Thursday, October 10, 2019

WHAT ARTIFICIAL INTELLIGENCE CANNOT DO , a grim note to the top 100 intellectuals of this planet , Part 2 - Capt Ajit Vadakayil



THIS POST IS CONTINUED FROM PART 1 BELOW--


https://ajitvadakayil.blogspot.com/2019/08/what-artificial-intelligence-cannot-do.html



  1. SOMEBODY ASKED ME

    CAPTAIN WHAT DO YOU PLAN TO ACHIEVE BY YOUR TEN PART POST ON ARTIFICIAL INTELLIGENCE

    https://ajitvadakayil.blogspot.com/2019/08/what-artificial-intelligence-cannot-do.html

    WELL--

    ONLY CAPT AJIT VADAKAYIL HAS THE CEREBRAL WHEREWITHAL ON THIS PLANET TO GIVE A CONDORS EYE VIEW ..ONLY CAPTAIN HAS THE ABILITY TO PULL BACK THE LENS..

    THE REST OF THE PLANET CAN GIVE ONLY "SEVEN BLIND MEN OF HINDOOSTAN " TUNNEL VISION VIEWS..

    A COMMIE BONG FEMALE ROHINI CHATTERJEE ( GRAND DAUGHTER OF SOMNATH CHATTERJEE ) INSULTED ME WITH A LYING POST BELOW--

    https://www.firstpost.com/living/open-letter-to-capt-vadakayil-the-man-who-wont-do-his-wifes-laundry-1192201.html

    I SAY LYING BECAUSE WHAT SHE WROTE ABOVE BELITTLING ME , HAS NOTHING TO DO WITH WHAT I WROTE BELOW..

    http://ajitvadakayil.blogspot.com/2013/10/karva-chauth-synchronising-fertility.html

    https://en.wikipedia.org/wiki/Somnath_Chatterjee

    AT THAT TIME I DECIDED TO KICK HER COMMIE TWAT..AND KICK IT GOOD.

    HER GRANDFATHER USED TO SPEAK TO MEDIA WITH PORTRAITS OF MARX/ LENIN/ ENGELS BEHIND HIM..

    AFTER I SHOT DOWN ROTHSCHILDs COMMUNISM, FEW DAYS BEFORE HIS DEATH-- SOMNATH BABY APPEARED FOR A PRESS INTERVIEW WITH A LARGE PORTRAIT OF CHE GUEVARA BEHIND HIM..

    http://ajitvadakayil.blogspot.com/2010/12/spirit-and-che-guevara-capt-ajit.html

    IT WAS A LESSON TO ALL.. DONT FUCK WITH ME ..

    UKKAD DEGA TERE KOH -- AARAM SEH!

    WHO SHOT DOWN BLOCKCHAIN/ BITCOIN WHEN THEY WERE FLYING HIGH?

    WHO FUCKED ALL THOSE WHO RUN SHELL COMPANIES ?

    capt ajit vadakayil
    ..

    PUT ABOVE COMMENT IN WEBSITES OF--
    ROHINI CHATTERJEE
    PINARAYI VIJAYAN
    KODIYERI BALAKRISHNAN
    PRAKASH KARAT
    BRINDA KARAT
    SITARAM YECHURY
    SUMEET CHOPRA
    DINESH VARSHNEY
    BINAYAK SEN
    SUDHEENDRA KULKARNI
    PRAKASH RAJ
    KAMALA HASSAN
    D RAJA
    ANNIE RAJA
    JOHN BRITTAS
    ADOOR GOPALAKRISHNAN
    ROMILA THAPAR
    IRFAN HABIB
    PMO
    PM MODI
    AMIT SHAH
    AJIT DOVAL

  1. https://timesofindia.indiatimes.com/spotlight/data-science-is-opening-opportunity-windows-for-graduates-across-the-world-heres-why-you-should-consider-learning-it/articleshow/71506557.cms

    SOMEBODY ASKED ME

    CAPTAIN, WHAT IS THE DIFFERENCE BETWEEN DATA SCIENCE, DATA MINING AND BIG DATA..

    I WILL SOON POST ON AI PART 2 - MY NEXT POST

    https://ajitvadakayil.blogspot.com/2019/08/what-artificial-intelligence-cannot-do.html

    WATCH HOW DEEP STATE TOOLS GOOGLE/ TWITTER/ FACEBOOK ETC HAVE SCREWED YOU..

    https://www.youtube.com/watch?v=hvMKFTBMmJM

    https://www.youtube.com/watch?v=ucRWyGKBVzo

    WHEN JACK DORSEY BANS YOUR TWITTER ACCOUNT AND ASKS FOR YOU MOBILE NUMBER-- HE WANTS TO TRACK YOU ( GOOGLE MAPS ) AND SPY ON YOU..

    PRISM IS A CODE NAME FOR A PROGRAM UNDER WHICH THE UNITED STATES NATIONAL SECURITY AGENCY (NSA) COLLECTS INTERNET COMMUNICATIONS FROM VARIOUS U.S. INTERNET COMPANIES PRISM BEGAN IN 2007 IN THE WAKE OF THE PASSAGE OF THE PROTECT AMERICA ACT UNDER THE BUSH ADMINISTRATION

    EDWARD SNOWDEN BLEW THE WHISTLE.. PUTIN GAVE HIM ASYLUM, OR HE WOULD HAVE BEEN ASSASSINATED UNDER ORDERS OF THE US PRESIDENT

    capt ajit vadakayil
    ..






Jaffa orange is the “the symbol of the Zionist enterprise and the state of Israel “, for Palestinians it symbolizes the loss of their homeland and its destruction.

This is a true incident.

Some homeless Palestinian boys used to play soccer by the side of a Jaffa orange farm in Israel..

One day the ball went inside the farm - enclosed by barbed wire.

Two boys went inside ( by tunneling under the barbed wire” and retrieved the ball.. They stole some oranges to be shared by the  kids playing..

The very next day a huge board came up on al 4 corners of the orange farm

ONE ORANGE HAS BEEN POISONED WITH CYANIDE

All Jews who saw this clever tactic went GA GA..  Media/ Police and even judges praised the deterrent. These damn vermins Arab boys cannot steal any more.

A week later the board was amended by an unknown force. It was an inserstion of the word MORE between words ONE and ORANGE.

ONE “MORE” ORANGE HAS BEEN POISONED WITH CYANIDE

Needless to say, the whole farm produce has to be destroyed.  

The Jew who thought he was damn clever had to eat crow.

IT IS A PIECE OF CAKE TO POISON DATA WHICH IS ABSORBED BY ARTIFICIAL INTELLIGENCE..  

WARS CAN BE WON FROM BEHIND A DESK..




CHINAs CURRENT ECONOMIC GROWTH IS JUST 3.9 % -- NOT 6.2 % AS IS BEING PROJECTED..

CHINAs GROWTH WILL CONTINUE FALLING.. SOE CHICKENS HAVE COME HOME TO ROOTS..

INDIA IS CURRENTLY THIS PLANETs FASTEST GROWING ECONOMY AT 10.1 %

THE BEGGAR WESTERN NATIONS ARE FEEDING FAKE DATA TO ARTIFICIAL INTELLIGENCE SYSTEMS TO SHOW THAT THEIR ECONOMIES ARE NOT IN RECESSION AND IS SMELLING OF ROSES....

ARTIFICIAL INTELLIGENCE ALGORITHMS WERE USED BY THE JEWISH DEEP STATE TO CRASH THE FINANCIAL MARKETS IN 2008.

THE 2008 FINANCIAL CRISIS BROUGHT THE WORLD TO ITS KNEES.

DEEP STATE CAN DO IT AGAIN—IT IS A PIECE OF CAKE IF YOU KNOW HOW TO POISON DATA. IN 2008 HARDLY ANYBODY KNEW ABOUT ARTIFICIAL INTELLIGENCE METHODS ..

DATA POISONING OCCURS WHEN AN ADVERSARY MODIFIES OR MANIPULATES PART OF THE DATASET UPON WHICH A MODEL WILL BE TRAINED, VALIDATED, AND TESTED. BY ALTERING A SELECTED SUBSET OF TRAINING INPUTS, A POISONING ATTACK CAN INDUCE A TRAINED AI SYSTEM INTO CURATED MISCLASSIFICATION,

TARGETED DATA POISONING IS WHEN AN ADVERSARY INTRODUCES A ‘BACKDOOR’ INTO THE INFECTED MODEL WHEREBY THE TRAINED SYSTEM FUNCTIONS NORMALLY UNTIL IT PROCESSES MALICIOUSLY SELECTED INPUTS THAT TRIGGER ERROR OR FAILURE.

WHEN DATA IS DELIBERATELY MANIPULATED TO DECEIVE FOR BETTER OR FOR WORSE– IT POSES A GREAT RISK TO INTEGRITY OF A SYSTEM WE HAVE GROWN TO TRUST

IT IS BETTER THAT THE WORD KNOWS THAT SUCH A THING IS POSSIBLE..   DATA-POISONING IS A NEW AND EXTREMELY POWERFUL TOOL FOR THOSE WHO WISH TO SOW DECEPTION AND MISTRUST IN OUR SYSTEMS—AND IT IS SO SIMPLE TO DO IT, BUT HARD TO DETECT

THE RISK IS AMPLIFIED BY THE CONVERGENCE OF AI WITH OTHER TECHNOLOGIES: DATA-POISONING CAN INFECT COUNTRY-WIDE GENOMICS DATABASES, AND POTENTIALLY WEAPONIZE BIOLOGICAL RESEARCH, NUCLEAR FACILITIES, MANUFACTURING SUPPLY CHAINS, FINANCIAL TRADING STRATEGIES AND POLITICAL DISCOURSE. UNFORTUNATELY, MOST OF THESE FIELDS ARE GOVERNED IN SILOS, WITHOUT A GOOD UNDERSTANDING OF HOW NEW TECHNOLOGIES MIGHT, THROUGH CONVERGENCE, CREATE SYSTEM-WIDE RISKS AT A GLOBAL LEVEL.

DATA POISONINGCAN HAPPEN WHEN AN ATTACKER MODIFIES THE MACHINE LEARNING PROCESS BY PLACING INACCURATE DATA INTO A DATASET, MAKING THE OUTPUTS LESS ACCURATE. THE GOAL OF THIS TYPE OF ATTACK IS TO COMPROMISE THE MACHINE LEARNING PROCESS AND TO MINIMIZE THE ALGORITHM’S USEFULNESS.

ADVERSARIAL MACHINE LEARNING ATTACKS CAN BE CLASSIFIED AS EITHER MISCLASSIFICATION INPUTS OR DATA POISONING. MISCLASSIFICATION INPUTS ARE THE MORE COMMON VARIANT, WHERE ATTACKERS HIDE MALICIOUS CONTENT IN THE FILTERS OF A MACHINE LEARNING ALGORITHM. THE GOAL OF THIS ATTACK IS FOR THE SYSTEM TO MISCLASSIFY A SPECIFIC DATASET. BACKDOOR TROJAN ATTACKS CAN BE USED TO DO THIS AFTER A SYSTEMS DEPLOYMENT.

THERE ARE TWO MAIN TYPES OF ATTACKS THAT RELY ON ADVERSARIAL DATA: POISONING ATTACKS, IN WHICH THE ATTACKER PROVIDES INPUT SAMPLES THAT SHIFT THE DECISION BOUNDARY IN HIS FAVOR, AND EVASION ATTACKS, IN WHICH AN ATTACKER CAUSES THE MODEL TO MISCLASSIFY A SAMPLE.

THE MOST COMMON TYPE OF DATA POISONING ATTACK IS MODEL SKEWING, WHICH RESULTS IN THE CLASSIFIER CATEGORIZING BAD INPUTS AS GOOD ONES. THE ATTACKER POLLUTES TRAINING DATA IN SUCH A WAY THAT THE BOUNDARY BETWEEN WHAT THE CLASSIFIER CATEGORIZES AS GOOD DATA, AND WHAT THE CLASSIFIER CATEGORIZES AS BAD, SHIFTS IN HIS FAVOR

POISONING ATTACKS WORK BY FEEDING DATA POINTS INTO THESE SYSTEMS THAT SLOWLY SHIFT THE ‘CENTER OF MASS’ OVER TIME. THIS PROCESS IS OFTEN REFERRED TO AS A BOILING FROG STRATEGY. POISONED DATA POINTS INTRODUCED BY THE ATTACKER BECOME PART OF PERIODIC RETRAINING DATA, AND EVENTUALLY LEAD TO FALSE POSITIVES AND FALSE NEGATIVES, BOTH OF WHICH RENDER THE SYSTEM UNUSABLE.

CYBERCRIMINALS USE REVERSE ENGINEERING TO EXTRACT A REPLICA OF THE AI MODEL AND CARRY OUT THESE ATTACKS WHICH OFTEN GO UNNOTICED FOR A LONG PERIOD. THEREFORE, AI MODELS CAN BE SKEWED USING SOPHISTICATED TECHNOLOGIES TO PRODUCE A TARGETED RESULT.

CONTINUED TO 2--
.
  1. CONTINUED FROM 1--

    TROJAN ATTACKS’ SPECIFICITY DIFFERENTIATES THEM FROM THE MORE GENERAL CATEGORY OF “DATA POISONING ATTACKS”, WHEREBY AN ADVERSARY MANIPULATES AN AI’S TRAINING DATA TO MAKE IT JUST GENERALLY INEFFECTIVE.

    IN THE INITIAL EXAMPLE THE TROJAN WAS INSERTED BY MANIPULATING BOTH THE TRAINING DATA AND ITS LABELS. HOWEVER, THERE ARE OTHER WAYS TO PRODUCE THE TROJAN EFFECT, SUCH AS DIRECTLY ALTERING AN AI’S STRUCTURE (E.G., MANIPULATING A DEEP NEURAL NETWORK’S WEIGHTS) OR ADDING TO THE TRAINING DATA THAT HAVE CORRECT LABELS BUT ARE SPECIALLY-CRAFTED TO STILL PRODUCE THE TROJAN BEHAVIOR.

    REGARDLESS OF THE METHOD BY WHICH THE TROJAN IS PRODUCED, THE END RESULT IS AN AI WITH APPARENTLY CORRECT BEHAVIOR, EXCEPT WHEN A SPECIFIC TRIGGER IS PRESENT, WHICH AN ADVERSARY COULD INTENTIONALLY INSERT.

    DATA POISONING ATTACKS LEVERAGES ON THE MANY DATA SOURCES WITH MASSIVE CORPUS WHICH MAKE IT ALMOST IMPOSSIBLE TO VALIDATE AND CURATE THE DATA

    DATA POISONING BY ENEMIES IN HEALTH CARE SYSTEMS CAN EVEN ALTER THE DOSAGE OF CRITICAL MEDICINES.. KOSHER INSURANCE COMPANIES LOVE EUTHANASIA – IT IS A PIECE OF CAKE TO SHIFT FALSE POSITIVES AND FALSE-NEGATIVES.

    DATA POISONING IS A FIELD OF CYBERSECURITY—INDIA MUST PAY ATTENTION—THE TIME IS NOW !

    ABHE CHOOT KA DHAKKAN , TUU GANGA BAJOO ( HACKING ) KYON DEK RAHA HAI BHAYYA HAMESHA .. GHADI GHODI JAMUNA BAJOO ( DATA POISONING ) BHI DEKHA KARO..

    WHEN I PEN MY TEN PART POST ON AI I WILL WRITE ABOUT DATA POISONING IN DETAIL.

    https://ajitvadakayil.blogspot.com/2019/08/what-artificial-intelligence-cannot-do.html

    Capt ajit vadakayil
    ..






In “data poisoning”, false data is continually smuggled into a machine learning system’s training set to prevent it from achieving mastery

Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data.  

It is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. .




A Data Scientist will look at the data from many angles, sometimes angles not known earlier.  Data science is the study of data.




Computer data is information processed or stored by a computer. This information may be in the form of text documents, images, audio clips, software programs, or other types of data.

Data is distinct pieces of information, usually formatted in a special way. An example of data is an email. Data are plain facts. The word "data" is plural for "datum."

When data are processed, organized, structured or presented in a given context so as to make them useful, they are called Information.

Data themselves are fairly useless, but when these data are interpreted and processed to determine its true meaning, they becomes useful and can be named as Information.

Information is data that has been processed in such a way as to be meaningful to the person who receives it. it is any thing that is communicated. Data is the raw material that can be processed by any computing machine. Data can be represented in the form of numbers and words which can be stored in computer's language


To successfully evaluate big data sets, data scientists use a variety of tools from fields including computer science, predictive analytics, statistics, and artificial intelligence.

Data science is the same concept as data mining and big data--  use the most powerful hardware, the most powerful programming systems, and the most efficient algorithms to solve problems.

"Big data" is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software.

Data with many cases (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate.

Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. Big data was originally associated with three key concepts: volume, variety, and velocity.

When we handle big data, we may not sample but simply observe and track what happens. Therefore, big data often includes data with sizes that exceed the capacity of traditional usual software to process within an acceptable time  and value.

Current usage of the term big data tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set



I will be more elaborate about Data mining.



The purpose of data mining, is to identify useful and understandable patterns by analyzing large sets of data. These data patterns help predict industry or information trends, and then determine what to do about them.




Data mining discovers previously unknown patterns and knowledge, whereas machine learning reproduces known patterns and knowledge.


Data Mining can cull existing information to highlight patterns, and serves as foundation for AI and machine learning.  It is the process of identifying patterns in big data volumes for data-driven decision-making in various areas of life. You can consider data mining between artificial intelligence and statistics.

Data mining is  one of the newer methods that market research companies are employing, but it serves as a foundation for both artificial intelligence and machine learning. ...  It is  a method for framing various hypotheses.

This data mining method helps to classify data in different classes.



Data Mining as the name suggest refers to extracting or mining knowledge from large amounts of data.   

It is a multidisciplinary field, drawing work from areas including database technology, artificial intelligence, machine learning, neural networks, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing, and data visualization.  


Data mining is a process used by companies to turn raw data into useful information. By using software to look for patterns in large batches of data, businesses can learn more about their customers and develop more effective marketing strategies as well as increase sales and decrease costs.

Data mining is an integral part of coding programs with the information, statistics, and data necessary for AI to create a solution.

Data mining is used to uncover hidden patterns in the underlying data which can be used for decision making process

The data mining technique in mined data is used by the AI systems for creating solutions. Data mining serves as a foundation for artificial intelligence. Data mining is a part of programming codes with information and data necessary for AI systems.

Machine learning is a type of data mining technique. Both Data Mining and Machine learning are areas which have been inspired by each other, though they have many things in common, yet they have different ends.

Data mining is performed by humans on certain data sets with the aim to find out interesting patterns between the items in a data set. Data mining uses techniques developed by machine learning for predicting the outcome.

Data mining is a technique of examining a large pre-existing database and extracting new information from that database, which is easy to understand.


Data mining is performed by humans on certain data sets with the aim to find out interesting patterns between the items in a data set.   Data mining uses techniques developed by machine learning for predicting the outcome.

Machine Learning is the ability of a computer to learn from mined datasets.

Data mining is done without any preconceived hypothesis, hence the information that comes from the data is not to answer specific questions of the organization.

Data mining is about extrapolating patterns and new knowledge from the data you’ve already collected.   It is the process of finding anomalies, patterns and correlations within large data sets to predict outcomes

Data mining is the practice of analyzing, examining data from different perspectives and summarizing to generate new information..   It is the process used by companies to turn raw data into useful information..

Data mining uses algorithms drawn from disciplines as diverse as statistics, artificial intelligence, and computer science in order to develop models from data; that is, in order to find interesting structures or recurrent themes according to criteria determined beforehand, and to extract the largest possible amount of knowledge useful to companies.

It groups together all technologies capable of analyzing database information in order to find useful information and possible significant and useful relationships within the data.


Algorithms perform the data mining.. An algorithm in data mining (or machine learning) is a set of heuristics and calculations that creates a model from data. To create a model, the algorithm first analyzes the data you provide, looking for specific types of patterns or trends.


An "algorithm" is any set of rules for doing something. ... A "heuristic" is an algorithm that does not guarantee a correct solution. A "good" heuristic is one that will get you either the correct, or a good enough solution most of the time.


A heuristic algorithm is one that is designed to solve a problem in a faster and more efficient fashion than traditional methods by sacrificing optimality, accuracy, precision, or completeness for speed.




Generally speaking, a heuristic is a "rule of thumb," or a good guide to follow when making decisions. In computer science, a heuristic has a similar meaning, but refers specifically to algorithms. ... As more sample data is tested, it becomes easier to create an efficient algorithm to process similar types of data.

Data mining often refers generally to the idea of probing deeply into some mountain of data. This informal use of the term usually says little about the techniques used to do the probing. In contrast, the more formal use of the term refers specifically to using computational techniques to uncover patterns in huge data sets.


 Here the techniques range widely from statistics to artificial intelligence.. Data mining is also known as knowledge discovery in databases (KDD)..   It is  the process of analyzing data from different perspectives and summarizing it into useful and actionable information. 

As a key step in the knowledge discovery from data (KDD) process, it is intended to extract interesting (nontrivial, implicit, previously unknown, and potentially useful) patterns.  KDD is a field of science that is used to find out the properties of datasets.



KDD is the overall process of extracting knowledge from data while Data Mining is a step inside the KDD process, which deals with identifying patterns in data.  Data mining refers to the application of algorithms for extracting patterns from data without the additional steps of the KDD process.

Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.

The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.  Data mining refers to a technologically driven process of using algorithms to analyze data from multiple perspectives and extract meaningful patterns that can be used to predict future behavior.



Data mining is also often referred to as ‘analytics’ or ‘knowledge discovery’ because its objective is precisely to generate knowledge or discover patterns of information amongst that data from which useful knowledge can be obtained.

Data mining has become an important complement of other data analysis tools because the amounts of information available these days sometimes are impossible to be analyzed with traditional methods; however it has also become a buzzword.

Data mining is a complex topic, has links with multiple core fields such as computer science, and adds value to rich seminal computational techniques from statistics, information retrieval, machine learning, and pattern recognition

Data mining is the interpretation by a human of the data in context, the algorithm process, and the results that make value. An evaluation is open ended as report, analysis, or prediction

Data mining techniques commonly used in academic analytics include regression analysis, rule induction, association rule discovery, clustering, neural networks, sequential patterns ,  classification,  prediction and decision trees.


Data Mining as the name suggests “mines” data using components of artificial intelligence, traditional statistics, etc.  Data Mining unlike Data Analytics is performed without any said hypothesis.  

Data Mining does not aim to answer specific questions.   The main difference is that Data Analytics looks for specific answers with a specific hypothesis, whereas, Data Mining does not have a specific answer to fulfill.



Data analysis refers to reviewing data from past events for patterns. Data analysis is analyzing huge amounts of data requires incredible computing power, and IaaS is the most economical way to get it. Companies use Infrastructure as a Service for data mining and analysis.


Data Analysis is the process of inspecting, cleansing, transforming, and modeling data to discover useful information, and support decision-making. The many different types of data analysis include data mining, a predictive technique used for modeling and knowledge discovery, and business intelligence, which relies on aggregation and focuses on business information.

When leveraging data mining methods, complex statistical models, and machine learning technologies, advanced analytics allows for making effective data-based decisions and building sentiment analysis or recommendation systems that lead to predictive analytics.

At its core, predictive analytics encompasses a variety of statistical techniques (including machine learning, predictive modelling and data mining) and uses statistics (both historical and current) to estimate, or ‘predict’, future outcomes. .


Analytics has huge scope in Business Analytics and Business Intelligence. Data Mining on the other hand uses techniques, both mathematical and scientific, to find patterns and trends. Data mining is one of the newer methods that market research companies are employing, but it serves as a foundation for both artificial intelligence and machine learning





Data Mining is commonly defined as the analysis of data for relationships and. patterns that have not previously been discovered by applying statistical and. mathematical methods. Business intelligence (BI) describes processes and. procedures for systematically gathering, storing, analyzing, and providing access Data mining is one of the activities in Data Analysis. ...

 On the other hand, Data Analysis tests a given hypothesis. While Data mining is based on Mathematical and scientific methods to identify patterns or trends, Data Analysis uses business intelligence and analytics models.


Data mining is the best collection of techniques you have for making the most out of the data you’ve already gathered. As long as you apply the correct logic, and ask the right questions, you can walk away with conclusions that have the potential to revolutionize your enterprise.

Data mining  can include statistical algorithms, machine learning, text analytics, time series analysis and other areas of analytics.  It includes the study and practice of data storage and data manipulation.

Data mining can  identify clients with high-risk profiles, or use cybersurveillance to pinpoint warning signs of fraud. Fraud detection is a topic applicable to many industries including banking and financial sectors, insurance, government agencies and law enforcement, and more. 

Through the use of sophisticated data mining tools, millions of transactions can be searched to spot patterns and detect fraudulent transactions.

 In data mining, anomaly detection (also outlier detection ) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. 

Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions


Data mining application is to process data and evaluate them. That covers any input file, which implicitly requires some structure, in order to perform some algorithm on it. The algorithm can split into sequential or parallel, and perform over the computer processing unit or the graphics processing unit.

The choice on which ones effects the performance of the algorithm in speed. As any algorithm, the terminal result is an output. A modified definition is a terminal result of multiple output. As any algorithm, the terminal result is an output. A modified definition is a terminal result of multiple output.

The performance is entirely mechanical and programmed into the computer. It is the interpretation by a human of the data in context, the algorithm process, and the results that make value. An evaluation is open ended as report, analysis, or prediction. It depends on the expertise of the person that performs a data mining.

Kind of knowledge to be mined:--
Characterization.
Discrimination.
Association and Correlation Analysis.
Classification.
Prediction.
Clustering.
Outlier Analysis.
Evolution Analysis.




Decision tree learning is a method commonly used in data mining.   In computer science, Decision tree learning uses a decision tree (as a predictive model) to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining and machine learning



Backpropagation (backward propagation) is an important mathematical tool for improving the accuracy of predictions in data mining and machine learning. ... Artificial neural networks use backpropagation as a learning algorithm to compute a gradient descent with respect to weights.


Essentially, backpropagation is an algorithm used to calculate derivatives quickly. ... Because backpropagation requires a known, desired output for each input value in order to calculate the loss function gradient, it is usually classified as a type of supervised machine learning.


Backpropagation is a two-step process, where the inputs are fed into the neural network via forward propagation and multiplied with (initially random) weights and bias before they are transformed via an activation function.

Apriori Algorithm is a data mining procedure used to extract association rules from data. These rules are useful because they can find patterns of correlation in data in the form of events.








Data Mining is the process of collecting data, aggregating it according to type and sorting through it to identify patterns and predict future trends. An ecommerce company, for example, would use data mining to analyze customer data and give product suggestions through the “customers who bought this item also bought” window.   

Data mining tools include Tanagra, R and Weka (a suite of machine learning algorithms for data mining).

AI engineers spend as much time thinking about data as algorithms. They need lots of good quality data to perform effective ML (  machine learning ) , and even more to test the  results. 'Data mining', is a field of computation focused on the automated identification of patterns  and anomalies in datasets.

The dataset could be anything from text posted on social media to precise measurements of underground geological formations, and the mining process could deploy  ANNs, statistics and modelling to identify useful features.

 'Big data' refers to datasets that are so  large and complex – including content from different sources, in different formats, and with  different degrees of authenticity and accuracy – that they cannot be stored or processed in the same way as smaller datasets. 

This brings us to 'data in the wild', which usually refers to data that was  produced for one purpose but remains somehow accessible and can be used for other purposes, perhaps outside the control of its original producer.

So a research project might apply data mining techniques to social media platforms and blogs to research different individual's emotional and behavioural responses to news stories. Since this 'data in the wild' was not intended for research purposes, its use might be unreliable, unethical, or even illegal.

Data Mining, Machine Learning, and Deep Learning are all in the same family..


Now I must break into a song..   

IN TRINIDAAAD THEY WAS A FAMAALIEE



While all three disciplines listed above are in the same family, it’s essential to understand how they differ.  At a basic level, Machine Learning uses the same algorithms and techniques like data mining, but the types of predictions the two provide vary. 

Data mining discovers previously unknown patterns and knowledge, whereas Machine Learning reproduces known patterns and knowledge. ML then automatically applies that information to additional datasets, and, ultimately, the business strategy and outcomes.

One of the most basic techniques in data mining is learning to recognize patterns in your data sets. This is usually a recognition of some aberration in your data happening at regular intervals, or an ebb and flow of a certain variable over time

Classification is a more complex data mining technique that forces you to collect various attributes together into discernable categories, which you can then use to draw further conclusions, or serve some function. For example, if you’re evaluating data on individual customers’ financial backgrounds and purchase histories, you might be able to classify them as “low,” “medium,” or “high” credit risks. You could then use these classifications to learn even more about those customers.


Prediction is one of the most valuable data mining techniques, since it’s used to project the types of data you’ll see in the future. In many cases, just recognizing and understanding historical trends is enough to chart a somewhat accurate prediction of what will happen in the future. For example, you might review consumers’ credit histories and past purchases to predict whether they’ll be a credit risk in the future.


For businesses, data mining is used to discover patterns and relationships in the data in order to help make better business decisions. Data mining can help spot sales trends, develop smarter marketing campaigns, and accurately predict customer loyalty.

Again, data mining is the process of finding anomalies, patterns and correlations within large data sets to predict outcomes. Using a broad range of techniques, you can use this information to increase revenues, cut costs, improve customer relationships, reduce risks and more.

Sifting through very large amounts of data for useful information. Data mining uses artificial intelligence techniques, neural networks, and advanced statistical tools. ... In contrast to an expert system, data mining attempts to discover hidden rules underlying the data

Data mining spans a wide range of applications in medicine and population health (study of drug implications, disease outbreak), bioinformatics (protein interactions, gene sequence analysis), engineering (intrusion detection and network security, flow classification, Web mining), business (fraud detection, decision support systems, risk analysis, forecasting market trend), and environmental studies (flood prediction)


The data mining process is classified in two stages: Data preparation/data preprocessing and data mining. The data preparation process includes data cleaning, data integration, data selection, and data transformation. The second phase includes data mining, pattern evaluation, and knowledge representation

Data mining process:
Identifying the source information.
Picking the data points that need to be analyzed.
Extracting the relevant information from the data.
Identifying the key values from the extracted data set.
Interpreting and reporting the results

Data Mining outcomes represent a valuable support for decision making.

Data mining is seen as an increasingly important tool by modern business to transform data into business intelligence giving an informational advantage. It is currently used in a wide range of profiling practices, such as marketing, surveillance, fraud detection, and business partner selection.


Bottom-up data mining analyses raw data in an attempt to discover hidden trends and groups, whereas the aim of top-down data mining is to test a specific hypothesis

Pre-processing involves preparing the dataset for analysis by the data mining software to be used. This may involve resolving undesirable data characteristics such as missing data (non-complete fields), irrelevant fields, non-variant fields, skewed fields, and outlying data points.


Data Transformation is defined as the process of transforming data into appropriate form required by mining procedure.


Essentially, a cloud data service is a remote version of a data center – located somewhere away from your company's physical premises – that lets you access your data through the internet. ... A data center traditionally refers to server hardware on your premises to store and access data through your local network

When it comes to storing and accessing massive amounts of data by your company or organization, cloud data services are a cost-effective alternative to setting up and running a data center.


Essentially, a cloud data service is a remote version of a data center – located somewhere away from your company's physical premises – that lets you access your data through the internet. The cloud provider performs ongoing maintenance and updates, often owning multiple data centers in several geographic locations to safeguard your data during outages and other failures.


A data center is ideal for companies that need a dedicated system that gives them full control over not only their data but the hardware itself. Because only the company is using this hardware infrastructure, a data center is more suited for an organization that has to run many types of applications and complex workloads.

A data center, however, has limited capacity. You're responsible for purchasing and installing more equipment and the latest technology if your company needs to expand the storage and workload of the data center.

A cloud data system has potentially unlimited capacity, based on your vendor's offerings and service plans. The disadvantage is that you do not have as much control over the remotely located hardware, since the cloud vendor owns and manages the data center system. 


Furthermore, unless you pay to have a private cloud within the vendor's network, your company will be sharing hardware resources with other cloud users.

With a cloud vendor, your company will be entrusting its data to a third party. It's up to the cloud provider to ensure it has the most up-to-date security certifications. If your cloud resides on several data centers in different locations, each location will need the proper security measures.

Your cloud data can be accessed by anyone with the proper credentials from anywhere with an internet connection. This is convenient, but it also opens a wide array of access points, all of which need to be protected to ensure that data transmitted through them is secure.


A data center is physically connected to your company's local network. This makes it easier to ensure that only people with company-approved credentials and devices can access stored apps and information. You are responsible for your own security, though.

If your company builds a data center from the ground up, this will take a lot of time, and your company will be responsible for the system's maintenance and administration. Operating a large data center can cost a fortune..

Data centers need electricity to power their servers, storage equipment, backups, and power cooling infrastructure; most servers require temperatures below 26 deg C  to operate, and cooling can comprise up to 40% of electricity usage in conventional data centers. The U.S. is home to 3 million data centers, or roughly one for every 100 Americans.

A large number are clustered in Loudoun County in northern Virginia. Tech giants like Amazon, Microsoft, and Google operate data centers there, and county officials claim that 70% of the world's Internet traffic flows through the area's data centers.

A cloud service is by far more cost-effective, especially for small companies. It does not require anywhere as much time or money to set up and run. The cloud service is available for your company's use almost immediately upon registration. As your company's data needs change over time, the cloud vendor should be able to scale your service up or down quickly. Most companies have a range of subscription plans to account for this.





Developing in the cloud enables users to get their applications to market quickly. Hardware failures do not result in data loss because of networked backups. Cloud computing uses remote resources, saving organizations the cost of servers and other equipment. 

Since cloud computing systems are internet-based, service outages are always an unfortunate possibility and can occur for any reason.

Cloud is the technology of distributed data processing in which some scalable information resources and capacities are provided as a service to multiple external customers through Internet technology. ... Customers also have the opportunity of paid use of it, usually through the Internet.

The cloud’s capability to scale vertically and horizontally makes it the perfect platform for Big Data hosting and processing. With vertical scaling, it is possible to expand the limit of a server by including assets as required by applications. Horizontal scaling enables organizations to extend hardware assets as processing necessities increase.

Cloud-based frameworks offer high bandwidth, huge amounts of memory, and scalable processing capacity to help Big Data applications with enhanced real-time processing and analysis of streaming information. The cloud is an unmistakable choice for applications running huge workloads and storing huge volumes of data.


With the worldwide cloud market revenue expected to hit $230 Billion in 2019, the amount of cloud implementations has seen an upward trend in the last few years.




Traditional Data Center
Cloud Data Center (CDC)
Location
On-premises, physically accessible
Virtualized, remote hardware
Management
Internal, business’s responsibility
Outsourced to third-party provider
Administration
In-house IT professionals
Employees of the service provider
Reliability
Co-location makes failures dependent, onus is on the business for downtime and repairs
Provider is trusted to meet its promises 
of availability and reliability
Pricing
Business pays directly for planning, people, hardware, software, and environment
Business pays per use, by resources 
provisioned
Scalability
Possible, but involves challenges and delay
Completely, instantly scalable


Data is collected from drones and satellites.  AI based Robots are able to perform the tasks given by a human. They have sensors to detect physical data from the real world such as light, heat, temperature and movement etc.

When you start accepting the fact that human thinking is gray, then data should be used as more of an assistant or a way to kind of assist with your decision making.

Knowlege Engineering  was invented to build data structures and rule-based reasoning systems to capture and articulate this “knowledge.”  AI has data fed to it, it then follows algorithms to help IT take data, refine it into Information. AI has nothing to do with Wisdom. It’s “knowledge” is Stored “Information”.

Artificial intelligence has clearly come a long way. Its ability to learn vast amounts of data, recognize patterns, and spit out results has improved countless industries and will improve countless more in the coming years. However, the problem with achieving true artificial intelligence is actually its greatest strength: the fact that it can't learn like a human.

We are very good at gathering data and developing algorithms to reason with that data  But that reasoning is only as good as the data, which, for the AI we have now, is one step removed from reality.

Python is a more popular language over C++ for AI and leads with a 57% vote among developers. That is because Python is easy to learn and implement. With its many libraries, they can also be used for data analysis. ... C++ being a lower-level language requires more experience and skill to master.
.
What is Artifical Intelligence?  Is it analyzing lots of data and finding meaningful patterns

The process of analyzing  massive data sets  and information  and storing them and analyzing them in suitable and sophisticated methods give computers immense capabilities .

AI’s main limitation is that it learns from given data. There is no other way that knowledge can be integrated, unlike human learning. This means that any DELIBERATE inaccuracies in the data will be reflected in the results.

Data utilization is one of the significant restrictions of Artificial Intelligence. For any program to begin, it requires data

Companies will need data analysts, data scientists, developers, cybersecurity experts, network engineers and IT professionals with a variety of skills to build and maintain their infrastructure to support AI and to use artificial intelligence technologies
.
Machine Learning is a technique of parsing data, learn from that data and then apply what they have learned to make an informed decision

A parser is a compiler or interpreter component that breaks data into smaller elements for easy translation into another language. A parser takes input in the form of a sequence of tokens or program instructions and usually builds a data structure in the form of a parse tree or an abstract syntax tree.
.
A parser is commonly used as a component of an interpreter or a compiler. The overall process of parsing involves three stages:--

Lexical Analysis: A lexical analyzer is used to produce tokens from a stream of input string characters, which are broken into small components to form meaningful expressions.

Syntactic Analysis: Checks whether the generated tokens form a meaningful expression. This makes use of a context-free grammar that defines algorithmic procedures for components. These work to form an expression and define the particular order in which tokens must be placed.

Semantic Parsing: The final parsing stage in which the meaning and implications of the validated expression are determined and necessary actions are taken.
.
Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data

The future of cybersecurity will be driven by a new class of subtle and stealthy attackers that has recently emerged. Their aim is not to steal data, but rather to manipulate or change it. There is little doubt that artificial intelligence (AI) will be used by attackers to drive the next major upgrade in cyber weaponry and will ultimately pioneer the malicious use of AI

Imagine an oil rig using faulty geo-prospection data to drill for oil in the wrong place, or a physician making a diagnosis using compromised medical records
.
In the current world of data deluge, it is nearly impossible for humans alone to analyse the billions of logs generated from the existing infrastructure components. Integrating AI into the existing systems including Security Monitoring Solutions, SIEM, Intrusion Detection Systems, Cryptographic technologies and Video vigilance systems can help in addressing many of these challenges to a larger extent.

The amount of data we generate every day is staggering—currently estimated at 2.7 quintillion bytes—and it’s the resource that makes deep learning possible. Since deep-learning algorithms require a ton of data to learn from, this increase in data creation is one reason that deep learning capabilities have grown in recent years.



What is an algorithm?

An algorithm is a set of rules used to calculate the answer to a problem. Algorithms power many computer programs. Algorithms are used to curate and tailor content on social media sites, mobile phones, and search engines based on the particular user. It is a sequence of unambiguous instructions.
An algorithm is a step-by-step procedure for solving a problem.

It is used for data processing, calculations, mathematical problems, and other computer-related operations.

A computer program is viewed as an elaborate algorithm.

There are many types of algorithms. The most fundamental types of algorithms are described below:

Recursive algorithms
Dynamic programming algorithm
Backtracking algorithm
Divide and conquer algorithm
Greedy algorithm
Brute Force algorithm
Randomized algorithm

Advantages of an Algorithm:
It is easy to write.
It is easy to understand.
It uses easy techniques to understand the logic.
It is easy to identify mistakes.
Every step has its own logical sequence.
It is easy to debug.
No need to understand a particular programming language.
It’s flexible.
It allows writing code in any language.
It is written in simple English language.
A non-technical user can also easily understand.

Disadvantages of an Algorithm:
It is time-consuming.
It is difficult to show looping and branching.
It is not suitable for big programs.
It is difficult for big tasks to fit into an algorithm.



THE TERM ARTIFICIAL INTELLIGENCE IS A MISNOMER..  SO SAYS CAPT AJIT VADAKAYIL..

ALGORITHMS ARE TUNNEL VISIONED. 

JUST BECAUSE A RAT BURROWING A TUNNEL IS FAAAAST, YOU CANNOT COMPARE IT WITH A CONDORs VISION.



A computer is designed to make faster calculus, algorithms, and other kinds of very useful tasks, however, the computer cannot take advantage of anything that it does, in conclusion, computers are not really intelligent.

A person who wins a discussion with his spouse at the expense of their relationship is less intelligent than who wins the discussion and keep a good relationship.

WARS CAN BE AVOIDED BY EMOTIONAL INTELLIGENCE

Emotional intelligence skills ( LEADERSHIP ) such as influencing, persuading, social understanding and empathy will become differentiators as artificial intelligence and machine learning take over
Intelligent machines will eventually be able to see, hear, smell, sense, move, think, create and speak and possess hajaaar LOGOS. 

BUT THEY WILL NEVER HAVE CONSCIOUS EMOTIONAL INTELLIGENCE , THE ETHOS AND PATHOS WHICH A HUMAN LEADER HAS.




Facebook and Amazon use predictive algorithms to make recommendations based on a user’s reading or purchasing history.

Machines will do algorithmic, rule-based learning better than we can; but they won’t do rule-breaking creativity anything like we can do it.

Awareness is something beyond computation for it is the ability to halt the processing in the brain machine at will to take stock of what is going on. .  No algorithm can determine when given a description of an arbitrary computer program and an input, whether the program will halt or continue to run forever.

If halting to an arbitrary input at randomly chosen time is impossible from a computability point of view, and the aware mind does it, then it is clear that consciousness is not computable.

Machine learning uses data to feed an algorithm that can understand the relationship between the input and the output. When the machine finished learning, it can predict the value or the class of new data point.

Whenever a machine completes tasks based on a set of stipulated rules that solve problems (algorithms), such an “intelligent” behavior is what is called artificial intelligence.

ML uses algorithms to parse data, learn from it, and then make a decision or prediction about something in the world. Rather than hard-coding software with specific instructions to accomplish a particular task, a machine is “trained” using large amounts of data and algorithms that give it the ability to learn how to perform a task or predict an outcome.

Unlike software that has been programmed manually and performed tasks with specific instructions (like Computer Vision software), Machine Learning algorithms are designed in such a way that they can learn and improve over time when exposed to new data.

Just because an AI algorithm can turn voice to text doesn’t mean it understands what it is processing.

A ‘black box’ algorithm cannot be trusted in critical situations.

In computer programming and software engineering, black box testing is used to check that the output of a program is as expected, given certain inputs. The term "black box" is used because the actual program being executed is not examined

Most algorithms produce an outcome or answer that humans rely on for making a decision. Most algorithms, however, do not explain how they arrived at that answer. Lack of explainability weakens accountability and does not foster trust.

It’s unclear what structures the statistical AI algorithms really learn.. the algorithms just separate data examples and do not  have a true understanding of the content.

Lack of context is one area that makes it  difficult for algorithms.

With humor,  you've really got to have a deep understanding of the world, how things work, how people work. It's indicative of something that really is intelligent


Algorithms have never needed to explain themselves to us before because we wrote them.

Many AI algorithms are capable of learning from data ( this is overhyped ) ; they can enhance themselves by learning new heuristics (strategies, or "rules of thumb", that have worked well in the past), or can themselves write other algorithms. .

Compared with humans, existing AI lacks several features of human "commonsense reasoning"; .
A programming algorithm is a computer procedure that is a lot like a recipe (called a procedure) and tells your computer precisely what steps to take to solve a problem or reach a goal.

Algorithms are nothing more than computer programs making decisions based on rules: either rules that we gave them, or rules they figured out themselves based on examples we gave them. In both cases, humans are in control of these algorithms and how they behave. If an algorithm is flawed, it’s our doing



In order for some instructions to be an algorithm, it must have the following characteristics:----
Clear and Unambiguous: Algorithm should be clear and unambiguous. Each of its steps should be clear in all aspects and must lead to only one meaning.
Well-Defined Inputs: If an algorithm says to take inputs, it should be well-defined inputs.
Well-Defined Outputs: The algorithm must clearly define what output will be yielded and it should be well-defined as well.
Finiteness: The algorithm must be finite, i.e. it should not end up in an infinite loops or similar.
Feasible: The algorithm must be simple, generic and practical, such that it can be executed upon will the available resources. It must not contain some future technology, or anything.
Language Independent: The Algorithm designed must be language-independent, i.e. it must be just plain instructions that can be implemented in any language, and yet the output will be same, as expected.

Inorder to write an algorithm, following things are needed as a pre-requisite:---
The problem that is to be solved by this algorithm.
The contraints of the problem that must be considered while solving the problem.
The input to be taken to solve the problem.
The output to be expected when the problem the is solved.
The solution to this problem, in the given contraints.
Then the algorithm is written with the help of above parameters such that it solves the problem.

How to Analyse an Algorithm?

For a standard algorithm to be good, it must be efficient. Hence the efficiency of an algorithm must be checked and maintained. It can be in two stages:---
Priori Analysis: “Priori” means “before”. Hence Priori analysis means checking the algorithm before its implementation. In this, the algorithm is checked when it is written in the form of theoretical steps. This Efficiency of an algorithm is measured by assuming that all other factors, for example, processor speed, are constant and have no effect on the implementation. This is done usually by the algorithm designer. It is in this method, that the Algorithm Complexity is determined.
Posterior Analysis: “Posterior” means “after”. Hence Posterior analysis means checking the algorithm after its implementation. In this, the algorithm is checked by implementing it in any programming language and executing it. This analysis helps to get the actual and real analysis report about correctness, space required, time consumed etc.

The two factors of Algorithm Complexity are:--
Time Factor: Time is measured by counting the number of key operations such as comparisons in the sorting algorithm.
Space Factor: Space is measured by counting the maximum memory space required by the algorithm.


Artificial intelligence is not here to replace us.  It augments our abilities and makes us better at what we do.  

Because AI algorithms learn differently than humans, they look at things differently in an objective manner . They can see relationships and patterns that escape us, because we are conscious humans. Our subjective brains are squelched to discard what is not important..


AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms tailor made by human , allowing the software to learn automatically from patterns or features in the data

Advanced algorithms are being developed and combined in new ways to analyze more data faster and at multiple levels. This intelligent processing is key to identifying and predicting rare events, understanding complex systems and optimizing unique scenarios.

In using the algorithms that study natural language and creating new language that mirrors natural use, AI breaks every sentence down into definable segments that function as commands, and in turn determines what action is an appropriate response to those sentences. .

Machine learning advancements such as neural networks and deep learning algorithms can discover hidden patterns in unstructured data sets and uncover new information

No wonder Jews were very upset when Google’s AI bot Tay declared that THERE WAS NO HOLOCAUST.




Artificial Intelligence (AI) is often portrayed as a kind of magic technology that will take over humanity in a fully autonomous and self-learning manner. In reality, however, AI is mainly a combination of machine learning and smart programming, which actually requires a lot of human effort..

Chatbots are programmed to recognize patterns in the input they receive. Based on those patterns, they then provide a pre-scripted answer. This already requires quite a bit of human labor, both manual and intellectual. When creating a new chatbot, you have to write out ‘conversation trees’.

We still need humans to define ‘good’ and ‘bad’ conversations, and to correct the algorithm as to which response to give. That way, the chatbot can ‘learn’ not to make the same mistake again, and advances the pattern that the chatbot recognizes in the input. This may sound like self-learning, but humans are constantly providing the necessary feedback.

In addition to the Data Scientist, the end users are also frequently called on to provide that feedback.





AI gets the most out of data. When algorithms are self-learning, the data itself can become intellectual property. The answers are in the data; you just have to apply AI to get them out. Since the role of the data is now more important than ever before, it can create a competitive advantage.

 If you have the best data in a competitive industry, even if everyone is applying similar techniques, the best data will win.

Artificial intelligence is not here to replace us. It augments our abilities and makes us better at what we do. Because AI algorithms learn differently than humans, they look at things differently. They can see relationships and patterns that escape us. 

This human, AI partnership offers many opportunities. It can:--

Bring analytics to industries and domains where it’s currently underutilized.
Improve the performance of existing analytic technologies, like computer vision and time series analysis.
Break down economic barriers, including language and translation barriers.
Augment existing abilities and make us better at what we do.
Give us better vision, better understanding, better memory and much more. .

The principle limitation of AI is that it learns from the data. There is no other way in which knowledge can be incorporated. That means any inaccuracies in the data will be reflected in the results. And any additional layers of prediction or analysis have to be added separately.

Today’s AI systems are trained to do a clearly defined task. The system that plays poker cannot play solitaire or chess. The system that detects fraud cannot drive a car or give you legal advice. In fact, an AI system that detects health care fraud cannot accurately detect tax fraud or warranty claims fraud.

In other words, these systems are very, very specialized. They are focused on a single task and are far from behaving like humans..

AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software to learn automatically from patterns or features in the data. AI is a broad field of study that includes many theories, methods and technologies

Advanced algorithms are being developed and combined in new ways to analyze more data faster and at multiple levels. This intelligent processing is key to identifying and predicting rare events, understanding complex systems and optimizing unique scenarios.


Emphasis is placed on fundamental algorithms and advanced methods of algorithmic design, analysis, and implementation. 

Techniques to be covered include amortization, randomization, fingerprinting, word-level parallelism, bit scaling, dynamic programming, network flow, linear programming, fixed-parameter algorithms, and approximation algorithms. Domains include string algorithms, network optimization, parallel algorithms, computational geometry, online algorithms, external memory, cache, and streaming algorithms, and data structures..






Machine learning uses data to feed an algorithm that can understand the relationship between the input and the output. When the machine finished learning, it can predict the value or the class of new data point.

Machine learning isn’t a tool or equipment type per se, but rather it’s a set of software algorithms used by the system to help find defects.

Every machine learning model is based on algorithms. Some of the most widely used algorithms at this time include:--

Linear regression: This is a prediction based on relatively consistent information. If you know you walk five miles an hour and you just walked for two hours, you’ve probably traveled 10 miles

Logistic regression: A scientist may have figured out some of the differences between tumors to the extent he can estimate which ones are malignant based on the general data he has about them. These estimates aren’t completely accurate, they merely provide means with which to define the likelihood of an event

Linear discriminant analysis: This is like logistic regression but is capable of handling multiple classes of data in order to predict the probability of events

Decision trees: These are widely used algorithms that employ logical progression to reach decisions. Each node represents an attribute, each link a decision and each leaf an outcome

Naive Bayes: This is a probabilistic model that works by analyzing multiple predictors in order to figure things out. It’s the kind of algorithm used in spam filters or recommendation systems. For example, the algorithm may notice you like certain songs or styles of music and will then attempt to predict others you may enjoy based on characteristics it sees in those songs

K-nearest neighbors: This supervised machine learning algorithm attempts to identify clusters of data. For example, it may notice that you like certain songs and will then figure out which other songs you will also like based on what it has been taught about those songs. The challenge with this is that as the quantity of data grows, both the size of the algorithm and its performance slow

Learning vector quantization: This algorithm could be seen as attempting to harness the power of KNN (above) in a smaller engine. Rather than hanging onto the entire data set, this artificial neural network algorithm lets you decide how many sets of data to keep as it evolves and then continues to make predictions based on any given value’s proximity to the information it holds

Support vector machines: These supervised learning models try to divide any set of results into two sets along a notional hyperplane, a decision boundary that helps classify the information. Support vectors are those data points that sit closest to the hyperplane the model identifies. The AI then attempts to define and act on data in accordance to its position vis-à-vis the hyperplane. It may also attempt to classify new data within the model by working out where it sits in relation to those support vectors

Bagging and random forest: These models attempt to combine data from multiple machine learning algorithms. Bootstrap aggregation (bagging) attempts to reduce the variance between these data sets. The idea is that by doing so the AI can use multiple models to help it deliver more accurate results. Random forest attempts to improve on standard bagging models by combining multiple slightly different models together. Each decision tree reflects its own unique subset of data. The idea is that this creates a more dependable average result

Algorithms are yet another sector in this rapidly evolving industry that is on an accelerated innovation path 

What goes as “artificial intelligence” today are neural networks. A neural network is a computer algorithm that imitates certain functions of the human brain. It contains virtual “neurons” that are arranged in “layers” which are connected with each other. 

The neurons pass on information and thereby perform calculations, much like neurons in the human brain pass on information and thereby perform calculations.




In artificial intelligence, an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection.

An evolutionary algorithm (EA) is an algorithm that uses mechanisms inspired by nature and solves problems through processes that emulate the behaviors of living organisms..

A genetic algorithm relies on binary representation of individuals: an individual is a string of bits, on which the mutation + crossover are easy to be implemented. ... Genetic algorithms are a type of evolutionary algorithm based on evolutionary biology and chromosome representations with evolutionary operators..  



Genetic algorithms are a metaheuristic used for all kinds of optimization problems. While they have applications in machine learning, they have as many applications elsewhere. ... Think about it as Meta Machine Learning algorithm that can generate more problem-specific algorithms

Just as artificial neural networks capture the imagination by comparing algorithms with neurons in an animate brain, genetic algorithms appeal to the metaphor of evolution, nature’s most widely known optimization algorithm. 

Genetic and evolutionary algorithms approach mathematical optimization (how do I maximize or minimize a certain value?) in similar ways. What they have in common are ideas drawn from biology: natural selection, reproduction and genetic mutation

The main difference between genetic algorithms and (most) neural networks is that genetic algorithms use a form of sampling to measure the relationship between a change in a parameter and a change in the fitness (loss), whereas neural networks give you a means to directly calculate that relationship without sampling. 


So the speed-up you get in training a neural network is the speedup associated with not having to collect a number of samples that scales with the number of parameters you want to tune.

 Generative adversarial networks, or GANs, are a powerful type of neural network used for unsupervised machine learning. Made up of two competing models which run in competition with one another, GANs are able to capture and copy variations within a dataset  

For GANs, the discriminative model not only provides the fitness function, it provides the derivatives of the fitness function with respect to each of the inputs to the discriminator. So, unlike a genetic algorithm, you don't need any sampling - you simply calculate the gradient and update accordingly.





A cryptographic algorithm, or cipher, is a set of well-defined but complex mathematical instructions used to encrypt or decrypt data. The encryption and decryption processes depend on a crypto-graphic key selected by the entities participating in the encryption and decryption process.

Three types of cryptography: secret-key, public key, and hash function. Use of the three cryptographic techniques for secure communication. The art of protecting information by transforming it (encrypting it) into an unreadable format, called cipher text. Only those who possess a secret key can decipher (or decrypt) the message into plain text. ...

Cryptography is used to protect e-mail messages, credit card information, and corporate data. A cryptographic system typically consists of algorithms, keys, and key management facilities.

There are two basic types of cryptographic systems: symmetric ("private key") and asymmetric ("public key"). Neural cryptography is a branch of cryptography dedicated to analyzing the application of stochastic algorithms, especially artificial neural network algorithms, for use in encryption and cryptanalysis.

Artificial neural networks are well known for their ability to selectively explore the solution space of a given problem. This feature finds a natural niche of application in the field of cryptanalysis. At the same time, neural networks offer a new approach to attack ciphering algorithms based on the principle that any function could be reproduced by a neural network, which is a powerful proven computational tool that can be used to find the inverse-function of any cryptographic algorithm.




Encryption is an interesting piece of technology that works by scrambling data so it is unreadable by unintended parties Triple DES was designed to replace the original Data Encryption Standard (DES) algorithm, which hackers eventually learned to defeat with relative ease. At one time, Triple DES was the recommended standard and the most widely used symmetric algorithm in the industry.

RSA is a public-key encryption algorithm and the standard for encrypting data sent over the internet. It also happens to be one of the methods used in our PGP and GPG programs.  Unlike Triple DES, RSA is considered an asymmetric algorithm due to its use of a pair of keys. Blowfish is yet another algorithm designed to replace DES.

This symmetric cipher splits messages into blocks of 64 bits and encrypts them individually.  Blowfish is known for both its tremendous speed and overall effectiveness as many claim that it has never been defeated. The Advanced Encryption Standard (AES) is the algorithm trusted as the standard by the U.S. Government and numerous organizations. 

Although it is extremely efficient in 128-bit form, AES also uses keys of 192 and 256 bits for heavy duty encryption purposes.

Machines will do algorithmic, rule-based learning better than we can; but they won’t do rule-breaking creativity anything like we can do it.


They won’t be able to care for the elderly, depressed colleagues, or stressed out and overwhelmed employees. Nor will they be able to discern what tough choices to make as leaders that balances the need to make profit, based on past data, whilst also delivering purpose and forging a future that no amount of data can predict.

With AI, it is necessary to keep an eye on the work to check whether the algorithms are still working within the desired parameters. AI and ML without human interference might drift from the set path. But working in partnership with AI, researchers are relieved of the burden of menial work.

As the algorithms become more robust and are given more autonomy to act without human intervention, we need to ensure that appropriate monitoring, alerts and controls are put in place
ML is a subset of artificial intelligence; in fact, it’s simply a technique for realizing AI.

It is a method of training algorithms such that they can learn how to make decisions. Training in machine learning entails giving a lot of data to the algorithm and allowing it to learn more about the processed information.

Machine learning is a subset of AI in which computer systems are taught to learn on their own. Algorithms allow the computer to analyse data to detect patterns and gain knowledge or abilities without having to be specifically programmed.

 Machine Learning is a technique which develops complex algorithms for processing large data and delivers results to its users. It uses complex programs which can learn through experience and make predictions.

Machine Learning algorithms are designed in such a way that they can learn and improve over time when exposed to new data.

The algorithms are improved by itself through regular input of training data. The goal of machine learning is to understand data and build models from data that can be understood and used by humans.

Whenever we receive a new information, the brain tries to compare it to a known item before making sense of it — which is the same concept deep learning algorithms employ.

For example, artificial neural networks (ANNs) are a type of algorithms that aim to imitate the way our brains make decisions.

Sophisticated AI systems use deep learning to solve computational tasks quickly, using networks of layered algorithms that communicate with each other to solve more and more complex problems.

Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input.

Logistic regression is a popular method, which determines the strength of cause and effect relationships between variables in data sets. It can be used to create an algorithm which predicts whether a transaction is ‘good’ or not.   It is a predictive analysis algorithm and based on the concept of probability. It is a simple Algorithm that you can use as a performance baseline..  

Logistic Regression is the go-to method for binary classification. It gives you a discrete binary outcome between 0 and 1. To say it in simpler words, it’s outcome is either one thing or another. Logistic regression gives you a discrete outcome but linear regression gives a continuous outcome.

.All analytics tools require quality data. AI algorithms and models require quality data. Without an appropriate amount of quality data analytics tools and AI models will not make accurate predictions or provide the best insights. So, any data fed to analytics tools and AI models must be collected, cleaned, and normalized. Data scientists spend nearly 80% of their time preparing data for use in models and business systems. .

Machine learning, gives ‘computers the ability to learn without being explicitly programmed’. Machine learning evolved from the study of pattern recognition and explores the notion that algorithms can learn from and make predictions on data. And, as they begin to become more ‘intelligent’, these algorithms can overcome program instructions to make highly accurate, data-driven decisions.

Predictive analytics is driven by predictive modelling. It’s more of an approach than a process. Predictive analytics and machine learning go hand-in-hand, as predictive models typically include a machine learning algorithm. 

These models can be trained over time to respond to new data or values, delivering the results the business needs. Predictive modelling largely overlaps with the field of machine learning.

There are two types of predictive models. They are Classification models, that predict class membership, and Regression models that predict a number. These models are then made up of algorithms. The algorithms perform the data mining and statistical analysis, determining trends and patterns in data.

 Predictive analytics software solutions will have built in algorithms that can be used to make predictive models. The algorithms are defined as ‘classifiers’, identifying which set of categories data belongs to.

The most widely used predictive models are:--

Decision trees:
Decision trees are a simple, but powerful form of multiple variable analysis. They are produced by algorithms that identify various ways of splitting data into branch-like segments. Decision trees partition data into subsets based on categories of input variables, helping you to understand someone’s path of decisions.

Regression (linear and logistic)
Regression is one of the most popular methods in statistics. Regression analysis estimates relationships among variables, finding key patterns in large and diverse data sets and how they relate to each other.

Neural networks
Patterned after the operation of neuronsin the human brain, neural networks (also called artificial neural networks) are a variety of deep learning technologies. They’re typically used to solve complex pattern recognition problems – and are incredibly useful for analysing large data sets. They are great at handling nonlinear relationships in data – and work well when certain variables are unknown

Other classifiers:---

Time Series Algorithms: Time series algorithms sequentially plot data and are useful for forecasting continuous values over time.
Clustering Algorithms: Clustering algorithms organise data into groups whose members are similar.
Outlier Detection Algorithms: Outlier detection algorithms focus on anomaly detection, identifying items, events or observations that do not conform to an expected pattern or standard within a data set.
Ensemble Models: Ensemble models use multiple machine learning algorithms to obtain better predictive performance than what could be obtained from one algorithm alone.
Factor Analysis: Factor analysis is a method used to describe variability and aims to find independent latent variables.
Naïve Bayes: The Naïve Bayes classifier allows us to predict a class/category based on a given set of features, using probability.
Support vector machines: Support vector machines are supervised machine learning techniques that use associated learning algorithms to analyse data and recognise patterns.

Each classifier approaches data in a different way, therefore for organisations to get the results they need, they need to choose the right classifiers and models.

AI adapts through progressive learning algorithms to let the data do the programming. AI finds structure and regularities in data so that the algorithm acquires a skill: 



The algorithm becomes a classifier or a predictor. So, just as the algorithm can teach itself how to play chess, it can teach itself what product to recommend next online. And the models adapt when given new data. Back propagation is an AI technique that allows the model to adjust, through training and added data, when the first answer is not quite right.

Some of the most famous algorithms are:--

Q-learning
Deep Q network
State-Action-Reward-State-Action (SARSA)
Deep Deterministic Policy Gradient (DDPG)

Training an algorithm requires to follow a few standard steps:--

Collect the data
Train the classifier
Make predictions

The first step is necessary, choosing the right data will make the algorithm success or a failure. The data you choose to train the model is called a feature.  .

The objective is to use these training data to classify the type of object. The first step consists of creating the feature columns. Then, the second step involves choosing an algorithm to train the model. When the training is done, the model will predict what picture corresponds to what object.

AI algorithms can influence the manufacturing supply chain by detecting the patterns of demand for products across geographies, socioeconomic segments, and time, and predicting market demand. This, in turn, will affect inventory, raw material sourcing, financing decisions, human staffing, energy consumption, and maintenance of equipment..



Up until recently, learning algorithms were exclusively designed  for networks with short-term memory but these algorithms have now  been generalized to train networks composed of two sub-networks, one  dedicated to long term-storage and one more specialized for online computation..

Machine learning algorithms are programs (math and logic) that adjust themselves to perform better as they are exposed to more data. ... So a machine-learning algorithm is a program with a specific way to adjusting its own parameters, given feedback on its previous performance making predictions about a dataset.

Machine learning algorithms are the engines of machine learning, meaning it is the algorithms that turn a data set into a model. Which kind of algorithm works best (supervised, unsupervised, classification, regression, etc.) depends on the kind of problem you’re solving, the computing resources available, and the nature of the data.

5 most commonly used machine learning algorithms.--
Linear Regression.
Logistic Regression.
Decision Tree.
Naive Bayes.
kNN.








In general, AI algorithms are “black boxes” in the sense we know little to nothing about their inner workings and therefore can’t explain how they’re coming to their decisions.

Understanding AI’s logic and reasoning is more than just double-checking the machine’s work. We have to be able to show what went into making that specific decision Clear visibility into the facts leads to more understanding between all parties, better governance, and a much better customer experience.

In computer programming and software engineering, black box testing is used to check that the output of a program is as expected, given certain inputs. The term "black box" is used because the actual program being executed is not examined.

Algorithms affects all aspects of our daily lives from using your phone alarm clock to wake up in the morning to the music and podcast you listen to, from the smart devices in your home to your social media interactions, from your smart fridge to your web browsing activity, from ride sharing and food delivery services to sending and receiving text messages, phone calls, and emails. Algorithms are weaved into the tapestry of our lives.

Algorithms are embedded in our every day lives whether we like it or not.  Like I said before, algorithm is a process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer.. as unambiguous specifications for performing calculation, data processing, automated reasoning, and other tasks.

The ‘black box problem’ refers to opaque calculations in complex algorithms.  Black box algorithm refers to a machine learning model where you know what goes in and what comes out, but you don’t know or understand the inner workings of the algorithm or how the algorithm is producing its results. 

Black box algorithms are usually complex machine learning models as opposed to simplified machine learning models like logistic regression. Typically, black box algorithms produce better outcomes with greater accuracy than simple algorithms. 



Black box algorithms gain their names from the mystery they present in exactly how they work and the difficulty in explaining why the result is what they are. Machine learning algorithms are responsible for black box algorithms

For a data scientist to be able to communicate with non technical people in a business environment is critical. So, if a data scientist doesn’t understand the model they are working with, then they can’t explain it to non-technical people. 

And, if they can’t explain how they got the results they have and how they came to this conclusion to business leaders, then the results from the black box algorithm might be rejected by business leaders even if the results are accurate. 

Humans have a natural tendency to throw away or reject anything they don’t understand. Also, if data scientists don’t understand the model they are working with, they can’t communicate it and explain it to their fellow data scientists



You can’t document something you don’t know exists. Proper documentation is important in programming. Documentation ensures consistency, helps you remember the work you did, and explain your work to others. If you can’t and don’t understand the inner workings of your algorithms, then you can’t document it.

If you don’t understand how the algorithms is working, then you won’t know how to tweak it to improve the algorithms and thus improve your results.

If you don’t understand the inner workings of the algorithms, the why and how it is deriving the outcomes it is producing, then you have no way of knowing if the algorithm is being consistent in its production process. You won’t know for a fact if the mechanical workings that lead to the last result is equivalent to the mechanical workings that lead to the current result.

If you don’t understand the algorithm, then you won’t be able to check, spot and correct the biases that might be built into the algorithm. You won’t even know the algorithm has biases that you should compensate or correct for.

Interpretability is crucial for several reasons. If researchers don’t understand how a model works, they can have difficulty transferring learnings into a broader knowledge base..  Interpretability is essential for guarding against embedded bias or debugging an algorithm. It also helps researchers measure the effects of trade-offs in a model. 

More broadly, as algorithms play an increasingly important role in society, understanding precisely how they come up with their answers will only become more critical. Linear models are user-friendly because they are simple and easy to understand. However, achieving the highest accuracy for large modern datasets often requires more complex and expressive models, like neural networks

In some ML-enabled applications, the black box issue doesn’t matter because users have no choice if they want to leverage the machine’s intelligence. Providing explainability for sophisticated machine learning models is an area of active research. Roughly speaking, there are five main approaches:

Use simpler models. This sacrifices accuracy for explainability.

Combine simpler and more sophisticated models. The more sophisticated model provides the recommendation, the simpler model provides rationales. This often works well, but there are gaps when the models disagree.




Use intermediate model states. For example, in computer vision, states in intermediate layers of the model are excited by certain patterns. These can be visualized as features (like heads, arms, and legs) to provide a rationale for image classification.

Use attention mechanisms. Some of the most sophisticated models have a mechanism to direct “attention” towards the parts of the input that matter the most (i.e., setting higher weights). These can be visualized to highlight the parts of an image or a text that contribute the most to a particular recommendation.

Modify inputs. If striking out a few words or blacking out a few parts of an image significantly changes overall model results, chances are these inputs play a significant role in the classification. They can be explored by running the model on variants of the input, with results are highlighted to the user.

Ultimately, human decision making can only be explained to some degree. It is the same for sophisticated algorithms. However, it is software providers’ responsibility to accelerate research on technical transparency to build further trust in intelligent software.

AI can scan tons of data and use that as a basis to quickly make decisions for any new situation based on that history of patterns.” one of the “pitfalls” of AI is that it uses a lot of “black box” techniques. These methods have an innate tendency to perpetuate and amplify biases present in the data with respect to factors such as race, gender and education,..  

Traditional algorithms range from simple business rules to highly complex decision engines that require greater involvement of data scientists in tuning, maintenance and re-calibration. As a result, they afford greater transparency and control than AI that runs in auto-pilot mode… 

The programmer must include all the rules and regulations for the algorithm to work properly because it has no common sense and no idea of things that are obviously wrong to us because the program does not understand it. You won't need data to develop an algorithm, but the algorithm needs to be perfect with very specific and clear action plans to properly work..

Automated decision-making has received a lot of bad press in recent years, mostly because people don't trust the technology behind it. A big part of that mistrust is due to black box AI, which produces outcomes from the murky depths of data, using algorithms. Explainable AI, however, is the bright future of artificial intelligence for business. 

As any maths teacher will tell you, writing the answers won’t get you the best grade. You have to show your working.. Deep learning networks are made of a huge number of connections, and in the middle of the process sits lots of data and the algorithms that interpret that data. Black box AI is where we understand the inputs and we know the outcomes, but we don’t know how the AI has arrived from A to B.

We should have the legal right to know why algorithms come to specific decisions about us. But there’s a clash, as software owners often claim that increasing transparency could risk revealing their intellectual property, or help individuals find loopholes to game the system

This black-box paradox is particularly problematic in healthcare, where the method used to reach a conclusion is vitally important. Responsibility for making life and death decisions, physicians are unwilling to entrust those decisions to a black box.




.KOSHER MEDICAL INSURANCE COMPANIES ARE MISUSING BLACK BOX ALGORITHMS FOR EUTHANASIA ( MAKE PROFITS FROM HUMAN LIFE )


Black box phenomena presents an operational reality where some AI becomes an obfuscatory force. Instead of being a force for positive informational use, the black box algorithm becomes yet another impenetrable data layer. It heaps on additional difficulties in building and maintaining trust that the information it presents is indeed as valuable as the end user expects it to be. 

This lack of transparency is essentially like a drug that does not disclose its side effects. Go ahead and use it, but you don’t know exactly what you’re getting yourself into and you may not like what happens next. Since there is no universal standard for tagging algorithmic bias, and since it is unlikely that AI developers will voluntarily disclose their algorithmic bias profile, end users find themselves dealing with AI applications that have zero accountability. 

Not only can use of these applications yield untrustworthy data, but the end user might not even be aware of that risk.

The human will not be able to trust the AI’s ability to learn from its mistake nor will the human be able to offer constructive feedback to the AI. Consequently, the human will lose faith in the AI.

Explainable AI, as opposed to black box AI, provides transparency for the part of the artificial intelligence process where algorithms interpret data.

This means two main business problems are solved:--

Accountability – we know how an automated decision is reached and can trace the path of reasoning if needed.
Auditability – we can review processes, test, and refine them more accurately, and predict and prevent future failures or gaps.

Ultimately, by opening up the black box of AI, we can build trust and thus explainability.
Explainable AI (XAI), Interpretable AI, or Transparent AI refer to techniques in artificial intelligence (AI) which can be trusted and easily understood by humans. It contrasts with the concept of the "black box" in machine learning where even their designers cannot explain why the AI arrived at a specific decision.

Explainable AI (XAI) refers to methods and techniques in the application of artificial intelligence technology (AI) such that the results of the solution can be understood by human experts. It contrasts with the concept of the "black box" in machine learning where even their designers cannot explain why the AI arrived at a specific decision.  

XAI is an implemention of the social right to explanation.   Vested interests claim that transparency rarely comes for free and that there are often trade-offs between the accuracy and the explanaibility of a solution.

The technical challenge of explaining AI decisions is sometimes known as the interpretability problem.   Another consideration is info-besity (overload of information), thus, full transparency may not be always possible or even required.



In machine learning or deep learning, the trained model is a ‘black box’ where designers cannot explain why the model takes a particular decision or which features are considered while making a decision. To get explanations ‘glass box’ models are required, which increase transparency, accountability, and trustworthiness without sacrificing learning performance. 

The explanation provided by XAI should be easily understood by humans. In other words, it is knowledge extraction from ‘black box’ models and representation in a human-understandable format.

The performance of deep neural networks has reached or even exceeded the human level on many complex tasks. In spite of their huge success, they are not effective because of their inability to explain their decision in a human-understandable format. 

It is challenging to get insights into their internal working; they are black boxes for humans. As we move forward with building more intelligent and robust AI systems, we should get to the bottom of these black boxes. There are many fields such as healthcare, defense, finance, security, transportation, etc. where we can not blindly trust the AI models.

Life-changing decisions are happening in the dark. Machine-learning algorithms now determine decisions from loan applications to cancer diagnoses. In France, they place children in schools. In the US, they determine prison sentences. They can set credit scores and insurance rates, and decide the fate of job candidates and university applicants.

But these programs are often unaccountable. To arrive at their decisions, machine-learning algorithms automatically build complex models based on big data sets, so that even the people using them may not be able to explain why or how a particular conclusion is reached. They’re a black box.

Most black box AIs do not provide reproducible research because classification and predictive accuracy is low. Accuracy tends to be low in such platforms because these tools try to ‘boil the ocean’ and solve very general problems. However, there’s no such thing as ‘general artificial intelligence’. 

Opaquely producing “AI” outputs that are incapable of classifying language according to specific concepts faster than human experts will not engender trust with portfolio managers, bankers, traders and analysts and will not help them make better decisions and generate alpha in the digital age. Rather, such black box tools will continue to fuel the hype around “AI”. 

Transparency builds trust. And most marketed AI is operating behind a set of tinted windows because transparency would reveal that these tools produce low semantic accuracy, no predictive insights- no alpha.


One major barrier to clear regulations for AI application in healthcare is the adaptive nature of deep learning systems. Unlike a physical device such as an ultrasound, AI-based SAMD can learn continuously. This is excellent news for patients, researchers, and clinicians—so long as the algorithm is learning correctly. 

AI BLACK BOX ALGORITHMS ARE USED BY WHITE JEWS TO SCREW MUSLIM PALESTINIANS IN ISRAEL—AND THE WORLD TURNS A NELSONs EYE..



A black teen steals something and gets rated high-risk for committing future crime by an algorithm used in courtroom sentencing, while a white man steals something of similar value and gets rated low-risk.

WHEN A WHITE  JEW DOES SERIAL MURDERS , HE IS MENTALLY SICK..   

BUT IF A BLACK MUSLIM DOES ONE SINGLE MURDER OUT OF RAGE EMANATING FROM ENDLESS HUMILIATION HE IS A TERRORIST

Algorithmic bias can result when the initial data used to train an AI system isn’t diverse enough (say, if it includes mostly white men) or if it reflects biased decisions authorities made in the past. 

For example, if officers overpoliced a certain neighborhood, yielding a high rate of arrests there, and that arrest data is used to train an AI, the system could end up reinforcing the old bias. “If a defendant is labeled ‘high risk’ by a recidivism algorithm, that can mean the difference between a fine and a prison sentence.”

IF THE JOKER MOVIE HAD A BLACK MUSLIM HERO INSTEAD OF A WHITE JEW, THE MOVIE WOULD HAVE NEVER HIT THE SCREENS



Deep neural networks, today’s most popular models, whose outputs are determined by millions of finely-tuned parameters and are therefore black boxes for us.

Again, black box  is a metaphor describing how people are unable to see or understand how technologies work and is particularly used to characterize the lack of understanding of how an algorithm works.

While we can understand the outputs of artificial intelligence (AI) – in terms of recommendations, decisions and so on – the processes to achieve them are too complicated for us to understand.

Concerns about the black box nature of AI center on its apparent lack of accountability, potential unseen biases and the inability to have clear visibility into what is driving an AI’s potentially life-changing decisions.

KOSHER BIG BROTHER WANTS TO TAKE OVER THE PLANET BY BLACK BOX AI SYSTEMS..   CAPT AJIT VADAKAYIL WONT ALLOW THIS..

CAN ANYBODY TELL WHICH BROWN BLOGGER SHOT DOWN BLACK CHAIN / BITCOIN AND SHELL COMPANIES ?

BATAO NAH?

PLEAJJJE !



Machine-learning systems pour through petabytes of data to zero in on suspicious correlations. But often the processes that lead these systems to flag questionable transactions are shrouded in mystery—a “black box.”  Regulators have to understand how and why a bank pinpointed a specific transaction or individual. This is essential for fairness and legal transparency.

KOSHER BOEING HAS ESCAPED MANSLAUGHTER  LIABILITIES AFTER THE CRASH OF TWO SUPERMAX 737 AIRLINE PLANES


Recent tragic events such as the Lion Air and Ethiopian accidents.. It's the robot's fault! It is wrong to think that the terms autonomous and unmanned are interchangeable, or even that an unmanned system is an AI system. Automation is of course a large part of unmanned operations and an AI solution may form part of the platform or the supporting operations.

Until now, there has been an assumption that unmanned systems would not involve people on board and so considerations of surface damage, rather than passenger liability, have been the major issue. In this regard, the position for unmanned operations is no different to manned operations, but the current increasing pace of interest in urban air mobility applications shows that it is now time to consider the passenger situation further.

Historically, aviation has relied heavily on the training and skills of flight crew as the last line of defence when other systems fail..

 AI applications and their algorithms tend to operate in black boxes – closed systems that give limited insight into how they reach their outcomes – and this can pose problems for key (human) decision makers in a business, many of whom are unaware of how AI systems make their decisions. 

Data is input and trained by the AI solution, but the output – the decision on what the next move should be – is made by the software.

What does this mean for manufacturer liability? That will require a proper risk assessment: to what extent do AI functions need to be failsafe and what is the default situation if they are not functioning correctly?



Increasingly, complex algorithms and machine learning-based systems are being used to achieve business goals, accelerate performance, and create differentiation. But they often operate like black boxes for decision making, and are not controlled appropriately, though they are vulnerable to a variety of risks.

We MUST  learn to harness the power of complex algorithms while managing the accompanying risks with a robust algorithmic risk management framework.

When algorithms and humans make sufficiently similar decisions their collaboration does not achieve improved outcomes. On the other hand, when algorithms fail, humans may not be able to compensate for their errors. Even if algorithms do not officially make decisions, they anchor human decisions in serious ways.

Today in USA individuals don’t even know that a choice not to hire, promote, or extend a loan to them was informed by a statistical algorithm.   

Kosher artificial intelligence would always choose Jew Albert Einstein to be the clerk in the peer review section or invention patent section , so that he can steal.


Understanding how a neural network comes to its decisions has been a long-standing challenge for artificial intelligence (AI) researchers. As the neural part of their name suggests, neural networks are brain-inspired AI systems intended to replicate the way that humans learn. They consist of input and output layers, and layers in between that transform the input into the correct output. 

Some deep neural networks have grown so complex that it’s practically impossible to follow this transformation process. That's why they are referred to as "black box” systems, with their exact goings-on inside opaque even to the engineers who build them.





Deep learning, by its very nature, is specifically a dark black box.  Deep-learning systems are increasingly moving out of the lab into the real world, from piloting self-driving cars to mapping crime and diagnosing disease. But pixels maliciously added to medical scans could fool a DNN into wrongly detecting cancer

Neural network is one of the best tools for data mining tasks due to its high accuracy. However, one of the drawbacks of neural network is its black box nature. This limitation makes neural network useless for many applications which require transparency in their decision-making process
The black box problems are usually addressed by so called rule extraction.

Deep learning is a class of machine learning algorithms that uses multiple layers to progressively extract higher level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces.

AI applications have been referred to as a ‘black box’ Where not even engineers can decipher why the machine  made a certain decision. This can significantly hinder  their effectiveness, and cause concern. 

The use of ‘black  box’ algorithms makes it difficult not only to identify when  things go wrong, but also to determine who is responsible  in the event on any damage or ethical lapse. This makes it prone to data poisoning.  In the medical field even a small error like wrong dosage can be devastating

An algorithm does not read the image as a human brain does. The computer reads numbers, not images. All images are composed of pixels of varying intensity, across multiple channels of colors.

A deep learning algorithm like neural network applies multiple filters on each layer, which are passed on the entire slice, followed by activations, regularizations, forward and back propagation and in the end optimizing the weights associated with each layer to minimize the loss.

The optimized weights that are generated for each unit in every layer of the network after completing the training process over millions and billions of images can now be applied to any new image and the algorithm will be able to classify the image.




How are these algorithms ‘fooled’?

The algorithms do not see the image, it just reads the pixel intensity as numbers and uses pre-trained weights to classify the image. If a carefully constructed noise is added to the image, the results of weight-pixels multiplication can be manipulated to generate the output probabilities such that the algorithm classifies the image as an entirely different thing.

There are ways to trick a ‘black box’ algorithm too.   

Researchers at MIT designed a way to fool state-of-the-art Google’s Cloud Vision API by targeting specific parts of the image and manipulating a few pixels that human eye cannot detect The researchers also found a way to generate adversarial examples for any ‘black-box’ network to manipulate the network into thinking whatever they wanted it to (yes, not just incorrect classification but ‘whatever’ results they wanted). 

For example, they fooled the algorithm into believing a photo of machine guns was a picture of a helicopter, by just manipulating a few pixels. The image would still look good to the human eye



This is something that posed a threat to AI-based Airport security scanners, which scan the luggage for potentials weapons or explosives. If someone wants to bypass this, they would just have to put certain other objects at the specific positions in their luggage or an adversarial patch so that the image being fed to the AI-enabled scanner gets distorted and the luggage passes the security scanner

Algorithm fooling another algorithm by learning from it — Generative Adversarial Networks (GANs):




The architecture of GAN consists of two networks — one a Generator and other a Discriminator. In the case of images, The Generator generates an image, and the Discriminator compares it with the actual image and sends the feedback to the Generator, which now generates a better image which is closer to the actual image.

The loop continues till the Generator generates an image that the Discriminator cannot differentiate from the real image, thus passing an artificially generate an image as actual image. This works for other domains too — speech, text, videos, music, etc.

Not just fooling the AI algorithms, but fooling the humans too!

These adversarial attacks are not just restricted to Image-classifier algorithms. Generative Adversarial Networks can be used to ‘generate’ an entirely new data (image, sound, text, video) after training them on real-world data. It can generate an entirely new face by training on a lot of examples, or a new voice sample by training the algorithm on voice, or an entirely new video of someone — the world of Deepfakes .


Deepfakes are touted as the next big security nightmare, and they are becoming better and better every day. Here is a video of someone impersonating Barack Obama, and honestly, it is difficult to tell if the video is fake. This can be used with destructive intentions to change public perception about anyone, or inciting a mob, or hurting religious sentiments leading to riots and violence!
How all these can be avoided to make AI better and more secure:

This list is not exhaustive but covers some of the points. The one thing that is paramount is that with increased usage, the algorithms need to be more secure. We might be moving away for the currently open-sourced networks and technologies to more secure and restrictive algorithms and technology platforms which will be used in a high-security environment and will not be available to everyone.

Also, the deep learning algorithms need to be trained explicitly in detecting frauds, possibly by a brute force approach or any other method. GANs are becoming better and better at generating fake images that can fool humans, and these will require special attention. AI algorithms can themselves be used to detect a fake image or a video, better than a human.

Also, additional inputs can be taken into account which is difficult to replicate — for example, an airport security scanner might take into account the facial image, gait, height, iris scan etc to become more foolproof.

There is a lot that is being done to build more and more accurate AI algorithms, but before we take them up and build our lives around these algorithms, we need to secure them and make sure they are not susceptible to fraud easily. Frauds and hacks will still happen, but it will be more difficult to do. Maybe an AI algorithm can better solve the problem of fooling an AI algorithm.

Adversarial machine learning is a technique used in machine learning to fool or misguide a model with malicious input. While adversarial machine learning can be used in a variety of applications, this technique is most commonly used to execute an attack or cause a malfunction in a machine learning system. The same instance of an attack can be changed easily to work on multiple models of different datasets or architectures.

Adversarial machine learning can be considered as either a white or black box attack. In a white box attack, the attacker knows the inner workings of the model being used and in a black box attack, the attacker only knows the outputs of the model.


Cloud computing and access to powerful CPUs/GPUs are increasing the risk of these adversaries becoming experts at wielding these AI/ML tool sets, which were designed for the good guys to use.

When combined with AI, ML provides automation platforms for exploit kits and, essentially, we're fast approaching the industrialization of automated intelligence to break down cyber defenses that were constructed with AI and ML.


Many of these successful exploit kits enable a new level of automation that makes attackers more intelligent, efficient, and dangerous.

Below :  A “global bypass” method to fool the Cylance algorithm.  The bypass method, in this case, involved taking strings from a nonmalicious file and adding it to the malicious file to trick the system into thinking it was benign. The method is said to work because Cylance’s machine-learning algorithm has been trained to favor a benign file, causing it to ignore malicious code if it sees strings from the benign file attached to a malicious file.




The adversarial use of artificial intelligence (AI) and machine learning (ML) in malicious ways by attackers may be embryonic, but the prospect is becoming real. It's evolutionary: AI and ML gradually have found their way out of the labs and deployed for security defenses, and now they're increasingly being weaponized to overcome these defenses by subverting the same logic and underlying functionality.

One improvement on the horizon is the ability to enable teams in the security operations center to understand how ML models reach their conclusions rather than having to flat-out trust that the algorithms are doing their jobs. So, when the model says there is anomalous risky behavior, the software can explain the reasoning behind the math and how it came to that conclusion.

This is extremely important when it's difficult to detect if adversaries have injected bad data — or "poisoned" it — into defensive enterprise security tools to retrain the models away from their attack vectors. Adversaries can create a baseline behavioral paradigm by poisoning the ML model data, so their adversarial behaviors artificially attain a low risk score within the enterprise and are allowed to continue their ingress.




A poisoning attack happens when the adversary is able to inject bad data into your model’s training pool, and hence get it to learn something it shouldn’t. The most common result of a poisoning attack is that the model’s boundary shifts in some way,

Poisoning attacks come in two flavors — those targeting your ML’s availability, and those targeting its integrity (also known as “backdoor” attacks).

As of today, poisoning attacks have been studied against sentiment analysis, malware clustering, malware detection, worm signature detection, DoS attack detection, intrusion detection, more intrusion detection, even more intrusion detection, and my personal favorite: social media chatbots.

How much the attacker knows about your system — before they launch the attack — is important. When thinking about information access, it is common to distinguish between two types of adversaries: WhiteBox (knows the internals of your model) and BlackBox (doesn’t).

When we talk about evasion attacks, that knowledge is what mainly defines how powerful of an attack the adversary can mount.

In poisoning attacks, however, there is a second just as important dimension that defines the attacker’s capability — adversarial access — or how deep they can get their hands into your system. 

Just like information access, adversarial access comes in levels (from most dangerous to least):---
Logic corruption
Data manipulation
Data injection
Transfer learning

Logic corruption is the most dangerous scenario. Logic corruption happens when the attacker can change the algorithm and the way it learns. At this stage the machine learning part stops to matter, really, because the attacker can simply encode any logic they want. 

You might just as well have been using a bunch of if statements. The adversary has the ability to meddle with the learning algorithm.  These attacks are referred as logic corruption. Apparently, it becomes very difficult to design  counter strategy against these adversaries who can alter the learning logic, thereby controlling the model itself.

Data Injection: The adversary does not have any access to the training data as well as to the learning algorithm but has ability to augment a new data to the training set. He can corrupt  the target model by inserting adversarial samples into the training dataset.

Data Modification: The adversary does not have access to the learning algorithm but has full access to the training data. He poisons the training data directly by modifying the data before it is used for training the target model.

Artificial Intelligence (AI) can indeed be a black box.  Users can't really figure out what drives the algorithm - what data has the machine been fed with, how it is nurtured and why machines make the decisions they do..


10 Algorithms: Machine Learning Engineers Need to Know
In a world where nearly all manual tasks are being automated, the definition of manual is changing. Machine Learning algorithms can help computers play chess, perform surgeries, and get smarter and more personal.

We are living in an era of constant technological progress, and looking at how computing has advanced over the years, we can predict what’s to come in the days ahead.

One of the main features of this revolution that stands out is how computing tools and techniques have been democratized. In the past five years, data scientists have built sophisticated data-crunching machines by seamlessly executing advanced techniques. The results have been astounding.

How Learning These Vital Algorithms Can Enhance Your Skills in Machine Learning
If you're a data scientist or a machine learning enthusiast, you can use these techniques to create functional Machine Learning projects:

.An algorithm is simply a set of actions to be followed in order to get to a solution. When it comes to ML, the algorithms involve taking data and performing calculations to find an answer. The complexity of these calculations differs depending on the task. The best algorithm allows you to get the right answer in the most efficient manner.

If an algorithm works longer than a human does, it’s useless. If it offers incorrect information, it’s unnecessary. Algorithms get training to learn how to process information. The efficiency, the accuracy, and the speed depend on the training quality.

When you use an algorithm to come up with the right answer, it doesn’t automatically mean using AI and/or ML. But if you are using AI and ML, you are taking advantage of the algorithms.

All humans have eyes, but not all creatures who have eyes are human.

These days, we hear about AI and ML being used whenever an algorithm exists. Using an algorithm to predict event outcomes doesn’t involve machine learning. Using the outcome to improve the future predictions does.

There are three types of Machine Learning techniques:

All three techniques are used in this list of 10 common Machine Learning Algorithms:

1. Linear Regression
To understand the working functionality of this algorithm, imagine how you would arrange random logs of wood in increasing order of their weight. There is a catch; however – you cannot weigh each log. You have to guess its weight just by looking at the height and girth of the log (visual analysis) and arrange them using a combination of these visible parameters. This is what linear regression is like.

In this process, a relationship is established between independent and dependent variables by fitting them to a line. This line is known as the regression line and represented by a linear equation Y= a *X + b.

In this equation:

Y – Dependent Variable
a – Slope
X – Independent variable
b – Intercept
The coefficients a & b are derived by minimizing the sum of the squared difference of distance between data points and the regression line.





2. Logistic Regression

Logistic Regression is used to estimate discrete values (usually binary values like 0/1) from a set of independent variables. It helps predict the probability of an event by fitting data to a logit function. It is also called logit regression.

These methods listed below are often used to help improve logistic regression models:-- 
include interaction terms
eliminate features
regularize techniques
use a non-linear model




3. Decision Tree

It is one of the most popular machine learning algorithms in use today; this is a supervised learning algorithm that is used for classifying problems. It works well classifying for both categorical and continuous dependent variables. In this algorithm, we split the population into two or more homogeneous sets based on the most significant attributes/ independent variables.




4. SVM (Support Vector Machine)


SVM is a method of classification in which you plot raw data as points in an n-dimensional space (where n is the number of features you have). The value of each feature is then tied to a particular coordinate, making it easy to classify the data. Lines called classifiers can be used to split the data and plot them on a graph.




5. Naive Bayes

A Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.

Even if these features are related to each other, a Naive Bayes classifier would consider all of these properties independently when calculating the probability of a particular outcome.

A Naive Bayesian model is easy to build and useful for massive datasets. It's simple and is known to outperform even highly sophisticated classification methods.


Looking forward to becoming a Machine Learning Engineer? Check out the Machine Learning Certification course and get certified today.




6. KNN (K- Nearest Neighbors)

This algorithm can be applied to both classification and regression problems. Apparently, within the Data Science industry, it's more widely used to solve classification problems. It’s a simple algorithm that stores all available cases and classifies any new cases by taking a majority vote of its k neighbors. The case is then assigned to the class with which it has the most in common. A distance function performs this measurement.

KNN can be easily understood by comparing it to real life. For example, if you want information about a person, it makes sense to talk to his or her friends and colleagues!

Things to consider before selecting KNN:-- 
KNN is computationally expensive
Variables should be normalized, or else higher range variables can bias the algorithm

Data still needs to be pre-processed.




7. K-Means
This is an unsupervised algorithm that solves clustering problems. Data sets are classified into a particular number of clusters (let's call that number K) in such a way that all the data points within a cluster are homogenous and heterogeneous from the data in other clusters.

How K-means forms clusters:--
The K-means algorithm picks k number of points, called centroids, for each cluster.
Each data point forms a cluster with the closest centroids i.e., K clusters.
It now creates new centroids based on the existing cluster members.

With these new centroids, the closest distance for each data point is determined. This process is repeated until the centroids do not change.




8. Random Forest
A collective of decision trees is called a Random Forest. To classify a new object based on its attributes, each tree is classified, and the tree “votes” for that class. The forest chooses the classification having the most votes (over all the trees in the forest).

Each tree is planted & grown as follows:-- 
If the number of cases in the training set is N, then a sample of N cases is taken at random. This sample will be the training set for growing the tree.
If there are M input variables, a number m<

Each tree is grown to the most substantial extent possible. There is no pruning. 





9. Dimensionality Reduction Algorithms
In today's world, vast amounts of data are being stored and analyzed by corporates, government agencies, and research organizations. As a data scientist, you know that this raw data contains a lot of information - the challenge is in identifying significant patterns and variables.


Dimensionality reduction algorithms like Decision Tree, Factor Analysis, Missing Value Ratio, and Random Forest can help you find relevant details.




10. Gradient Boosting & AdaBoost
These are boosting algorithms used when massive loads of data have to be handled to make predictions with high accuracy. Boosting is an ensemble learning algorithm that combines the predictive power of several base estimators to improve robustness.


In short, it combines multiple weak or average predictors to build a strong predictor. These boosting algorithms always work well in data science competitions like Kaggle, AV Hackathon, CrowdAnalytix. These are the most preferred machine learning algorithms today. Use them, along with Python and R Codes, to achieve accurate outcomes.






If you want to build a career in machine learning, start right away. The field is increasing, and the sooner you understand the scope of machine learning tools, the sooner you'll be able to provide solutions to complex work problems.



Most AI is based on machine learning, where software learns to spot patterns in large data sets.



Machine learning (ML) is the technique perhaps most closely associated with AI today: a computer-driven algorithm in which a statistical model is developed iteratively (ie it ‘learns’) in order to optimize the model’s performance at solving a given problem. As with people, external feedback can aid this process, in which case the ML is called ‘supervised learning’.




Machine learning (ML) is a subset of AI and is based on the idea of writing computer algorithms that automatically upgrade themselves by discovering patterns in existing data, without being explicitly programmed. 

It is also used to automatically analyse the way interconnected systems work in order to detect cyber-attacks and limit their damage. The entire processing of ML tools depends on data. The more data an algorithm obtains, the more accurate it becomes and thus, the more effective the results it delivers.



Unlike machine learning, AI learns by acquiring and then applying knowledge. The aim of AI is to find the optimal solution by training computers to respond as well as (or better than) a human. 

Artificial Intelligence is ideal for situations where adapting to new scenarios is important.

Machine learning uses past experiences to look for learned patterns, while Artificial Intelligence uses the experiences to acquire knowledge and skills, then applies that knowledge to new scenarios.


Data annotation (also called data labeling) is quite critical to practical machine learning at scale. It is one of the most dependable ways to improve your model performance (much more dependable than tuning your architecture), so it becomes a very key lever.

Annotation in machine learning is a process of labeling the data on images containing specific objects like humans, vehicles, objects to make it recognizable for machines.


Google has developed a solution that promises to cut down on labeling time dramatically. It’s called Fluid Annotation, and it employs machine learning to annotate class labels and outline every object and background region in a picture Google claims it can accelerate the creation of labeled data sets by a factor of three.

Annotation literally means to label a given data like image, video etc.. for further references purposes. This is done by assigning some sort of keywords on the particular area of text, image etc.. 

This is achieved by the annotations which can be from image, text, video, or audio

Annotation is often the hardest part of the artificial intelligence (AI) training process. This is especially true for computer vision – traditional annotation tools require human annotators to outline each object in a given image

Fluid Annotation, an intuitive human-machine collaboration interface for annotating the class label and outline of every object and background region in an image. 

Fluid Annotation starts from the output of a strong neural network model, which the annotator can edit by correcting the labels of existing regions, adding new regions to cover missing objects, and removing incorrect regions. 

Fluid annotation has several attractive properties: (a) it is very efficient in terms of human annotation time; (b) it supports full images annotation in a single pass, as opposed to performing a series of small tasks in isolation, such as indicating the presence of objects, clicking on instances, or segmenting a single object known to be present. 

Fluid Annotation subsumes all these tasks in one unified interface. (c) it empowers the annotator to choose what to annotate and in which order. This enables to put human effort only on the errors the machine made, which helps using the annotation budget effectively.




Artificial intelligence (AI) is  a field of study that tries to make computers smart.  It is the concept of making machines capable of performing tasks without human intervention, such as building smart machines.   Artificial intelligence is about imparting a cognitive ability to a machine.

AI is the ability of a machine or a computer program to think and learn. The concept of AI is based on the idea of building machines capable of thinking, acting, and learning like humans.

Artificial Intelligence uses models developed by Machine Learning and other algorithms to lead to intelligent behavior. AI is very much programming based.

 Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems.  Deep Learning can automatically discover the features to be used for classification, ML requires these features to be provided manually.

Deep learning is a subset of machine learning. Usually, when people use the term deep learning, they are referring to deep artificial neural networks.

Deep learning, approach within machine learning, is loosely modeled on the brain’s “neural networks”. In a deep learning mesh, you have “neurons” which have discrete layers and connections to other “neurons” — much like the neurons in our own brains do. Each layer of neurons picks out a specific feature to learn

The idea behind machine learning is that the machine can learn without human intervention. .
Machine learning is a tool to analyze, understand and identify a pattern in the data.

Machines, do not mind going through the same routine, over and over, and they perform routine, repetitive tasks much faster and more efficiently than people do.

Fundamental issue of machine learning: its inability to learn like a human. The technology intrinsically differs from the way humans learn about the world around them, and that’s not going to be an easy problem to fix.

 AI continues to struggle with some of the most basic world views of the human brain, due to its inability to learn the way we do.

Deep learning technically is machine learning and functions in the same way but it has different capabilities. The main difference between deep and machine learning is, machine learning models become better progressively but the model still needs some guidance. 

If a machine learning model returns an inaccurate prediction then the programmer needs to fix that problem explicitly but in the case of deep learning, the model does it by himself. Automatic car driving system is a good example of deep learning.

Machine learning is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead.  It is a subset of artificial intelligence.

All machine learning counts as AI, but not all AI counts as machine learning. For example, symbolic logic – rules engines, expert systems and knowledge graphs – could all be described as AI, and none of them are machine learning..

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves .

One of the most important features of machine learning algorithms is that they are able to analyze vast amounts of transaction data and flag suspicious transactions with highly accurate risk scores in real-time. This risk-based analytics approach detects complex patterns that are difficult for analysts to identify, meaning banks and financial organizations are far more operationally efficient while detecting more fraud. 

The algorithms take into account several factors, including the customer’s location, the device used, and other contextual data points to build up a detailed picture of each transaction. This approach improves real-time decisions and better protects customers against fraud, all without impacting the user experience. 

With AI, data analysis is completed in milliseconds, efficiently detecting complex patterns that can be difficult for a human analyst to identify.  This reduces the amount of manual work spent on monitoring all transactions, because fewer cases require human attention.


https://en.wikipedia.org/wiki/Anthony_Levandowski

IN A 2017 COURT FILING OVER AN INTELLECTUAL PROPERTY SUIT WITH UBER, GOOGLE REVEALED THAT ONE OF THE LEADERS OF ITS SELF-DRIVING CAR DEPARTMENT, ANTHONY LEVANDOWSKI, RECEIVED US$120 MILLION IN PAY AND INCENTIVES SINCE 2007.

AI EXPERTS GET MORE MONEY THAN TOP BANKERS

capt ajit vadakayil
..


Often confused with artificial intelligence, machine learning actually takes the process one step further by offering the data necessary for a machine to learn and adapt when exposed to new data

Again, deep learning is a subset of machine learning, and machine learning is a subset of AI, which is an umbrella term for any computer program that does something smart. In other words, all machine learning is AI, but not all AI is machine learning

.Machine Learning. Data systems that modify themselves by building, testing and discarding models recursively in order to better identify or classify input data.
.
As the name suggests, machine learning can be loosely interpreted to mean empowering computer systems with the ability to “learn”.

Machine learning  is a field that aims to teach computers to learn from examples (or “Data”) and perform a task without being explicitly programmed to do so.  The intention of ML is to enable machines to learn by themselves using the provided data and make accurate predictions.
.
ML is an application of AI that provide system the ability to automatically learn and improve from experience . It is the ability of a computer to learn from mined data sets.

The machine learning algorithms take the information representing the relationship between items in data sets and build models so that it can predict future outcomes. These models are nothing but actions which will be taken by the machine to get to a result.



Machine Learning develops complex algorithms for processing large data and delivers results to its users. It uses complex programs which can learn through experience and make predictions.

The algorithms are improved by itself through regular input of training data. The goal of machine learning is to understand data and build models from data that can be understood and used by humans.

The machine learning algorithms take the information representing the relationship between items in data sets and build models so that it can predict future outcomes. These models are nothing but actions which will be taken by the machine to get to a result.   It gives computers the ability to learn without being explicitly programmed

Machine learning automates analytical model building. It uses methods from neural networks, statistics, operations research and physics to find hidden insights in data without being explicitly programmed where to look or what to conclude.

Machine learning has developed based on the ability to use computers to probe the data for structure, even if we do not have a theory of what that structure looks like. The test for a machine learning model is a validation error on new data, not a theoretical test that proves a null hypothesis.

 Because machine learning often uses an iterative approach to learn from data, the learning can be easily automated. Passes are run through the data until a robust pattern is found.

Machine Learning is essentially the process by which machines acquire knowledge. It generally focuses on analyzing data for patterns and relationships.

As well as picking up known patterns, machine learning is able to go a step further and ‘learn’ new patterns, without the need for human intervention. This allows models to adapt over time to uncover previously unknown patterns, or identify new tactics that might be employed by fraudsters.

AI machine learning analyzes data, makes assumptions, learns and provides predictions at a scale and depth of detail impossible for individual human analysts. Assumptions made on scanty information can be dangerous.

Organizations and technology companies are employing machine learning based predictive analytics to gain an edge over the rest of the market. Machine learning advancements such as neural networks and deep learning algorithms can discover hidden patterns in unstructured data sets and uncover new information. But building a comprehensive data analysis and predictive analytics strategy requires big data and progressive IT systems.

Machine learning automates analytical model building. It uses methods from neural networks, statistics, operations research and physics to find hidden insights in data without explicitly being programmed for where to look or what to conclude.

A neural network is a type of machine learning that is made up of interconnected units (like neurons) that processes information by responding to external inputs, relaying information between each unit. The process requires multiple passes at the data to find connections and derive meaning from undefined data.

Deep learning uses huge neural networks with many layers of processing units, taking advantage of advances in computing power and improved training techniques to learn complex patterns in large amounts of data. Common applications include image and speech recognition.

Cognitive computing is a subfield of AI that strives for a natural, human-like interaction with machines. Using AI and cognitive computing, the ultimate goal is for a machine to simulate human processes through the ability to interpret images and speech – and then speak coherently in response.

 One of the main ideas behind machine learning is that the computer can be trained to automate tasks that would be exhaustive or impossible for a human being. The clear breach from the traditional analysis is that machine learning can take decisions with minimal human intervention.

Machine learning is a process that enables the analysis of a large amount of data. It is a faster process in learning the risk factors, and profitable opportunities. They have a feature of learning from their mistakes and experiences. 

Even they can eliminate making errors on the same work for that it requires some time to understand the reason. When machine learning is combined with Artificial Intelligence and other cognitive technologies it can be a large field to gather an immense amount of information and then rectify the errors and learn from further experiences, developing in a smarter, faster and accuracy handling technique.

In the process of machine learning, the high amount of data is used and on the other hand, many algorithms are used and tested. Hence there is a huge change to experience many errors. Because while you are training your dataset at that particular many algorithms is used if there is any mistake in the algorithm then it can lead the user to several irrelevant advertisements.

These blunders are a common issue that is experienced many times. Because when these mistakes happen, it is not easy to find out the main source for which the issue is been created and to find out that particular issue and rectifying it, takes a longer time.


Data is the main focus for data science and learning is the main focus for machine learning and that is where the difference lies.

Machine learning (ML) models can be astonishingly good at making predictions provided they hve enough good data to glean from , but they often can’t yield  explanations for their forecasts in terms that humans can easily understand. 

The features from which they draw conclusions can be so numerous, and their calculations so complex, that researchers can find it impossible to establish exactly why an algorithm produces the answers it does.  It is possible, however, to determine how a machine learning algorithm arrived at its conclusions.

Machine learning is capable of generalizing information from large data sets, and then detects and extrapolates patterns in order to apply that information to new solutions and actions. Obviously, certain parameters must be set up at the beginning of the machine learning process so that the machine is able to find, assess, and act upon new data.

Text mining (also referred to as text analytics) is an artificial intelligence (AI) technology that uses natural language processing (NLP) to transform the free (unstructured) text in documents and databases into normalized, structured data suitable for analysis or to drive machine learning (ML) algorithms.

AI can be broken down into  the following categories: 1) systems that think like humans; 2) systems that act like humans; 3) systems that  think rationally; and 4) systems that act rationally.

In reality, AI is considered more of a field than an easily definable “thing,” and it can be broken down into  many subfields, such as machine learning, robotics, neural networks, vision, natural language processing,  and speech processing. 

There is significant crossover among these sub-fields. AI also draws from fields other  than computer science, including psychology, neuroscience, cognitive science, philosophy, linguistics, probability, and logic.

“Narrow AI”—what is currently in use—is the single-task application of artificial intelligence for uses such  as image recognition, language translation, and autonomous vehicles. Machines currently perform more  accurately than humans at these types of tasks. 

In the future, researchers hope to achieve “artificial general  intelligence” (AGI). This would involve systems that exhibit intelligent behavior across a range of cognitive  tasks.   Do not expect  that these capabilities will be achieved for at least decades


Machine learning works like this:--
(1) Programmers begin with a historical data set, which is divided into a training set and a test set.
(2) They then choose a model, a mathematical structure that characterizes a range of possible decision making rules. This model includes adjustable parameters. The model is like a box, and the parameters are the adjustable knobs on the box.
(3) They define an objective function used to evaluate the desirability of the outcome.
(4) They train the model, which is the process of adjusting the parameters to maximize the objective function.
(5) Once trained, they use the test dataset to evaluate the accuracy and effectiveness of the model. Ideally, the model should perform similarly on the test data. The goal is to be able to generalize the model, so it is  accurate in response to cases it has never seen before.

If a machine learning  model is not continually updated with new data that reflects current reality, it will naturally become less  accurate over time.

The use of AI in algorithmic decision-making has introduced a new set of challenges. Because machine learning algorithms use statistics, they also have the same problems with biased data and measurement rror as their deterministic predecessors. 

However, ML systems differ in a few key ways. First, whereas  traditional statistical modeling is about creating a simple model in the form of an equation, machine  learning is much more fine-tuned. It captures a multitude of patterns that cannot be expressed in a single  equation. Second, unlike deterministic algorithms, machine learning algorithms calibrate themselves.

Because they identify so many patterns, they are too complex for humans to understand, and thus it is  not possible to trace the decisions or recommendations they make .  In addition, many machine learning algorithms constantly re-calibrate themselves through feedback. An example of this are e-mail spam filters,  which continually learn and improve their spam detection capabilities as users mark email as spam.

Another issue is the impact of error rates. Because of their statistical basis, all ML systems have error rates. Even though in many cases ML systems are far more accurate than human beings, there is danger in assuming that simply because a system’s predictions are more accurate than a human’s, the outcome is  necessarily better.  Even if the error rate is close to zero, in a tool with millions of users, thousands could be affected by error rates.

Machine Learning is an algorithm that can learn from data without relying on rules-based programming.  Statistical modeling is a formalization of relationships between variables in the data in the form of mathematical equations.

Machine learning models are designed to make the most accurate predictions possible. Statistical models are designed for inference about the relationships between variables.

Statistics and statistical models are not the same. Statistics is the mathematical study of data. You cannot do statistics unless you have data. A statistical model is a model for the data that is used either to infer something about the relationships within the data or to create a model that is able to predict future values. Often, these two go hand-in-hand.

Machine learning is all about results, it is likely working in a company where your worth is characterized solely by your performance. Whereas, statistical modeling is more about finding relationships between variables and the significance of those relationships, whilst also catering for prediction

Machine learning is built upon a statistical framework. This should be overtly obvious since machine learning involves data, and data has to be described using a statistical framework. However, statistical mechanics, which is expanded into thermodynamics for large numbers of particles, is also built upon a statistical framework. The concept of pressure is actually a statistic, and temperature is also a statistic.

The major difference between statistics and machine learning is that statistics is based solely on probability spaces


In terms of statistics vs machine learning, machine learning would not exist without statistics, but machine learning is pretty useful in the modern age due to the abundance of data humanity has access to since the information explosion.

Machine learning (ML) is a class of algorithms that may include a statistical method with the objective of providing an understanding of the patterns and structures in a data set. These algorithms perform tasks without specifying instructions. It is dependent on patterns and inference. 

In its simplest form, it takes data and provides an understanding of the data through a process that is called training. After training, any data that is provided as input, a matching output is generated.

What are the differences between Statistical Modeling and Machine learning?

Statistical forecasting has its origin in Classical statistics whereas machine learning has its origins in computers science. 

Machine learning makes fewer assumptions about the data and therefore can be applied to different types of data. Statistical forecasting sometimes requires that assumptions be made to the distribution of the data. This can be a restriction on the type of data.

Machine Learning (ML) has become an important element in decision-making today. It has revolutionized the entire process of decision-making with the shortest possible time required for a decision. Each movement of the individuals, material, finished goods, etc. are captured and stored as data and used for decision-making through Artificial Intelligence (AI).

One can use machine learning to optimize the forecasting process. From detecting unusual patterns in the data, categorize data into different classes of time series and match a time series to a method. So, machine learning .provides the building block for the intelligent and smarter forecast with fast forecast runtime without compromising accuracy.

Machine learning is the field that deals with creating algorithms that learn from data, so that programs and systems can accomplish tasks without an explicit set of programmed instructions— for example, image recognition technology often relies on machine learning algorithms that parse huge numbers of pictures, learning to identify objects and other features within those images over time and after analyzing large volumes of image data.

The field of machine learning started as a subarea of artificial intelligence research, but it has since evolved to become its own distinct branch within AI research and development.

Where machine learning is a broad discipline that encompasses how computers can understand and “learn” from data, statistical learning focuses on taking raw data and turning it into actionable information, and it is the basis for machine learning algorithms.

Since statistical learning may be used to develop the underlying models that govern how a machine learning algorithm understands data, the two fields are very closely intertwined. One basic example of this in action is a linear regression algorithm, which is a type of machine learning algorithm that was developed based on the principles of statistics.

Knowledge of statistics is also critical when troubleshooting issues with machine learning algorithms as well as solving broader data analytics problems. For example, imagine if a machine learning algorithm is highly accurate in a test environment, but becomes less accurate when used on a real-world data set. 

Statistics expertise helps professionals understand why and how to address the underlying issue. Statistics knowledge also paves the way for a variety of data careers, ranging from marketing analysis to data science.

The use cases for machine learning span across many industries, but what generally makes a good machine learning problem is a matter of scale. Since machine learning algorithms learn from data, they can be used more effectively when there is a large volume of information available. 

For example, researchers can study the behavior of computer programs to identify likely instances of malware; however, researchers have access to billions of data points from sources like event logs and other security analysis tools. 

Analyzing this information manually would take decades, but machine learning can drastically cut down on the time it takes to parse this data and reach actionable conclusions.


Machine learning and statistics are increasingly being applied to customer service roles in relation to these platforms. Chatbots and machine learning systems are trained to respond to the most common user complaints and questions, allowing companies to focus their customer service agents on addressing complex or highly escalated cases. 

In this way, they can maintain fast response time to customer interactions, while making sure that high level requests are given the level of detail that will keep customers satisfied with the response. 

When it comes down to it, the difference between statistics and machine learning is that machine learning encompasses the convergence of a variety of techniques and technologies that may include statistics and statistical modeling, whereas statistics focuses on using data to make predictions and create models for analysis.

While it is important to use statistics for machine learning to create more sophisticated algorithms, not every problem is a machine learning problem. For example, machine learning can help to automate data analysis, but not all data sets will be large enough to justify automation—in this case, statistics can still be used without machine learning to identify patterns and extract actionable information.

Again, today's artificial intelligence is based on machine learning. It is about finding patterns in seas of data—correlations that would not be immediately intuitive or comprehensible to humans—and then using those patterns to make decisions

The term artificial intelligence (AI) covers technologies where machines mimic human intelligence to solve complex problems. On one side we find methods where an algorithm, a ‘recipe’ on how to handle a specific set of inputs, drives the computing process that determines or suggests an output. 

Machine learning (ML) resides in this domain, where multiple methods of various levels of complexity are applied to solve different kinds of problems. Some of these techniques need a dataset to ‘train’ the algorithm on how to handle the information. Algorithmic bias is often inherited from the datasets used to train the algorithm. 

Some systems ‘learn’ how to achieve the optimal result with no supervision. Artificial neural networks mimic the way our brain is constructed. Millions of calculations are performed and sent between the nodes of the network, generating complexity that can become impossible to explain.

Algorithm driven chatbots reply to our questions in text or spoken language.



The biggest problem with machine learning systems is that we ourselves don't quite understand everything they're supposedly learning, nor are we certain they're learning everything they should or could be. We've created systems that draw mostly, though never entirely, correct inferences from ordinary data, by way of logic that is by no means obvious.

Machine learning is put to use when linear regression or best-fit curves are insufficient -- when math can't explain the relationship.

AI is usually undertaken in conjunction with machine learning and data analysis. Machine learning takes data and looks for underlying trends. If it identifies a practical problem, software designers can take that knowledge and use it to diagnose specific problems. 

Data are robust enough that algorithms can detect usage patterns. Data can come in the form of digital information, satellite images, visual information, text, or structured data.

AI – in particular, both machine learning and deep learning – take large data sets as input, distill the essential lessons from those data, and deliver conclusions based on them.

Learning and Adaptation can be collectively called as machine learning which can be defined as the branch of computer science which enables computer systems to learn and respond to queries on the basis of experience and knowledge rather than from predefined programs.

Pattern recognition is the process of recognizing patterns by using the machine learning algorithm. Pattern recognition can be defined as the classification of data based on knowledge already gained or on statistical information extracted from patterns and/or their representation.

 Pattern matching may sound like a simple idea, but it's being used to create some highly advanced AI tools, powering everything from image recognition to natural language applications.

Much of the power of machine learning rests in its ability to detect patterns. Much of the basis of this power is the ability of machine learning algorithms to be trained on example data such that, when future data is presented, the trained model can recognize that pattern for a particular application. 



If you can train a system on a pattern, then you can detect that pattern in the future. Indeed, pattern matching in machine learning -- and its counterpart in anomaly detection -- is what makes many applications of AI work, from image recognition to conversational applications.

There are a wide range of use cases for AI-enabled pattern and anomaly detection systems. In particular, pattern recognition -- one of the seven core patterns of AI applications -- is being applied to fraud detection and analysis, finding outliers and anomalies in big stacks of data; recommendation systems, providing deep insight into large pools of data; and other applications that depend on identification of patterns through training.

One of the challenges with existing fraud detection systems is that they are primarily rules-based, using predefined notions of what constitutes fraudulent or suspicious behavior. The problem is that humans are particularly creative at skirting rules and finding ways to fool systems. 

Companies looking to reduce fraud, suspicious behavior or other risk are finding solutions in machine learning systems that can either be trained to recognize patterns of fraudulent behavior or, conversely, find outliers and anomalies to learned acceptable behavior.

Financial systems, especially banking and credit card processing institutions, are early adopters in using machine learning to enable real-time identification of potentially fraudulent transactions. AI-based systems are able to handle millions of transactions per minute and use trained models to make millisecond decisions as to whether a particular transaction is legitimate. 

These models can identify which purchases don't fit usual spending patterns or look at interactions between paying parties to decide if something should be flagged for further inspection.

Cyber security firms are also finding significant value in the application of machine learning-based pattern and anomaly systems to bolster their capabilities. 

Rather than depending on signature-based systems, which are primarily oriented toward responding to attacks that have already been reported and analyzed, machine learning-based systems are able to detect anomalous system behavior and block those behaviors from causing problems to the systems or networks.

BELOW: MY ELDER SON IS AN EXPERT IN JAVA/ PYTHON/ C++


Python is widely considered as the preferred language for teaching and learning Ml (Machine Learning). Python offers concise and readable code. Python is more intuitive than other programming languages..

Python is one among the most popular dynamic programming languages that is being used today. ... When compared to other programming languages like C++ and Java, it requires the programmer to develop lesser codes. It offers automatic memory management and several standard libraries for the programmer.

Here are the top languages that are most commonly used for making the AI projects:--

Python is considered to be in the first place in the list of all AI development languages due to the simplicity. ... Python is highly productive as compared to other programming languages like C++ and Java. Python has simple programming syntax, code readability and English-like commands that make coding in Python lot easier and efficient.

R is mainly used for statistical analysis while Python provides a more general approach to data science. R and Python are state of the art in terms of programming language oriented towards data science. Learning both of them is, of course, the ideal solution. ... Python is a general-purpose language with a readable syntax.

Python is easy to learn and implement. With its many libraries, they can also be used for data analysis
Python combines remarkable power with very clear syntax. ... Python is also usable as an extension language for applications written in other languages that need easy-to-use scripting or automation interfaces. .

Java Is Faster Than Python

Java is pegged to be 25 times faster than Python. In terms of concurrency, Java beats Python. Java is excellent when it comes to scaling applications, which makes it the best choice for building large and more complex ML and AI applications

Python is faster than R, when the number of iterations is less than 1000. Below 100 steps, python is up to 8 times faster than R, while if the number of steps is higher than 1000, R beats Python when using lapply function!

C++ is an object-oriented programming language and has influenced other languages, such as C#, Java and the latest version of C.    C++ is mostly used when the program is simple, and execution speed is the most important. C++ is still much faster than Java. ... C++ is extremely useful for AI projects.

Python is better for  data manipulation and repeated tasks, while R is good for ad hoc analysis and exploring datasets. ... R has a steep learning curve, and people without programming experience may find it overwhelming. Python is generally considered easier to pick up.

Python 3.7, the latest version of the language aimed at making complex tasks simple

Python is also better for data manipulation and repeated tasks. The future scope of Python is bright as it also helps in the analysis of a large amount of data through its high-performance libraries and tools. One of the most popular Python libraries for the data visualization is Matplotlib.

Java, however, is not recommended for beginners as it is a more complex program. Python is more forgiving as you can take shortcuts such as reusing an old variable. Additionally, many users find Python easier to read and understand than Java. At the same time, Java code can be written once and executed from anywhere.

Despite being a general purpose language, Python has made its way into the most complex technologies such as Artificial Intelligence, Machine Learning, Deep Learning, and so on.

Less Code: Implementing AI involves tons and tons of algorithms. Thanks to Pythons support for pre-defined packages, we don’t have to code algorithms. And to make things easier, Python provides “check as you code” methodology that reduces the burden of testing the code.
Prebuilt Libraries: Python has 100s of pre-built libraries to implement various Machine Learning and Deep Learning algorithms. So every time you want to run an algorithm on a data set, all you have to do is install and load the necessary packages with a single command. Examples of pre-built libraries include NumPy, Keras, Tensorflow, Pytorch, and so on.
Ease of learning: Python uses a very simple syntax that can be used to implement simple computations like, the addition of two strings to complex processes such as building a Machine Learning model.
Platform Independent: Python can run on multiple platforms including Windows, MacOS, Linux, Unix, and so on. While transferring code from one platform to the other you can make use of packages such as PyInstaller that will take care of any dependency issues.

Massive Community Support: Python has a huge community of users which is always helpful when we encounter coding errors. Apart from a huge fan following, Python has multiple communities, groups, and forums where programmers post their errors and help each other.


Python is a dynamic programming language which supports object-oriented, imperative, functional and procedural development paradigms. Scikit and TensorFlow are two popular machine learning libraries available to Python developers. 

C++ is one of the oldest and most popular programming languages. Microsoft used C++.. Most of the machine learning platforms support C++ including TensorFlow. Tensorflow supports Python, Java, C++


BELOW: THAMBI SUNDAR PICHAI CAN COMPROMISE HIS INTEGRITY-- WHEN ASKED BY HIS JEWISH DEEP STATE MASTERS 

THAMBI SANK MY BLOG POSTS BECAUSE I SUPPORTED TRUMP AGAINST ROTHSCHILDs CANDIDATE HILLARY..





































  • WE THE PEOPLE OF INDIA , WOULD LIKE TO ALERT CHINSE PRESIDENT XI AND THE PEOPLE OF CHINA-- TO A FRAUD PERPETUATED BY INDIAN PM MODI, JUST TO MILK TAMIL VOTES..


    CHINESE PRESIDENT XIs VISIT TO INDIA IS NOT TO HONOUR HIM, BUT FOR FELLOW WITHOUT CHARACTER MODI TO MILK TAMIL VOTES..
    BODHI DHARMA IS NOT A TAMILIAN PRINCE.

    BODHI DHARMA WAS A MALAYALI KALARI EXPERT WHO INTRODUCED KUNG FU AND TEA CEREMONY TO CHINESE.

    http://ajitvadakayil.blogspot.com/2011/12/kalaripayattu-oldest-and-deadliest.html

    http://ajitvadakayil.blogspot.com/2009/06/tea-capt-ajit-vadakayil.html


    THE NAME OF MAMMALAPURAM IS IN REALITY MAHABALIPURAM.  .NAMED AFTER A 12,000 YEAR OF KERALA KING WHO OWNED THIS PORT..

    THE SEASHORE TEMPLES AT MAHABALIPURAM ARE 12,000 YEARS OLD..  YOU GET VERY GOOD GROUND WATER THERE .. THE CALICUT KING OWNED MAHABALIPURAM PORT—PAID GOOD MONEY, SINCE HE WAS THE RICHEST KING ON THE PLANET..

    https://ajitvadakayil.blogspot.com/2019/10/perumal-title-of-calicut-thiyya-kings.html

    MAHABALIPURAM WAS AN ANCIENT PORT OWNED BY THE CALICUT KING FOR HIS SHIPS TO TRANSSHIP SPICES FROM A PORT WAREHOUSE DURING SOUTH WEST MONSOONS..

    CHINESE ADMIRAL ZHENG HE NEVER VISITED TAMIL NADU.. HE DIED IN CALICUT/ KERALA.

    http://ajitvadakayil.blogspot.com/2011/09/chinese-admiral-zheng-he-in-calicut.html

    HERE IS A SHOCKER -- ALL CHINESE BUDDHIST TRAVELERS TO INDIA , NEVER EXISTED.. PALI SPEAKING BUDDHA HIMSELF IS FAKE.

    https://www.indiatoday.in/news-analysis/story/modi-xi-summit-at-mamallapuram-and-story-of-tamil-prince-who-took-buddhism-to-china-1608130-2019-10-11

    BUDDHIST CHINESE TRAVELLER HIUEN TSANG NEVER EXISTED..

    BODHI NAME OF BODHI DHARMA -- IS ACTUALLY BUDDHIMAN ( MAN OF WISDOM )--  IT HAS NOTHING TO DO WITH BODH GAYA , THE PLACE OF BIRTH OF FAKE PALI SPEAKING PIG EATING BUDDHA CREATED BY JEW ROTHSCHILD ( WHO CREATED MAO AND THE COMMUNIST REVOLUTION )

    https://ajitvadakayil.blogspot.com/2019/06/deliberately-buried-truths-about-buddha.html

    WE ASK KERALA CM PINARAYI VIJAYAN TO ESTABLISH THE TRUTH.. ENOUGH OF TAMIL PRIDE TRYING TO PROVE THAT MALAYALAM IS A PARASITE TAPEWORM FROM TAMILs STOMACH..

    http://ajitvadakayil.blogspot.com/2013/08/chinese-revolution-biggest-genocide-on.html

    capt ajit vadakayil
    ..

  • PUT ABOVE COMMENT IN WEBSITES OF-
    PRESIDENT XI OF CHINA
    INDIAN AMBASSADOR TO CHINA
    CHINESE AMBASSADOR TO INDIA
    CHINA GLOBAL TIMES EDITOR HU XIJIN
    EXTERNAL AFFAIRS MINISTER/ MINISTRY
    CM OF KERALA PINARAYI VIJAYAN
    GOVERNOR OF KERALA
    DGP OF KERALA
    ALL MLAs/ MPs OF KERALA
    ALL MEDIA OF KERALA
    KERALA HIGH COURT CHIEF JUSTICE
    E SREEDHARAN
    ALL CELEBRITIES OF KERALA
    ENTIRE BBC GANG
    ALL KERALA COLLECTORS
    PMO
    PM MODI
    AMIT SHAH
    HOME MINISTRY
    AJIT DOVAL
    RAW
    CBI
    IN
    NIA
    ED
    IB
    I&B MINISTRY
    JAVEDEKAR
    CJI GOGOI
    ALL SUPREME COURT JUDGES
    ATTORNEY GENERAL
    ALL HIGH COURT CHIEF JUSTICES
    ALL SUPREME COURT LAWYERS
    CMs OF ALL INDIAN STATES
    DGPs OF ALL STATES
    GOVERNORS OF ALL STATES
    PRESIDENT OF INDIA
    VP OF INDIA
    SPEAKER LOK SABHA
    SPEAKER RAJYA SABHA
    JACK DORSEY
    MARK ZUCKERBERG
    THAMBI SUNDAR PICHAI
    CEO OF WIKIPEDIA
    QUORA CEO ANGELO D ADAMS
    QUORA MODERATION TEAM
    KURT OF QUORA
    GAUTAM SHEWAKRAMANI
    DAVID FRAWLEY
    STEPHEN KNAPP
    WILLIAM DALRYMPLE
    KONRAED ELST
    WALLIAM DARYLMPLE
    FRANCOIS GAUTIER
    DEFENCE MINISTER - MINISTRY
    ALL THREE ARMED FORCE CHIEFS.
    RAJEEV CHANDRASHEKHAR
    MOHANDAS PAI
    SURESH GOPI
    MOHANLAL
    MATA AMRITANANDAMAYI
    ALL CONGRESS SPOKESMEN
    RAHUL GANDHI
    SONIA GANDHI
    PRIYANKA VADRA
    SHASHI THAROOR
    ARUNDHATI ROY
    ANNA VETTICKAD
    FAZAL GHAFOOR ( MES KERALA)
    MAMMOOTY
    DULQER SALMAN
    AANIE RAJA
    JOHN BRITTAS
    ADOOR GOPALAKRISHNAN
    NITI AYOG
    AMITABH KANT
    ZAKKA JACOB

    WEBSITES OF DESH BHAKT LEADERS
    SPREAD OF SOCIAL MEDIA







  • .
    .





  • I NEVER THOUGHT I WILL BE REDUCED TO HIS..

    ALL BECAUSE OF A FELLOW WITHOUT INTEGRITY-- NARENDRA DAMODARDAS MODI..

  • ALL MY MALAYALI READERS IN GULF AND USA .. TAKE THIS ISSUE UP..

    ENOUGH IS ENOUGH !

  • SEND THE ABOVE MESSAGE BY SCREEN SHOT ALSO TO--

    ROMILA THAPAR
    IRFAN HABIB
    NIVEDITA MEMON
    AYESHA KIDWAI
    VC OF JNU/ DU/ JU / TISS / FTII
    ALL SOCIAL SCIENCES PROFESSORS OF JNU/ DU/ JU / TISS
    ALL NALANDA UNIVERSITY PROFESSORS
    RAM MADHAV
    RAJ THACKREY
    UDDHAV THACKREY
    VIVEK OBEROI
    GAUTAM GAMBHIR
    ASHOK PANDIT
    ANUPAM KHER
    KANGANA RANAUT
    VIVEK AGNIHOTRI
    KIRON KHER
    MEENAKSHI LEKHI
    SMRITI IRANI
    PRASOON JOSHI
    MADHUR BHANDARKAR
    SWAPAN DASGUPTA
    SONAL MANSINGH
    MADHU KISHWAR
    SUDHIR CHAUDHARY
    GEN GD BAKSHI
    SAMBIT PATRA
    RSN SINGH
    SWAMY
    RAJIV MALHOTRA
    SADGURU JAGGI VASUDEV
    SRI SRI RAVISHANKAR
    BABA RAMDEV
    RSS
    VHP
    AVBP
    .
    NCM
    NHRC
    NCW
    REKHA SHARMA
    SWATI MALLIWAL
    CHETAN BHAGAT
    DEVDUTT PATTANAIK
    AMISH TRIPATI
    ASADDUDIN OWAISI
    KUNHALIKUTTY
    RANA AYYUB
    AVED AKTHAR
    MAHESH BHATT
    SHABANA AZMI
    AMITABH BACHCHAN
    PRITISH NANDI
    ASHISH NANDI
    PAVAN VARMA
    RAMACHANDRA GUHA
    JOHN DAYAL
    KANCHA ILIAH
    SHOBHAA DE
    FATHER CEDRIC PERIERA
    DANIEL RAJA
    BRINDA KARAT
    PRAKASH KARAT
    SITARAM YECHURY
    SUMEET CHOPRA
    DINESH VARSHNEY
    BINAYAK SEN
    SUDHEENDRA KULKARNI
    PRAKASH RAJ
    RAJNIKANTH
    KAANIYA MURTHY
    SUDHA MURTHY
    AUDREY TRUSHCKE
    WENDY DONIGER
    SHELDON POLLOCK
    ANURAG KASHYAP
    APARNA SEN
    MANI RATNAM
    KOKONA SEN SHARMA
    SHYAM BENEGAL
    SHUBHA MUDGAL
    SAUMITRA CHETTERJEE
    NAYANTHARA SEHGAL
    THE ENTIRE BBC GANG
    MUKESH AMBANI
    LAXMI MITTAL
    RATAN TATA
    MAHINDRA
    AZIM PREMJI
    KUMARMANGALAM
    RAHUL BAJAJ
    NAVEEN JINDAL
    THE QUINT
    THE SCROLL
    THE WIRE
    THE PRINT
    MK VENU
    MADHU TREHAN
    CLOSET COMMIE ARNAB GOSWMI
    RAJDEEP SARDESAI
    PAAGALIKA GHOSE
    NAVIKA KUMAR
    ANAND NARASIMHAN
    SRINIVASAN JAIN
    SONAL MEHROTRA KAPOOR
    VIKRAM CHANDRA
    NIDHI RAZDAN
    FAYE DSOUZA
    RAVISH KUMAR
    PRANNOY JAMES ROY
    AROON PURIE
    VINEET JAIN
    RAGHAV BAHL
    SEEMA CHISTI
    DILEEP PADGOANKAR
    VIR SANGHVI
    KARAN THAPAR
    BARKHA DUTT
    SHEKHAR GUPTA
    SIDHARTH VARADARAJAN
    ARUN SHOURIE
    N RAM
    SANJAY DUBEY

    WEBSITES OF DESH BHAKT LEADERS
    SPREAD OF SOCIAL MEDIA


  • THIS POST IS NOW CONTINUED TO PART 3 ,BELOW-







    CAPT AJIT VADAKAYIL
    ..

    223 comments:


    1. WE THE PEOPLE OF INDIA , WOULD LIKE TO ALERT CHINSE PRESIDENT XI AND THE PEOPLE OF CHINA-- TO A FRAUD PERPETUATED BY INDIAN PM MODI, JUST TO MILK TAMIL VOTES..

      CHINESE PRESIDENT XIs VISIT TO INDIA IS NOT TO HONOUR HIM, BUT FOR FELLOW WITHOUT CHARACTER MODI TO MILK TAMIL VOTES..

      BODHI DHARMA IS NOT A TAMILIAN PRINCE.

      BODHI DHARMA WAS A MALAYALI KALARI EXPERT WHO INTRODUCED KUNG FU AND TEA CEREMONY TO CHINESE.

      http://ajitvadakayil.blogspot.com/2011/12/kalaripayattu-oldest-and-deadliest.html

      http://ajitvadakayil.blogspot.com/2009/06/tea-capt-ajit-vadakayil.html

      THE NAME OF MAMMALAPURAM IS IN REALITY MAHABALIPURAM. .NAMED AFTER A 12,000 YEAR OF KERALA KING WHO OWNED THIS PORT..

      THE SEASHORE TEMPLES AT MAHABALIPURAM ARE 12,000 YEARS OLD.. YOU GET VERY GOOD GROUND WATER THERE .. THE CALICUT KING OWNED MAHABALIPURAM PORT—PAID GOOD MONEY, SINCE HE WAS THE RICHEST KING ON THE PLANET..

      https://ajitvadakayil.blogspot.com/2019/10/perumal-title-of-calicut-thiyya-kings.html

      MAHABALIPURAM WAS AN ANCIENT PORT OWNED BY THE CALICUT KING FOR HIS SHIPS TO TRANSSHIP SPICES FROM A PORT WAREHOUSE DURING SOUTH WEST MONSOONS..

      CHINESE ADMIRAL ZHENG HE NEVER VISITED TAMIL NADU.. HE DIED IN CALICUT/ KERALA.

      http://ajitvadakayil.blogspot.com/2011/09/chinese-admiral-zheng-he-in-calicut.html

      HERE IS A SHOCKER -- ALL CHINESE BUDDHIST TRAVELERS TO INDIA , NEVER EXISTED.. PALI SPEAKING BUDDHA HIMSELF IS FAKE.

      https://www.indiatoday.in/news-analysis/story/modi-xi-summit-at-mamallapuram-and-story-of-tamil-prince-who-took-buddhism-to-china-1608130-2019-10-11

      BUDDHIST CHINESE TRAVELLER HIUEN TSANG NEVER EXISTED..

      BODHI NAME OF BODHI DHARMA -- IS ACTUALLY BUDDHIMAN ( MAN OF WISDOM )-- IT HAS NOTHING TO DO WITH BODH GAYA , THE PLACE OF BIRTH OF FAKE PALI SPEAKING PIG EATING BUDDHA CREATED BY JEW ROTHSCHILD ( WHO CREATED MAO AND THE COMMUNIST REVOLUTION )

      https://ajitvadakayil.blogspot.com/2019/06/deliberately-buried-truths-about-buddha.html

      WE ASK KERALA CM PINARAYI VIJAYAN TO ESTABLISH THE TRUTH.. ENOUGH OF TAMIL PRIDE TRYING TO PROVE THAT MALAYALAM IS A PARASITE TAPEWORM FROM TAMILs STOMACH..

      http://ajitvadakayil.blogspot.com/2013/08/chinese-revolution-biggest-genocide-on.html

      capt ajit vadakayil
      ..

      ReplyDelete
      Replies

      1. PUT ABOVE COMMENT IN WEBSITES OF-
        PRESIDENT XI OF CHINA
        INDIAN AMBASSADOR TO CHINA
        CHINESE AMBASSADOR TO INDIA
        CHINA GLOBAL TIMES EDITOR HU XIJIN
        EXTERNAL AFFAIRS MINISTER/ MINISTRY
        CM OF KERALA PINARAYI VIJAYAN
        GOVERNOR OF KERALA
        DGP OF KERALA
        ALL MLAs/ MPs OF KERALA
        ALL MEDIA OF KERALA
        KERALA HIGH COURT CHIEF JUSTICE
        E SREEDHARAN
        ALL CELEBRITIES OF KERALA
        ENTIRE BBC GANG
        ALL KERALA COLLECTORS
        PMO
        PM MODI
        AMIT SHAH
        HOME MINISTRY
        AJIT DOVAL
        RAW
        CBI
        IN
        NIA
        ED
        IB
        I&B MINISTRY
        JAVEDEKAR
        CJI GOGOI
        ALL SUPREME COURT JUDGES
        ATTORNEY GENERAL
        ALL HIGH COURT CHIEF JUSTICES
        ALL SUPREME COURT LAWYERS
        CMs OF ALL INDIAN STATES
        DGPs OF ALL STATES
        GOVERNORS OF ALL STATES
        PRESIDENT OF INDIA
        VP OF INDIA
        SPEAKER LOK SABHA
        SPEAKER RAJYA SABHA
        JACK DORSEY
        MARK ZUCKERBERG
        THAMBI SUNDAR PICHAI
        CEO OF WIKIPEDIA
        QUORA CEO ANGELO D ADAMS
        QUORA MODERATION TEAM
        KURT OF QUORA
        GAUTAM SHEWAKRAMANI
        DAVID FRAWLEY
        STEPHEN KNAPP
        WILLIAM DALRYMPLE
        KONRAED ELST
        WALLIAM DARYLMPLE
        FRANCOIS GAUTIER
        DEFENCE MINISTER - MINISTRY
        ALL THREE ARMED FORCE CHIEFS.
        RAJEEV CHANDRASHEKHAR
        MOHANDAS PAI
        SURESH GOPI
        MOHANLAL
        MATA AMRITANANDAMAYI
        ALL CONGRESS SPOKESMEN
        RAHUL GANDHI
        SONIA GANDHI
        PRIYANKA VADRA
        SHASHI THAROOR
        ARUNDHATI ROY
        ANNA VETTICKAD
        FAZAL GHAFOOR ( MES KERALA)
        MAMMOOTY
        DULQER SALMAN
        AANIE RAJA
        JOHN BRITTAS
        ADOOR GOPALAKRISHNAN
        NITI AYOG
        AMITABH KANT
        ZAKKA JACOB

        WEBSITES OF DESH BHAKT LEADERS
        SPREAD OF SOCIAL MEDIA

        Delete


      2. Capt. Ajit VadakayilOctober 11, 2019 at 7:46 PM
        I NEVER THOUGHT I WILL BE REDUCED TO HIS..

        ALL BECAUSE OF A FELLOW WITHOUT INTEGRITY-- NARENDRA DAMODARDAS MODI..

        Delete

        Capt. Ajit VadakayilOctober 11, 2019 at 7:47 PM
        ALL MY MALAYALI READERS IN GULF AND USA .. TAKE THIS ISSUE UP..

        ENOUGH IS ENOUGH !

        Delete

        Capt. Ajit VadakayilOctober 11, 2019 at 7:49 PM
        SEND THE ABOVE MESSAGE BY SCREEN SHOT ALSO TO--

        ROMILA THAPAR
        IRFAN HABIB
        NIVEDITA MEMON
        AYESHA KIDWAI
        VC OF JNU/ DU/ JU / TISS / FTII
        ALL SOCIAL SCIENCES PROFESSORS OF JNU/ DU/ JU / TISS
        ALL NALANDA UNIVERSITY PROFESSORS
        RAM MADHAV
        RAJ THACKREY
        UDDHAV THACKREY
        VIVEK OBEROI
        GAUTAM GAMBHIR
        ASHOK PANDIT
        ANUPAM KHER
        KANGANA RANAUT
        VIVEK AGNIHOTRI
        KIRON KHER
        MEENAKSHI LEKHI
        SMRITI IRANI
        PRASOON JOSHI
        MADHUR BHANDARKAR
        SWAPAN DASGUPTA
        SONAL MANSINGH
        MADHU KISHWAR
        SUDHIR CHAUDHARY
        GEN GD BAKSHI
        SAMBIT PATRA
        RSN SINGH
        SWAMY
        RAJIV MALHOTRA
        SADGURU JAGGI VASUDEV
        SRI SRI RAVISHANKAR
        BABA RAMDEV
        RSS
        VHP
        AVBP
        .
        NCM
        NHRC
        NCW
        REKHA SHARMA
        SWATI MALLIWAL
        CHETAN BHAGAT
        DEVDUTT PATTANAIK
        AMISH TRIPATI
        ASADDUDIN OWAISI
        KUNHALIKUTTY
        RANA AYYUB
        AVED AKTHAR
        MAHESH BHATT
        SHABANA AZMI
        AMITABH BACHCHAN
        PRITISH NANDI
        ASHISH NANDI
        PAVAN VARMA
        RAMACHANDRA GUHA
        JOHN DAYAL
        KANCHA ILIAH
        SHOBHAA DE
        FATHER CEDRIC PERIERA
        DANIEL RAJA
        BRINDA KARAT
        PRAKASH KARAT
        SITARAM YECHURY
        SUMEET CHOPRA
        DINESH VARSHNEY
        BINAYAK SEN
        SUDHEENDRA KULKARNI
        PRAKASH RAJ
        RAJNIKANTH
        KAANIYA MURTHY
        SUDHA MURTHY
        AUDREY TRUSHCKE
        WENDY DONIGER
        SHELDON POLLOCK
        ANURAG KASHYAP
        APARNA SEN
        MANI RATNAM
        KOKONA SEN SHARMA
        SHYAM BENEGAL
        SHUBHA MUDGAL
        SAUMITRA CHETTERJEE
        NAYANTHARA SEHGAL
        THE ENTIRE BBC GANG
        MUKESH AMBANI
        LAXMI MITTAL
        RATAN TATA
        MAHINDRA
        AZIM PREMJI
        KUMARMANGALAM
        RAHUL BAJAJ
        NAVEEN JINDAL
        THE QUINT
        THE SCROLL
        THE WIRE
        THE PRINT
        MK VENU
        MADHU TREHAN
        CLOSET COMMIE ARNAB GOSWMI
        RAJDEEP SARDESAI
        PAAGALIKA GHOSE
        NAVIKA KUMAR
        ANAND NARASIMHAN
        SRINIVASAN JAIN
        SONAL MEHROTRA KAPOOR
        VIKRAM CHANDRA
        NIDHI RAZDAN
        FAYE DSOUZA
        RAVISH KUMAR
        PRANNOY JAMES ROY
        AROON PURIE
        VINEET JAIN
        RAGHAV BAHL
        SEEMA CHISTI
        DILEEP PADGOANKAR
        VIR SANGHVI
        KARAN THAPAR
        BARKHA DUTT
        SHEKHAR GUPTA
        SIDHARTH VARADARAJAN
        ARUN SHOURIE
        N RAM
        SANJAY DUBEY

        WEBSITES OF DESH BHAKT LEADERS
        SPREAD OF SOCIAL MEDIA

        Delete
      3. Hi Sir,

        I will e-mail your message.

        Our Indian heros are Sire. Bodhidharma, Grand Sire. Bhisma, Lord Ayyappa etc. They should be promoted as heroes not people like Gandhi etc.

        Thank you,
        Soujanya

        Delete
    2. GRETA THUNBERG IS A PUPPET OF THE DEEP STATE

      SHE HAS BEEN UNLEASHED WITH THE PRIME OBJECTIVE OF MAKING A GULLIBLE FELLOW LIKE MODI DECLARE THAT "INDIA WILL MOTHBALL OUR ENORMOUS RESERVES OF COAL"..

      ALL THESE WESTERN NATIONS HAVE FOREST FIRES EVERY YEAR.. DUE TO 100% NEGLIGENCE..

      THE BEEF LOBBY EMITS MAXIMUM GREENHOUSE GASES ( METHANE - FART / NITROUS OXIDE- URINE DUNG )

      INDIA HAS THE LOWEST PER CAPITA BEEF CONSUMPTION..

      OUR HUMPED COWS FART ONLY 5% METHANE AS WHAT THEIR WESTERN HUMPLESS COUNTERPARTS DO.

      MODI DOES NOT HAVE THE BALLS TO PROTECT BHARATMATA..

      MDDI DOES NOT HAVE THE GAAND MEIN TATTI TO SHOW THE KOSHER MIRROR TO HIS JEWISH MASTERS ABOUT NITRO-FERTILISERS AND NITRO-EXPLOSIVES .

      MODI IS AFRAID THAT HE WONT GET THE NOBEL PRIZE..

      https://ajitvadakayil.blogspot.com/2019/10/greta-thunberg-puppet-of-jewish-deep.html

      MODI MUST KNOW THIS-- MALALA AND SATYARTHI ARE HIDING AFTER THEIR WON THE NOBEL PEACE PRIZE.

      DESCENDANTS OF 65 PARSIS WHO WERE KNIGHTED BY ROTHSCHILD BEFORE 1947 ARE ALL HIDING..

      RAVISH KUMAR OF NDTV GOT THE MAGSAYSAY AWARD-- HE WAS FEELING LIKE A PIECE OF SHIT AT THE AWARD CEREMONY..

      BECAUSE HE KNOWS-- EVERYBODY KNOWS THAT TO GET A MAGSAYSAY AWARD YOU HAVE TO BE A DESH DROHI..

      INDIAs PER CAPITA CARBON EMISSION AND ELECTRICITY CONSUMPTION IS THE LEAST ON THE PLANET.. MODI HIDES THIS SHOCKING TRUTH..

      THIS IS WHY MODI HAS AN IDIOT LIKE KAYASTHA PRAKASH JAVEDEKAR AS THE ENVIRONMENT MINISTER..

      KAYASTHAS HAVE BLED BHARATMATA MAXIMUM TILL TODAY..

      http://ajitvadakayil.blogspot.com/2019/07/we-never-heard-words-kayastha-and.html

      AFTER SIX YEARS ON PM CHAIR-- HAS MODI AMENDED THE NCERT SYLLABUS.. TO PASS IAS YOU HAVE TO REPEAT CHITPAVAN JEW LOK MANYA BAL GANGADHARA TILAKs LIES, THAT THE BLUE EYED WHITE SKINNED BLONDE MAN WROTE OUR VEDAS AND GAVE US SANSKRIT.

      https://en.wikipedia.org/wiki/The_Arctic_Home_in_the_Vedas

      ARYANS ARE KERALA NAMBOODIRI ELDER BROTHERS WITH SWASTIKA A SYMBOL

      SEMITES ARE KERALA NAMBOODIRI YOUNGER BROTHERS WITH SIX POINTED STAR AS SYMBOL..

      https://ajitvadakayil.blogspot.com/2019/10/perumal-title-of-calicut-thiyya-kings.html

      IMMORAL FELLOW MODI IS HAND IN GLOVE WITH JEW ROTHSCHILD.. HE GAVE AWAY BODHI DHARMA TO TAMILS.. HE DECLARED AT UN, THAT TAMIL IS THE OLDEST LANGUAGE .. ALL FOR VOTES..

      ROTHSCHILD SANK THE DANAVA CIVILIZATION WHICH PRECEEDED THE VEDIC CIVILIZATION BY DOZENS OF MILLENNIUMS..

      ALL NINE VISHNU AVATARS HAVE TO DO WITH KERALA, WHY ?

      https://ajitvadakayil.blogspot.com/2019/09/onam-our-only-link-to-planets-oldest.html

      capt ajit vadakayil
      ..

      ReplyDelete
      Replies
      1. PUT ABOVE COMMENT BY SCREENSHOT IN WEBSITES OF-
        CM OF KERALA PINARAYI VIJAYAN
        GOVERNOR OF KERALA
        DGP OF KERALA
        ALL MLAs/ MPs OF KERALA
        ALL MEDIA OF KERALA
        KERALA HIGH COURT CHIEF JUSTICE
        E SREEDHARAN
        ALL CELEBRITIES OF KERALA
        ENTIRE BBC GANG
        ALL KERALA COLLECTORS
        PMO
        PM MODI
        AMIT SHAH
        HOME MINISTRY
        AJIT DOVAL
        RAW
        CBI
        IN
        NIA
        ED
        IB
        I&B MINISTRY
        JAVEDEKAR
        CJI GOGOI
        ALL SUPREME COURT JUDGES
        ATTORNEY GENERAL
        ALL HIGH COURT CHIEF JUSTICES
        ALL SUPREME COURT LAWYERS
        CMs OF ALL INDIAN STATES
        DGPs OF ALL STATES
        GOVERNORS OF ALL STATES
        PRESIDENT OF INDIA
        VP OF INDIA
        SPEAKER LOK SABHA
        SPEAKER RAJYA SABHA
        JACK DORSEY
        MARK ZUCKERBERG
        THAMBI SUNDAR PICHAI
        CEO OF WIKIPEDIA
        QUORA CEO ANGELO D ADAMS
        QUORA MODERATION TEAM
        KURT OF QUORA
        GAUTAM SHEWAKRAMANI
        DAVID FRAWLEY
        STEPHEN KNAPP
        WILLIAM DALRYMPLE
        KONRAED ELST
        WALLIAM DARYLMPLE
        FRANCOIS GAUTIER
        DEFENCE MINISTER - MINISTRY
        ALL THREE ARMED FORCE CHIEFS.
        RAJEEV CHANDRASHEKHAR
        MOHANDAS PAI
        SURESH GOPI
        MOHANLAL
        MATA AMRITANANDAMAYI
        ALL CONGRESS SPOKESMEN
        RAHUL GANDHI
        SONIA GANDHI
        PRIYANKA VADRA
        SHASHI THAROOR
        ARUNDHATI ROY
        ANNA VETTICKAD
        FAZAL GHAFOOR ( MES KERALA)
        MAMMOOTY
        DULQER SALMAN
        AANIE RAJA
        JOHN BRITTAS
        ADOOR GOPALAKRISHNAN
        NITI AYOG
        AMITABH KANT
        ZAKKA JACOB
        ROMILA THAPAR
        IRFAN HABIB
        NIVEDITA MEMON
        AYESHA KIDWAI
        VC OF JNU/ DU/ JU / TISS / FTII
        ALL SOCIAL SCIENCES PROFESSORS OF JNU/ DU/ JU / TISS
        ALL NALANDA UNIVERSITY PROFESSORS
        RAM MADHAV
        RAJ THACKREY
        UDDHAV THACKREY
        VIVEK OBEROI
        GAUTAM GAMBHIR
        ASHOK PANDIT
        ANUPAM KHER
        KANGANA RANAUT
        VIVEK AGNIHOTRI
        KIRON KHER
        MEENAKSHI LEKHI
        SMRITI IRANI
        PRASOON JOSHI
        MADHUR BHANDARKAR
        SWAPAN DASGUPTA
        SONAL MANSINGH
        MADHU KISHWAR
        SUDHIR CHAUDHARY
        GEN GD BAKSHI
        SAMBIT PATRA
        RSN SINGH
        SWAMY
        RAJIV MALHOTRA
        SADGURU JAGGI VASUDEV
        SRI SRI RAVISHANKAR
        BABA RAMDEV
        RSS
        VHP
        AVBP
        NCM
        NHRC
        NCW
        REKHA SHARMA
        SWATI MALLIWAL
        CHETAN BHAGAT
        DEVDUTT PATTANAIK
        AMISH TRIPATI
        ASADDUDIN OWAISI
        KUNHALIKUTTY
        RANA AYYUB
        AVED AKTHAR
        MAHESH BHATT
        SHABANA AZMI
        AMITABH BACHCHAN
        PRITISH NANDI
        ASHISH NANDI
        PAVAN VARMA
        RAMACHANDRA GUHA
        JOHN DAYAL
        KANCHA ILIAH
        SHOBHAA DE
        FATHER CEDRIC PERIERA
        DANIEL RAJA
        BRINDA KARAT
        PRAKASH KARAT
        SITARAM YECHURY
        SUMEET CHOPRA
        DINESH VARSHNEY
        BINAYAK SEN
        SUDHEENDRA KULKARNI
        PRAKASH RAJ
        RAJNIKANTH
        KAANIYA MURTHY
        SUDHA MURTHY
        AUDREY TRUSHCKE
        WENDY DONIGER
        SHELDON POLLOCK
        ANURAG KASHYAP
        APARNA SEN
        MANI RATNAM
        KOKONA SEN SHARMA
        SHYAM BENEGAL
        SHUBHA MUDGAL
        SAUMITRA CHETTERJEE
        NAYANTHARA SEHGAL
        THE ENTIRE BBC GANG
        MUKESH AMBANI
        LAXMI MITTAL
        RATAN TATA
        MAHINDRA
        AZIM PREMJI
        KUMARMANGALAM
        RAHUL BAJAJ
        NAVEEN JINDAL
        THE QUINT
        THE SCROLL
        THE WIRE
        THE PRINT
        MK VENU
        MADHU TREHAN
        CLOSET COMMIE ARNAB GOSWMI
        RAJDEEP SARDESAI
        PAAGALIKA GHOSE
        NAVIKA KUMAR
        ANAND NARASIMHAN
        SRINIVASAN JAIN
        SONAL MEHROTRA KAPOOR
        VIKRAM CHANDRA
        NIDHI RAZDAN
        FAYE DSOUZA
        RAVISH KUMAR
        PRANNOY JAMES ROY
        AROON PURIE
        VINEET JAIN
        RAGHAV BAHL
        SEEMA CHISTI
        DILEEP PADGOANKAR
        VIR SANGHVI
        KARAN THAPAR
        BARKHA DUTT
        SHEKHAR GUPTA
        SIDHARTH VARADARAJAN
        ARUN SHOURIE
        N RAM
        SANJAY DUBEY

        WEBSITES OF DESH BHAKT LEADERS
        SPREAD OF SOCIAL MEDIA

        Delete
    3. https://timesofindia.indiatimes.com/india/ed-probes-praful-patels-alleged-land-deal-with-dawood-man/articleshow/71560936.cms

      EVERYBODY KNOW THAT TELGI WAS WORKING FOR DAWOOD , WITH SOME MARATHA MANOOS LEADERS IN CAHOOTS..

      ONLY INDIAN SECURITY AGENCIES AND PM DOES NOT KNOW?

      WHY?..

      http://ajitvadakayil.blogspot.com/2014/12/telgi-stamp-paper-scam-must-be-exhumed.html

      EVEN TODAY A BIG PERCENTAGE OF LAND DEALS IN MUMBAI ( AND REST OF INDIA ) IS STILL OF TELGIs FAKE STAMP PAPER..

      TEGI DID NOT MAKE MONEY OUT OF STAMP PAPER AS IS ESTABLISHED BY OUR JUDICIARY. THIS IS PEANUTS..

      DAWOOD HIS AGENTS AND MARATHA MANOOS POLITICIANS MADE HUMONGOUS MONEY OUT OF GRABBED LAND..

      TELGI UNDERWENT A NARCO TRUTH SERUM TEST LIVE ON TV, ON HIS OWN ACCORD.. HE NAMES ONLY TWO MARATHA MANOOS POLITICIANS , THEDA FACE AND A FORMER AVIATION MINISTER WHO BY HIS OWN ADMISSION ON TV, WAS BORN IN A POOR FARMERs FAMILY..

      THE ENRON SCAM INVOLVING MARATHA MANOOS POLITICIANS IS STILL BURIED.. MODI GOES AND GIVES THEDA FACE A PADMA AWARD..WHY?

      capt ajit vadakayil
      ..

      ReplyDelete
      Replies
      1. PUT ABOVE COMMENT IN WEBSITES OF--
        PUT ABOVE COMMENT BY SCREENSHOT IN WEBSITES OF-
        CM OF KERALA PINARAYI VIJAYAN
        GOVERNOR OF KERALA
        DGP OF KERALA
        ALL MLAs/ MPs OF KERALA
        ALL MEDIA OF KERALA
        KERALA HIGH COURT CHIEF JUSTICE
        E SREEDHARAN
        ALL CELEBRITIES OF KERALA
        ENTIRE BBC GANG
        ALL KERALA COLLECTORS
        PMO
        PM MODI
        AMIT SHAH
        HOME MINISTRY
        AJIT DOVAL
        RAW
        CBI
        IN
        NIA
        ED
        IB
        I&B MINISTRY
        JAVEDEKAR
        CJI GOGOI
        ALL SUPREME COURT JUDGES
        ATTORNEY GENERAL
        ALL HIGH COURT CHIEF JUSTICES
        ALL SUPREME COURT LAWYERS
        CMs OF ALL INDIAN STATES
        DGPs OF ALL STATES
        GOVERNORS OF ALL STATES
        PRESIDENT OF INDIA
        VP OF INDIA
        SPEAKER LOK SABHA
        SPEAKER RAJYA SABHA
        JACK DORSEY
        MARK ZUCKERBERG
        THAMBI SUNDAR PICHAI
        CEO OF WIKIPEDIA
        QUORA CEO ANGELO D ADAMS
        QUORA MODERATION TEAM
        KURT OF QUORA
        GAUTAM SHEWAKRAMANI
        DAVID FRAWLEY
        STEPHEN KNAPP
        WILLIAM DALRYMPLE
        KONRAED ELST
        WALLIAM DARYLMPLE
        FRANCOIS GAUTIER
        DEFENCE MINISTER - MINISTRY
        ALL THREE ARMED FORCE CHIEFS.
        RAJEEV CHANDRASHEKHAR
        MOHANDAS PAI
        SURESH GOPI
        MOHANLAL
        MATA AMRITANANDAMAYI
        ALL CONGRESS SPOKESMEN
        RAHUL GANDHI
        SONIA GANDHI
        PRIYANKA VADRA
        SHASHI THAROOR
        ARUNDHATI ROY
        ANNA VETTICKAD
        FAZAL GHAFOOR ( MES KERALA)
        MAMMOOTY
        DULQER SALMAN
        AANIE RAJA
        JOHN BRITTAS
        ADOOR GOPALAKRISHNAN
        NITI AYOG
        AMITABH KANT
        ZAKKA JACOB
        ROMILA THAPAR
        IRFAN HABIB
        NIVEDITA MEMON
        AYESHA KIDWAI
        VC OF JNU/ DU/ JU / TISS / FTII
        ALL SOCIAL SCIENCES PROFESSORS OF JNU/ DU/ JU / TISS
        ALL NALANDA UNIVERSITY PROFESSORS
        RAM MADHAV
        RAJ THACKREY
        UDDHAV THACKREY
        VIVEK OBEROI
        GAUTAM GAMBHIR
        ASHOK PANDIT
        ANUPAM KHER
        KANGANA RANAUT
        VIVEK AGNIHOTRI
        KIRON KHER
        MEENAKSHI LEKHI
        SMRITI IRANI
        PRASOON JOSHI
        MADHUR BHANDARKAR
        SWAPAN DASGUPTA
        SONAL MANSINGH
        MADHU KISHWAR
        SUDHIR CHAUDHARY
        GEN GD BAKSHI
        SAMBIT PATRA
        RSN SINGH
        SWAMY
        RAJIV MALHOTRA
        SADGURU JAGGI VASUDEV
        SRI SRI RAVISHANKAR
        BABA RAMDEV
        RSS
        VHP
        AVBP
        NCM
        NHRC
        NCW
        REKHA SHARMA
        SWATI MALLIWAL
        CHETAN BHAGAT
        DEVDUTT PATTANAIK
        AMISH TRIPATI
        ASADDUDIN OWAISI
        KUNHALIKUTTY
        RANA AYYUB
        AVED AKTHAR
        MAHESH BHATT
        SHABANA AZMI
        AMITABH BACHCHAN
        PRITISH NANDI
        ASHISH NANDI
        PAVAN VARMA
        RAMACHANDRA GUHA
        JOHN DAYAL
        KANCHA ILIAH
        SHOBHAA DE
        FATHER CEDRIC PERIERA
        DANIEL RAJA
        BRINDA KARAT
        PRAKASH KARAT
        SITARAM YECHURY
        SUMEET CHOPRA
        DINESH VARSHNEY
        BINAYAK SEN
        SUDHEENDRA KULKARNI
        PRAKASH RAJ
        RAJNIKANTH
        KAANIYA MURTHY
        SUDHA MURTHY
        AUDREY TRUSHCKE
        WENDY DONIGER
        SHELDON POLLOCK
        ANURAG KASHYAP
        APARNA SEN
        MANI RATNAM
        KOKONA SEN SHARMA
        SHYAM BENEGAL
        SHUBHA MUDGAL
        SAUMITRA CHETTERJEE
        NAYANTHARA SEHGAL
        THE ENTIRE BBC GANG
        MUKESH AMBANI
        LAXMI MITTAL
        RATAN TATA
        MAHINDRA
        AZIM PREMJI
        KUMARMANGALAM
        RAHUL BAJAJ
        NAVEEN JINDAL
        THE QUINT
        THE SCROLL
        THE WIRE
        THE PRINT
        MK VENU
        MADHU TREHAN
        CLOSET COMMIE ARNAB GOSWMI
        RAJDEEP SARDESAI
        PAAGALIKA GHOSE
        NAVIKA KUMAR
        ANAND NARASIMHAN
        SRINIVASAN JAIN
        SONAL MEHROTRA KAPOOR
        VIKRAM CHANDRA
        NIDHI RAZDAN
        FAYE DSOUZA
        RAVISH KUMAR
        PRANNOY JAMES ROY
        AROON PURIE
        VINEET JAIN
        RAGHAV BAHL
        SEEMA CHISTI
        DILEEP PADGOANKAR
        VIR SANGHVI
        KARAN THAPAR
        BARKHA DUTT
        SHEKHAR GUPTA
        SIDHARTH VARADARAJAN
        ARUN SHOURIE
        N RAM
        SANJAY DUBEY

        WEBSITES OF DESH BHAKT LEADERS
        SPREAD OF SOCIAL MEDIA

        Delete
    4. Captain Ji,

      Congratulations.Good news. Turkey got what it deserved. Trump has put sanctions on Turkey. Pak and Turkey will beg together. And Russia will cancel S400 as well.

      https://www.washingtonpost.com/politics/trump-says-he-will-soon-issue-order-authorizing-sanctions-on-turkey-over-its-incursion-into-syria/2019/10/14/ec0b9746-eea5-11e9-b2da-606ba1ef30e3_story.html



      ReplyDelete

    5. https://www.thenewsminute.com/article/kerala-cpi-conduct-seminar-vedas-upanishads-counter-hindutva-politics-110404

      NJALILEVEETTIL EDAVALATHU BALARAMAN IS THE FULL NAME.. I HAVE DONE RESEARCH ON HIS ACTIVITIES.

      NE BALARAM WAS TAUGHT BUDDHISM AND PALI IN CALCUTTA BY JEW ROTHSCHILD.. HE WAS NOT AN ATHEIST OR A COMMUNIST..

      NE BALARAM WAS A TEACHER IN PERAVOOR UP SCHOOL.. A SMALL VILLAGE IN WAYANAD..

      NOT A SINGLE COMMIE STALWART FROM KERALA KNOWS EVEN A VESTIGE OF COMMUNISM OR MARXISM..

      GREAT COMMIE LEADERS E.M.S. NAMBOODIRIPAD, AK GOPALAN , K. DAMODARAN, N.E. BALARAM , M.S. DEVADAS , GAURI , P KRISHNA PILLAI , C BHASKARAN, PRAKASH KARAT ARE ALL PRETENDERS ..

      IF ANY COMMUNIST IN KERALA CAN EXPLAIN “MOOLADHANAM “ ( DAS KAPITAL ) TO ME, I WILL STOP BLOGGING..

      NONE OF THEM KNOW THAT JEW ROTHSCHILD INVENTED COMMUNISM AND GERMAN JEW KARL MAX WAS HIS FIRST COUSIN..

      LENIN/ STALIN/ TROTSKY/ ENGELS WERE ARE JEWS..

      https://ajitvadakayil.blogspot.com/2019/10/perumal-title-of-calicut-thiyya-kings.html

      https://ajitvadakayil.blogspot.com/2019/09/onam-our-only-link-to-planets-oldest.html

      capt ajit vadakayil
      ..

      ReplyDelete
      Replies

      1. PUT ABOVE COMMENT IN WEBSITES OF--
        NE BALARAM MEMORIAL TRUST KANNUR
        P SANTHOSH KUMAR
        CN CHANDRAN
        CM PINARAYI VIJAYAN
        KODIYERI BALAKRISHNAN
        PRAKASH KARAT
        ALL MLAs/ MPs OF KERALA
        ALL COLLECTORS OF KERALA
        ALL MEDIA OF KERALA
        A PADMAKUMAR DEVASWOM BOARD PRESIDENT
        MATA AMRITANANDAMAYI
        SRI SRI RAVISHANKAR
        SADGURU JAGGI VASUDEV
        BABA RAMDEV
        PMO
        PM MODI
        AMITABH KANT
        NITI AYOG
        AMIT SHAH
        HOME MINISTRY
        AJIT DOVAL
        RAW
        CBI
        IN
        NIA
        ED
        IB
        I&B MINISTRY
        JAVEDEKAR
        CJI GOGOI
        ALL SUPREME COURT JUDGES
        ATTORNEY GENERAL
        LAW MINISTER PRASAD
        LAW MINISTRY
        CMs OF ALL INDIAN STATES
        DGPs OF ALL STATES
        GOVERNORS OF ALL STATES
        PRESIDENT OF INDIA
        VP OF INDIA
        SPEAKER LOK SABHA
        SPEAKER RAJYA SABHA
        DAVID FRAWLEY
        STEPHEN KNAPP
        WILLIAM DALRYMPLE
        KONRAED ELST
        FRANCOIS GAUTIER
        DEFENCE MINISTER - MINISTRY
        ALL THREE ARMED FORCE CHIEFS.
        RAJEEV CHANDRASHEKHAR
        MOHANDAS PAI
        RAM MADHAV
        RAJ THACKREY
        UDDHAV THACKREY
        VIVEK OBEROI
        GAUTAM GAMBHIR
        ASHOK PANDIT
        ANUPAM KHER
        KANGANA RANAUT
        VIVEK AGNIHOTRI
        KIRON KHER
        MEENAKSHI LEKHI
        SMRITI IRANI
        PRASOON JOSHI
        MADHUR BHANDARKAR
        SWAPAN DASGUPTA
        SONAL MANSINGH
        MADHU KISHWAR
        SUDHIR CHAUDHARY
        GEN GD BAKSHI
        SAMBIT PATRA
        RSN SINGH
        SWAMY
        RAJIV MALHOTRA
        RSS
        VHP
        AVBP
        CHETAN BHAGAT
        DEVDUTT PATTANAIK
        AMISH TRIPATI
        PAVAN VARMA
        RAMACHANDRA GUHA
        JOHN DAYAL
        KANCHA ILIAH
        SHOBHAA DE
        ANNA VETTICKAD
        JOHN BRITTAS
        DANIEL RAJA
        BRINDA KARAT
        SITARAM YECHURY
        SUMEET CHOPRA
        DINESH VARSHNEY
        BINAYAK SEN
        SUDHEENDRA KULKARNI
        PRAKASH RAJ
        THE QUINT
        THE SCROLL
        THE WIRE
        THE PRINT
        MK VENU
        MADHU TREHAN
        CLOSET COMMIE ARNAB GOSWMI
        RAJDEEP SARDESAI
        PAAGALIKA GHOSE
        NAVIKA KUMAR
        ANAND NARASIMHAN
        SRINIVASAN JAIN
        VISHNU SOM
        SONAL MEHROTRA KAPOOR
        VIKRAM CHANDRA
        NIDHI RAZDAN
        FAYE DSOUZA
        RAVISH KUMAR
        PRANNOY JAMES ROY
        AROON PURIE
        VINEET JAIN
        RAGHAV BAHL
        N RAM
        SEEMA CHISTI
        BARKHA DUTT
        SHEKHAR GUPTA
        SIDHARTH VARADARAJAN
        ARUN SHOURIE
        RAHUL EASHWAR
        WEBSITES OF DESH BHAKT LEADERS
        SPREAD OF SOCIAL MEDIA

        Delete
      2. Sir, sent to --

        KODIYERI BALAKRISHNAN
        https://www.facebook.com/KodiyeriB

        CN CHANDRAN
        https://www.facebook.com/cnchandran.cpi

        Kadakampally Surendran
        https://www.facebook.com/kadakampally/?__tn__=%2Cd%2CP-R&eid=ARCMXIoak5sZTuxpDHuJXSBlIhqlrDeBR8eCHC_LGJGRMMnDdBhxgz9B4XAg3I-cbZesVG2XYM4J2Gen

        Prakash Karat
        https://www.facebook.com/prakashkaratofficial/

        @neethujoseph_15 (the news minute )

        edexlive@gmail.com

        sent screenshots to Kerala handles (Media,collectors, few MPs and MLAs )
        Sent emails to Kerala ids..

        mohan.pai@manipalglobal.com fayedsouza@gmail.com
        editor@thequint.com
        letters@scroll.in
        editorial@thewire.in
        feedback@theprint.in
        contact@newslaundry.com
        contact@republicworld.com
        mail@rajdeepsardesai.net
        navikakumar@timesnow.tv
        vikramchandra@berkeley.edu
        ravish@ndtv.com
        aroonpurie@aajtak.com
        vineet.jain@timesgroup.com
        raghav.bahl@thequint.com
        seema.chishti@expressindia.com
        connect@virsanghvi.com
        karanthapar@itvindia.net
        barkha.dutt@gmail.com
        svaradarajan@gmail.com
        sv@thewire.in
        nram.thehindu@gmail.com
        ramachandraguha@yahoo.in
        connect@mygov.nic.in
        as-niti@gov.in
        amitabh.kant@nic.in

        Delete
    6. Dear Captain

      What is RECP, is it good/bad for Farmers?

      https://twitter.com/IndiaIpw/status/1183945293265432577?s=20

      http://www.cambodiaproperty.news/lifestyle/asean-officials-hope-to-draw-india-into-recp-deal-in-bangkok/

      https://economictimes.indiatimes.com/news/economy/foreign-trade/kerala-raises-concern-on-the-proposed-rcep-agreement/articleshow/62838519.cms?from=mdr

      ReplyDelete
      Replies
      1. THE REGIONAL COMPREHENSIVE ECONOMIC PARTNERSHIP (RCEP) IS A PROPOSED FREE TRADE AGREEMENT (FTA) BETWEEN THE TEN MEMBER STATES OF THE ASSOCIATION OF SOUTHEAST ASIAN NATIONS (ASEAN) (BRUNEI, CAMBODIA, INDONESIA, LAOS, MALAYSIA, MYANMAR, THE PHILIPPINES, SINGAPORE, THAILAND, VIETNAM) AND ITS SIX FTA PARTNERS (CHINA, JAPAN, INDIA, SOUTH KOREA, AUSTRALIA AND NEW ZEALAND).

        RCEP NEGOTIATIONS WERE FORMALLY LAUNCHED IN NOVEMBER 2012 AT THE ASEAN SUMMIT IN CAMBODIA

        INDIA’S EXPERIENCE WITH FREE TRADE AGREEMENTS HAS NEVER BEEN GOOD BECAUSE WE SENT SLAVES IN DEEP STATE PAYROLL TO NEGOTIATE..

        INDIA IS LIKE THE CAT WHO FELL IN HOT WATER.. NOW WE SHALL BE CIECUMSPECT WITH COLD WATER .. WE NEED TO BE CAREFUL WHILE GETTING INTO AN AGREEMENT WITH LARGE ECONOMIES SUCH AS CHINA.

        CHINA POSES A GREAT THREAT TO NOT ONLY EXISTING INDUSTRIAL GOODS MANUFACTURERS, BUT ALSO THE COUNTRY'S SMALL AND MEDIUM ENTREPRENEURS.

        MODI HAS FAILED TO CREATE JOBS.

        ONCE COMPLETED, RCEP WILL HAVE A SYSTEMIC IMPACT ON THE GLOBAL ECONOMY. ITS SIXTEEN MEMBERS ACCOUNT FOR APPROXIMATELY ONE-THIRD OF WORLD GDP, MAKING IT A LARGER TRADE BLOC THAN NAFTA OR THE EU.

        FREE TRADE IS A DEEP STATE AGENDA , WHEN YOU LOSE YOUR SOVERIEGNITY AS SOON AS YOU SIGN ON THE DOTTED LINE ..

        CHINA WILL DUMP ITS GOODS INTO INDIA ( AND POLLUTING INDUSTRES) ONCE THE PACT IS SIGNED. CHINAs SOIL AND WATER HAS BEEN SEVERELY DEGRADED BY MAKING CHEAP TOYS FOR WHITE KIDS..

        FREE TRADE IS A POLICY TO ELIMINATE DISCRIMINATION AGAINST IMPORTS AND EXPORTS. BUYERS AND SELLERS FROM DIFFERENT ECONOMIES MAY VOLUNTARILY TRADE WITHOUT A GOVERNMENT APPLYING TARIFFS, QUOTAS, SUBSIDIES OR PROHIBITIONS ON GOODS AND SERVICES.

        PROPOSED TARIFF ELIMINATION UNDER RCEP WOULD RENDER OUR EXPORTS UNCOMPETITIVE.

        RULES OF ORIGIN SPECIFY THE RULES BY WHICH A PRODUCT IS CONSIDERED TO BE “MADE IN” A COUNTRY FOR THE PURPOSE OF CUSTOMS CLEARANCE.

        THE HIGHEST VALUE ADDITION WITH THE HELP OF INDIGENOUS INPUTS MUST BE DONE IN THE COUNTRY FROM WHICH A PRODUCT IS EXPORTED.

        AS TRADE BARRIERS ARE ELIMINATED, AND PREDATORY PRICING COMES INTO PLAY, CERTAIN GOODS MAY BE CHEAPER TO OBTAIN OVERSEAS THAN TO MAKE DOMESTICALLY. ..

        NEW STARTUPS IN INDIA WILL BE ELIMINATED.. WE HAVE 1300 MILLION PEOPLE AND WE NEED TO PROVIDE JOBS..

        INDIA IS RUSHING TO BE THIS PLANET’S NO 1 SUPERPOWER IN 14 YEARS .. WE NEED TO WARD OF DEEP STATE SPONSORED FOREIGN COMPETITORS. ... THE PROTECTION OF TARIFFS, QUOTAS, OR SUBSIDIES ALLOWS DOMESTIC COMPANIES TO HIRE LOCALLY.

        SORRY WE DON’T WANT JEWS TO DECIDE OUR WATANs POLICIES. WE HAVE AN ELECTED GOVT FOR THAT.

        GLOBALIZATION PROCESS HAS BEEN ENGINEERED BY KOSHER CORPORATE ELITES,.. ONE OF THE MAIN OBJECTIVES OF FREE TRADE MOVEMENTS ABROAD HAS BEEN TO TAP CHEAPER LABOR SOURCES.

        FREE TRADE HAS BEEN GIVEN AN AURA OF VIRTUE BY DEEP STATE PAYROLL TRAITORS.

        JUST AS "FREEDOM" MUST BE GOOD, SO GLOBALIZATION HINTS AT INTERNATIONALISM AND SOLIDARITY BETWEEN COUNTRIES,

        AND WE DON’T NEED COMMIE TRAITORS LIKE ABHIJIT BANERJEE AND AMARTYA SEN TO DECIDE WHAT BHARATMATA CANNOT DO..

        COMMIE GITA GOPINATH ( BLOOD RELATIVE OF COMMIE AK GOPALAN ) HAS BEEN CHOSEN AS IMF CHIEF TO PUSH INDIA INTO GLOBALISATION.

        JNU WOMAN NIRMALA SITARAMAN SOLD BHARATMATA AT THE KOSHER MANDI DURING WTO NAIROBI.. WE LOST OUR SOVEREINITY..

        Capt ajit vadakayil
        ..

        Delete
    7. https://timesofindia.indiatimes.com/tv/news/hindi/sa-re-ga-ma-pa-2011-winner-azmat-hussain-auditions-for-indian-idol-11-talks-about-his-drug-addiction-phase/articleshow/71581520.cms

      I HAVE WARNED THESE YOUNG SCHOOL SINGERS-- NEVER STOP YOUR STUDIES..

      YOU CANNOT MAKE IT BIG IN SINGING IN BOLLYWOOD UNLESS YOU HAVE A GOD FATHER --OR SHOVE YOUR PRICK INTO THE ASSHOLE OF SOME GAY JEW DIRECTORS..

      ReplyDelete
    8. https://timesofindia.indiatimes.com/india/abhijit-was-an-economist-by-accident-but-is-an-ace-cook-says-mother-nirmala/articleshow/71589170.cms

      THIS TATTU IS COOKING FOR HIS JEWESS WIFE..

      ReplyDelete
      Replies
      1. Why does his wife look like a man? Thick neck, no breasts, strong jawline, deep set eyes and that jewish thin upper lip. Their economic theories are silly on top of that - it is what u said Captain - Amartya Sen has been put to pasture this is try #2 by R.

        Bengalis brag about their Nobel prizes but have u noticed they are all poverty related: Mother Teresa, Amartya Sen, Abhijit Banerjee even that Yusuf from Bangladesh.

        Delete
    9. https://www.business-standard.com/article/politics/story-in-numbers-india-has-suicide-rate-higher-than-the-global-average-119101300672_1.html

      THIS IS A LIE..

      COMMIES ARE INFLATING THE FARMER SUICIDES.. EVERY DEATH IS COUNTED AS A SUICIDE..

      INDIA HAS THE LOWEST SUICIDE RATE ON THE PLANET-- BY PERCENTAGE.. WE HAVE 1300 MILLION PEOPLE..

      WE HAVE A DEEP STATE AGENT AS I&B MINISTER-- KAYASTHA PRAKASH JAVEDEKAR..

      ReplyDelete
    10. Namaste Captain,

      Have finished Person of Interest all seasons. Now I can dedicate more time to post comments with more thought in it.

      Last season felt like an abrupt ending.. Heard that they were asked not to continue the series.

      Glued to your AI blog page now

      ReplyDelete
    11. i've commented on TOI regarding banerjee, as usual it's censored.

      ReplyDelete
    12. Dear Capt Ajit sir,
      Yday Arnab tried hard to get RSS/BJP spokespersons Sharda and N.Kohli to accept arresting small fry Praful Patel first, then Sharad Pawar....maybe he got some money from INC/NCP to have them arrested and win elections...which BJP think tank will not do till Maharashtra elections are over.

      ReplyDelete
    13. Dear Capt Ajit sir,
      Unless the Kosher Rothschild agents are in power, local jaichands are working in tandem....as below.
      BJP releases its manifesto for Maharashtra elections, promises to demand Bharat Ratna for Veer Savarkar, Jyotiba Phule and Savitribai Phule..The BJP working President JP Nadda and Maharashtra Chief Minister Devendra Fadnavis today released the party’s manifesto for the upcoming state assembly polls. The manifesto states the party’s demand to confer the Bharat Ratna award to Mahatma Jyotiba Phule, Savitribai Phule and Veer Savarkar.
      https://www.opindia.com/2019/10/bjp-maharashtra-manifesto-assembly-elections/?fbclid=IwAR1u7wwR1_BeS7DkQTFqQ65iKvOp2d7dC2e2YcSgY8Bwf_f1DFjgjQrYBcg

      ReplyDelete
    14. I found this passage in Wikipedia that you might find interesting:

      Umichand
      Umichand (also known as Amin Chand or Amir Chand) was a Sikh businessman who had come to Kolkata from Amritsar when the British were just making forays into the country. He played a major role in establishing the British in India. He had earned fabulously in business with the British East India Company and donated all his wealth for religious purposes at the time of his death.[7][8] He was known as 'the Rothschild of his day'.

      Does this mean that people knew of Rothschilds back then and historians have hidden this fact?

      ReplyDelete
    15. https://en.wikipedia.org/wiki/Omichund

      http://ajitvadakayil.blogspot.com/2011/07/back-swing-of-john-galt-capt-ajit.html

      THE BASTARDS WHO MADE IT POSSIBLE FOR JEW ROTHSCHILD TO WIN THE BATTLE OF PLASSEY ( ON PAPER ) WERE--

      JAIN JEW JAGAT SETH
      MARWARI JEW UMICHAND
      PIR ALI MUSLIM MIR JAFFAR ( TAGORE AND RAMOHAN ROY WERE PIR AI MUSLIMS )
      KAYASTHA DURLABH RAM
      MARWARI JEW MANIK CHAND

      http://ajitvadakayil.blogspot.com/2019/07/we-never-heard-words-kayastha-and.html

      THE MARWARI JAIN JEWS OF OSIYA, JODHPUR DISTRICT WERE NAMED AS OSWAL. ROTSHCILD USED OSWALS AS LOCAL FINANCIERS.

      OSWAL JAGAT SHETH WAS ROTHSCHILD’S OPIUM DRUG RUNNER.

      IN THE 1757 BATTLE OF PLASSEY THE SETH SIDED WITH ROTHSCHILD AND PLAYED A KEY ROLE IN BRINGING DOWN SIRAJ UD-DAULAH . OPIUM TRADERS ROTHSCHILD TOOK OVER INDIA VIDE THE BATTLE OF PLASSEY IN 1757.

      JAGAT SETH JEWS INCLUDE MAHDAB RAI AND SWARUP CHAND. THEY WERE OPIUM AGENTS AND BANKING PARTNERS OF ROTHSCHILD .

      YOU HAVE NO IDEA HOW THESE CRYPTO JEW MARWARIS AND JAINS SCREWED BHARATMATA.

      AFTER ROTHSCILD LEFT INDIA IN 1947, HIS ASSETS BECAME BENAMI –WITH CRYPTO JEW MARWARI JAINS ACTING AS MERE FRONTS.

      WHO DOES NOT KNOW THE SUPER RICH --THE JAGAT SETHS -- THE GOPALDAS MANOHARDAS -THE GANERIWALA - THE PITTIE FAMILIES --

      THE TERM MARWARI LITERALLY REFERS TO SOMEONE WHO HAILS FROM OR IS AN INHABITANT OF MARWAR - THE ERSTWHILE JODHPUR STATE. THIS TERM GAINED CURRENCY INITIALLY IN BENGAL, WHERE THE TRADERS FROM SHEKHAWATI AND OTHER PARTS OF RAJASTHAN ESTABLISHED THEIR BUSINESS EMPIRES.

      DISTINCT IN THEIR DRESS, CUSTOMS AND LANGUAGE, THE CRYPTO JEW TRADERS AND MERCHANTS OF RAJASTHAN CAME TO BE KNOWN AS MARWARIS. IN ANTWERP THEY WERE DIAMOND AGENTS OF JEW ROTHSCHILD.. IN CALCUTTA THEY WERE OPIUM MIDDLEMEN..

      NATHURAM SARAF SERVED AS A BANIA TO THE FIRM OF MILLER KINSELL AND GHOSE, RAMKUMAR CHOKHANI OF NAWALGARH WAS THE BANIA FOR LUDWIG DUKE. HARIRAM GOENKA WAS GUARANTEE BROKER TO THE RALLI BROTHERS, ONKARMAL JATIA TO ANDREWE YULE AND ANANDIIAL PODDAR TO TOYOTO MENKA KESHA.

      THE PODDARS AND RUIAS OF RAMGARH HAD SET UP FIRMS IN MUMBAI AND RAMNARAIN RUIA AND GOVINDRAM GHANSHYAMDAS WERE FIRMLY ENTRENCHED IN THE BROKER TRADE. BILASIRAI KEDIA, GULRAJ SINGHANIA AND RAMDAYAL NEVATIA, FROM FATEHPUR, AND NATHURAM PODDAR AND JOKHIRAM RUIA OF RAMGARH, WERE KING PINS IN THE R CONTROLLED OPIUM TRADE .

      ROTHSCHILD ENCOURAGED THE BIRLAS, GOENKAS, DALMIAS, RUIAS, PODDARS AND SINGHANIAS AMONGST OTHERS, TO EXPAND, DIVERSIFY, AND GET INTO CORE SECTORS

      THE HUNDI, SERVED BOTH AS A CASHLESS REMITTANCE FACILITY ENABLING LONG-DISTANCE INLAND TRADE AND ALSO A SOURCE OF MOBILE CREDIT, BY VIRTUE OF IT BEING FREELY TRANSFERABLE THROUGH SUCCESSIVE ENDORSEMENTS BEFORE BEING FINALLY PRESENTED TO THE DRAWEE.

      CONTINUED TO 2--

      ReplyDelete
      Replies
      1. CONTINUED FROM 1--

        IT WAS THE LUBRICANT THAT GREASED THE WHEELS OF COMMERCE, BY CONNECTING SOME 1,700 NATIONWIDE PRODUCE MANDIS AND 12 NODAL MONEY MARKETS HANDLING THE BULK OF DISCOUNTING OF THESE BILLS AT THE TURN OF THE CENTURY.

        THESE FIRMS WERE MAGNETS FOR ATTRACTING FELLOW RAJASTHANI CLANSMEN, WHO COULD JOIN AS CLERKS, MANAGERS, BROKERS AND PARTNERS. G.D. BIRLA’S GRANDFATHER, SHIVNARAYAN WORKED WITH TARACHAND GHANSHYAMDAS; SO DID THE GRANDFATHER OF THE GLOBAL STEEL CZAR, LAKSHMI NIWAS MITTAL.

        THERE WAS SEVARAM RAMRIKHDAS THAT EMPLOYED, AMONG OTHERS, THE RPG GROUP PATRIARCH, RAMA PRASAD GOENKA’S GRANDFATHER’S GREAT-GRANDFATHER, RAMDUTT. THE SEVARAM RAMRIKHDAS FIRM’S DIVISION RESULTED IN INDEPENDENT OFFSHOOTS AT KANPUR, MIRZAPUR, FARRUKHABAD AND KOLKATA: THE SINGHANIAS ARE DESCENDENTS OF THE KANPUR LINE.

        THE MARWARIS PIONEERED TRADING IN INDIGENOUS OPTIONS (SATTA), GIVING THE BUYERS THE RIGHT, BUT WITH NO OBLIGATION, TO BUY OR SELL A CERTAIN COMMODITY AT A SPECIFIED FUTURE DATE AND PRICE. THESE COULD BE TEJI (CALL) OR MANDI (PUT), WITH THE PREMIUM PAID BY THE BUYER OF THE OPTION KNOWN AS NAZRANA.

        THEY KNOW HOW TO WHEEL AND DEAL .

        WHEN GANDHI WAS THROWN INTO JAIL BY ROTHSCHILD, THE SAME NIGHT HE WOULD BE WHISKED AWAY TO BIRLAS PALACE OR JEW AGA KHANs PALACE WERE TWO NAKED TEENAGED GIRLS WOULD BE WAITING WITH ENEMA KIT AND BLANKET.

        http://ajitvadakayil.blogspot.com/2019/07/how-gandhi-converted-opium-to-indigo-in.html

        http://ajitvadakayil.blogspot.in/2010/11/drug-runners-of-india-capt-ajit.html

        LET ME QUOTE WIKIPEDIA:

        QUOTE - G. D. BIRLA'S FATHER, BALDEODAS BIRLA, WAS ADOPTED FROM THE NAVALGARH BIRLA FAMILY. BALDEODAS'S FORTUNE WAS MADE IN PARTNERSHIP WITH HIS NEPHEW, FULCHAND SODHANI, THROUGH SPECULATION IN THE OPIUM TRADE RUNNING INTO MORE THAN 10 MILLION RUPEES, IN WHICH HIS ELDER BROTHER JUGAL KISHORE BIRLA HAD EARNED A NAME-UNQUOTE

        JAGAT SETH IS A TITLE—WORLD LORD. JAGAT SETHS INCLUDE MAHDAB RAI AND SWARUP CHAND. THEY WERE OPIUM AGENTS AND BANKING PARTNERS OF ROTHSCHILD .

        http://ajitvadakayil.blogspot.com/2011/02/murky-truths-of-sepoys-mutiny-1857.html

        capt ajit vadakayil
        ..

        Delete
      2. PUT ABOVE COMMENT IN WEBSITES OF--
        MATA AMRITANANDAMAYI
        SRI SRI RAVISHANKAR
        SADGURU JAGGI VASUDEV
        BABA RAMDEV
        PMO
        PM MODI
        AMITABH KANT
        NITI AYOG
        AMIT SHAH
        HOME MINISTRY
        AJIT DOVAL
        RAW
        CBI
        IN
        NIA
        ED
        IB
        I&B MINISTRY
        JAVEDEKAR
        CJI GOGOI
        ALL SUPREME COURT JUDGES
        ATTORNEY GENERAL
        LAW MINISTER PRASAD
        LAW MINISTRY
        CMs OF ALL INDIAN STATES
        DGPs OF ALL STATES
        GOVERNORS OF ALL STATES
        PRESIDENT OF INDIA
        VP OF INDIA
        SPEAKER LOK SABHA
        SPEAKER RAJYA SABHA
        DAVID FRAWLEY
        STEPHEN KNAPP
        WILLIAM DALRYMPLE
        KONRAED ELST
        FRANCOIS GAUTIER
        DEFENCE MINISTER - MINISTRY
        ALL THREE ARMED FORCE CHIEFS.
        RAJEEV CHANDRASHEKHAR
        MOHANDAS PAI
        RAM MADHAV
        RAJ THACKREY
        UDDHAV THACKREY
        VIVEK OBEROI
        GAUTAM GAMBHIR
        ASHOK PANDIT
        ANUPAM KHER
        KANGANA RANAUT
        VIVEK AGNIHOTRI
        KIRON KHER
        MEENAKSHI LEKHI
        SMRITI IRANI
        PRASOON JOSHI
        MADHUR BHANDARKAR
        SWAPAN DASGUPTA
        SONAL MANSINGH
        MADHU KISHWAR
        SUDHIR CHAUDHARY
        GEN GD BAKSHI
        SAMBIT PATRA
        RSN SINGH
        SWAMY
        RAJIV MALHOTRA
        RSS
        VHP
        AVBP
        CHETAN BHAGAT
        DEVDUTT PATTANAIK
        AMISH TRIPATI
        PAVAN VARMA
        RAMACHANDRA GUHA
        JOHN DAYAL
        KANCHA ILIAH
        SHOBHAA DE
        ANNA VETTICKAD
        JOHN BRITTAS
        DANIEL RAJA
        BRINDA KARAT
        SITARAM YECHURY
        SUMEET CHOPRA
        DINESH VARSHNEY
        BINAYAK SEN
        SUDHEENDRA KULKARNI
        PRAKASH RAJ
        THE QUINT
        THE SCROLL
        THE WIRE
        THE PRINT
        MK VENU
        MADHU TREHAN
        CLOSET COMMIE ARNAB GOSWMI
        RAJDEEP SARDESAI
        PAAGALIKA GHOSE
        NAVIKA KUMAR
        ANAND NARASIMHAN
        SRINIVASAN JAIN
        VISHNU SOM
        SONAL MEHROTRA KAPOOR
        VIKRAM CHANDRA
        NIDHI RAZDAN
        FAYE DSOUZA
        RAVISH KUMAR
        PRANNOY JAMES ROY
        AROON PURIE
        VINEET JAIN
        RAGHAV BAHL
        N RAM
        SEEMA CHISTI
        BARKHA DUTT
        SHEKHAR GUPTA
        SIDHARTH VARADARAJAN
        ARUN SHOURIE
        RAHUL EASHWAR
        WEBSITES OF DESH BHAKT LEADERS
        SPREAD OF SOCIAL MEDIA

        Delete
      3. Sir, sent to -

        Mata Amritanandamayi
        https://www.amritapuri.org/eservices/contact

        narendramodi1234@gmail.com
        contact@amitshah.co.in
        prakash.j@sansad.nic.in
        info.nia@gov.in
        information@cbi.gov.in
        38ashokroad@gmail.com
        mos-defence@gov.in
        swamy39@gmail.com
        connect@mygov.nic.in
        info.nia@gov.in
        contact@amitshah.co.in
        jscpg-mha@nic.in
        rmo@mod.nic.in
        17akbarroad@gmail.com
        webmaster.indianarmy@nic.in
        proiaf.dprmod@nic.in
        pronavy.dprmod@nic.in
        connect@mygov.nic.in
        as-niti@gov.in
        vch-niti@nic.in
        amitabh.kant@nic.in
        secy.ncert@nic.in
        minister.hrd@gov.in
        pstohrm@gov.in
        info.nia@gov.in
        ed-del-rev@nic.in
        17akbarroad@gmail.com
        rmo@mod.nic.in
        mos-defence@gov.in
        webmaster.indianarmy@nic.in
        pronavy.dprmod@nic.in
        proiaf.dprmod@nic.in
        connect@mygov.nic.in
        ravis@sansad.nic.in
        minister.inb@gov.in
        prakash.j@sansad.nic.in
        supremecourt@nic.in
        nalsa-dla@nic.in
        info.nia@gov.in
        ed-del-rev@nic.in
        jspna-moib@gov.in
        dirj1-doj@nic.in
        amitabh.kant@nic.in
        prakash.j@sansad.nic.in
        minister.hrd@gov.in
        m.yadav@gov.in,
        annadurai@nic.in,
        rg.ngt@nic.in,
        ngtpb.pg@gmail.com,
        drrameshpokhriyal@gmail.com,
        secy.dhe@nic.in,
        r.subra@nic.in,
        subrahyd@gmail.com,
        krishna.kumari@nic.in,
        km.harigovindan@nic.in,
        js.saini@nic.in,
        sandhu.edu@nic.in,
        m-mohan@gov.in,
        madhu.ranjan@gov.in,
        jsfa.edu@gov.in,
        subba.rao61@nic.in,
        mmohan.edu@nic.in,
        praveen.nandwana@gov.in,
        vcm.ugc@nic.in,
        secy.ncert@nic.in
        wim@nic.in
        Info@janamtv.com
        amitabh.kant@nic.in
        ambuj.sharma38@nic.in
        as-niti@gov.in
        bjphqo@gmail.com
        contact@amitshah.co.in
        contactus@rss.org
        connect@mygov.nic.in
        eam@mea.gov.in
        info.nia@gov.in
        Info@vhp.org
        mphamirpur@gmail.com
        mib.inb@nic.in
        ms-ncw@nic.in
        news.dd@doordarshan.gov.in
        pmo@govmu.org
        mail@rajdeepsardesai.net
        sagarika.ghosh@springer.com
        vedicinstitute@gmail.com
        srinandan@aol.com
        koenraadelst@hotmail.com
        fgautier26@gmail.com; secy.ncert@nic.in
        sircar.j@gmail.com
        minister.hrd@gov.in ; swami39@gmail.com
        infinity.foundation.india@gmail.com
        as-niti@gov.in;vch-niti@nic.in
        amitabh.kant@nic.in
        contact@republicworld.com
        fayedsouza@timesgroup.com
        vineetjain@timesgroup.com

        Delete
    16. https://timesofindia.indiatimes.com/business/india-business/indian-buyers-slash-malaysian-palm-oil-purchases-fearing-duty-hike-traders/articleshow/71592640.cms

      KERALA POLITICIANS ( CONGRESS/ COMMIES ) HAVE IMPORTED PALM OIL FROM MALAYSIA FOR KICKBACKS..

      PALM OIL IS MOST DANGEROUS FOR HEATH

      THE ONLY GOOD VEG OIL IS VIRGIN COCONUT OIL-- WHICH WE IN KERALA HAD PLENTY..

      http://ajitvadakayil.blogspot.com/2012/08/coconut-oil-is-good-for-cooking-ignore.html

      ReplyDelete
    17. https://photogallery.indiatimes.com/news/india/apj-abdul-kalam-life-in-pics/APJ-Abdul-Kalam/articleshow/48245824.cms'

      REPLACE ALL ROAD AND MONUMENTS NAMED AFTER DESH DROHI KATHIAWARI JEW GANDHI WITH ABDUL KALAM-- HE IS OUR HERO !

      ReplyDelete
    18. https://twitter.com/TarekFatah/status/1183780569156595718

      THE SHIRK PREACHERS OF KERALA ARE MOSTLY CRYPTO JEWS..

      THEY ARE 100 TIMES WORSE THAN THIS MULLAH..

      http://ajitvadakayil.blogspot.com/2011/07/cracked-heels-and-prayer-marks-capt.html

      IN THE PHOTO BELOW, MM AKBARs ZEBIBA MARK IS IN THE WRONG PLACE

      https://scroll.in/article/870255/kerala-islamic-preacher-held-for-spreading-hate-through-schools-says-others-used-the-same-textbook

      HERE IS MM AKBAR AT CALICUT

      https://www.youtube.com/watch?v=B1uxnSNzkVI

      ReplyDelete
    19. https://photogallery.indiatimes.com/beauty-pageants/miss-world/miss-india-usa-shree-saini-collapses-a-night-before-miss-world-america-finale-mother-asks-for-prayers/articleshow/71593780.cms?picid=71594014

      LOOKS LIKE A HIJRA WITH NARROW HIPBONES..

      ReplyDelete
    20. Respected Sir,
      Can one listen to discourses on the holy Bhagavad Gita by Swami Chinmayananda during one's menstrual cycle.
      Thank you

      ReplyDelete
    21. https://www.youtube.com/watch?v=Xu28PjOrgZY

      HEY KERALA COMMIE SASHI KUMAR..

      YOU ARE SO STUPID THAT YOU DONT EVEN KNOW THAT SADGURU RAPED YOU..

      SASHI KUMAR, TO PUNISH YOU, I MAY WRITE A POST ON HOW DEEP STATE HELPED YOU SET UP ASIANET.. I KNOW THE DIRTY SECRETS..

      YOU WILL BE IN THE SAME BOAT AS PAPA CHID SOONER THAN LATER.. I KNOW MEMBERS OF YOUR FAMILY AND YOUR CLOSE ASSOCIATES, WHO DONT LIKE THE SLIME IN YOU..

      HERE IS A SAYING " MAALATHIL KEDEKUNNA MOORKHAN PAAMBINE KONATHIL KETARUTHE"..

      NEVER EXTRACT A COBRA FROM ITS HOLE AND PUT IT INSIDE YOUR LANGOT..

      HEY COMMIE SASHI KUMAR-- CAN YOU UNDERSTAND THE CONTENTS OF THE POST BELOW?

      http://ajitvadakayil.blogspot.com/2018/09/sanatana-dharma-hinduism-exhumed-and.html

      ALL THESE STORIES OF EMPERORS ASHOKA AND HARSHA VARDHANA CONVERTING FROM BAAAAD HINDUISM TO GOOOOOD BUDDHISM ARE ALL COOKED UP..

      PALI SPEAKING, PIG EATING BUDDHA NEVER EXISTED.. ASHOKA / HARSHA VARDHANA NEVER EXISTED.. ALL WERE COOKED UP BY JEW ROTHSCHILD WHO INVENTED COMMUNISM..

      http://ajitvadakayil.blogspot.com/2019/06/deliberately-buried-truths-about-buddha.html

      capt ajit vadakayil
      ..

      ReplyDelete
      Replies
      1. PUT ABOVE COMMENTS IN WEBSITES OF--
        SASHI KUMAR
        SADGURU JAGGI VASUDEV
        NE BALARAM MEMORIAL TRUST KANNUR
        P SANTHOSH KUMAR
        CN CHANDRAN
        CM PINARAYI VIJAYAN
        KODIYERI BALAKRISHNAN
        PRAKASH KARAT
        ALL MLAs/ MPs OF KERALA
        ALL COLLECTORS OF KERALA
        ALL MEDIA OF KERALA
        A PADMAKUMAR DEVASWOM BOARD PRESIDENT
        MATA AMRITANANDAMAYI
        SRI SRI RAVISHANKAR
        BABA RAMDEV
        PMO
        PM MODI
        AMITABH KANT
        NITI AYOG
        AMIT SHAH
        HOME MINISTRY
        AJIT DOVAL
        RAW
        CBI
        IN
        NIA
        ED
        IB
        I&B MINISTRY
        JAVEDEKAR
        CJI GOGOI
        ALL SUPREME COURT JUDGES
        ATTORNEY GENERAL
        LAW MINISTER PRASAD
        LAW MINISTRY
        CMs OF ALL INDIAN STATES
        DGPs OF ALL STATES
        GOVERNORS OF ALL STATES
        PRESIDENT OF INDIA
        VP OF INDIA
        SPEAKER LOK SABHA
        SPEAKER RAJYA SABHA
        DAVID FRAWLEY
        STEPHEN KNAPP
        WILLIAM DALRYMPLE
        KONRAED ELST
        FRANCOIS GAUTIER
        DEFENCE MINISTER - MINISTRY
        ALL THREE ARMED FORCE CHIEFS.
        RAJEEV CHANDRASHEKHAR
        MOHANDAS PAI
        RAM MADHAV
        RAJ THACKREY
        UDDHAV THACKREY
        VIVEK OBEROI
        GAUTAM GAMBHIR
        ASHOK PANDIT
        ANUPAM KHER
        KANGANA RANAUT
        VIVEK AGNIHOTRI
        KIRON KHER
        MEENAKSHI LEKHI
        SMRITI IRANI
        PRASOON JOSHI
        MADHUR BHANDARKAR
        SWAPAN DASGUPTA
        SONAL MANSINGH
        MADHU KISHWAR
        SUDHIR CHAUDHARY
        GEN GD BAKSHI
        SAMBIT PATRA
        RSN SINGH
        SWAMY
        RAJIV MALHOTRA
        RSS
        VHP
        AVBP
        CHETAN BHAGAT
        DEVDUTT PATTANAIK
        AMISH TRIPATI
        PAVAN VARMA
        RAMACHANDRA GUHA
        JOHN DAYAL
        KANCHA ILIAH
        SHOBHAA DE
        ANNA VETTICKAD
        JOHN BRITTAS
        DANIEL RAJA
        BRINDA KARAT
        SITARAM YECHURY
        SUMEET CHOPRA
        DINESH VARSHNEY
        BINAYAK SEN
        SUDHEENDRA KULKARNI
        PRAKASH RAJ
        BABA RAMDEV
        PMO
        PM MODI
        AMITABH KANT
        NITI AYOG
        AMIT SHAH
        HOME MINISTRY
        AJIT DOVAL
        RAW
        CBI
        IN
        NIA
        ED
        IB
        I&B MINISTRY
        JAVEDEKAR
        CJI GOGOI
        ALL SUPREME COURT JUDGES
        ATTORNEY GENERAL
        LAW MINISTER PRASAD
        LAW MINISTRY
        CMs OF ALL INDIAN STATES
        DGPs OF ALL STATES
        GOVERNORS OF ALL STATES
        PRESIDENT OF INDIA
        VP OF INDIA
        SPEAKER LOK SABHA
        SPEAKER RAJYA SABHA
        DAVID FRAWLEY
        STEPHEN KNAPP
        WILLIAM DALRYMPLE
        KONRAED ELST
        FRANCOIS GAUTIER
        DEFENCE MINISTER - MINISTRY
        ALL THREE ARMED FORCE CHIEFS.
        RAJEEV CHANDRASHEKHAR
        MOHANDAS PAI
        RAM MADHAV
        RAJ THACKREY
        UDDHAV THACKREY
        VIVEK OBEROI
        GAUTAM GAMBHIR
        ASHOK PANDIT
        ANUPAM KHER
        KANGANA RANAUT
        VIVEK AGNIHOTRI
        KIRON KHER
        MEENAKSHI LEKHI
        SMRITI IRANI
        PRASOON JOSHI
        MADHUR BHANDARKAR
        SWAPAN DASGUPTA
        SONAL MANSINGH
        MADHU KISHWAR
        SUDHIR CHAUDHARY
        GEN GD BAKSHI
        SAMBIT PATRA
        RSN SINGH
        SWAMY
        RAJIV MALHOTRA
        RSS
        VHP
        AVBP
        CHETAN BHAGAT
        DEVDUTT PATTANAIK
        AMISH TRIPATI
        PAVAN VARMA
        RAMACHANDRA GUHA
        JOHN DAYAL
        KANCHA ILIAH
        SHOBHAA DE
        ANNA VETTICKAD
        JOHN BRITTAS
        DANIEL RAJA
        BRINDA KARAT
        SITARAM YECHURY
        SUMEET CHOPRA
        DINESH VARSHNEY
        BINAYAK SEN
        SUDHEENDRA KULKARNI
        PRAKASH RAJ
        ROMILA THAPAR
        BUDDHIST PRIYANKA VADRA
        IRFAN HABIB
        NIVEDITA MEMON

        WEBSITES OF DESH BHAKT LEADERS
        SPREAD OF SOCIAL MEDIA

        Delete
      2. Sir, Sent to --

        https://www.facebook.com/sashi.kumar.33671
        https://twitter.com/karuppadanna

        Kodiyeri Blakrishnan
        https://www.facebook.com/KodiyeriB

        Cn Chandran
        https://www.facebook.com/cnchandran.cpi

        Kadakampally Surendran
        https://www.facebook.com/kadakampally/?__tn__=%2Cd%2CP-R&eid=ARCMXIoak5sZTuxpDHuJXSBlIhqlrDeBR8eCHC_LGJGRMMnDdBhxgz9B4XAg3I-cbZesVG2XYM4J2Gen

        Prakash Karat
        https://www.facebook.com/prakashkaratofficial/

        Sitaram Yechury ( posted message as a reply to one of his FB post)
        https://www.facebook.com/ComradeSRY/?eid=ARBp2Q62aIyFEl6A01Bw49gs6dYllvW0HWot9A5cEcNTFupfJhyzd47XdQ5Sb3IR0B3Q-gcC-ayYtauP

        Used snapshots.

        @NSA_AjitDoval @MrAjitDoval @CBItweets @Ajit_Doval @dir_ed @RAWHeadOffice @NIA_India @cbic_india @nib_india @KollamNanbans

        @IASassociation @kannurcollector @CollectorWyd @Collectortnv @balakiran_ias @BPIAS @vijayakumariti @collectorkkd @KeralaGovernor @drthomasisaac @oommen_chandy @JBrittas @LOQUA17 @pcgeorgepoonjar @marunadannews @Mohanlal @SChidanandapuri @YourSwamijiSP @BJPKerala1 @rsskeralashakha @RahulEaswar @People4Dharma @HinduCharter @gopi_suresh @b_kodiyeri @kadakampalli @Yathish_IPS @E_Sreedharan @parasaran @KeralaPoliceA @keralapolice @janamtvnational @KeralaNews24x7 @PIBTvpm @THKerala @TOIKochiNews @kairalionline @manoramanews @manoramaonline @ManoramaTopNews @mathrubhumi @mathrubhumieng @mathrubhuminews @mahaabvp @asianet @asianetnewstv @DDNewsMalayalam @IeMalayalam @livenewskerala @marunadannews @Metrom_com_au @ML_Express @Nri_malayalee @thatsMalayalam @ZeeMalayalam @ddmalayalam @advprathibha @Forumkeralam1 @kerala_kaumudi @thenewsminute @drthomasisaac @gsudhakaran_gs @rojimjohn @amariffmla @kssabarinadhan @adv_b_satyan @vtbalram @vijayanpinarayi @hibieden @chennithala @kcjoseph99 @mbrajeshcpm @kadakampalli @ibsathishonline @karkalasunil @thiruvanchoor @shailajateacher @vs1923 @bjprajagopal @shafiksu @ckhareendranmla @vdsatheesan @mlapayyannur @tpramakrishnan @aliminister @kunnappilli @oommen_chandy @rajumla7 @shamseeran @georgemthomas1 @vssivakumr @ptthomasinc @kunhiramanmla @maniyashaan @apanilkumar1 @abdulwahabpv @ksomaprasad @mpveerendran

        @RajatSharmaLive @CMOKerala @CPIMKerala @sagarikaghose @Shehla_Rashid @RanaAyyub @ReallySwara @TeestaSetalvad @kavita_krishnan @JohnDayal @Kancha_ilaiah @prakashraaj @SudheenKulkarni @annavetticad @SitaramYechury @brindacpim @Ram_Guha @PavanK_Varma @virsanghvi @KaranThapar_TTP @UmarKhalidJNU @seemay @ayesha_kidwai @DineshVarsh @kramdas @awardreturner
        @Ram_Guha @svaradarajan @Arundatiroy @Nidhi @DeShobhaa
        @Koenraad_Elst @chetan_bhagat @authoramish @devduttmyth
        @mammukka @dulQuer @PKKunhalikutty @ikamalhaasan @VinodDua7 @AnnieRaja92 @PavanK_Varma @madhutrehan @supriya_sule @nramind @suhelseth @devduttmyth @ShashiTharoor

        @RajivMessage @rajeev_mp @KirronKherBJP @M_Lekhi @smritiirani @madhukishwar @sudhirchaudhary @GeneralBakshi @sambitswaraj @ssrajamouli @Mohanlal @AnupamPKher @prasoonjoshi_ @hindulegalcell

        regards.

        Delete
    22. https://twitter.com/DrGPradhan/status/1184095971765936128

      CONMAN HALWAI DOCTOR IS SHOCK AND AWE MODE

      240 MILLION PEOPLE READ HIS TWEETS ON HINDI HEARTLAND POLITICS.. WHEN YOU GLEAN IT, IT COMES BACK TO SQUARE ONE' APUN AWAAL "..

      TEE HEEEEEEE

      ReplyDelete
      Replies
      1. PUT THIS IN TWITTER SITES OF -
        HALWAI
        HONEY PREET JAIN
        AMIT SHAH
        HOME MINISTRY
        CBI
        NIA
        ED
        AJIT DOVAL
        PMO
        PM MODI

        Delete
    23. Dear captain,

      Asoka, Andal, Abraham...etc starts with 'A'

      and

      DasA, buddhA, sarA...etc ends with 'A'


      are these coincidence......or delibrately kept....for themselfes (R) to figure it out easily and push their agenda....

      Love and gratitude always,
      Jai hind.

      ReplyDelete
    24. Dear Capt Ajit sir,

      Arnab let the cat out of the bag...Iqbal Mirchi is India's largest drug dealer and Praful Patel having nexus with him on land deals and not attaching his properties when he's on the Committee on Home Affairs (1995–1996), the Committee on Finance (1996–97)...however Amit Shah has hinted at broader actions post investigation by CBI/ED sooner.

      ReplyDelete
      Replies
      1. HOTEL SEA ROCK WAS BOMBED BY JEW DAWOOD IBRAHIM, SO THAT SEA ROCK HOTEL COULD BE BOUGHT BY JEWS..

        SURESH NANDA WHOSE FATHER ADMIRAL SM NANDA ( CNS ) CAME FROM AN IMPOVERISHED FAMILY BOUGH THE HOTEL IN 2005 FOR 330 CRORES.. FROM WHERE DID HE GET THE MONEY?

        SM NANDA WORKED AS A CLERK IN KARACHI PORT TRUST UNDER THE GRANDFATHER OF ONE OF MY PAKSIATNI OFFICERS ..

        SURESH NANDA WAS AN ARMS DEALER -- WHO WAS TAKEN CARE BY MOSSAD..

        https://en.wikipedia.org/wiki/Suresh_Nanda

        WHY IS SURESH NANDA NOT INVESTIGATED.. BECAUSE BJP POLITICANS AND DEEP STATE PAYROLL JUDGES ARE INVOLVED ?

        I USED TO EAT AT "THE EARTHEN OWEN" REVOLVING RESTAURANT .. YOU WILL ALWAYS FIND BOLLYWOOD FILM STARS TALKING TO JEWISH MAFIA THERE..

        https://en.wikipedia.org/wiki/Sardarilal_Mathradas_Nanda

        https://en.wikipedia.org/wiki/1999_Delhi_hit-and-run_case

        WE ASK BJP-- WHY BE SELECTIVE ?

        WE ALL KNOW MOSSAD AGENT GEORGE FERNANDES , WHO BECAME DEFENCE MINISTER-- AND WHO WAS WEARING A SIKH TURBAN WITH SWAMY AND MODI IN 1976..

        capt ajit vadakayil
        ..

        Delete
      2. PUT ABOVE COMMENT IN WEBSITES OF
        PMO
        PM MODI
        AJIT DOVAL
        RAW
        ED
        NIA
        IB
        CBI
        AMIT SHAH
        HOME MINISTRY

        Delete
      3. Used screenshots -

        sent to,

        @RAWHeadOffice @NIA_India @nib_india ‏ @Vikram_Sood @rawnksood @SpokespersonMoD @PMOIndia @narendramodi @AmitShahOffice @HMOIndia @AmitShah @NSA_AjitDoval @MrAjitDoval @CBItweets @Ajit_Doval @dir_ed @adgpi

        Delete
      4. Your Registration Number is : PMOPG/E/2019/0615465

        Delete
      5. Tweet:
        https://twitter.com/AghastHere/status/1184331353631973376?s=20

        Handles:
        @RAWHeadOffice @NIA_India @nib_india ‏ @Vikram_Sood @rawnksood @SpokespersonMoD @PMOIndia @narendramodi @AmitShahOffice @HMOIndia @AmitShah @NSA_AjitDoval @MrAjitDoval @CBItweets @Ajit_Doval @dir_ed @adgpi

        Thanks, Sriram, for sharing handles.

        Delete

    25. Geeta NOctober 15, 2019 at 8:10 PM
      Guru Ji what is reason that city like bhopal and even small town vidisha are running freemanson club mostly rich businessman are part of these club they have monthly meeting in dress code, I wonder there agenda?

      ReplyDelete
      Replies
      1. Vidisha, Bhopal have rotor clubs, which are freemason clubs

        Khajuraho has a freemason lodge

        Looking forward to the reply from captain Kalki

        Delete
    26. Dear Capt Ajit sir,
      Why do you think Germany is proactively closing all 84 of its coal plants ? https://www.latimes.com/world/europe/la-fg-germany-coal-power-20190126-story.html

      ReplyDelete
      Replies
      1. Video of Pune Electric Bus Getting Charged Through a Diesel Generator Surfaces, Twitter Has a Field Day

        https://www.news18.com/news/auto/video-of-pune-electric-bus-getting-charged-through-a-diesel-generator-surfaces-twitter-has-a-field-day-2343281.html

        Delete
    27. https://twitter.com/realDonaldTrump/status/1184125314147983361?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Etweet

      ReplyDelete
    28. Captain guruji,

      If a man falls in love with a woman who is seven years older to the man, and they want to marry, what would you advice??

      Pranam guruji

      ReplyDelete
    29. https://twitter.com/VishnuNDTV/status/1184084585975402497?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Etweet

      THIS BLOGSITE WARNS VISHNU SOM OF NDTV..

      YOU KNOW NOTHING OF ECONOMICS.. YOU ARE GUILTY OF UNFAIRLY RUNNING DOWN THE ECONOMY OF THE WATAN, WITH YOUR LIES , USING DESH DROHIS LIKE AMARTYA SEN, RAGHURAM RAJAN , ABHIJIT BANNERJEE ETC TO SHORE UP YOUR LIES..

      VISHNU SOM, YOU ARE NOW CONSIDERED A DESH DROHI, JUST LIKE YOUR BOSS PRANOY JAMES ROY.. YOU GET OVERJOYED WHEN SOMETHING BAD HAPPENS TO BHARATMATA.. YOU HAVE TRULY STEPPED INTO BARKHA DUTTs SHOES..

      INDIA IS THE ONLY ROARING ECONOMY ON THE PLANET.. NO AMOUNT OF LIES FROM DEEP STATE AGENTS AMARTYA SEN, RAGHURAM RAJAN, ABHIJIT BANNERJEE CAN WISH IT AWAY..

      ABHIJEET BANNERJEE, WE THE PEOPLE OF INDIA DONT WANT YOUR ADVISE.. JUST LAY OFF BHARATMATA ..

      DEEP STATE DARLING ABHIJIT BANNERJEE WAS ONE OF THE 108 SIGNATORIES WHO LIED THAT MODI COOKED UP STATISTICS PUBLISHED BY CENTRAL STATISTICAL OFFICE NSSO JUST PRIOR TO 2019.. THIS WAS A POLITICAL MOVE..

      SEE BELOW LINK.. COMMIE ABHIJIT BANERJEE IS NO 5 IN THE LIST..

      https://thewire.in/economy/108-economists-social-scientists-raise-red-flags-over-interference-in-data-estimation

      IF YOU WANT TO KNOW REAL ECONOMICS READ QUESTIONS TO QUORA FROM 686 TO 820 BELOW..

      https://ajitvadakayil.blogspot.com/2019/06/archived-questions-to-quora-from-capt.html

      Capt ajit vadakayil
      ..

      ReplyDelete
      Replies
      1. PUT ABOVE COMMENT IN WEBSITES OF--

        MIT DEAN ( WARNING HIM THAT HIS PROFESSOR ABHIJIT BANNERJEE IS INVOLVED IN POLITICS )
        TRUMP
        AMBASSADOR TO FROM INDIA-USA
        ESTHER DUFLO
        AMARTYA SEN
        RAGHURAM RAJAN
        VISHNU SOM
        PRANNOY JAMES ROY
        RBI GOVERNOR
        RBI
        FINANCE MINISTRY CENTRE AND ALL STATES
        ALL DEANS OF INDIAN ECONOMICS AND BUSINESS SCHOOLS
        PMO
        PM MODI
        AMITABH KANT
        NITI AYOG
        AMIT SHAH
        HOME MINISTRY
        AJIT DOVAL
        RAW
        CBI
        IN
        NIA
        ED
        IB
        I&B MINISTRY
        JAVEDEKAR
        CJI GOGOI
        ALL SUPREME COURT JUDGES
        ATTORNEY GENERAL
        CMs OF ALL INDIAN STATES
        DGPs OF ALL STATES
        GOVERNORS OF ALL STATES
        PRESIDENT OF INDIA
        VP OF INDIA
        SPEAKER LOK SABHA
        SPEAKER RAJYA SABHA
        JACK DORSEY
        MARK ZUCKERBERG
        THAMBI SUNDAR PICHAI
        CEO OF WIKIPEDIA
        QUORA CEO ANGELO D ADAMS
        QUORA MODERATION TEAM
        KURT OF QUORA
        GAUTAM SHEWAKRAMANI
        DAVID FRAWLEY
        STEPHEN KNAPP
        WILLIAM DALRYMPLE
        KONRAED ELST
        FRANCOIS GAUTIER
        DEFENCE MINISTER - MINISTRY
        ALL THREE ARMED FORCE CHIEFS.
        RAJEEV CHANDRASHEKHAR
        MOHANDAS PAI
        RAM MADHAV
        RAJ THACKREY
        UDDHAV THACKREY
        VIVEK OBEROI
        GAUTAM GAMBHIR
        ASHOK PANDIT
        ANUPAM KHER
        KANGANA RANAUT
        VIVEK AGNIHOTRI
        KIRON KHER
        MEENAKSHI LEKHI
        SMRITI IRANI
        PRASOON JOSHI
        MADHUR BHANDARKAR
        SWAPAN DASGUPTA
        SONAL MANSINGH
        MADHU KISHWAR
        SUDHIR CHAUDHARY
        GEN GD BAKSHI
        SAMBIT PATRA
        RSN SINGH
        SWAMY
        SWAMYs BROTHER SA IYER
        RAJIV MALHOTRA
        SADGURU JAGGI VASUDEV
        SRI SRI RAVISHANKAR
        BABA RAMDEV
        RSS
        VHP
        AVBP
        THE ENTIRE BBC GANG
        MUKESH AMBANI
        LAXMI MITTAL
        RATAN TATA
        MAHINDRA
        AZIM PREMJI
        KUMARMANGALAM
        RAHUL BAJAJ
        NAVEEN JINDAL
        PAVAN VARMA
        RAMACHANDRA GUHA
        THE QUINT
        THE SCROLL
        THE WIRE
        THE PRINT
        MK VENU
        CLOSET COMMIE ARNAB GOSWMI
        RAJDEEP SARDESAI
        PAAGALIKA GHOSE
        NAVIKA KUMAR
        ANAND NARASIMHAN
        SRINIVASAN JAIN
        SONAL MEHROTRA KAPOOR
        VIKRAM CHANDRA
        NIDHI RAZDAN
        FAYE DSOUZA
        RAVISH KUMAR
        AROON PURIE
        VINEET JAIN
        RAGHAV BAHL
        SEEMA CHISTI
        SHEKHAR GUPTA
        SIDHARTH VARADARAJAN
        ARUN SHOURIE
        N RAM
        WEBSITES OF DESH BHAKT LEADERS
        SPREAD OF SOCIAL MEDIA

        Delete
      2. https://www.facebook.com/nirmala.sitharaman/

        Tulu@mit.edu,
        narendramodi1234 ,
        contact@amitshah.co.in,
        prakash.j@sansad.nic.in,
        info.nia@gov.in,
        information@cbi.gov.in,
        38ashokroad@gmail.com,
        mos-defence@gov.in,
        Anantha@mit.edu,
        banerjee@mit.edu,
        eduflo@mit.edu,
        mosfinance@nic.in,
        appointment.fm@gov.in,
        secyexp@nic.in,
        pramod.das@gov.in,
        mathewag@nic.in

        sent screenshots and emails to many..

        Delete
    30. Namaskaara Captain - Belated Birthday wishes. Also belated wishes of Mahanavami and Vijaya Dashami to fellow readers. I couldn't wish on time through the comment sections, but definitely prayed for everyone's "Shreyas" on those auspicious days.

      I was on a leisurely trip to Europe for couple of weeks. I visited Athens and Rome to also experience some of the highlights from your previous blogs. Wanted to share couple of my observations

      Greece
      *******
      Santorini and Mykonos - The homes on those islands felt lot like Indian home. Both in concept and in making. Similar homes can be seen especially in the rural South India.

      Athens - Was staying with in the heart of the city closer by to the Acropolis, Ancient Agora and the old Monastiraki market. Ancient Agora gave a vibe of "Naaga Bana"/Snake Grove of Malabar area. I don't know whether there were snakes, but I definitely feel that it had a huge ayurvedic garden. The Church of the Holy Apostles in the corner must have been a temple in that ayurvedic garden. There were lot of hindu motifs of Peacocks on the door. Swastika mark on the stones that were tucked away and locked in the ruins area

      The temples at the Acropolis and of Hephaestus definitely had the hindu motif of "Mango Leaves" in the entry door way. While growing up we used to adorn Mango leaves on the door during every festival.

      The ancient Agora was used as the venue of the accession of the new members to EU back in 2003.

      Italy
      ******
      Rome - felt the magic of Pantheon and Colosseum. The usage of facade to cover from inside the dome was very evident. In one of the section, there was a slight opening to see how the facade would have been erected to cover up the hindu art. But one can see the Kerala influence of wood usage for the door and the arch outside. Very similar to temples in Kerala and Mangalore. What was the reason that the temples in Greece and Rome have bigger dimension when compared to temples in Kerala? Is the reason that India is naturally so beautiful, that constructing smaller size would be apt vs. in Athens and Rome, it is the other way round? Also, the "Dwaja Sthamba" in temple converted into church was every evident.

      Also, the guide was telling us that Fountain of Trevi the "3" is denoting the junction of three roads where the ancient acqueduct terminates. From you blogs it is very evident that you it must has to have similar concept of "Trevni Sangama" from the santhan dharma if the acqueduct architects were from India/Kerala. There must be the mobius coil underneath that fountain. Hence the coin throwing ceremony to ask for something that is needed.


      Also, I noticed that Emblem of Greek orthodox church has a striking resemblance to the Ghandaberunda symbol of Mysore Kingdom/Current Karnataka Government.

      I saw your comment in the previous article of Queen Helena and Ancient India using "Yavana" to refer to Greeks.

      Dhanyaawada
      Kiran

      ReplyDelete
    31. SOMEBODY CALLED ME UP AND CRIED

      CAPTAIN, YOU HAVE CALLED DAWOOD IBRAHIM A JEW..

      YES-- DAVID ABRAHAM.. A KONKAN BENE ISRAEL JEW..

      IF YOU SEE THE MEMONS -- PAPA ABDUL RAZZAK MEMON HAD SIX SONS. ALL NAMED AFTER JEWISH HEROES.. SOLOMON -SULEMAN / ABRAHAM -IBRAHIM / JACOB - YAKUB / JOB - AYUB / JESUS - ESSA / JOSEPH -YUSUF ..

      I HAVE WRITTEN SIX TIMES BEFORE THAT THE RDX FOR 1993 MUMBAI BLASTS CAME FROM A COCHIN MATTANCHERY JEW ISAACs SHIP IN A CONTAINER, WHICH WAS UNLOADED LAST AND TAKEN AWAY WITH CUSTOMS ESCORT..

      I HAVE SPOKEN ABOUT JEW ISAAC WITH A ISRAELI WHITE JEW EX-CAPTAIN ( A nice RESPECTABLE MAN ) , WHO OFFERED TO ISAAC TO RUN HIS MAFIA SHIPS ( BRIBING EVERYWHERE ) THE LEGITIMATE WAY.. HE REFUSED..

      ONE OF MY JUNIOR OFFICERS WAS ISAACs HENCHMAN.. ONCE IN BRAZIL, FILIPINO CREW WENT ON STRIKE BECAUSE THEIR WAGES WERE NOT PAID..

      ISAAC WENT ALL THE WAY TO BRAZIL .. HE ASKED FOR THE RING LEADER , A FILIPINO THIRD ENGINEER AND ASKED HIM WHY HE WAS NOT WORKING.. THE THIRD ENGINEER SAID HE HAS PAIN AT THE BACK..

      ISAAC HAD A RETRACTABLE STEEL ROD WITH HIM -- HE GAVE HIM SUCH A HARD BLOW ON THE SAME SPOT THAT THE THIRD ENGINEER WENT FLYING ACROSS THE ROOM. THEN HE ASKED HIM " IS YOU PAIN STILL THERE " TO WHICH THE FILIPINO SAID-- IT IS GONE AND WENT BACK TO WORK EL PRONTO ..

      ALL DUES WERE PAID IN USD CASH BY ISAAC . HE HAD MORE THAN ONE LAKH USD IN CASH WITH HIM.

      MY JUNIOR OFFICER ( WITH ISAAC GIVEN MOBILE PHONE ) WENT TO COLLECT HIM AT THE PORT GATE ON A CYCLE.. HE DID NOT HAVE A PASS, BUT BRIBED THE BRAZILIAN GUARD IN USD.

      WHILE PIGGY BACKING THE CYCLE RIDE FROM PORT GATE TO SHIP-- COCHIN JEW ISAAC ASKS FROM THE CARRIER SEAT " MAIN CHAAR FLIGHT PAKADKE AAYA HOON - PROBLEM KYA HAI JAHAAZ PEH "

      WHEN HOTEL SEA ROCK OPERATED BEFORE THE BLAST IN 1993, THE REVOLVING RESTAURANT ON TOP "THE EARTHEN OWEN " WAS A HUB OF MAFIA JEW ACTIVITY..

      YOU COULD SEE ALL BOLLYWOOD JEWS AND THE MUMBAI UNDERWORLD MAFIA JEWS. SETTING UP DEALS..

      CENTURIES AGO, GERMAN JEW ROTHSCHILD DUMPED A SHIPLOAD OF BENE ISRAEL JEWS FROM NORTH KERALA OFF THE KONKAN COAST.. THEY WADED ASHORE AND CLAIMED TO BE SHIP WRECKED..

      THEIR DESCENDANTS WERE THE CHITPAVAN BRAHMINS LIKE RANADE, TILAK, GOKHALE, SAVARKAR etc / PESHWAS WHO KICKED OUT EMPEROR SHAHU / KATHIWARI-PALANPURI-MARWARI JEWS ..

      MOST BOLLYWOOD KHANS ARE DESCENDANTS OF PASHTUN JEW PATHANS -- WHO DID OPIUM RETAIL STREET DRUG RUNNING FOR JEW ROTHSCHILD..( THE JOB NIGERIANS DO TODAY AT NIGHT)..

      KHAN MARKET SPONSORED BY THE LUTYENS BABUS IN DELHI WAS A JEWISH MARKET , WHERE YOU COULD BUY OPIOD DRUGS AND SMUGGLED FOREIGN GOODS..

      http://ajitvadakayil.blogspot.com/2019/07/how-gandhi-converted-opium-to-indigo-in.html

      HOTEL SEA ROCK WAS BOMBED BY KONKAN JEW DAWOOD IBRAHIM ( USING JEW MEMONS ) , SO THAT THE SINDHI OWNED SEA ROCK HOTEL COULD BE BOUGHT BY JEWS.. THEY NEVER KNEW THE POWER OF RDX .. ALL THEY WANTED WAS TO STOP THE REVOLVING RESTAURANT..

      SURESH NANDA WHOSE FATHER ADMIRAL SM NANDA ( CNS ) CAME FROM AN IMPOVERISHED FAMILY BOUGHT THE HOTEL IN 2005 FOR 330 CRORES.. FROM WHERE DID HE GET THE MONEY?

      SM NANDA WORKED AS A CLERK IN KARACHI PORT TRUST UNDER THE GRANDFATHER OF ONE OF MY PAKSIATNI OFFICERS ..

      SURESH NANDA WAS AN ARMS DEALER -- WHO WAS TAKEN CARE BY MOSSAD..

      https://en.wikipedia.org/wiki/Suresh_Nanda

      WHY IS SURESH NANDA NOT INVESTIGATED.. BECAUSE BJP POLITICANS AND DEEP STATE PAYROLL JUDGES ARE INVOLVED ?

      WE ASK AJIT DOVAL-- RAW KOH KUCHCH NAHIN PATHA ?

      capt ajit vadakayil
      ..

      ReplyDelete
      Replies
      1. PMO
        PM MODI
        AMIT SHAH
        HOME MINISTRY
        AJIT DOVAL
        RAW
        CBI
        IN
        NIA
        ED
        IB
        I&B MINISTRY
        PRAKASH JAVEDEKAR
        LAW MINISTER PRASAD
        CJI GOGOI
        LAW MINISTRY
        ATTORNEY GENERAL

        Delete
      2. https://twitter.com/kkarthikeyan09/status/1184330873669181446?s=20

        Delete
      3. Sent to --

        webmaster.indianarmy@nic.in,
        armyveteranscell@gmail.com,
        veteranscell-army@nic.in,
        info.nia@gov.in,
        information@cbi.gov.in,
        38ashokroad@gmail.com,
        mos-defence@gov.in,
        narendramodi1234@gmail.com,
        contact@amitshah.co.in,
        prakash.j@sansad.nic.in,

        Delete
      4. Sent message to IB from its website and mail to-
        alokmittal.nia@gov.in

        Delete
    32. https://timesofindia.indiatimes.com/india/india-falls-to-102-in-hunger-index-8-ranks-below-pakistan/articleshow/71606116.cms

      NOBODY IN INDIA DIES OF HUNGER DESPITE 35% OF OUR FOOD ( VEGGIES/ FRUITS/ GRAINS ) ROT IN STORAGE..

      ALL THIS IS FAKE NEWS BY DEEP STATE CONTROLLED BENAMI MEDIA..

      I HAVE SEEN THIS PLANET FOR 40 YEARS..

      SHOWING MUMBAI STREET BEGGAR KIDS EATING -- CUTS NO ICE !

      AMARTYA SEN/ RAGHURAM RAJAN , ABHIJIT BANNERJEE , SAM PITRODA ETC IS BEHIND THIS FALSE CAMPAIGN ..

      capt ajit vadakayil
      ..

      ReplyDelete
      Replies
      1. PUT ABOVE COMMENT IN WEBSITES OF--
        PMO
        PM MODI
        AMIT SHAH
        HOME MINISTRY
        AJIT DOVAL
        RAW
        CBI
        IN
        NIA
        ED
        IB
        I&B MINISTRY
        PRAKASH JAVEDEKAR
        LAW MINISTER PRASAD
        CJI GOGOI
        LAW MINISTRY
        ATTORNEY GENERAL
        NIRMALA SITARAMAN
        FINANCE MINISTRY
        AMITABH KANT
        NITI AYOG

        Delete
    33. https://www.business-standard.com/article/pti-stories/indian-economy-on-a-shaky-ground-nobel-awardee-banerjee-119101400968_1.html

      WE THE PEOPLE ASK PM MODI..

      ABHIJIT BANNERJEE , IS A DEEP STATE AGENT --JUST LIKE AMARTYA SEN AND RAGHURAM RAJAN..

      HE IS GUILTY OF LYING USING A PLATFORM GIVEN TO HIM BY FORD FOUNDATION TO BRING HIS OWN WATAN DOWN..

      I HAVE READ SOME OF HIS WORKS INCLUDING HIS BOOK “POOR ECONOMICS “ AND HEARD HIS VIEWS -- AND I CONSIDER HIM A MEDIOCRE BRAIN WHO HAS REACHED PLACES BY SPONSORSHIP FROM JEWISH NEOCONS ..

      MODI, WE THE PEOPLE ASK YOU TO WARN HIM..

      WE ASK DONALD TRUMP TO NOTE THIS FELLOW AND PROFILE HIM..

      MODI / TRUMP-- WE ASK YOU TO WARN MIT THAT A PROFESSOR NAMED ABHIJIT BANNERJEE IS DOING DIRTY COMMIE POLITICS WHICH MAY WARRANT HIS ARREST NEXT TIME HE COMES TO INDIA..

      DEEP STATE DARLING ABHIJIT BANNERJEE WAS ONE OF THE 108 SIGNATORIES WHO LIED THAT MODI COOKED UP STATISTICS PUBLISHED BY CENTRAL STATISTICAL OFFICE NSSO JUST PRIOR TO 2019 TO PAINT A ROSY PICTURE OF INDIAN ECONOMY ..

      THIS WAS A SLIMY POLITICAL MOVE, UNBECOMING OF A PROFESSER OF MIT WHO MUST HAVE INTEGRITY .. ACADEMIC STATURE OF MIT IS NOW IN QUESTION LIKE COMMIE INSTITUTION JNU OF INDIA..

      SEE BELOW LINK.. COMMIE ABHIJIT BANERJEE IS NO 5 IN THE LIST..

      https://thewire.in/economy/108-economists-social-scientists-raise-red-flags-over-interference-in-data-estimation

      ROTHSCHILDs ECONOMICS PUSHED BY THE LIKES OF AMARTYA SEN ( WITH A EMMA ROTHSCHILD WIFE ) IS THE BANE OF THIS PLANET..

      IF YOU WANT TO KNOW REAL ECONOMICS READ QUESTIONS TO QUORA FROM 686 TO 820 BELOW..

      https://ajitvadakayil.blogspot.com/2019/06/archived-questions-to-quora-from-capt.html

      WE ASK WHICH PROFESSOR WITH INTEGRITY WILL MARRY HIS OWN DOCTORAL STUDENT ? IS ABHIJIT BANNERJEEs WIFE ESTHER DUFLO ( A JOINT NOBEL PRIZE WINNER ) WORTHY OF HER DEGREE ?

      Capt ajit vadakayil
      ..

      ReplyDelete
      Replies

      1. PUT ABOVE COMMENT IN WEBSITES OF--

        MIT DEAN ( WARNING HIM THAT HIS PROFESSOR ABHIJIT BANNERJEE IS INVOLVED IN POLITICS )
        TRUMP
        AMBASSADOR TO FROM INDIA-USA
        ESTHER DUFLO
        ARUNDHATI TULI ( DIVORCED WIFE OF ABHIJIT BANNERJI )
        AMARTYA SEN
        RAGHURAM RAJAN
        MANMOHAN SINGH
        VISHNU SOM
        PRANNOY JAMES ROY
        RBI GOVERNOR
        RBI
        FINANCE MINISTRY CENTRE AND ALL STATES
        ALL DEANS OF INDIAN ECONOMICS AND BUSINESS SCHOOLS
        PMO
        PM MODI
        AMITABH KANT
        NITI AYOG
        AMIT SHAH
        HOME MINISTRY
        AJIT DOVAL
        RAW
        CBI
        IN
        NIA
        ED
        IB
        I&B MINISTRY
        JAVEDEKAR
        CJI GOGOI
        ALL SUPREME COURT JUDGES
        ATTORNEY GENERAL
        CMs OF ALL INDIAN STATES
        DGPs OF ALL STATES
        GOVERNORS OF ALL STATES
        PRESIDENT OF INDIA
        VP OF INDIA
        SPEAKER LOK SABHA
        SPEAKER RAJYA SABHA
        JACK DORSEY
        MARK ZUCKERBERG
        THAMBI SUNDAR PICHAI
        CEO OF WIKIPEDIA
        QUORA CEO ANGELO D ADAMS
        QUORA MODERATION TEAM
        KURT OF QUORA
        GAUTAM SHEWAKRAMANI
        DAVID FRAWLEY
        STEPHEN KNAPP
        WILLIAM DALRYMPLE
        KONRAED ELST
        FRANCOIS GAUTIER
        DEFENCE MINISTER - MINISTRY
        ALL THREE ARMED FORCE CHIEFS.
        RAJEEV CHANDRASHEKHAR
        MOHANDAS PAI
        RAM MADHAV
        RAJ THACKREY
        UDDHAV THACKREY
        VIVEK OBEROI
        GAUTAM GAMBHIR
        ASHOK PANDIT
        ANUPAM KHER
        KANGANA RANAUT
        VIVEK AGNIHOTRI
        KIRON KHER
        MEENAKSHI LEKHI
        SMRITI IRANI
        PRASOON JOSHI
        MADHUR BHANDARKAR
        SWAPAN DASGUPTA
        SONAL MANSINGH
        MADHU KISHWAR
        SUDHIR CHAUDHARY
        GEN GD BAKSHI
        SAMBIT PATRA
        RSN SINGH
        SWAMY
        SWAMYs BROTHER SA IYER
        RAJIV MALHOTRA
        SADGURU JAGGI VASUDEV
        SRI SRI RAVISHANKAR
        BABA RAMDEV
        RSS
        VHP
        AVBP
        THE ENTIRE BBC GANG
        MUKESH AMBANI
        LAXMI MITTAL
        RATAN TATA
        MAHINDRA
        AZIM PREMJI
        KUMARMANGALAM
        RAHUL BAJAJ
        NAVEEN JINDAL
        PAVAN VARMA
        RAMACHANDRA GUHA
        THE QUINT
        THE SCROLL
        THE WIRE
        THE PRINT
        MK VENU
        CLOSET COMMIE ARNAB GOSWMI
        RAJDEEP SARDESAI
        PAAGALIKA GHOSE
        NAVIKA KUMAR
        ANAND NARASIMHAN
        SRINIVASAN JAIN
        SONAL MEHROTRA KAPOOR
        VIKRAM CHANDRA
        NIDHI RAZDAN
        FAYE DSOUZA
        RAVISH KUMAR
        AROON PURIE
        VINEET JAIN
        RAGHAV BAHL
        SEEMA CHISTI
        SHEKHAR GUPTA
        SIDHARTH VARADARAJAN
        ARUN SHOURIE
        N RAM
        WEBSITES OF DESH BHAKT LEADERS
        SPREAD OF SOCIAL MEDIA

        Delete
      2. I ASK MY READERS TO GIVE ABHIJIT BANNERJEE A PIECE OF YOUR MIND..

        HE IS COMING TO INDIA NEXT WEEK TO LAUNCH HIS BOOK.

        Delete
      3. Email Id Of His First Wife

        Tulu@mit.edu

        Email id of Present Dean Of MIT college of engineering

        Anantha@mit.edu

        https://news.mit.edu/contact

        Sent Trump Via White House Form, to pm modi PMOPG/E/2019/0615614, governors, CM's and dgp of the states, to ministers and some important ministries and media people

        Delete
      4. Correction

        the email id of first wife is

        tuli@mit.edu

        Delete
      5. Many Jews in the USA university system are not worthy of their degree. Most people know this but obviously you can't say anything or else the Kosher Nostra will come down on you hard.

        In fact it helps to be Jewish to get tenure.

        Delete
      6. Dear Captain,

        Sent to PMO (PMOPG/E/2019/0615670)

        https://twitter.com/kkarthikeyan09/status/1184326594476699649?s=20

        abhijit banerjee started discharging his duty within hours of getting nobel price i.e., to attack Indian interests and project that Indian economy is in bad state

        Thanks and Regards,
        Karthikeyan K

        Delete
      7. Sent mails to-
        appointment.fm@gov.in
        mphamirpur@gmail.com
        Anantha@mit.edu
        asen@fas.harvard.edu
        shaktikanta.das@nic.in
        contact@amitshah.co.in
        amitabh.kant@nic.in

        Trump and putin listen to you and follow your advice,but it is very difficult to wake up this modi,who is pretending sleeping..

        Delete
      8. Sarah Brady
        Assistant Dean for Finance
        sarahb@mit.edu

        Delete
      9. FYI an additional piece of information.

        Department of Economics in MIT falls under School of Humanities, Arts, and Social Sciences (SHASS).
        Dean is Ms. Melissa Nobles.
        Email: mnobles@mit.edu

        Delete
    34. https://timesofindia.indiatimes.com/india/social-media-used-to-taint-judiciary/articleshow/71605927.cms

      A part of the social media is controlled by the IT cell in Pakistan. How can the SC take action against social media comments?

      Complaining to the media that judges are not getting important (lucrative) cases has proven that Indian judiciary is as corrupt & incompetent as the political landscape. The SC judges (most of them appointed by previous government) had audaciously rejected NJAC which had been passed by both Houses.

      The issue of Gender equality at Sabarimala is still unsettled. The Mandala season (Vrischikam month) is nearing and people are in panic mood again because the same Pinarayi is the CM of the state. If LDF wins at least 2 out of 5 seats in the coming bye-elections on October 21st, then Pinarayi will again start the war cry against Hindus. The state government has completed the master plan for the International airport at Sabari hills. Speaking to a public gathering at Konni, as part of the election campaign, Kerala CM affirmed that the proposed Sabarimala airport will be realized soon.

      ReplyDelete
    35. Hi capt,

      First time i have heard truth in twitter about kill chips.

      https://twitter.com/ashokkmrsingh/status/1184321483306237952

      ReplyDelete
    36. https://timesofindia.indiatimes.com/india/these-indians-are-not-having-kids-to-save-the-planet/articleshow/70910606.cms

      deep state agenda..

      ReplyDelete
    37. WE ASK DONALD TRUMP AND PM MODI?

      WHY HAS THE NEXUS OF TOYOTA AND ISIS NEVER BEEN EXPOSED?

      REASON IS THE SAUDI MIDDLEMAN JEW ABDUL LATIF JAMEEL IS A DEEP STATE AGENT.

      THE ABDUL LATIF JAMEEL POVERTY ACTION LAB (J-PAL) IS A GLOBAL RESEARCH CENTER WORKING TO REDUCE POVERTY BY ENSURING THAT POLICY IS INFORMED BY KOSHER DEEP STATE POLICIES .

      J-PAL WAS FOUNDED IN 2003 AS THE "POVERTY ACTION LAB" BY MIT PROFESSORS ABHIJIT BANERJEE, ESTHER DUFLO AND SENDHIL MULLAINATHAN. THE INFOSYS PRIZE 2018 IN SOCIAL SCIENCES WAS AWARDED TO PROF. SENDHIL MULLAINATHAN.

      ALL RESEARCH AND STATISTICS BY ABDUL LATIF JAMEEL POVERTY ACTION LAB (J-PAL) IS UNRELIABLE—IT IS DELIBERATELY SKEWED TO SHOW INDIA IN VERY POOR LIGHT.

      HTTPS://EN.WIKIPEDIA.ORG/WIKI/ABDUL_LATIF_JAMEEL_POVERTY_ACTION_LAB

      ABDUL LATIF JAMEEL POVERTY ACTION LAB (JPAL) IS A VERY SHADY OUTFIT THAT WORKS WITH NOTORIOUS ORGANIZATIONS LIKE FORD FOUNDATION AND TERRORIST-LINKED SAUDI ABDUL LATIF JAMEEL FAMILY THAT HAD A ROLE IN SUPPLYING TOYOTA CARS TO ISIS.

      JEW ABDUL LATIF JAMEEL OF SAUDI ARABIA SUPPLIED TOYOTA CRUISERS TO ISIS ( CREATED /FUNDED /ARMED BY JEWS ).. THE FOUNDER, JEW ABDUL LATIF JAMEEL, RAN THE COMPANY FROM ITS FORMATION UNTIL HIS DEATH IN 1993, WHEN HIS SON, JEW MOHAMMED ABDUL LATIF JAMEEL, BECAME CHAIRMAN AND PRESIDENT..

      TILL NOW NOBODY HAS FIGURED OUT HOW TOYOTA, THE WORLD’S SECOND LARGEST AUTO MAKER, SOLD MORE THAT A HUNDRED THOUSAND BRAND NEW SUVS TO ISIS WITH PEDESTALS AND TRUCKBEDS STRENGTHENED AND DAMPED FOR FITTING LARGE MACHINE GUNS WITHHEAVY RE-COIL .. ( TOYOTA HILUX PICKUPS, AN OVERSEAS MODEL SIMILAR TO THE TOYOTA TACOMA, AND TOYOTA LAND CRUISERS )..

      TOYOTA LAND CRUISER AND HILUX MARKETED BY ABDUL LATIF JAMEEL MOTORS HAVE EFFECTIVELY BECOME PART OF THE ISIS BRAND.

      TOYOTA DEALERSHIP IN SYRIA WAS OPENLY RUN BY ABDUL LATIF JAMEEL..

      http://www.diariodivic.it/isis-toyota-vehicles-as-part-of-the-war/

      https://www.alj.com/en/about/story/

      https://en.wikipedia.org/wiki/Hassan_Jameel

      US Citizen Abhijit Vinayak Banerjee officially became Christian after marrying crypto Jewess Ester Duflo.

      FORMER IAS IQBAL SINGH DHALIWAL IS EXECUTIVE DIRECTOR OF J-PAL AT DEPARTMENT OF ECONOMICS, MASSACHUSETTS INSTITUTE OF TECHNOLOGY. .. HE IS THE HUSBAND OF KERALA COMMIE GITA GOPINATH WHOSE IS A BLOOD RELATIVE OF JEW ROTHSCHILD AGENT AK GOPALAN .

      GITA GOPINATH WORKED AS THE ECONOMIC ADVISER TO THE COMMIE CHIEF MINISTER OF KERALA PINARAYI VIJAYAN BEFORE APPOINTED AS CHIEF ECONOMIST OF THE INTERNATIONAL MONETARY FUND IN OCTOBER 2018.

      BOTH COMMIE GITA GOPINATH AND COMMIE ABHIJIT BANNERJEE ARE PROTÉGÉS OF AMARTYA SEN WHO MARRIED JEWESS EMMA OF THE WORLD’S RICHEST ROTHSCHILD FAMILY.

      http://ajitvadakayil.blogspot.com/2011/09/amartya-sen-gets-nobel-prize-for.html

      ABHIJEET BANERJEE LIKE HIS MENTOR AMARTYA SEN IS A RABID MODI HATER.. HE WAS THE ONE WHO FRAMED THE POPULIST #NYAY SCHEME OF RAHUL GANDHI DURING ELECTIONS TO FOLLOW UP ON SEN'S MNREGA DEBACLE

      THIS FORD FOUNDATION CAUCUS ARE CHEERLEADERS OF RAHUL GANDHI. KAVITA RAMDAS DAUGHTER OF DEEP STATE DARLING ADMIRAL LAKSHMINARAYAN RAMDAS IS SENIOR ADVISOR TO THE FORD FOUNDATION'S PRESIDENT, DARREN WALKER..

      https://www.youtube.com/watch?v=sWnjUtZE-IM

      THE LATEST BOOK OF ABHIJEET BANERJEE WAS CO-EDITED WITH GITA GOPINATH, RAGHURAM RAJAN, AND MIHIR S. SHARMA

      MIHIR SHARMA IS A LOBBYIST FOR THE ITALIAN JEWISH SONIA MAINO FAMILY , A PERCEPTION MOLDER FOR JEWISH DEEP STATE TOOL BLOOMBERG .

      WE ASK WHICH PROFESSOR WITH INTEGRITY WILL MARRY HIS OWN DOCTORAL STUDENT ? IS ABHIJIT BANNERJEEs WIFE ESTHER DUFLO ( A JOINT NOBEL PRIZE WINNER ) WORTHY OF HER DEGREE ?

      Capt ajit vadakayil
      ..

      ReplyDelete
      Replies
      1. PUT ABOVE COMMENT IN WEBSITES OF--

        MIT DEAN ( WARNING HIM THAT HIS PROFESSOR ABHIJIT BANNERJEE IS INVOLVED IN POLITICS )
        TRUMP
        AMBASSADOR TO FROM INDIA-USA
        PUTIN
        AMBASSADOR TO FROM RUSSIA -INDIA
        ESTHER DUFLO
        ARUNDHATI TULI ( DIVORCED WIFE OF ABHIJIT BANNERJI )
        AMARTYA SEN
        RAGHURAM RAJAN
        GITA GOPINATH
        IQBAL SINGH DHALIWAL
        MANMOHAN SINGH
        MIHIR S SHARMA
        ADMIRAL L RAMDAS
        WIFE LALITA RAMDAS
        DAUGHTER KAVITA RAMDAS
        VISHNU SOM
        PRANNOY JAMES ROY
        RBI GOVERNOR
        RBI
        FINANCE MINISTRY CENTRE AND ALL STATES
        ALL DEANS OF INDIAN ECONOMICS AND BUSINESS SCHOOLS
        PMO
        PM MODI
        AMITABH KANT
        NITI AYOG
        AMIT SHAH
        HOME MINISTRY
        AJIT DOVAL
        RAW
        CBI
        IN
        NIA
        ED
        IB
        I&B MINISTRY
        JAVEDEKAR
        CJI GOGOI
        ALL SUPREME COURT JUDGES
        ATTORNEY GENERAL
        CMs OF ALL INDIAN STATES
        DGPs OF ALL STATES
        GOVERNORS OF ALL STATES
        PRESIDENT OF INDIA
        VP OF INDIA
        SPEAKER LOK SABHA
        SPEAKER RAJYA SABHA
        JACK DORSEY
        MARK ZUCKERBERG
        THAMBI SUNDAR PICHAI
        CEO OF WIKIPEDIA
        QUORA CEO ANGELO D ADAMS
        QUORA MODERATION TEAM
        KURT OF QUORA
        GAUTAM SHEWAKRAMANI
        DAVID FRAWLEY
        STEPHEN KNAPP
        WILLIAM DALRYMPLE
        KONRAED ELST
        FRANCOIS GAUTIER
        DEFENCE MINISTER - MINISTRY
        ALL THREE ARMED FORCE CHIEFS.
        RAJEEV CHANDRASHEKHAR
        MOHANDAS PAI
        RAM MADHAV
        RAJ THACKREY
        UDDHAV THACKREY
        VIVEK OBEROI
        GAUTAM GAMBHIR
        ASHOK PANDIT
        ANUPAM KHER
        KANGANA RANAUT
        VIVEK AGNIHOTRI
        KIRON KHER
        MEENAKSHI LEKHI
        SMRITI IRANI
        PRASOON JOSHI
        MADHUR BHANDARKAR
        SWAPAN DASGUPTA
        SONAL MANSINGH
        MADHU KISHWAR
        SUDHIR CHAUDHARY
        GEN GD BAKSHI
        SAMBIT PATRA
        RSN SINGH
        SWAMY
        SWAMYs BROTHER SA IYER
        RAJIV MALHOTRA
        SADGURU JAGGI VASUDEV
        SRI SRI RAVISHANKAR
        BABA RAMDEV
        RSS
        VHP
        AVBP
        THE ENTIRE BBC GANG
        MUKESH AMBANI
        LAXMI MITTAL
        RATAN TATA
        MAHINDRA
        AZIM PREMJI
        KUMARMANGALAM
        RAHUL BAJAJ
        NAVEEN JINDAL
        PAVAN VARMA
        RAMACHANDRA GUHA
        THE QUINT
        THE SCROLL
        THE WIRE
        THE PRINT
        MK VENU
        CLOSET COMMIE ARNAB GOSWMI
        RAJDEEP SARDESAI
        PAAGALIKA GHOSE
        NAVIKA KUMAR
        ANAND NARASIMHAN
        SRINIVASAN JAIN
        SONAL MEHROTRA KAPOOR
        VIKRAM CHANDRA
        NIDHI RAZDAN
        FAYE DSOUZA
        RAVISH KUMAR
        AROON PURIE
        VINEET JAIN
        RAGHAV BAHL
        SEEMA CHISTI
        SHEKHAR GUPTA
        SIDHARTH VARADARAJAN
        ARUN SHOURIE
        N RAM
        WEBSITES OF DESH BHAKT LEADERS
        SPREAD OF SOCIAL MEDIA

        Delete
      2. ALSO PUT IN WEBSITES OF--
        JAPANESE PM SHINZŌ ABE
        AMBASSADOR TO FROM JAPAN- INDIA
        EXTERNAL AFFIARS MINISTER/ MINISTRY
        EVERY MP AND MLA OF INDIA
        ENTIRE BBC GANG
        ENTIRE INDIAN MEDIA
        CM PINARAYI VIJAYAN
        KODIYERI BALAKRISHNAN
        HIGH COURT CHIEF JUSTICES OF ALL STATES IN INDIA
        DEANS OF ALL IITs
        FAZAL GHAFOOR ( MES )
        MOHANLAL
        MAMOOTY
        SURESH GOPI
        AK ANTONY
        OMMEN CHANDY
        ALL CONGRESS SPOKESMEN
        RAHUL GANDHI
        SONIA GANDHI
        PRIYANKA VADRA
        SAM PITRODA
        NCM
        NHRC
        NCW
        REKHA SHARMA
        SWATI MALLIWAL
        SHASHI THAROOR
        CHETAN BHAGAT
        DEVDUTT PATTANAIK
        AMISH TRIPATI
        ASADDUDIN OWAISI
        KUNHALIKUTTY
        RANA AYYUB
        DULQER SALMAN
        JAVED AKTHAR
        MAHESH BHATT
        SHABANA AZMI
        AMITABH BACHCHAN
        PRITISH NANDI
        ASHISH NANDI
        JOHN DAYAL
        KANCHA ILIAH
        ARUNDHATI ROY
        SHOBHAA DE
        FATHER CEDRIC PERIERA
        ANNA VETTICKAD
        DANIEL RAJA
        BRINDA KARAT
        PRAKASH KARAT
        SITARAM YECHURY
        SUMEET CHOPRA
        DINESH VARSHNEY
        BINAYAK SEN
        SUDHEENDRA KULKARNI
        PRAKASH RAJ
        KAMALA HASSAN
        RAJNIKANTH
        JOHN BRITTAS
        KAANIYA MURTHY
        SUDHA MURTHY
        ANURAG KASHYAP
        APARNA SEN
        MANI RATNAM
        ADOOR GOPALAKRISHNAN
        KOKONA SEN SHARMA
        SHYAM BENEGAL
        SHUBHA MUDGAL
        SAUMITRA CHETTERJEE
        NAYANTHARA SEHGAL
        DILEEP PADGOANKAR
        VIR SANGHVI
        KARAN THAPAR
        BARKHA DUTT
        ARUN SHOURIE
        SANJAY DUBEY
        KAPIL SIBAL
        ABHISHEK MANU SINGHVI
        ALL COLLECTORS OF MAJOR INDIAN CITIES
        ALL PROFESSORS OF MIT

        Delete
      3. Hello Captain,

        Have sent screenshots to Donald Trump, Vishnu Som and Prakash Javedkar.

        Here are the links:

        https://twitter.com/manntharkan/status/1184403138616401920

        https://twitter.com/manntharkan/status/1184404452075327489

        https://twitter.com/manntharkan/status/1184406537982361600

        Kind regards,

        Delete
      4. https://twitter.com/punithdg619/status/1184411562372558848

        Delete
      5. https://twitter.com/IwerePm/status/1184396073747415041
        Sent to Donalt Trump Narendra Modi, cabinet ministers - Amit, Nirmala,Piyush,Gadkari, Rajnath and others

        Delete
      6. https://twitter.com/kkarthikeyan09/status/1184434526656679936?s=20

        Delete
      7. https://twitter.com/vkgjain/status/1184449438539907072?s=19

        Delete
      8. https://twitter.com/shree1082002/status/1184491353385750529

        Delete
      9. Tweets:
        https://twitter.com/AghastHere/status/1184522369634689024?s=20
        https://twitter.com/AghastHere/status/1184522587084328962?s=20

        Handles (First Set):
        @realDonaldTrump @narendramodi @AbeShinzo @IndianEmbTokyo @IndianEmbassyUS @harshvshringla @USAmbIndia @KremlinRussia_E @IndEmbMoscow @RusEmbIndia @RaghuramRRajan @iqbaldhali @AmitShah @HMOIndia @nsitharaman @FinMinIndia @ianuragthakur @DasShaktikanta @RBI @PrakashJavdekar@mihirssharma @kramdas @laramdas @VishnuNDTV @PrannoyRoyNDTV @NIA_India @dir_ed @MIB_India

        Email Message:
        MIT Dean,
        Why is an active professor from SHASS J-PAL, allowed to meddle with international politics and criticize the way governments conduct their economic affairs.
        Are there no checks and balances under your capacity to monitor such faculty, before it turns into diplomatic incident between two great nations?

        Email IDs:
        mnobles@mit.edu, eduflo@mit.edu, tuli@mit.edu, asen@fas.harvard.edu, gopinath@fas.harvard.edu

        Delete
      10. Posted on NIA latest tweet, verified

        https://twitter.com/NIA_India/status/1183714733272133632?s=20

        Delete
      11. I have emailed again to MIT Dean and others as below:

        Respected sir/madam,
        Hide quoted text

        How can MIT allow an active professor like Mr. Abhijeet Banerjee interfere in political activities of another nation?!

        Mr. Banerjee engages in rumour mongering about Indian economy just because he is a rabid hater of current Indian government.
        LIKE HIS MENTOR AMARTYA SEN.
        Abhijeet also framed the populist scheme of the opposition during the most recent Indian election TO FOLLOW UP ON SEN'S MNREGA DEBACLE.

        Do you want MIT to be looked upon as a political org instead of being the beacon of science and technology?!

        Please rein in this couple of Abhijeet and Esther and monitor activities of their lab whose founder was involved in selling reconfigured Toyota SUVs with Machine guns to ISIS..

        As you know, TILL NOW NOBODY HAS FIGURED OUT HOW TOYOTA, THE WORLD’S SECOND LARGEST AUTO MAKER, SOLD MORE THAT A HUNDRED THOUSAND BRAND NEW SUVS TO ISIS WITH PEDESTALS AND TRUCKBEDS STRENGTHENED AND DAMPED FOR FITTING LARGE MACHINE GUNS WITHHEAVY RE-COIL ..

        TOYOTA LAND CRUISER AND HILUX MARKETED BY ABDUL LATIF JAMEEL MOTORS HAVE EFFECTIVELY BECOME PART OF THE ISIS BRAND NOW.

        Visvanathan
        Chennai India

        Delete
      12. Namaste Capt Ajiji,

        Following video for the above message.

        https://twitter.com/Mohit_b_Handa/status/1184689900500611072

        Guys this is a clean upload with out any tags so that you can directly retweet it and make sure the spread of type of people increases while tagging.

        I have RT it below
        https://twitter.com/Mohit_b_Handa/status/1184692047409602560

        Remember capt's "HURRICANE FORCE " message above

        Delete
      13. Sent to trump and putin..
        Sent message to IB from its website...and mailed to- ed,nia,FM and her secretaries,RBI,amitshah,defense min and his secretaries, niti...

        Delete
    38. Namaste Capt,

      Maha Farmers earn lakhs from Moringa : https://www.thebetterindia.com/198892/maharashtra-farmer-earning-lakhs-moringa-superfood-growing-organic/

      ReplyDelete
    39. Capt. Sir,

      There was also a news report where Texas Plumber's truck ended up in Syria with ISIS. The truck was sold through auction, went to Turkey and then to Syria.

      https://www.youtube.com/watch?v=e4Js8BmbHB4

      ReplyDelete
    40. Captain,
      For the siltation of reservoir problem,I got the lukewarm response from jal ministry when I sent it as a grievance. So I sent mail to jal minister and his secretaries as below-

      https://drive.google.com/file/d/1Qd0h0iWessznHkQowKwXf-dDC9MjHXYJ/view?usp=drivesdkor

      For this I received the following reply from them along with hand book on reservoir sedimentation-

      https://drive.google.com/file/d/1_OxnpQfrGRzcxUAljjGhBTzYAMxxfiQH/view?usp=drivesdk

      Hand book link-

      https://drive.google.com/file/d/1_WCSsg_BzyAKcUTwmQJf2INxawwxM_XU/view?usp=drivesdk

      ReplyDelete
    41. Dear Capt Ajit sir,

      World Food Day is celebrated every year around the world on 16 October in honor of the date of the founding of the Food and Agriculture Organization of the United Nations in 1945.
      You are the only one who is thrusting on Organic farming in India, which is the need of the hour and Modi Govt Agri ministry is not even looking at it....

      ReplyDelete
    42. https://www.thechemicalengineer.com/news/shell-and-bp-join-consortium-to-tackle-methane-emissions/

      ReplyDelete
    43. SOMEBODY ASKED ME

      CAPTAIN, IS THERE IS A SAPT RISHI CONCEPT IN THE WEST?

      INDEED THERE IS..

      INDIAN SAPT RISHIs ARE COSMIC ALLEGORIES..

      http://ajitvadakayil.blogspot.com/2011/08/polaris-and-great-bear-capt-ajit.html

      BUT THE WESTERN SAPT RISHIS ARE REAL MORTAL AND WERE ALL KERALA DANAVA SAGES..

      THERE WAS A FLOOR MOSAIC MURAL OF THEM AT BAALBEK TEMPLE IN LEBANON , TEMPLE OF APOLLO AT DELPHI, AND TEMPLE AT ANTIOCH - WHICH WAS DESTROYED BY THE POPE..

      https://ajitvadakayil.blogspot.com/2019/08/secrets-of-12000-year-old-baalbek.html

      ONE OF THE HINDU SAGES ( PERIANDER ) BUILT THE CORINTH CANAL , 6.5 KM LONG , 2700 YEARS AGO..

      THE CANAL FELL INTO RUIN BY SABOTAGE.. BUT IT HAS BEEN REVIVED RECENTLY FOR PASSENGER SHIPS..

      IN 1978 I WENT TO SEE THIS CORINTH CANAL WHEN MY SHIP CARRYING 3 LAKH TONNES OF OIL BERTHED FOR UNLOADING AT AGIOI THEODOROI AT GREECE - JUST 15 KM FROM THE BERTH..

      HERE IS A SHIP 72 FEET WIDE PASSING THROUGH THE CORINTH CANAL..

      https://www.youtube.com/watch?v=qYuyURXBxsA

      https://www.hindustantimes.com/it-s-viral/cruise-ship-squeezes-through-tiny-canal-incredible-video-captured/story-fZhb0JlWVoBUKy0mTVGToK.html

      THE POPE CONVERTED THE SEVEN SAGES TO TYRANTS WHO FUCKED DEAD WOMEN..

      I STOOD ON THE BRIDGE IN 1978 AT MIDNIGHT AND WATCHED A SHIP SAIL DOWN BELOW..

      https://theculturetrip.com/europe/greece/articles/a-brief-history-of-the-corinth-canal/

      capt ajit vadakayil
      ..

      ReplyDelete
    44. https://timesofindia.indiatimes.com/life-style/health-fitness/de-stress/masaba-gupta-admits-going-for-therapy-urges-fans-to-open-up-and-seek-help/articleshow/71596228.cms

      BUFFALO BECOMES BRAVEHEART !

      TEE HEEEEEEE

      ReplyDelete
    45. https://indianexpress.com/article/opinion/columns/govt-calling-the-supreme-court-shots-narendra-modi-6070659/

      UNCONSTITUTIONAL NJAC?

      OH YEAH MELORD ?

      WAIT TILL WE FORM MILITARY COURTS TO HANG THE DESH DROHI JUDGES IN DEEP STATE PAYROLL-- THE ONES WHO CREATED THE NAXAL RED CORRIDOR..

      FOR A LONG TIME OUR JUDGES CONSTITUTED PARSIS , CRYPTO JEWS AND KAYASTHAS -- ALL AGENTS OF JEW ROTHSCHILD !

      ReplyDelete
    46. Latest theory about moon lander failure is that a neighbor paid a hacker a huge amount to sabotage the landing

      ReplyDelete
    47. https://photogallery.indiatimes.com/sports/cricket/glenn-maxwells-pics-with-indian-girlfriend-spark-marriage-rumours-on-social-media/articleshow/70872519.cms?picid=70872581

      SLUTS

      ReplyDelete


    48. namiOctober 16, 2019 at 6:37 PM
      This means that from spring 2020, all adults in England will be considered an organ donor when they die unless they had recorded a decision not to donate or are in one of the excluded groups. This is commonly referred to as an 'opt out' system. You can record your decision to opt in or out on the Organ Donor Register.
      https://www.organdonation.nhs.uk › ...
      What is the opt out system? - NHS Organ Donation

      ReplyDelete
    49. SOMEBODY ASKED ME

      CAPTAIN WHAT DO YOU PLAN TO ACHIEVE BY YOUR TEN PART POST ON ARTIFICIAL INTELLIGENCE

      https://ajitvadakayil.blogspot.com/2019/08/what-artificial-intelligence-cannot-do.html

      WELL--

      ONLY CAPT AJIT VADAKAYIL HAS THE CEREBRAL WHEREWITHAL ON THIS PLANET TO GIVE A CONDORS EYE VIEW ..ONLY CAPTAIN HAS THE ABILITY TO PULL BACK THE LENS..

      THE REST OF THE PLANET CAN GIVE ONLY "SEVEN BLIND MEN OF HINDOOSTAN " TUNNEL VISION VIEWS..

      A COMMIE BONG FEMALE ROHINI CHATTERJEE ( GRAND DAUGHTER OF SOMNATH CHATTERJEE ) INSULTED ME WITH A LYING POST BELOW--

      https://www.firstpost.com/living/open-letter-to-capt-vadakayil-the-man-who-wont-do-his-wifes-laundry-1192201.html

      I SAY LYING BECAUSE WHAT SHE WROTE ABOVE BELITTLING ME , HAS NOTHING TO DO WITH WHAT I WROTE BELOW..

      http://ajitvadakayil.blogspot.com/2013/10/karva-chauth-synchronising-fertility.html

      https://en.wikipedia.org/wiki/Somnath_Chatterjee

      AT THAT TIME I DECIDED TO KICK HER COMMIE TWAT..AND KICK IT GOOD.

      HER GRANDFATHER USED TO SPEAK TO MEDIA WITH PORTRAITS OF MARX/ LENIN/ ENGELS BEHIND HIM..

      AFTER I SHOT DOWN ROTHSCHILDs COMMUNISM, FEW DAYS BEFORE HIS DEATH-- SOMNATH BABY APPEARED FOR A PRESS INTERVIEW WITH A LARGE PORTRAIT OF CHE GUEVARA BEHIND HIM..

      http://ajitvadakayil.blogspot.com/2010/12/spirit-and-che-guevara-capt-ajit.html

      IT WAS A LESSON TO ALL.. DONT FUCK WITH ME ..

      UKKAD DEGA TERE KOH -- AARAM SEH!

      WHO SHOT DOWN BLOCKCHAIN/ BITCOIN WHEN THEY WERE FLYING HIGH?

      WHO FUCKED ALL THOSE WHO RUN SHELL COMPANIES ?

      capt ajit vadakayil
      ..

      PUT ABOVE COMMENT IN WEBSITES OF--
      ROHINI CHATTERJEE
      PINARAYI VIJAYAN
      KODIYERI BALAKRISHNAN
      PRAKASH KARAT
      BRINDA KARAT
      SITARAM YECHURY
      SUMEET CHOPRA
      DINESH VARSHNEY
      BINAYAK SEN
      SUDHEENDRA KULKARNI
      PRAKASH RAJ
      KAMALA HASSAN
      D RAJA
      ANNIE RAJA
      JOHN BRITTAS
      ADOOR GOPALAKRISHNAN
      ROMILA THAPAR
      IRFAN HABIB
      PMO
      PM MODI
      AMIT SHAH
      AJIT DOVAL

      ReplyDelete
      Replies
      1. Sir, sent to --

        @RohiniChatterji @firstpost

        ADOOR GOPALAKRISHNAN -- adoorg@gmail.com

        @vijayanpinarayi @b_kodiyeri @PMOIndia @narendramodi @AmitShah @AmitShahOffice @HMOIndia

        @irfhabib @sagarikaghose @Shehla_Rashid @RanaAyyub @ReallySwara @TeestaSetalvad @kavita_krishnan @JohnDayal @Kancha_ilaiah @prakashraaj @SudheenKulkarni @annavetticad @SitaramYechury @brindacpim @Ram_Guha @PavanK_Varma @virsanghvi @KaranThapar_TTP @UmarKhalidJNU @seemay @ayesha_kidwai @DineshVarsh @kramdas @awardreturner @Ram_Guha @svaradarajan @Arundatiroy @Nidhi @DeShobhaa
        @Koenraad_Elst @chetan_bhagat @authoramish @devduttmyth
        @mammukka @dulQuer @PKKunhalikutty @ikamalhaasan @VinodDua7 @AnnieRaja92 @PavanK_Varma @madhutrehan @supriya_sule @nramind @suhelseth @devduttmyth @ShashiTharoor

        Happy Karva chauth to all women readers.

        Delete
    50. https://www.freepressjournal.in/india/ayodhya-case-sunni-waqf-board-lawyer-rajiv-dhawan-tears-up-submissions-made-by-another-lawyer

      A LAWYER DESTROY THE OPPOSITION LAWYERs EVIDENCE IN THE PRESENCE OF THE JUDGE. THIS IS THE MEANING OF CONTEMPT OF COURT..

      NOT WHAT OUR STUPID JUDGE CONTEND-- THESE JUDGES ARE THE BOTTOM DREGS OF THE SCHOOL CEREBRAL BARREL AND TRAUMATIZED DISCARDS OF THE LOSER LAWYER POOL..

      THEY WERE FOOT SLOGGING UNSUCCESSFUL LAWYERS . .THEY BECAME JUDGES SO THAT THEY CAN C0CK A SNOOK AT THEIR SUCCESSFUL LAWYER COLLEAGUES. .

      OTHER DEVELOPED NATIONS DONT HAVE "CONTEMPT OF COURT " ANY MORE.

      TRUTH CANNOT BE BRANDED AS VILIFICATION OR DEFAMATION ....

      INDIA IS A DEMOCRACY.. CITIZENS HAVE RIGHT OF FREE SPEECH AGAINST JUDICIARY .... DOES OF OUR CONSTITUTION SAY OTHERWISE ? ....

      BY THE TIME A LAWYER BECOMES A JUDGE HE IS CORRUPT, AND HE IS READY TO SELL HIS OWN MOTHER IN THE WH0REHOUSE PROVIDED THE PRICE IS RIGHT . . .

      COURT CLERKS KNOW THE MORAL FIBRE OF THE JUDGES AS THEY WERE LAWYERS BEFORE WHO GAVE THEM A CUT .... THERE IS A NEXUS BETWEEN LAWYERS AND JUDGES. . .

      TODAY OUR COLLEGIUM JUDICIARY IS PACKED WITH ANTI-HINDU AND ANTI-WATAN JUDGES. . WHAT IS THIS TAAREQ PEH TAREEQ PEH TAREEQ ?.

      WE WANT THE LAW MINISTER RAVI SHANKAR PRASAD TO KNOW HIS OWN POWERS -- RIGHT NOW HE IS JUST FLOWING WITH THE TIDE , AS HE DOES NOT HAVE THE GUTS .. .

      CRIMINALS MOCK OUR SYSTEM SAYING COCKILY INTO TV CAMERAS -- WE TRUST THE INDIAN JUDICIARY . . THEY USE FOREIGN FUNDS TO PRODUCE FALSE WITNESSES OUT OF THIN AIR. . .

      INDIA IS THE ONLY NATION ON THIS PLANET , WHERE COLLEGIUM JUDICIARY ELECTS JUDGES . .THEY SCUTTLED NJAC . . .

      IN THE RECENT PAST COLLEGIUM JUDICIARY SAVED SEVERAL TRAITORS WHO HAVE TRIED TO KILL BHARATMATA . .

      PEOPLE LIKE JEW NOAM CHOMSKY GIVE FIATS TO COLLEGIUM MELORDS -- TO CREATE THE RED CORRIDOR.

      WE KNOW COLLEGIUM JUDGES HAVE FOREIGN SUPPORT WHEN THEIR LEGISTLATE , DO EXTREME JUDICIAL OVERREACH . . . . .

      BHARATMATA IS RACING TO BE THIS PLANETS NO 1 SUPERPOWER IN 15 YEARS --BEFORE THAT THE NEW WORLD ORDER WANTS INDIA TO IMPLODE. ..

      OUR COLLEGIUM JUDICIARY BURNT MIDNIGHT OIL TO OPEN CHAMIYA BARS ( WH0RE HOUSES IN MUMBAI ) BREAD WINNERS OF ENTIRE FAMILIES HAVE BEEN ROTTING IN JAIL WITHOUT A TRIAL FOR THREE DECADES ..

      ONE OF THE REASONS FOR NAXALISM , IS BECAUSE OF THE COLLEGIUM JUDICIARY . . ..

      POOR UNDERTRIALS WHO ARE BREAD WINNERS ARE KEPT IN JAIL FOR DECADES WITHOUT A TRIAL . .. .

      POOR PEOPLE DO NOT HAVE THE WHEREWITHAL TO PROVIDE BAIL MONEY OR PERSONAL SURETY . . .WITH ANOTHER MAN COMING AND SHOWING ALL THOSE DOCUMENTS WHICH ARE REQUIRED FOR THE GUARANTEE . ...

      WHY IS THE INDIAN GOVT INSISTING ON BAIL MONEY FROM VERY POOR PEOPLE ?

      JUSTICE KATJU GOT BUSHWHACKED AND AMBUSHED BY CJI GOGOI FOR WRITING TRUTH IN HIS BLOG…. COLLEGIUM JUDGES ARE NOT GOD..

      ONE STUP1D JUDGE HAD SLAPPED BOTH "SEDITION" AND "CONTEMPT OF COURT " ON ARUN JAITLEY FOR SAYING CONSTITUTION DOES NOT SUPPORT "COLLEGIUM SYSTEM" BUT ONLY NJAC ….

      OUR FAILED LAWYERS TURNED COLLEGIUM JUDGES DO NOT HAVE THE CEREBRAL WHEREWITHAL TO UNDERSTAND THAT SANE/ FAIR JUDGMENTS MUST BE WITHIN THE PERIMETER OF CONTEXT --AND NATURAL JUSTICE MUST BE INHERENT....

      THE MELORDS CANT EVEN UNDERSTAND THE MEANING OF CIRCUMSTANTIAL EVIDENCE ...

      THE SUPREME COURT JUDGES ARE UNABLE TO EVEN INTERPRET THE CONSTITUTION...

      THE CHIEF JUSTICE OF INDIA DOES NOT HAVE POWERS TO RULE INDIA..JUDGES DO NOT HAVE THE POWERS OF JUDICIAL REVIEW OF LAWS CREATED ( SEC 370/ 35A ) BY LAW MAKERS..

      THE SYSTEM HAS BROKEN DOWN..

      READ ALL 8 PARTS OF THE POST BELOW--................................... …………………………….. JUSTICE BE DAMNED , ENFORCE THE LAW ! .. NOT ANY MORE, IN FREE INDIA !! Vadakayil …………………………
      Capt ajit vadakayil
      ..'

      ReplyDelete
      Replies
      1. PUT ABOVE COMMENT IN WEBSISTES OF-

        PMO
        PM MODI
        AMITABH KANT
        NITI AYOG
        AMIT SHAH
        HOME MINISTRY
        AJIT DOVAL
        RAW
        CBI
        IN
        NIA
        ED
        IB
        I&B MINISTRY
        JAVEDEKAR
        CJI GOGOI
        ALL SUPREME COURT JUDGES
        ATTORNEY GENERAL
        CMs OF ALL INDIAN STATES
        DGPs OF ALL STATES
        GOVERNORS OF ALL STATES
        PRESIDENT OF INDIA
        VP OF INDIA
        SPEAKER LOK SABHA
        SPEAKER RAJYA SABHA
        SOLI BABY
        KATJU BABY
        FALI BABY
        SALVE BABY

        Delete
      2. Sir, sent to -

        narendramodi1234@gmail.com
        contact@amitshah.co.in
        prakash.j@sansad.nic.in
        info.nia@gov.in
        information@cbi.gov.in
        38ashokroad@gmail.com
        mos-defence@gov.in
        alokmittal.nia@gov.in
        nslsa.nagaland@yahoo.in,
        oslsa@nic.in,
        ms@pulsa.gov.in,
        rslsajp@gmail.com,
        sikkim_slsa@live.com,
        telenganaslsa@gmail.com,
        tnslsa@dataone.in,
        tnslsa@gmail.com,
        salsatripura@gmail.com,
        upslsa@up.nic.in,
        slsa-uk@nic.in,
        highcourt_ua@nic.in,
        wbstatelegal@gmail.com,
        andslsa2013@gmail.com,
        secylaw2016@gmail.com,
        slsa_utchd@yahoo.com,
        reg.slsa-dnh@gov.in,
        damanourt@gmail.com,
        dlsathebest@gmail.com,
        veebhaskr@gmail.com,
        lslsa-lk@nic.in,
        msutplsa@gmail.com
        supremecourt@nic.in
        proiaf.dprmod@nic.in
        pronavy.dprmod@nic.in
        pd.ncert@nic.in
        cbm.ncert@nic.in
        dceta.ncert@nic.in
        deme.ncert@nic.in ,
        esdhead@gmail.com,
        esdhead.ncert@gov.in
        ntse2@yahoo.co.in
        chairperson.ldd@gmail.com
        deme.ncert@nic.in
        esdhead@gmail.com
        esdhead.ncert@gov.in
        ncert.media@gmail.com
        dek.ncert@gmail.com
        nalsa-dla@nic.in,
        sclc@nic.in,
        apslsauthority@rediffmal.com,
        apslsauthority@yahoo.com,
        apslsa2013@rediffmail.com,
        aslsa@gmail.com,
        assamslsa@gmail.com,
        bslsa_87@yahoo.in,
        cgslsa.cg@nic.in,
        rajnishshrivastav@gmail.com,
        reg-high.goa@nic.in,
        msguj.lsa@nic.in,
        hslsa.haryana@gmail.com,
        mslegal-hp@nic.in,
        jhalsaranchi@gmail.com,
        jhalsa_ranchi@yahoo.co.in,
        karslsa@gmail.com,
        kelsakerala@gmail.com,
        mplsajab@nic.in,
        mslsa-bhc@nic.in,
        legalservices@maharashtra.gov.in,
        maslsa.imphal@gmail.com,
        megshillong@gmail.com,
        mizoramslsa@gmail.com,

        Delete
      3. Sent to-

        Dep secretary-kg.thang@nic.in
        Rsprasad-ravis@sansad.nic.in
        Law secretary-secylaw-dla@nic.in

        Delete
    51. http://ajitvadakayil.blogspot.com/2013/10/karva-chauth-synchronising-fertility.html

      TODAY IS KARVA CGAUTH-- 17TH OCT 2019.

      3RD DAY AFTER FULL MOON..

      http://ajitvadakayil.blogspot.com/2016/10/amavasya-poornima-ekadashi-fasting.html

      ReplyDelete
      Replies
      1. Sant Rampal followers trending on twitter once again and spreading lies about Karva Chauth. His followers spreading nonsense against hinduism everyday. Kabir follower Sant Rampal belongs to jail.

        Delete
      2. is it ok to drink small amount coconut milk during ekadashi fast ?

        Delete
    52. The lawyer who destroyed Hindu evidence is a Hindu name.
      Would he dare to do the same with a Muslim?

      He is probably thinking he will as usual get away with a apology tendered ir some such silly thing. No financial implications like banning him in court for a year or so.

      ReplyDelete
    53. https://timesofindia.indiatimes.com/india/in-latest-theatrics-dhavan-shreds-ram-birthplace-map/articleshow/71623282.cms

      .
      GOGOI IS THE NO 1 DESH DROHI OF INDIA...

      WE ALL REMEMBER HOW GOGOI BUSHWHACKED CJI KATJU FOR WRITING TRUTHS IN A BLOG..

      GOGOI DID A ILLEGAL PRESS CONFERENCE.. .

      GOGOI TREATED THE CBI CHIEF LIKE A CLASS ROOM DUNCE ..

      WHEN GOGOI RETIRES WE THE PEOPLE WILL DEAL WITH HIM..

      GOGOI IS DRUNK WITH POWER..

      HIS POMPOUS BODY LANGUAGE IS INSUFFERABLE..

      ReplyDelete
    54. I wished my friend and told her about how a woman is grateful today. Will share your post for details. Knowing the significance, she is totally putting her heart and soul into celebrating. Thank you very much for the awesome post. Many women will benefit.

      Regards,
      Prapulla

      ReplyDelete
    55. SOMEBODY ASKED ME

      WHY ARE YOU SO UPSET WITH MOSSAD DARLINGS GUJU NO 2 MODI/ KAYASTHA JAVEDEKAR/ KAYASTHA PRASAD..

      BHARATMATAs FEET ARE BEING BLED BY THIS DEEP STATE DARLING GRUESOME THREESOME..

      EXAMPLE:

      JUST AS AN EXPERIMENT ( ALL MY READERS KNOW ABOUT THIS) I SENT A QUORA ARTICLE WHERE A INDIAN MOTHER MAKES HER PRE-TEEN SON AND DAUGHTER HAVE SEX WITH EACH OTHER AS SOON AS THEY COME BACK FROM SCHOOL.. AND SHE MASTURBATES WHOLE WATCHING HER SON HUMP HIS OWN SISTER..

      THIS WAS SENT TO ALL-- PM / PMO/ CJI/ LAW MINISTER/ I&B MINISTER-- MORE THAN 30 AGENCIES..

      WHAT HAPPENED ?

      95% OF AGENCIES IGNORED AS USUAL.. ON A SUBJECT OF NATIONAL IMPORTANCE , WHERE QUORA IS MALIGNING INDIA..AND ENCOURAGING PEDOPHILIA / INCEST/ BESTIALITY..

      5% REPLIED-- "YEH HAMAARA ARTICLE MEIN NAHIN LIKHELA HAI".. THIS IS NOT OUR PROBLEM..

      FINALLY PMO SAYS-- COMPLAIN TO CYBER CELL..

      WHEN MY READERS COMPLAINED TO CYBER CELL.. REPLY COMES-- THIS IS NOT OUR JOB-- GO TO POLICE STATION AND FILE FIR..

      SO WE MUST GO TO SOME KHAINI MUNCHING POT BELLIED HAWALDAR WHO SPEAKS ONLY MARATHI AND FILE A FIR ?

      THIS IS WHY SHELL COMPANIES HAVE SURVIVED IN INDIA FOR SO LONG..

      THERE IS NOBODY TO ACCEPT A TECHNICAL COMPLAINT AND MOST TOP MINISTERS DONT CARE AS THEY HAVE BEEN CHOSEN ONLY BECAUSE THEY GIVE EGO MASSAGE TO MODI..

      THE BABUS IN VARIOUS MINISTRIES ARE IN DEEP STATE PAYROLL.. 95% OF THESE BABUS DESERVE TO BE SACKED FOR BEING DESH DROHIS.. THIS INCLUDES MEMBERS OF CBI/ IB/ NIA/ ED/ RAW ETC..

      WE KNOW THE DESH DROHIS IN OUR VARIOUS AGENCIES AND MINISTRIES.. HOW DO THEIR CHILDREN GET SCHOLARSHIPS IN FOREIGN UNIVERSITIES LIKE COLUMBIA AND CAMBRIDGE ?

      HOW DID SAGARIKA GHOSE ( HER FATHER WAS BHASKAR GHOSE , TOP GUN OF DOORDARSHAN ) BECOME A RHODES SCHOLAR ?

      WE WATCH..

      MODI , WE ASK YOU TO WORK FOR BHARATMATA NOT YOUR JEWISH MASTERS .. WE KNOW WHAT YOU DID IN 1976 WEARING SIKH TURBAN ..

      ROTHSCHILDs KOSHER BANKS AND INSURANCE COMPANIES HAVE GONE FROM TOEHOLD TO FOOT HOLD TO DRIVERS SEAT.. BHARATMATA HAS BEEN KICKED INTO THE KOSHER ADULTERY/ HOMOSEXUALITY MANDI..

      ILLEGAL COLLEGIUM JUDGES ARE PLAYING GOD.. YESTERDAY CJI GOGOI ASKED LAWYER RAJEEV DHAWAN TO TEAR UP A CRUCIAL MAP ON AYODHYA EVIDENCE.. GOGOI TREATED THE CBI CHIEF LIKE A CLASS ROOM DUNCE ..

      capt ajit vadakayil
      ..

      ReplyDelete
      Replies
      1. PUT ABOVE COMMENT IN WEBSISTES OF-
        TRUMP
        AMBASSADOR TO FROM INDIA -USA
        PUTIN
        AMBASSADOR TO FROM INDIA-RUSSIA
        EXTERNAL AFFAIRS MINISTER/ MINISTRY
        PMO
        PM MODI
        JACK DORSEY
        MARK ZUCKERBERG
        THAMBI SUNDAR PICHAI
        CEO OF WIKIPEDIA
        QUORA CEO ANGELO D ADAMS
        QUORA MODERATION TEAM
        KURT OF QUORA
        GAUTAM SHEWAKRAMANI
        AMITABH KANT
        NITI AYOG
        AMIT SHAH
        HOME MINISTRY
        AJIT DOVAL
        RAW
        CBI
        IN
        NIA
        ED
        IB
        I&B MINISTRY
        JAVEDEKAR
        CJI GOGOI
        ALL SUPREME COURT JUDGES
        ATTORNEY GENERAL
        LAW MINISTER PRASAD
        LAW MINISTRY
        ALL STATES HIGH COURT CHIEF JUSTICES
        CMs OF ALL INDIAN STATES
        DGPs OF ALL STATES
        GOVERNORS OF ALL STATES
        PRESIDENT OF INDIA
        VP OF INDIA
        SPEAKER LOK SABHA
        SPEAKER RAJYA SABHA
        SOLI BABY
        KATJU BABY
        FALI BABY
        SALVE BABY
        EVERY MP AND MLA OF INDIA
        ENTIRE BBC GANG
        ENTIRE INDIAN MEDIA
        ALL CONGRESS SPOKESMEN
        RAHUL GANDHI
        SONIA GANDHI
        PRIYANKA VADRA
        SAM PITRODA
        NCM
        NHRC
        NCW
        REKHA SHARMA
        SWATI MALLIWAL
        SHASHI THAROOR
        CHETAN BHAGAT
        DEVDUTT PATTANAIK
        AMISH TRIPATI
        KAPIL SIBAL
        ABHISHEK MANU SINGHVI
        ALL COLLECTORS OF MAJOR INDIAN CITIES
        RBI GOVERNOR
        RBI
        DAVID FRAWLEY
        STEPHEN KNAPP
        WILLIAM DALRYMPLE
        KONRAED ELST
        FRANCOIS GAUTIER
        DEFENCE MINISTER - MINISTRY
        ALL THREE ARMED FORCE CHIEFS.
        RAJEEV CHANDRASHEKHAR
        MOHANDAS PAI
        RAM MADHAV
        RAJ THACKREY
        UDDHAV THACKREY
        VIVEK OBEROI
        GAUTAM GAMBHIR
        ASHOK PANDIT
        ANUPAM KHER
        KANGANA RANAUT
        VIVEK AGNIHOTRI
        KIRON KHER
        MEENAKSHI LEKHI
        SMRITI IRANI
        PRASOON JOSHI
        MADHUR BHANDARKAR
        SWAPAN DASGUPTA
        SONAL MANSINGH
        MADHU KISHWAR
        SUDHIR CHAUDHARY
        GEN GD BAKSHI
        SAMBIT PATRA
        RSN SINGH
        SWAMY
        SWAMYs BROTHER SA IYER
        RAJIV MALHOTRA
        SADGURU JAGGI VASUDEV
        SRI SRI RAVISHANKAR
        BABA RAMDEV
        RSS
        VHP
        AVBP
        THE ENTIRE BBC GANG
        MUKESH AMBANI
        LAXMI MITTAL
        RATAN TATA
        MAHINDRA
        AZIM PREMJI
        KUMARMANGALAM
        RAHUL BAJAJ
        NAVEEN JINDAL
        PAVAN VARMA
        RAMACHANDRA GUHA
        THE QUINT
        THE SCROLL
        THE WIRE
        THE PRINT
        MK VENU
        CLOSET COMMIE ARNAB GOSWMI
        RAJDEEP SARDESAI
        PAAGALIKA GHOSE
        NAVIKA KUMAR
        ANAND NARASIMHAN
        SRINIVASAN JAIN
        SONAL MEHROTRA KAPOOR
        VIKRAM CHANDRA
        NIDHI RAZDAN
        FAYE DSOUZA
        RAVISH KUMAR
        AROON PURIE
        VINEET JAIN
        RAGHAV BAHL
        SEEMA CHISTI
        SHEKHAR GUPTA
        SIDHARTH VARADARAJAN
        ARUN SHOURIE
        N RAM
        ARUNDHATI ROY
        SHOBHAA DE
        FAZAL GHAFOOR ( MES )
        MOHANLAL
        MAMOOTY
        SURESH GOPI
        AK ANTONY
        OMMEN CHANDY
        ASADDUDIN OWAISI
        KUNHALIKUTTY
        RANA AYYUB
        DULQER SALMAN
        JAVED AKTHAR
        MAHESH BHATT
        SHABANA AZMI
        AMITABH BACHCHAN
        PRITISH NANDI
        ASHISH NANDI
        JOHN DAYAL
        KANCHA ILIAH
        .
        FATHER CEDRIC PERIERA
        ANNA VETTICKAD
        DANIEL RAJA
        BRINDA KARAT
        PRAKASH KARAT
        SITARAM YECHURY
        SUMEET CHOPRA
        DINESH VARSHNEY
        BINAYAK SEN
        SUDHEENDRA KULKARNI
        PRAKASH RAJ
        KAMALA HASSAN
        RAJNIKANTH
        JOHN BRITTAS
        KAANIYA MURTHY
        SUDHA MURTHY
        ANURAG KASHYAP
        APARNA SEN
        MANI RATNAM
        ADOOR GOPALAKRISHNAN
        KOKONA SEN SHARMA
        SHYAM BENEGAL
        SHUBHA MUDGAL
        SAUMITRA CHETTERJEE
        NAYANTHARA SEHGAL
        DILEEP PADGOANKAR
        VIR SANGHVI
        KARAN THAPAR
        BARKHA DUTT
        ARUN SHOURIE
        SANJAY DUBEY

        WEBSITES OF DESH BHAKT LEADERS
        SPREAD OF SOCIAL MEDIA

        Delete
      2. Namaste captain Ajitji,

        Hope you & family are well as we haven't heard from you still.

        Below is the video for above message
        https://twitter.com/Mohit_b_Handa/status/1185501182837456896

        We will strive more & more to spread these messages.

        Will try best to make a hurricane impact

        Delete
    56. REGARDING TECHNICAL COMPLAINTS ( LIKE SHELL COMPANIE/ CRYPTO CURRENCIES ETC )..

      FOR PASSING SHIP CAPTAINs EXAM THERE IS A SUBJECT "SHIP MASTERs BUSINESS" -- WHICH IS ALL ABOUT MARITIME LAW..

      THERE IS A CHAPTER CALLED "MARITIME LIEN"..

      NORMALLY THE CAPTAIN GOES DOWN WITH HIS SINKING SHIP--ONLY FOR ONE REASON..

      IF THE SHIP IS 100% ABANDONED THEN ANYBODY CAN COME IN A SMALL BOAT , TIE A LINE TO THE SHIP-- AND THE SHIP WITH CARGO BECOMES HIS.. IF THE ORIGINAL SHIPOWNER/ CARGO OWNER WANTED THE SHIP/ CARGO BACK-- HE HAS TO PAY MILLIONS OF US DOLLARS..

      THIS IS WHY A SHIP CAPTAIN IS VERY CAREFUL EVEN WHILE PASSING A LINE TO A TUG WHICH HIS ENGINES FAIL INSIDE SUEZ CANAL..

      SO SO SO

      PICKLE JOHN FERNANDES WAS STUDYING FOR HIS MASTERS EXAM.. HIS HEAD WAS FULL OF SALVAGE CLAUSES AND MARITIME LIENS..

      WHEN HE WENT FOR HIS USUAL MORNING WALK AT MUMBAI MARINE DRIVE , HE SAW THAT THE BOMBAY FLOATING LIGHT ( IT IS A UNMANNED LIGHT VESSEL PAINTED RED ) LYING AGROUND AGAINST THE WAVE BREAKER ROCKS..

      FANDU BHAIYYA RAN AND BOUGHT A THIN PLASTIC LINE..

      THEN HE CLAMBERED OVER THE ROCKS DIVED INTO THE SEA AND TIED A LINE ON THE LIGHT SHIP AND TIED THE OTHER END TO THE SHORE ..

      A BIG CROWD WAS WATCHING HIM, AS IF HE HAS GONE MAD..

      THEN HE RAN LIKE USAIN BOLT TO THE NEAREST POLICE STATION AND CLAIMED "MARINE SALVAGE"..

      THE PANDU HAVALDAR WITH PAN JUICE DRIPPING FROM THE CORNER OF HIS MOUTH-- COULD NOT UNDERSTAND THE TECHNICAL SALVAGE LIEN JARGON FROM A DRIPPING WET MAN WHO APPEARED TO BE MAD..

      PANDU BHAIYYA BEAT UP PICKLE JOHN FANDU SEVERELY WITH HIS LATHI , KICKED HIS PIG SORPOTEL FARTING FAT ASS AND LOCKED HIM UP ..

      TEE HEEEEEEE..

      WHAT FANDU DID-- IF HE HAD DONE IN USA HE WOULD HAVE GOT A LOT OF MONEY..

      IN SCI SOME STUPID INDIAN CAPTAINS GAVE A LINE TO A EGYPTIAN TUG ( WITHOUT SPECIFYING TOWAGE CONTRACT ON VHF ) WHEN ENGINES FAILED IN SUEZ AND THE SUEZ PORT CLAIMED SALVAGE .. THEY GOT MASSIVE SALVAGE MONEY IN EGYPTIAN COURTS ..

      https://goldfinchwinslow.com/when-is-a-tow-a-salvage-or-a-salvage-a-tow/

      http://www.gard.no/Content/20823111/Gard%20Guidance%20on%20Maritime%20Claims_final.pdf

      capt ajit vadakayil
      ..

      ReplyDelete
      Replies
      1. PUT ABOVE COMMENT IN WEBSISTES OF-
        TRUMP
        AMBASSADOR TO FROM INDIA -USA
        PUTIN
        AMBASSADOR TO FROM INDIA-RUSSIA
        EXTERNAL AFFAIRS MINISTER/ MINISTRY
        PMO
        PM MODI
        JACK DORSEY
        MARK ZUCKERBERG
        THAMBI SUNDAR PICHAI
        CEO OF WIKIPEDIA
        QUORA CEO ANGELO D ADAMS
        QUORA MODERATION TEAM
        KURT OF QUORA
        GAUTAM SHEWAKRAMANI
        AMITABH KANT
        NITI AYOG
        AMIT SHAH
        HOME MINISTRY
        AJIT DOVAL
        RAW
        CBI
        IN
        NIA
        ED
        IB
        I&B MINISTRY
        JAVEDEKAR
        CJI GOGOI
        ALL SUPREME COURT JUDGES
        ATTORNEY GENERAL
        LAW MINISTER PRASAD
        LAW MINISTRY
        ALL STATES HIGH COURT CHIEF JUSTICES
        CMs OF ALL INDIAN STATES
        DGPs OF ALL STATES
        GOVERNORS OF ALL STATES
        PRESIDENT OF INDIA
        VP OF INDIA
        SPEAKER LOK SABHA
        SPEAKER RAJYA SABHA
        SOLI BABY
        KATJU BABY
        FALI BABY
        SALVE BABY
        EVERY MP AND MLA OF INDIA
        ENTIRE BBC GANG
        ENTIRE INDIAN MEDIA
        ALL CONGRESS SPOKESMEN
        RAHUL GANDHI
        SONIA GANDHI
        PRIYANKA VADRA
        SAM PITRODA
        NCM
        NHRC
        NCW
        REKHA SHARMA
        SWATI MALLIWAL
        SHASHI THAROOR
        CHETAN BHAGAT
        DEVDUTT PATTANAIK
        AMISH TRIPATI
        KAPIL SIBAL
        ABHISHEK MANU SINGHVI
        ALL COLLECTORS OF MAJOR INDIAN CITIES
        RBI GOVERNOR
        RBI
        DAVID FRAWLEY
        STEPHEN KNAPP
        WILLIAM DALRYMPLE
        KONRAED ELST
        FRANCOIS GAUTIER
        DEFENCE MINISTER - MINISTRY
        ALL THREE ARMED FORCE CHIEFS.
        RAJEEV CHANDRASHEKHAR
        MOHANDAS PAI
        RAM MADHAV
        RAJ THACKREY
        UDDHAV THACKREY
        VIVEK OBEROI
        GAUTAM GAMBHIR
        ASHOK PANDIT
        ANUPAM KHER
        KANGANA RANAUT
        VIVEK AGNIHOTRI
        KIRON KHER
        MEENAKSHI LEKHI
        SMRITI IRANI
        PRASOON JOSHI
        MADHUR BHANDARKAR
        SWAPAN DASGUPTA
        SONAL MANSINGH
        MADHU KISHWAR
        SUDHIR CHAUDHARY
        GEN GD BAKSHI
        SAMBIT PATRA
        RSN SINGH
        SWAMY
        SWAMYs BROTHER SA IYER
        RAJIV MALHOTRA
        SADGURU JAGGI VASUDEV
        SRI SRI RAVISHANKAR
        BABA RAMDEV
        RSS
        VHP
        AVBP
        THE ENTIRE BBC GANG
        MUKESH AMBANI
        LAXMI MITTAL
        RATAN TATA
        MAHINDRA
        AZIM PREMJI
        KUMARMANGALAM
        RAHUL BAJAJ
        NAVEEN JINDAL
        PAVAN VARMA
        RAMACHANDRA GUHA
        THE QUINT
        THE SCROLL
        THE WIRE
        THE PRINT
        MK VENU
        CLOSET COMMIE ARNAB GOSWMI
        RAJDEEP SARDESAI
        PAAGALIKA GHOSE
        NAVIKA KUMAR
        ANAND NARASIMHAN
        SRINIVASAN JAIN
        SONAL MEHROTRA KAPOOR
        VIKRAM CHANDRA
        NIDHI RAZDAN
        FAYE DSOUZA
        RAVISH KUMAR
        AROON PURIE
        VINEET JAIN
        RAGHAV BAHL
        SEEMA CHISTI
        SHEKHAR GUPTA
        SIDHARTH VARADARAJAN
        ARUN SHOURIE
        N RAM
        ARUNDHATI ROY
        SHOBHAA DE
        FAZAL GHAFOOR ( MES )
        MOHANLAL
        MAMOOTY
        SURESH GOPI
        AK ANTONY
        OMMEN CHANDY
        ASADDUDIN OWAISI
        KUNHALIKUTTY
        RANA AYYUB
        DULQER SALMAN
        JAVED AKTHAR
        MAHESH BHATT
        SHABANA AZMI
        AMITABH BACHCHAN
        PRITISH NANDI
        ASHISH NANDI
        JOHN DAYAL
        KANCHA ILIAH
        .
        FATHER CEDRIC PERIERA
        ANNA VETTICKAD
        DANIEL RAJA
        BRINDA KARAT
        PRAKASH KARAT
        SITARAM YECHURY
        SUMEET CHOPRA
        DINESH VARSHNEY
        BINAYAK SEN
        SUDHEENDRA KULKARNI
        PRAKASH RAJ
        KAMALA HASSAN
        RAJNIKANTH
        JOHN BRITTAS
        KAANIYA MURTHY
        SUDHA MURTHY
        ANURAG KASHYAP
        APARNA SEN
        MANI RATNAM
        ADOOR GOPALAKRISHNAN
        KOKONA SEN SHARMA
        SHYAM BENEGAL
        SHUBHA MUDGAL
        SAUMITRA CHETTERJEE
        NAYANTHARA SEHGAL
        DILEEP PADGOANKAR
        VIR SANGHVI
        KARAN THAPAR
        BARKHA DUTT
        ARUN SHOURIE
        SANJAY DUBEY

        WEBSITES OF DESH BHAKT LEADERS
        SPREAD OF SOCIAL MEDIA

        Delete
    57. https://timesofindia.indiatimes.com/business/india-business/indian-traders-cut-purchase-of-palm-oil-from-malaysia/articleshow/71629721.cms

      INDIA MUST PUT A TOTAL BAN OF PALM OIL IMPORTS FROM MALAYSIA.. REPLACE MALAYSIA WITH INDONESIA..

      PALM OIL IS UNHEALTHY OIL.. VIRGIN COCONUT OIL IS THE ONLY GOOD VEG OIL..

      http://ajitvadakayil.blogspot.com/2012/08/coconut-oil-is-good-for-cooking-ignore.html

      capt ajit vadakayil
      ..

      ReplyDelete


    58. Rishi KesariyaOctober 17, 2019 at 8:16 AM
      Sir,

      My wife is on her periods but today is Karwachauth fast so she has to fast.

      Due to her periods I'll have to perform the Karwachauth Pooja.

      Is it fine?

      Regards

      ReplyDelete
      Replies

      Capt. Ajit VadakayilOctober 17, 2019 at 9:34 AM
      SHE CAN FAST
      SHE CAN WATCH THE MOON

      NO PERFORMING PUJA !

      ReplyDelete
    59. INDIA GAVE BHARAT RATNA TO ROTHSCHILDs AGENT JEW ABDUL GHAFFAR KHAN !

      JEW TOLSTOY/ KATHIAWARI JEW GANDHI/ JEW ABDUL GHAFFAR KHAN WERE ALL ROTHSCHILDs AGENTS PREACHING NON-VIOLENCE AGAINST THE WHITE OPPRESSOR..

      http://ajitvadakayil.blogspot.com/2019/08/german-jew-leo-tolstoy-who-fanned-dying.html

      capt ajit vadakayil
      ..

      ReplyDelete
      Replies

      1. PUT ABOVE COMMENT IN WEBSISTES OF-
        TRUMP
        AMBASSADOR TO FROM INDIA -USA
        PUTIN
        AMBASSADOR TO FROM INDIA-RUSSIA
        EXTERNAL AFFAIRS MINISTER/ MINISTRY
        PMO
        PM MODI
        JACK DORSEY
        MARK ZUCKERBERG
        THAMBI SUNDAR PICHAI
        CEO OF WIKIPEDIA
        QUORA CEO ANGELO D ADAMS
        QUORA MODERATION TEAM
        KURT OF QUORA
        GAUTAM SHEWAKRAMANI
        AMITABH KANT
        NITI AYOG
        AMIT SHAH
        HOME MINISTRY
        AJIT DOVAL
        RAW
        CBI
        IN
        NIA
        ED
        IB
        I&B MINISTRY
        JAVEDEKAR
        CJI GOGOI
        ALL SUPREME COURT JUDGES
        ATTORNEY GENERAL
        LAW MINISTER PRASAD
        LAW MINISTRY
        ALL STATES HIGH COURT CHIEF JUSTICES
        CMs OF ALL INDIAN STATES
        DGPs OF ALL STATES
        GOVERNORS OF ALL STATES
        PRESIDENT OF INDIA
        VP OF INDIA
        SPEAKER LOK SABHA
        SPEAKER RAJYA SABHA
        SOLI BABY
        KATJU BABY
        FALI BABY
        SALVE BABY
        EVERY MP AND MLA OF INDIA
        ENTIRE BBC GANG
        ENTIRE INDIAN MEDIA
        ALL CONGRESS SPOKESMEN
        RAHUL GANDHI
        SONIA GANDHI
        PRIYANKA VADRA
        SAM PITRODA
        NCM
        NHRC
        NCW
        REKHA SHARMA
        SWATI MALLIWAL
        SHASHI THAROOR
        CHETAN BHAGAT
        DEVDUTT PATTANAIK
        AMISH TRIPATI
        KAPIL SIBAL
        ABHISHEK MANU SINGHVI
        ALL COLLECTORS OF MAJOR INDIAN CITIES
        RBI GOVERNOR
        RBI
        DAVID FRAWLEY
        STEPHEN KNAPP
        WILLIAM DALRYMPLE
        KONRAED ELST
        FRANCOIS GAUTIER
        DEFENCE MINISTER - MINISTRY
        ALL THREE ARMED FORCE CHIEFS.
        RAJEEV CHANDRASHEKHAR
        MOHANDAS PAI
        RAM MADHAV
        RAJ THACKREY
        UDDHAV THACKREY
        VIVEK OBEROI
        GAUTAM GAMBHIR
        ASHOK PANDIT
        ANUPAM KHER
        KANGANA RANAUT
        VIVEK AGNIHOTRI
        KIRON KHER
        MEENAKSHI LEKHI
        SMRITI IRANI
        PRASOON JOSHI
        MADHUR BHANDARKAR
        SWAPAN DASGUPTA
        SONAL MANSINGH
        MADHU KISHWAR
        SUDHIR CHAUDHARY
        GEN GD BAKSHI
        SAMBIT PATRA
        RSN SINGH
        SWAMY
        SWAMYs BROTHER SA IYER
        RAJIV MALHOTRA
        SADGURU JAGGI VASUDEV
        SRI SRI RAVISHANKAR
        BABA RAMDEV
        RSS
        VHP
        AVBP
        THE ENTIRE BBC GANG
        MUKESH AMBANI
        LAXMI MITTAL
        RATAN TATA
        MAHINDRA
        AZIM PREMJI
        KUMARMANGALAM
        RAHUL BAJAJ
        NAVEEN JINDAL
        PAVAN VARMA
        RAMACHANDRA GUHA
        THE QUINT
        THE SCROLL
        THE WIRE
        THE PRINT
        MK VENU
        CLOSET COMMIE ARNAB GOSWMI
        RAJDEEP SARDESAI
        PAAGALIKA GHOSE
        NAVIKA KUMAR
        ANAND NARASIMHAN
        SRINIVASAN JAIN
        SONAL MEHROTRA KAPOOR
        VIKRAM CHANDRA
        NIDHI RAZDAN
        FAYE DSOUZA
        RAVISH KUMAR
        AROON PURIE
        VINEET JAIN
        RAGHAV BAHL
        SEEMA CHISTI
        SHEKHAR GUPTA
        SIDHARTH VARADARAJAN
        ARUN SHOURIE
        N RAM
        ARUNDHATI ROY
        SHOBHAA DE
        FAZAL GHAFOOR ( MES )
        MOHANLAL
        MAMOOTY
        SURESH GOPI
        AK ANTONY
        OMMEN CHANDY
        ASADDUDIN OWAISI
        KUNHALIKUTTY
        RANA AYYUB
        DULQER SALMAN
        JAVED AKTHAR
        MAHESH BHATT
        SHABANA AZMI
        AMITABH BACHCHAN
        PRITISH NANDI
        ASHISH NANDI
        JOHN DAYAL
        KANCHA ILIAH
        .
        FATHER CEDRIC PERIERA
        ANNA VETTICKAD
        DANIEL RAJA
        BRINDA KARAT
        PRAKASH KARAT
        SITARAM YECHURY
        SUMEET CHOPRA
        DINESH VARSHNEY
        BINAYAK SEN
        SUDHEENDRA KULKARNI
        PRAKASH RAJ
        KAMALA HASSAN
        RAJNIKANTH
        JOHN BRITTAS
        KAANIYA MURTHY
        SUDHA MURTHY
        ANURAG KASHYAP
        APARNA SEN
        MANI RATNAM
        ADOOR GOPALAKRISHNAN
        KOKONA SEN SHARMA
        SHYAM BENEGAL
        SHUBHA MUDGAL
        SAUMITRA CHETTERJEE
        NAYANTHARA SEHGAL
        DILEEP PADGOANKAR
        VIR SANGHVI
        KARAN THAPAR
        BARKHA DUTT
        ARUN SHOURIE
        SANJAY DUBEY

        WEBSITES OF DESH BHAKT LEADERS
        SPREAD OF SOCIAL MEDIA

        Delete
      2. Sent screenshots to --

        @realDonaldTrump @mfa_russia @IndianEmbassyUS @USAndIndia @POTUS @IndEmbMoscow @MEAIndia @DrSJaishankar @RusEmbIndia @IndianDiplomacy @indiandiplomats

        @CMO_Odisha @CMofKarnataka @AndhraPradeshCM @ChhattisgarhCMO @CMOfficeUP @TelanganaCMO @MamataOfficial @CMMadhyaPradesh @VasundharaBJP @NitishKumar @Dev_Fadnavis @neiphiu_rio @sarbanandsonwal @PemaKhanduBJP @ArvindKejriwal @CMOTamilNadu @ncbn @CMOKerala @tarun_gogoi @RajCMO @capt_amarinder @RajGovOfficial @Naveen_Odisha @MamataBenerjee @virbhadrasingh @ysjagan @anandibenpatel @drramansingh @yadavakhilesh @SangmaConrad @vijayanpinarayi @PemaKhanduBJP @bhupeshbaghel @DrPramodPSawant @vijayrupanibjp @mlkhattar @jairamthakurbjp @dasraghubar @BSYBJP @vijayanpinarayi @NBirenSingh @ZoramthangaCM @OfficeOfKNath @vijayrupanibjp

        @CPMumbaiPolice @CPDelhi @CPBlr @DgpPradesh @D_Roopa_IPS @dgpup @dgpcidkarnataka @dgp_ap @DGP_MP @TelanganaDGP @DGPPunjabPolice @IPS_Association @AddlCPTraffic @AddlCPWest @smittal_ips @TheKeralaPolice @PoliceTamilnadu @DGP_FIRE @DGPOdisha @dgpgujarat @DGPMaharashtra @bihar_police @assampolice @knagarajips @CG_Police @DelhiPolice @DGP_Goa @JmuKmrPolice @cpkarimnagar @rama_rajeswari @DevenBhartiIPS @RSPraveenSwaero @IGWomenSafety @adgzonekanpur @adgzonelucknow @adgzonevaranasi

        @RajivMessage @rajeev_mp @KirronKherBJP @M_Lekhi @smritiirani @madhukishwar @sudhirchaudhary @GeneralBakshi @sambitswaraj @ssrajamouli @Mohanlal @AnupamPKher @prasoonjoshi_ @hindulegalcell

        @VishnuNDTV @sagarikaghose @Shehla_Rashid @RanaAyyub @ReallySwara @TeestaSetalvad @kavita_krishnan @JohnDayal @Kancha_ilaiah @prakashraaj @SudheenKulkarni @annavetticad @SitaramYechury @brindacpim @Ram_Guha @PavanK_Varma @virsanghvi @KaranThapar_TTP @UmarKhalidJNU @seemay @ayesha_kidwai @DineshVarsh @kramdas @awardreturner
        @Ram_Guha @svaradarajan @Arundatiroy @Nidhi @DeShobhaa
        @Koenraad_Elst @chetan_bhagat @authoramish @devduttmyth
        @mammukka @dulQuer @PKKunhalikutty @ikamalhaasan @VinodDua7 @AnnieRaja92 @PavanK_Varma @madhutrehan @supriya_sule @nramind @suhelseth @ShashiTharoor @irfhabib @laramdas

        Readers can have a note of these handles to spread comments.

        Delete
    60. https://timesofindia.indiatimes.com/india/nobody-dared-remove-article-370-despite-lot-of-talk-pm-modi/articleshow/71634684.cms

      BURN ALL NCERT HISTORY AND SOCIAL STUDIES BOOKS..

      REWRITE HISTORY OF INDIA..

      ROTHSCHILDs AGENT NEHRU WROTE THAN INDIAN CIVILIZATION IS JUST 6000 YEARS GOLD, IN HIS BOOK GLIMPSES OF WORLD HISTORY .

      SORRY, VEDAS WENT ON ORAL ROUTE FOR 330 CENTURIES BEFORE BEING PENNED DOWN 70 CENTURIES AGO..

      https://ajitvadakayil.blogspot.com/2019/09/onam-our-only-link-to-planets-oldest.html

      https://ajitvadakayil.blogspot.com/2019/10/perumal-title-of-calicut-thiyya-kings.html

      IN 14 YEARS INDIA WILL BE THIS PLANETs NO 1 SUPERPOWER- WHETHER AMARTYA SEN / RAGHURAN RAJAN/ ABHIJIT BANNERJEE/ GITA GOPINATH/ SUNDAR PICHAI LIKES IT OR NOT..

      INDIA IS THE ONLY ROARING ECONOMY ON THIS PLANET TODAY.. WE DONT CARE WHAT IMF/ WORLD BANK OR THEIR STOOGE AGENTS S&P / MOODYs/ FITCH THINK ABOUT US..

      THOSE DAYS OF INDIANS BEING SELF LOATHING IS LONG GONE-- GONNAAYY GONNE !

      capt ajit vadakayil
      ..

      ReplyDelete
      Replies
      1. PUT ABOVE COMMENTS IN WEBSITES OF--
        MIT DEAN ( WARNING HIM THAT HIS PROFESSOR ABHIJIT BANNERJEE IS INVOLVED IN POLITICS )
        TRUMP
        AMBASSADOR TO FROM INDIA-USA
        PUTIN
        AMBASSADOR TO FROM RUSSIA -INDIA
        PM OF UK
        AMBASSADOR TO FROM UK-INDIA
        ESTHER DUFLO
        ARUNDHATI TULI ( DIVORCED WIFE OF ABHIJIT BANNERJI )
        AMARTYA SEN
        RAGHURAM RAJAN
        GITA GOPINATH
        IQBAL SINGH DHALIWAL
        MANMOHAN SINGH
        MIHIR S SHARMA
        EXTERNAL AFFAIRS MINISTER/ MINISTRY
        RBI GOVERNOR
        RBI
        FINANCE MINISTRY CENTRE AND ALL STATES
        ALL DEANS OF INDIAN ECONOMICS AND BUSINESS SCHOOLS
        PMO
        PM MODI
        AMITABH KANT
        NITI AYOG
        AMIT SHAH
        HOME MINISTRY
        AJIT DOVAL
        RAW
        CBI
        IN
        NIA
        ED
        IB
        I&B MINISTRY
        JAVEDEKAR
        CJI GOGOI
        ALL SUPREME COURT JUDGES
        ATTORNEY GENERAL
        CMs OF ALL INDIAN STATES
        DGPs OF ALL STATES
        GOVERNORS OF ALL STATES
        PRESIDENT OF INDIA
        VP OF INDIA
        SPEAKER LOK SABHA
        SPEAKER RAJYA SABHA
        JACK DORSEY
        MARK ZUCKERBERG
        THAMBI SUNDAR PICHAI
        CEO OF WIKIPEDIA
        QUORA CEO ANGELO D ADAMS
        QUORA MODERATION TEAM
        KURT OF QUORA
        GAUTAM SHEWAKRAMANI
        DAVID FRAWLEY
        STEPHEN KNAPP
        WILLIAM DALRYMPLE
        KONRAED ELST
        FRANCOIS GAUTIER
        DEFENCE MINISTER - MINISTRY
        ALL THREE ARMED FORCE CHIEFS.
        CM PINARAYI VIJAYAN
        KODIYERI BALAKRISHNAN
        ALL MLAs/ MPs OF KERALA
        ADMIRAL L RAMDAS
        WIFE LALITA RAMDAS
        DAUGHTER KAVITA RAMDAS
        JULIO RIBEIRO
        SAM PITRODA
        MANMOHAN SINGH
        KAPIL SIBAL
        ABHISEX MAANGTHA SINGHVI
        RAJEEV CHANDRASHEKHAR
        MOHANDAS PAI
        RAM MADHAV
        RAJ THACKREY
        UDDHAV THACKREY
        VIVEK OBEROI
        GAUTAM GAMBHIR
        ASHOK PANDIT
        ANUPAM KHER
        KANGANA RANAUT
        VIVEK AGNIHOTRI
        KIRON KHER
        MEENAKSHI LEKHI
        SMRITI IRANI
        PRASOON JOSHI
        MADHUR BHANDARKAR
        SWAPAN DASGUPTA
        SONAL MANSINGH
        MADHU KISHWAR
        SUDHIR CHAUDHARY
        GEN GD BAKSHI
        SAMBIT PATRA
        RSN SINGH
        SWAMY
        SWAMYs BROTHER SA IYER
        RAJIV MALHOTRA
        SADGURU JAGGI VASUDEV
        SRI SRI RAVISHANKAR
        BABA RAMDEV
        RSS
        VHP
        AVBP
        THE ENTIRE BBC GANG
        MUKESH AMBANI
        LAXMI MITTAL
        RATAN TATA
        MAHINDRA
        AZIM PREMJI
        KUMARMANGALAM
        RAHUL BAJAJ
        NAVEEN JINDAL
        PAVAN VARMA
        RAMACHANDRA GUHA
        THE QUINT
        THE SCROLL
        THE WIRE
        THE PRINT
        MK VENU
        CLOSET COMMIE ARNAB GOSWMI
        RAJDEEP SARDESAI
        PAAGALIKA GHOSE
        NAVIKA KUMAR
        ANAND NARASIMHAN
        SRINIVASAN JAIN
        SONAL MEHROTRA KAPOOR
        VIKRAM CHANDRA
        VISHNU SOM
        NIDHI RAZDAN
        FAYE DSOUZA
        RAVISH KUMAR
        AROON PURIE
        VINEET JAIN
        N RAM
        PRANNOY JAMES ROY
        RAGHAV BAHL
        SEEMA CHISTI
        SHEKHAR GUPTA
        SIDHARTH VARADARAJAN
        ARUN SHOURIE
        EVERY MP AND MLA OF INDIA
        ENTIRE INDIAN MEDIA
        HIGH COURT CHIEF JUSTICES OF ALL STATES IN INDIA
        DEANS OF ALL IITs
        FAZAL GHAFOOR ( MES )
        MOHANLAL
        MAMOOTY
        SURESH GOPI
        AK ANTONY
        OMMEN CHANDY
        ALL CONGRESS SPOKESMEN
        RAHUL GANDHI
        SONIA GANDHI
        PRIYANKA VADRA
        NCM
        NHRC
        NCW
        REKHA SHARMA
        SWATI MALLIWAL
        SHASHI THAROOR
        CHETAN BHAGAT
        DEVDUTT PATTANAIK
        AMISH TRIPATI
        ASADDUDIN OWAISI
        KUNHALIKUTTY
        RANA AYYUB
        DULQER SALMAN
        JAVED AKTHAR
        MAHESH BHATT
        SHABANA AZMI
        AMITABH BACHCHAN
        PRITISH NANDI
        ASHISH NANDI
        JOHN DAYAL
        KANCHA ILIAH
        ARUNDHATI ROY
        SHOBHAA DE
        FATHER CEDRIC PERIERA
        ANNA VETTICKAD
        DANIEL RAJA
        BRINDA KARAT
        PRAKASH KARAT
        SITARAM YECHURY
        SUMEET CHOPRA
        DINESH VARSHNEY
        BINAYAK SEN
        SUDHEENDRA KULKARNI
        PRAKASH RAJ
        KAMALA HASSAN
        RAJNIKANTH
        JOHN BRITTAS
        KAANIYA MURTHY
        SUDHA MURTHY
        ANURAG KASHYAP
        APARNA SEN
        MANI RATNAM
        ADOOR GOPALAKRISHNAN
        KOKONA SEN SHARMA
        SHYAM BENEGAL
        SHUBHA MUDGAL
        SAUMITRA CHETTERJEE
        NAYANTHARA SEHGAL
        DILEEP PADGOANKAR
        VIR SANGHVI
        KARAN THAPAR
        BARKHA DUTT
        ARUN SHOURIE
        SANJAY DUBEY
        ALL COLLECTORS OF MAJOR INDIAN CITIES
        ALL PROFESSORS OF MIT

        SPREAD ON SOCIAL MEDIA

        Delete
    61. .

      Capt. Ajit VadakayilOctober 17, 2019 at 1:17 PM
      YOU CAN IMMERSE ASHES IN THE SEA

      Delete

      Vardhan TaleraOctober 17, 2019 at 2:49 PM
      Please tell what has happened to the soul of the deceased? My father involved other men who were not blood relatives in lighting the pyre and then called back the two women who had earlier left on my request.

      Delete

      Capt. Ajit VadakayilOctober 17, 2019 at 2:59 PM
      PYRE MUST BE LIT BY THE CLOSE BLOOD RELATIVE MALE..

      Delete

      Vardhan TaleraOctober 17, 2019 at 7:14 PM
      The pyre was first lit by real nephew, is the soul still stuck? Will the earlier mentioned presence of women keep the soul trapped? In that case does 1 year have to pass before his blood relatives can break a coconut to liberate the soul on Shraadh?

      Delete

      Capt. Ajit VadakayilOctober 17, 2019 at 7:21 PM
      NOTHING TO WORRY

      Delete

      Vardhan TaleraOctober 17, 2019 at 8:11 PM
      That's a major relief. Thank you a lot, you are the reason why this soul is not astray and on the correct path. I now need to help out his relatives who cooperated in cremating him regarding their buried dead ancestors. What explicit advice should I give them? Also times are changing and your silent revolution is working, one elderly samaj pramukh of Lingayat's had said yesterday that he was witness to 4-5 such cases of cremation.

      Delete

      Capt. Ajit VadakayilOctober 17, 2019 at 8:25 PM
      WHY DO MUSLIMS GO TO DARGAHs AND CHRISTIANS GO TO GRAVE YARDS..

      IT IS A TACIT ACKNOWLEDGEMENT THAT THEIR SOULS ARE TRAPPED ON EARTH FOREVER-- MISERABLE AND LONELY

      http://ajitvadakayil.blogspot.com/2012/11/rip-impossible-with-burial-world-is.html

      capt ajit vadakayil
      ..

      ReplyDelete
    62. https://timesofindia.indiatimes.com/social-humour-these-karwa-chauth-memes-are-breaking-internet/liveblog/71631043.cms

      DEEP STATE AGENTS AT WORK..

      THIS RITUAL IS SOLEMN. NOT TO BE RIDICULED..

      GUJJU NO 2 MODI HAS EMPLOYED NAPUNSAK KAYASTHAS PRAKASH JAVEDEKAR/ RAVI SHANKAR PRASAD.. THEY JUMP UP AND DOWN LIKE DEMENTED ORANGUTANS ONLY WHEN MODI IS INSULTED..

      http://ajitvadakayil.blogspot.com/2013/10/karva-chauth-synchronising-fertility.html

      WE WANT MIDGET VINEET JAIN TO BE FINED HEAVILY...

      capt ajit vadakayil
      ..

      PUT ABOVE COMMENTS IN WEBSITES OF--

      99.99 % OF MY READERS ARE ANTI-HINDU, ANTI-WATAN DESH DROHIS.. THEY COME HERE FOR FREE READING MATERIAL..

      I NOW PUT A SEVERE CURSE ON ALL THESE BASTARDS , THEIR FAMILIES AND THEIR FUTURE GENERATIONS ..

      PMO
      PM MODI
      AMITABH KANT
      NITI AYOG
      AMIT SHAH
      HOME MINISTRY
      AJIT DOVAL
      RAW
      CBI
      IN
      NIA
      ED
      IB
      I&B MINISTRY
      JAVEDEKAR
      CJI GOGOI
      ALL SUPREME COURT JUDGES
      ATTORNEY GENERAL
      CMs OF ALL INDIAN STATES
      DGPs OF ALL STATES
      GOVERNORS OF ALL STATES
      PRESIDENT OF INDIA
      VP OF INDIA
      SPEAKER LOK SABHA
      SPEAKER RAJYA SABHA
      DAVID FRAWLEY
      STEPHEN KNAPP
      WILLIAM DALRYMPLE
      KONRAED ELST
      FRANCOIS GAUTIER
      LAW MINISTER PRASAD
      LAW MINISTRY
      CJI GOGOI
      ATTORNEY GENERAL
      RAJEEV CHANDRASHEKHAR
      MOHANDAS PAI
      RAM MADHAV
      RAJ THACKREY
      UDDHAV THACKREY
      VIVEK OBEROI
      GAUTAM GAMBHIR
      ASHOK PANDIT
      ANUPAM KHER
      KANGANA RANAUT
      VIVEK AGNIHOTRI
      KIRON KHER
      MEENAKSHI LEKHI
      SMRITI IRANI
      PRASOON JOSHI
      MADHUR BHANDARKAR
      SWAPAN DASGUPTA
      SONAL MANSINGH
      MADHU KISHWAR
      SUDHIR CHAUDHARY
      GEN GD BAKSHI
      SAMBIT PATRA
      RSN SINGH
      SWAMY
      RAJIV MALHOTRA
      SADGURU JAGGI VASUDEV
      SRI SRI RAVISHANKAR
      BABA RAMDEV
      RSS
      VHP
      AVBP

      ReplyDelete
      Replies
      1. Tweets:
        https://twitter.com/AghastHere/status/1185244972783755264?s=20
        https://twitter.com/AghastHere/status/1185245067314892801?s=20
        https://twitter.com/AghastHere/status/1185245147723894784?s=20
        https://twitter.com/AghastHere/status/1185245204015714304?s=20

        Handles:
        @PMOIndia @narendramodi @AmitShah @HMOIndia @NITIAayog @amitabhk87 @NIA_India @dir_ed @MIB_India @PrakashJavdekar @CMOMaharashtra @Dev_Fadnavis @CMOKerala @vijayanpinarayi @CMOfficeAssam @sarbanandsonwal @CMO_Odisha @Naveen_Odisha @ArunachalCMO @PemaKhanduBJP @ashokgehlot51 @CMPuducherry @VNarayanasami @capt_amarinder @BjpBiplab @NBirenSingh @ysjagan @BSYBJP @vijayrupanibjp @NitishKumar @MamataOfficial @DrPramodPSawant @Neiphiu_Rio @SangmaConrad @myogiadityanath @ArvindKejriwal @mlkhattar @BiswabhusanHC @jagdishmukhi @AnusuiyaUikey @ADevvrat @SatyadeoNArya @KalrajMishra @KeralaGovernor @BSKoshyari @tathagata2 @jagdishmukhi @GovernorOdisha @vpsbadnore @anandibenpatel @jdhankhar1 @RSSorg @VHPDigital @ABVPVoice @rammadhavbjp @rajeev_mp @TVMohandasPai @RajThackeray @OfficeofUT @vivekoberoi @GautamGambhir @ashokepandit @prasoonjoshi_ @KanganaTeam @AnupamPKher @vivekagnihotri @KirronKherBJP @M_Lekhi @smritiirani @sonal_mansingh @imbhandarkar @sudhirchaudhary @GeneralBakshi @sambitswaraj @Swamy39 @RajivMessage @SriSri @SadhguruJV @yogrishiramdev

        PS: Thanks to the reader for updating the list of Women MPs in the spreadsheet.

        Delete
      2. Captain Sir,
        Your curse has hit like a thunder, suddenly without warning.. Don't know what to say, only that I have not done my best to spread your messages. My email has gone into spam database of nic email servers, twitter throws me captcha every 10-12 tweets. Only sending thru pmopg website and lesser tweets.

        Somehow do not even have moral authority to ask you to continue blogging. Hope and pray to God that things will become normal..

        Delete
      3. Apologies for irking you...current situation did not allow me to transmit your messages.

        Regards,
        Saurin

        Delete
      4. Sir,
        I saw your comment just today. I have been spreading your teachings and revelations to my family, friends and acquantances. I am not on whatsapp, linkedin etc. though there is a lot of pressure at work as I am against joining social media. I am only on email and have an old nokia feature phone. Please do not be angry with me as I am not coming here for free reading. You have opened my eyes and I now see through many false prophets and the depth of conspiracy against India and this reflects through all my interactions. Sir, please do not be angry with me.
        With Love,
        Shankar.

        Delete
    63. I HAVE LOT OF WHITE SKINNED READERS IN NASA.. WHO COME TO MY SITE OUT OF CURIOSITY

      THEY THINK SANATANA DHARMA IS SAVAGE SUPERSTITION..

      DURING THE NEXT SABARIMALA 3 LIGHT FLASHES OF JAN 14TH 2020 ( TWILIGHT ) WATCH HOW THE EARTHs MAGNETIC FIELD WHICH SUSTAINS OUR ATMOSPHERE , SPIKES.

      http://ajitvadakayil.blogspot.com/2018/11/5900-year-old-sabarimala-this-planets.html

      YET IT ALLOWS MORE PRANA IN FROM THE SAVITUR QUASAR..

      http://ajitvadakayil.blogspot.com/2017/07/gayatri-mantra-savitr-savitur-saraswati.html

      capt ajit vadakayil
      ..

      PUT ABOVE COMMENT IN WEBSITES OF-

      ATHEIST COMMIE PINARAYI VIJAYAN
      ATHEIST COMMIE KODIYERI BALAKRISHNAN
      ATHEIST COMMIE KADAKAMPALLI SURENDRAN
      ATHERIST COMME A PADMAKUMAR -- DEWASWON PRESIDENT
      ALL MPs/ MLAs OF KERALA
      ALL COLLECTORS OF KERALA
      ALL KERALA MEDIA

      ReplyDelete
      Replies
      1. Captain sir,

        How are you ? Feels like long time I have not seen your post.

        Delete
    64. Respected Sir,
      Hope all is well at your end!!
      Out of sight out of mind never works in your case for me sir...i get anxious!
      Guess you have taken a break to relax with your family.take care dear Sir.
      Love and regards

      ReplyDelete
    65. How are you sir ..? and hope you are doing well..

      ReplyDelete
    66. Dear Capt Ajit sir,
      This is the biggest revelation so far from your end...Earth's magnetic field which sustains our atmosphere spikes during the 3 flashes at Sabarimala on 14 Han 2020 :-)
      Sent this on twitter to Pinari Vijayan, Kodiyeri Balakrishnan, Kadakmpalli Surnedran, PM Modi, some cabinet ministers, Shashi Tharoor etc..
      https://twitter.com/IwerePm/status/1185251236573925376/photo/1

      ReplyDelete
    67. sent via mails sir, are you travelling ??

      ReplyDelete
    68. Namaste Captain Ajitji,

      No msgs today.. Hope alls fine with you and that you have just taken a break for personal reasons /commitments

      Awaiting your presence.

      I am working on videos on "SOMEBODY ASKED ME" messages of yours.

      ReplyDelete
    69. Dear Capt Ajit sir,
      It's been 2 days...no updates...are you travelling and on a holiday or visit to your son's family in USA...wish u are back to prid us in your blogs :-)
      It seems Hilary has the right perception that Russia will back Tulsi Gabbard to make sure Trump wins again.... what do you say ?
      https://www.yahoo.com/news/2020-hillary-clinton-tulsi-gabbard-russia-trump-third-party-191140891.html?.tsrc=fauxdal

      ReplyDelete
    70. Dear Sir, are you busy ? No comments published.

      Gratitude.

      ReplyDelete
    71. Pranaam Guruji,

      we have again failed to meet your expectation , we have again failed to deliver, we all are sorry.

      We request you to please come back, we will try our level best to post all your comments and blogs. Do not leave us this way..

      You silence worries us.




      Thanks & Regards,

      Shailender Parmar

      ReplyDelete
    72. Namastae Master Ji,

      Namaskaram - Hope all is well, could not see your comments & post for last 2 days.

      Grace us with your blessings always,

      Thanks with highest gratitude.

      ReplyDelete
    73. Mahodaya,

      Strength, Honor and Gratitude to you for everything you do.

      I am sorry I was away for some personal reasons. I hope you are well over there.

      :-)

      ReplyDelete
    74. Dear sir,

      I had a beautiful dream today where I actually saw you, we spent a lot of time together, and you blessed me..🙏

      It was a wonderful feeling I wanted to share with you. The funny part is I don’t remember my dreams on daily basis, very rarely I remember dreams and today’s dream was one amongst few handful dreams I have remembered.

      Contrary when my mom was alive she spent every day explaining her dream to us ..

      I feel blessed to have felt your presence, spend time with you and get your blessings. Seeking yours and Respected and beloved mam’s blessings 🙏🙏🙏

      ReplyDelete
    75. Dear Capt Ajit sir,

      This is what happens, if Kurds wants peach in their regions and some people strive hard to get everyone to understand world intrigue and not fall for their crypto jew masters traps...

      https://www.christianpost.com/news/kurdish-female-politician-who-worked-to-unite-christians-arabs-kurds-executed-in-syria-233434/?fbclid=IwAR36nB35YpviG4lw5s0slXUdKnBlV9wRT3QuM4tLNnbYFA6jq7LEnpb2K1A

      ReplyDelete
    76. Dear Capt Ajit sir,

      How many BJP leaders have condemned Kamlesh Tiwari gruesome murder by Islamic youth....that too under Yogi Adityanath's rule....Hindus are not safe in Modi's regime...a false flag and politically motivated incident....how to avoid such incidents and bring real politician culprits to be booked ?

      ReplyDelete
    77. Dear Capt Ajit sir,

      Kalki effect in place...

      Got his via FB "Just read in the Tamizh newspapers that Modiji is planning to come to Brahadeeswara Temple at Thanjavur in TamizhNadu along with the Israel premier!!

      If this is true , someone please tell him not to.

      Anybody who comes there ceases to rule after that !! "

      ReplyDelete
    78. Dear Ajit Sir,
      क्या आपने अल्‍प अवधि के लिए खुद पर विराम लगाया हुआ है!
      I am missing you please!
      Hope everything is fine!

      ReplyDelete
    79. Namaste Captain ,

      Not heard yet from you. We are really worried. Please response through you blog.

      ReplyDelete
    80. Capt. hope everything is fine...

      ReplyDelete
    81. Gurunatha!
      Hope all is well and my prayers!!!
      Vinith

      ReplyDelete
    82. Respected Sir, Hope you are doing fine..

      ReplyDelete
    83. Ajit garu, no comment from u since 2 days. Hope u nd ur family is safe. Love u sir.

      ReplyDelete
    84. Hope you are keeping well Captain. If on vacation, wishing you a wonderful time. Spread the message about Karva Chauth.

      Regards,
      Prapulla

      ReplyDelete
    85. This blog site has changed my life in multiplal ways. I was a dog man all mt life. This blog made me aware about cats . I changed my approach towards cats. Earlier i used to shoo them away as soon as i saw them. Once a cat cameto my room and started rubbing against me but i did not encourage and it went on.
      Capt inspired me to change my outlook towards Cats.
      A female cat started visiting my house. Instead of shooing off i started ignoring the cats presence. The cat usually used my house as a transit way. This went on for months. The cat used to appear on my room window, then cross the room and out through stairs. I used to completely ignore the cat to make her comfortable during her passage.

      I was pleasently surprised when i discovered new born kittens in one corner of my room used as storage one day. I came back to my chair and just then the cat came back and after eyeing me for a minute went to her kittens. After feeding them she went out of the room before eyeing me one more time.
      This became routine until she shifted the 5 of them one by one to my neighbours after 15 days.
      Cats in india takes her kitten to 6 different houses over 3 months or so . By this time the kitten grow up to be not dependent on the cat.
      The cat after her 6th house came back to myhouse with the remaining 2 male kittens.
      Then she disappeared from the colony altogether. Out of the two the bigger brother too went off after a few days.

      The second kitten remained. He is still living here. He goes out on his rounds in the neighbourhood. He comes back to sleep on our sofa. I feed him fish.
      He gives me great joy and has become popular in the neighbourhood too.

      He is six months old now. He follows me when i come out of my house. He tries to scare the dogs of the area and getting more confident by the day.

      The kittens Dad is a ginger tom cat. He is the main male cat of the area.
      He knows that they are his children. He used to visit them since they were with the mother.
      The bigger kitten also lives nearby and he scares the resident kitten whenever he comes .
      The dad also comes to visit. He is notthat afraid of his dad as much as his brother. His dad talks with him usually in their language. Today i saw him trying to scare his dad who was very patient.
      I am enjoying the company of a cat. I have graduated to cats from dogs thank to Captain Gji.
      I constantly worry about the cat as his brother living nearby is naturally stronger from birth and his instincts more cat like. He was the one who was friendly with me originally before he went off leaving the smaller bro behind.
      I never interfere when he come and intimidates my cat. I never interfere when his dad comes and my cat hides from him.
      I also do not interfere usually in his stand offs with dogs outside.
      I wish a good future for all Cats and animals.

      ReplyDelete
    86. Dear Sir,

      It has been four days, no blogging no comments. Are you traveling? is your health OK? Bit scared. You have a legitimate reasons to be upset over readers. But please do not punish everybody. Nobody can do what you have done for this great country of ours. God knows your hard work for Bharatmata, may god bless your family and our country. My humble gratitude to you as your pupil. Please forgive me if i did something wrong all these years.

      Only person who knows about Sanathan dharma and the deep state is you. If you don't do it, truth will sink forever. All these babus in ministries, MSM or in social media reading the blogs, they are just hiding from you. They all are hiding from the truth. At least future generations may realize the truth.

      There are many readers sending your messages. You may give clear instructions to all Indian readers. I don't know what to say. You know everything sir. Request you to please come back.

      Love and Gratitude.

      ReplyDelete
    87. Hi Sir,

      Hope you are doing good.


      Thanks
      Maheshwar

      ReplyDelete
    88. Respected captain
      Pranaam,

      Best wishes for you and your family .Hope all of you are keeping fine and having good time by Almighty's blessings.

      Regards

      ReplyDelete
    89. What r you upto Sir No update.
      I do my part though don't post it very often
      https://twitter.com/Vaibhav34632584/status/1186344915820638208?s=19

      ReplyDelete
    90. Captain,

      I am worried. Hope you are well. Please publish the comments when you are back.

      Regards,
      Muthu Swamynathan.

      ReplyDelete
    91. Dear Capt Ajit sir,

      Pls come back online, we need you to inspire billions to be on the path of consciousness and awaken the entire world....Kalki effect is on....you are our real Danava hero :-)

      ReplyDelete
    92. Sir.
      Are you in travel?

      Hope everything is fine.

      ReplyDelete
    93. Namaskaara_/\_
      You are touring i suppose.
      _/\_Alupa Raaja

      ReplyDelete
    94. Capt, where are you?

      In previous blog you asked why your regular readers are hiding?

      I just started a new course at university after dropping out of the previous one due to health issues and personal issues. I wanted my life to stablize first because I was in a really bad position. I don't want to let down my father, who didn't given up on me after my failure.

      I did visit your blog from time to time. Although I tried to fully focus on my studies, I find myself drawn to your website. Sometimes I spread your message, but I didn't report back to you, since I wasn't doing it regularly. I hope you're not upset with me captain.

      ReplyDelete
    95. Sir, hope everything is ok!
      Thankyou.

      ReplyDelete
    96. Hello Captain,

      I hope you are well.

      We have not heard from you for a few days and wondering if all is well?

      Please let us know.

      Kind regards,

      ReplyDelete
    97. Dear Ajit Sir,
      Please Pardon me!
      What has happened!?!?!?
      Seems you are very upset!
      The world will suffer an apocalypse if you don't resume blogging.
      Please do get back.
      It was always divinely satisfying to exchange some concerns with you here.
      Miss you!
      Regards!!!

      ReplyDelete
    98. Captain, Hope you are doing well and ok.

      ReplyDelete
    99. Namaskar Guruji, Please come back..... waiting for you ......... love you always:-)

      ReplyDelete
    100. Dear Capt Ajit sir,

      We are all concerned about you not posting for the last 6 days....pls let us know what's happening ? Are you in a hibernation mood or do you need any help ?
      Is there something wrong done by any of us ?

      ReplyDelete
    101. Respected Captain
      Pranaam

      Wish at these times since you are off the blog you are keeping well. Praying to god for you always.Eagerly waiting for your valuable view on today's political development.

      Wishing you all the best
      Regards

      ReplyDelete
    102. Dear Ajit Sir,

      You are vindicated again, not the first time but it's a warning again to Pathetic fellow Modi who actually is a worst PM.
      Maharashtra & Haryana elections again proved that there is serious loopholes in the strategy of BJP.
      BJP has tried to bank upon its fortune on the innocent people who layed their life for the country.
      It is only after the person dies that BJP jumps to take cognizance and builds it's fortune.

      It was silence of BJP in Sabarimala last time which cost BJP loose out in Rajasthan, MP & Chattisgarh.
      This time this ridiculous #SabkaViswas farce and the recent PMC Bank customers.
      Those helpless PMC Bank customers whose money BJP didnot release made Marathi voters boycott election.
      Only a little more than 45% voted overall in Maharashtra out of which Muslims are pro-active voters.
      There in Haryana, turncoats are welcomed in party to take party affairs while honest party workers of BJP are sidelined.
      If people will not teach BJP a lesson, then what else will people do!
      Your silence from this Blog cost BJP a lot.
      All their strategists who used to peek into this blog but never participated in discussions or arguments are now clueless.
      Disgraceful!

      That Halwai Conman Gaurav Pradhan has raised up his hand citing he didn't bother pre-elections calculation at all.
      क्या हरामी आदमी निकला वह गौरव प्रधान
      Apologies for my language but what a 3rd class fellow Halwai is...

      Rest are all over NEWS paper...

      Miss you!
      Regards!

      ReplyDelete
    103. where is Captain? It is so long he is not present on blog.

      ReplyDelete
    104. Captain did absolutely right
      I don't know about other readers unlike them i am already in trouble for so long just waiting for getting everything settle down after that i will show these useless readers what's the duty of a vadakayilian.

      ReplyDelete
    105. dear captain,

      It's been a while since you stopped blogging.

      Hope everything is going well with you and your family.

      ReplyDelete
    106. Dear sir posting it again because am very excited and do not want to miss wishing you today ….Wish you and your family a very happy Dhanteras. Please do keep writing and exposing all that is wrong … You have helped me a lot and want to again thank you .. Regards,

      ReplyDelete
    107. Dear Capt Ajit ji
      Sir.. happy dhanvantri diwas...
      Sir.. Our prayers on auspicious occasion of Deepawali.. that paramatma give you and your family all the good health and happiness of the world with his divine shakti...

      thank you for everything
      regards
      sameer

      ReplyDelete
    108. Dear Capt Ajit sir,
      What's happened sir, you are not posting or responding....pls let us know, if you need us to do something much more to set things right and help/support your divine work of exhuming the truth for the benefit of humanity at large :-)

      ReplyDelete
    109. Dear Captain,
      I hope you are doing well. I will pray for the same. Please take care.
      Regards,
      Hari

      ReplyDelete
    110. Namaste Captain Ajitji,

      HAPPY DIWALI TO YOU and FAMILY. Please come back. Our Diwali will not be worth it till you speak.

      Have made this video on Diwali. Posted on twitter.
      https://twitter.com/Mohit_b_Handa/status/1187715301225271296

      You can download video below to share personally with friends. Have added blog address in the end.
      https://drive.google.com/open?id=12SNewP7bfi1gXKr1GlJIJV9SobVOyAup

      At least Please communicate something if not publish comments.

      Regards
      Mohit

      ReplyDelete
    111. Namaste Captain,

      Sharing you the youtube link of Diwali video containing your messages sent sometime back

      https://youtu.be/j_sw_fcW0Dw

      HAPPY DIWALI TO YOU AGAIN, BUT NOT TO US TILL WE SEE YOU ACTIVE.

      ReplyDelete
    112. Capt, how are you? Hope you're doing well.

      ReplyDelete
    113. Respected sir

      Where are u . Missing your blogs. As you have pointed out most of us are parasites ( we want knowledge from your blog site but our involvement in your mission is almost zero) and I am one of them. Please captain give us one for chance everyone will try to contribute and take your mission to higher level. Please reply us. Thank you

      ReplyDelete
    114. Dear Captain
      Peace and love. I do not understand why some matters have come to pass. My wish is for all beings to be well.
      Thank you for everything.
      Peace and love to all readers. Till we meet again

      Cheers
      Ravi
      KL

      ReplyDelete
    115. Captain, I hope you are fine and just taking a break. Just worried to see no updates. Take care.

      ReplyDelete
    116. Apologies Capt. Pls respond.

      Thx
      Saurin

      ReplyDelete
    117. Captain Sir,

      Wish you, family, friends, your readers a very happy DEEPAVALI !!!!

      Please reconsider and come back, Somehow, all the joy seems to have gone out in our lives.

      Regards,
      C Prabhu

      ReplyDelete
    118. Dear Captain
      Happy Diwali to you and your family...
      Where are you Sir...?? Hope you are doing well...

      ReplyDelete
    119. Namaste Capt Ajitji,

      HAPPY DIWALI TO YOU AND FAMILY. Have made a special video for you on Diwali

      https://youtu.be/j_sw_fcW0Dw

      WE miss you and are eagerly awaiting your comeback. We Hope and pray that you are in a pink of health. Your last post has really got me thinking about you. This Diwali has been really DULL due to absence of your words.

      CHARAN SPARSH TO YOU









      https://youtu.be/j_sw_fcW0Dw

      ReplyDelete
    120. Dear sir,

      Hope you have taken break to relax and spend time with your family.

      Wishing you and your beautiful family a very happy Deepavali

      ReplyDelete
    121. Dear Captain,

      Wish you and your family Happy Deepavali.

      Your decision to stay offline bothers. You have crossed the dangers that no one sees and you have reached the heights of blogging that no one reaches.Your anger for not seeing enough changes in the administration is reasonable. But the readership of 1.02 billion will not be a waste. Ultimately truth finds its way and lights the hearts. I sincerely wish you end your silence.
      I thought you stayed offline due to elections not to influence any party but even after the results, you were silent. If you are vacating, I wish you and your family good time but please say hai to your waiting readers.

      Regards..

      ReplyDelete
    122. Ajit Sir where are you?? Missing you badly. Hope all is well with you and family. Wish you happy Diwali

      ReplyDelete
    123. Respected Captain Namaskaram Happy Diwali to You,Family and All readers.Hope and Pray that All is Well and you are in good health.With Gratitude RVK.

      ReplyDelete
    124. Dear Captain- Wish you and your family a very happy Diwali!! Hope you are doing well, haven't seen any updates in your blog since a long time.

      Sincerely,
      KP

      ReplyDelete
    125. Respected Captain , was busy for a while , but noticed , you have not been active over a week. I hope everything is fine and in good spirits with you Captain.

      Regards

      ReplyDelete
    126. Dear Capt Ajit sir,

      Are you busy with some Govt work or something personal, pls let us know why you are on hibernation mode and not blogging for the last 1 week ?

      ReplyDelete