THIS POST IS CONTINUED FROM PART 1 BELOW--
https://ajitvadakayil.blogspot.com/2019/08/what-artificial-intelligence-cannot-do.html
CAPT AJIT VADAKAYIL SAYS AI MUST MEAN “INTELLIGENCE AUGUMENTATION “ IN FUTURE ..
Let this be IA
Let this be IA
OBJECTIVE AI CANNOT HAVE A VISION,
IT CANNOT PRIORITIZE,
IT CANT GLEAN CONTEXT,
IT CANT TELL THE MORAL OF A STORY ,
IT CANT RECOGNIZE A JOKE, OR BE A JUDGE IN A JOKE CONTEST
IT CANT DRIVE CHANGE,
IT CANNOT INNOVATE,
IT CANNOT DO ROOT CAUSE ANALYSIS ,
IT CANNOT MULTI-TASK,
IT CANNOT DETECT SARCASM,
IT CANNOT DO DYNAMIC RISK ASSESSMENT ,
IT IS UNABLE TO REFINE OWN KNOWLEDGE TO WISDOM,
IT IS BLIND TO SUBJECTIVITY,
IT CANNOT EVALUATE POTENTIAL,
IT CANNOT SELF IMPROVE WITH EXPERIENCE,
IT CANNOT UNLEARN
IT IS PRONE TO CATASTROPHIC FORGETTING
IT DOES NOT UNDERSTAND BASICS OF CAUSE AND EFFECT,
IT CANNOT JUDGE SUBJECTIVELY TO VETO/ ABORT,
IT CANNOT FOSTER TEAMWORK DUE TO RESTRICTED SCOPE,
IT CANNOT MENTOR,
IT CANNOT BE CREATIVE,
IT CANNOT THINK FOR ITSELF,
IT CANNOT TEACH OR ANSWER STUDENTs QUESTIONS,
IT CANNOT PATENT AN INVENTION,
IT CANNOT SEE THE BIG PICTURE ,
IT CANNOT FIGURE OUT WHAT IS MORALLY WRONG,
IT CANNOT PROVIDE NATURAL JUSTICE,
IT CANNOT FORMULATE LAWS
IT CANNOT FIGURE OUT WHAT GOES AGAINST HUMAN DIGNITY
IT CAN BE FOOLED EASILY USING DECOYS WHICH CANT FOOL A CHILD,
IT CANNOT BE A SELF STARTER,
IT CANNOT UNDERSTAND APT TIMING,
IT CANNOT FEEL
IT CANNOT GET INSPIRED
IT CANNOT USE PAIN AS FEEDBACK,
IT CANNOT GET EXCITED BY ANYTHING
IT HAS NO SPONTANEITY TO MAKE THE BEST OUT OF SITUATION
IT CAN BE CONFOUNDED BY NEW SITUATIONS
IT CANNOT FIGURE OUT GREY AREAS,
IT CANNOT GLEAN WORTH OR VALUE
IT CANNOT UNDERSTAND TEAMWORK DYNAMICS
IT HAS NO INTENTION
IT HAS NO INTUITION,
IT HAS NO FREE WILL
IT HAS NO DESIRE
IT CANNOT SET A GOAL
IT CANNOT BE SUBJECTED TO THE LAWS OF KARMA
ON THE CONTRARY IT CAN SPAWN FOUL AND RUTHLESS GLOBAL FRAUD ( CLIMATE CHANGE DUE TO CO2 ) WITH DELIBERATE BLACK BOX ALGORITHMS, JUST FEW AMONG MORE THAN 60 CRITICAL INHERENT DEFICIENCIES.
HUMANS HAVE THINGS A COMPUTER CAN NEVER HAVE.. A SUBCONSCIOUS BRAIN LOBE, REM SLEEP WHICH BACKS UP BETWEEN RIGHT/ LEFT BRAIN LOBES AND FROM AAKASHA BANK, A GUT WHICH INTUITS, 30 TRILLION BODY CELLS WHICH HOLD MEMORY, A VAGUS NERVE , AN AMYGDALA , 73% WATER IN BRAIN FOR MEMORY, 10 BILLION MILES ORGANIC DNA MOBIUS WIRING ETC.
SINGULARITY , MY ASS !
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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
-
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..
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
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--
.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--
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
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.
n
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.
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
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.
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.
You might just as well have been using a bunch of if statements.
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.
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.
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
..
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 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.
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++
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
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 BECAUSE OF A FELLOW WITHOUT INTEGRITY-- NARENDRA DAMODARDAS MODI..
ALL MY MALAYALI READERS IN GULF AND USA .. TAKE THIS ISSUE UP..
ENOUGH IS ENOUGH !
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
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
..
ReplyDeleteWE 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
..
DeletePUT 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
DeleteCapt. 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
Hi Sir,
DeleteI 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
GRETA THUNBERG IS A PUPPET OF THE DEEP STATE
ReplyDeleteSHE 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
..
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https://timesofindia.indiatimes.com/india/ed-probes-praful-patels-alleged-land-deal-with-dawood-man/articleshow/71560936.cms
ReplyDeleteEVERYBODY 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
..
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Captain Ji,
ReplyDeleteCongratulations.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
ReplyDeletehttps://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
..
DeletePUT ABOVE COMMENT IN WEBSITES OF--
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Sir, sent to --
DeleteKODIYERI 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..
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Dear Captain
ReplyDeleteWhat 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
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).
DeleteRCEP 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
..
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
ReplyDeleteI 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..
https://timesofindia.indiatimes.com/india/abhijit-was-an-economist-by-accident-but-is-an-ace-cook-says-mother-nirmala/articleshow/71589170.cms
ReplyDeleteTHIS TATTU IS COOKING FOR HIS JEWESS WIFE..
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.
DeleteBengalis 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.
https://www.business-standard.com/article/politics/story-in-numbers-india-has-suicide-rate-higher-than-the-global-average-119101300672_1.html
ReplyDeleteTHIS 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..
Namaste Captain,
ReplyDeleteHave 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
i've commented on TOI regarding banerjee, as usual it's censored.
ReplyDeleteDear Capt Ajit sir,
ReplyDeleteYday 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.
Dear Capt Ajit sir,
ReplyDeleteUnless 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
I found this passage in Wikipedia that you might find interesting:
ReplyDeleteUmichand
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?
https://en.wikipedia.org/wiki/Omichund
ReplyDeletehttp://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--
CONTINUED FROM 1--
DeleteIT 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
..
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https://timesofindia.indiatimes.com/business/india-business/indian-buyers-slash-malaysian-palm-oil-purchases-fearing-duty-hike-traders/articleshow/71592640.cms
ReplyDeleteKERALA 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
https://photogallery.indiatimes.com/news/india/apj-abdul-kalam-life-in-pics/APJ-Abdul-Kalam/articleshow/48245824.cms'
ReplyDeleteREPLACE ALL ROAD AND MONUMENTS NAMED AFTER DESH DROHI KATHIAWARI JEW GANDHI WITH ABDUL KALAM-- HE IS OUR HERO !
https://twitter.com/TarekFatah/status/1183780569156595718
ReplyDeleteTHE 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
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
ReplyDeleteLOOKS LIKE A HIJRA WITH NARROW HIPBONES..
Respected Sir,
ReplyDeleteCan one listen to discourses on the holy Bhagavad Gita by Swami Chinmayananda during one's menstrual cycle.
Thank you
YES
Deletehttps://www.youtube.com/watch?v=Xu28PjOrgZY
ReplyDeleteHEY 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
..
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Used snapshots.
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regards.
https://twitter.com/DrGPradhan/status/1184095971765936128
ReplyDeleteCONMAN 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
PUT THIS IN TWITTER SITES OF -
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Dear captain,
ReplyDeleteAsoka, 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.
Dear Capt Ajit sir,
ReplyDeleteArnab 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.
HOTEL SEA ROCK WAS BOMBED BY JEW DAWOOD IBRAHIM, SO THAT SEA ROCK HOTEL COULD BE BOUGHT BY JEWS..
DeleteSURESH 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
..
PUT ABOVE COMMENT IN WEBSITES OF
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Deletesent to,
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Your Registration Number is : PMOPG/E/2019/0615465
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Handles:
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Thanks, Sriram, for sharing handles.
ReplyDeleteGeeta 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?
Vidisha, Bhopal have rotor clubs, which are freemason clubs
DeleteKhajuraho has a freemason lodge
Looking forward to the reply from captain Kalki
Dear Capt Ajit sir,
ReplyDeleteWhy 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
THIS IS TO GIVE GEELA WHOAL TO MODI
DeleteVideo of Pune Electric Bus Getting Charged Through a Diesel Generator Surfaces, Twitter Has a Field Day
Deletehttps://www.news18.com/news/auto/video-of-pune-electric-bus-getting-charged-through-a-diesel-generator-surfaces-twitter-has-a-field-day-2343281.html
https://twitter.com/realDonaldTrump/status/1184125314147983361?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Etweet
ReplyDeleteCaptain guruji,
ReplyDeleteIf 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
It would be like seriously raiding hell..
Deletehttps://twitter.com/VishnuNDTV/status/1184084585975402497?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Etweet
ReplyDeleteTHIS 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
..
PUT ABOVE COMMENT IN WEBSITES OF--
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sent screenshots and emails to many..
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.
ReplyDeleteI 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
SOMEBODY CALLED ME UP AND CRIED
ReplyDeleteCAPTAIN, 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
..
PMO
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https://twitter.com/kkarthikeyan09/status/1184330873669181446?s=20
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https://timesofindia.indiatimes.com/india/india-falls-to-102-in-hunger-index-8-ranks-below-pakistan/articleshow/71606116.cms
ReplyDeleteNOBODY 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
..
PUT ABOVE COMMENT IN WEBSITES OF--
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Sent screenshots ..
DeleteSent emails Captain.
ReplyDeletehttps://www.business-standard.com/article/pti-stories/indian-economy-on-a-shaky-ground-nobel-awardee-banerjee-119101400968_1.html
ReplyDeleteWE 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
..
DeletePUT ABOVE COMMENT IN WEBSITES OF--
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TRUMP
AMBASSADOR TO FROM INDIA-USA
ESTHER DUFLO
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AMARTYA SEN
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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
I ASK MY READERS TO GIVE ABHIJIT BANNERJEE A PIECE OF YOUR MIND..
DeleteHE IS COMING TO INDIA NEXT WEEK TO LAUNCH HIS BOOK.
Email Id Of His First Wife
DeleteTulu@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
Correction
Deletethe email id of first wife is
tuli@mit.edu
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.
DeleteIn fact it helps to be Jewish to get tenure.
Dear Captain,
DeleteSent 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
Sent mails to-
Deleteappointment.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..
Sarah Brady
DeleteAssistant Dean for Finance
sarahb@mit.edu
FYI an additional piece of information.
DeleteDepartment of Economics in MIT falls under School of Humanities, Arts, and Social Sciences (SHASS).
Dean is Ms. Melissa Nobles.
Email: mnobles@mit.edu
https://timesofindia.indiatimes.com/india/social-media-used-to-taint-judiciary/articleshow/71605927.cms
ReplyDeleteA 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.
Hi capt,
ReplyDeleteFirst time i have heard truth in twitter about kill chips.
https://twitter.com/ashokkmrsingh/status/1184321483306237952
https://timesofindia.indiatimes.com/india/these-indians-are-not-having-kids-to-save-the-planet/articleshow/70910606.cms
ReplyDeletedeep state agenda..
WE ASK DONALD TRUMP AND PM MODI?
ReplyDeleteWHY 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
..
PUT ABOVE COMMENT IN WEBSITES OF--
DeleteMIT 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
ALSO PUT IN WEBSITES OF--
DeleteJAPANESE 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
Hello Captain,
DeleteHave 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,
https://twitter.com/punithdg619/status/1184411562372558848
Deletehttps://twitter.com/IwerePm/status/1184396073747415041
DeleteSent to Donalt Trump Narendra Modi, cabinet ministers - Amit, Nirmala,Piyush,Gadkari, Rajnath and others
https://twitter.com/kkarthikeyan09/status/1184434526656679936?s=20
Deletehttps://twitter.com/vkgjain/status/1184449438539907072?s=19
Deletehttps://twitter.com/shree1082002/status/1184491353385750529
DeleteTweets:
Deletehttps://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
Posted on NIA latest tweet, verified
Deletehttps://twitter.com/NIA_India/status/1183714733272133632?s=20
I have emailed again to MIT Dean and others as below:
DeleteRespected 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
Namaste Capt Ajiji,
DeleteFollowing 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
Sent to trump and putin..
DeleteSent message to IB from its website...and mailed to- ed,nia,FM and her secretaries,RBI,amitshah,defense min and his secretaries, niti...
Namaste Capt,
ReplyDeleteMaha Farmers earn lakhs from Moringa : https://www.thebetterindia.com/198892/maharashtra-farmer-earning-lakhs-moringa-superfood-growing-organic/
Capt. Sir,
ReplyDeleteThere 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
Captain,
ReplyDeleteFor 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
Dear Capt Ajit sir,
ReplyDeleteWorld 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....
https://www.thechemicalengineer.com/news/shell-and-bp-join-consortium-to-tackle-methane-emissions/
ReplyDeleteSOMEBODY ASKED ME
ReplyDeleteCAPTAIN, 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
..
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
ReplyDeleteBUFFALO BECOMES BRAVEHEART !
TEE HEEEEEEE
https://indianexpress.com/article/opinion/columns/govt-calling-the-supreme-court-shots-narendra-modi-6070659/
ReplyDeleteUNCONSTITUTIONAL 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 !
Latest theory about moon lander failure is that a neighbor paid a hacker a huge amount to sabotage the landing
ReplyDeletehttps://photogallery.indiatimes.com/sports/cricket/glenn-maxwells-pics-with-indian-girlfriend-spark-marriage-rumours-on-social-media/articleshow/70872519.cms?picid=70872581
ReplyDeleteSLUTS
ReplyDeletenamiOctober 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
SOMEBODY ASKED ME
ReplyDeleteCAPTAIN 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
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Happy Karva chauth to all women readers.
https://www.freepressjournal.in/india/ayodhya-case-sunni-waqf-board-lawyer-rajiv-dhawan-tears-up-submissions-made-by-another-lawyer
ReplyDeleteA 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
..'
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Law secretary-secylaw-dla@nic.in
http://ajitvadakayil.blogspot.com/2013/10/karva-chauth-synchronising-fertility.html
ReplyDeleteTODAY IS KARVA CGAUTH-- 17TH OCT 2019.
3RD DAY AFTER FULL MOON..
http://ajitvadakayil.blogspot.com/2016/10/amavasya-poornima-ekadashi-fasting.html
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.
Deleteis it ok to drink small amount coconut milk during ekadashi fast ?
DeleteCOCONUT WATER--YES
DeleteCOCONUT MILK- NO
The lawyer who destroyed Hindu evidence is a Hindu name.
ReplyDeleteWould 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.
https://timesofindia.indiatimes.com/india/in-latest-theatrics-dhavan-shreds-ram-birthplace-map/articleshow/71623282.cms
ReplyDelete.
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..
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.
ReplyDeleteRegards,
Prapulla
SOMEBODY ASKED ME
ReplyDeleteWHY 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
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Namaste captain Ajitji,
DeleteHope 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
REGARDING TECHNICAL COMPLAINTS ( LIKE SHELL COMPANIE/ CRYPTO CURRENCIES ETC )..
ReplyDeleteFOR 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
..
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https://timesofindia.indiatimes.com/business/india-business/indian-traders-cut-purchase-of-palm-oil-from-malaysia/articleshow/71629721.cms
ReplyDeleteINDIA 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
..
ReplyDeleteRishi 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 !
INDIA GAVE BHARAT RATNA TO ROTHSCHILDs AGENT JEW ABDUL GHAFFAR KHAN !
ReplyDeleteJEW 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
..
DeletePUT ABOVE COMMENT IN WEBSISTES OF-
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Readers can have a note of these handles to spread comments.
https://timesofindia.indiatimes.com/india/nobody-dared-remove-article-370-despite-lot-of-talk-pm-modi/articleshow/71634684.cms
ReplyDeleteBURN 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
..
PUT ABOVE COMMENTS IN WEBSITES OF--
DeleteMIT DEAN ( WARNING HIM THAT HIS PROFESSOR ABHIJIT BANNERJEE IS INVOLVED IN POLITICS )
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ReplyDeleteCapt. 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
..
https://timesofindia.indiatimes.com/social-humour-these-karwa-chauth-memes-are-breaking-internet/liveblog/71631043.cms
ReplyDeleteDEEP 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
..
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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 ..
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PS: Thanks to the reader for updating the list of Women MPs in the spreadsheet.
Captain Sir,
DeleteYour 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..
Apologies for irking you...current situation did not allow me to transmit your messages.
DeleteRegards,
Saurin
Sir,
DeleteI 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.
I HAVE LOT OF WHITE SKINNED READERS IN NASA.. WHO COME TO MY SITE OUT OF CURIOSITY
ReplyDeleteTHEY 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
..
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ATHEIST COMMIE KADAKAMPALLI SURENDRAN
ATHERIST COMME A PADMAKUMAR -- DEWASWON PRESIDENT
ALL MPs/ MLAs OF KERALA
ALL COLLECTORS OF KERALA
ALL KERALA MEDIA
Captain sir,
DeleteHow are you ? Feels like long time I have not seen your post.
Respected Sir,
ReplyDeleteHope 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
How are you sir ..? and hope you are doing well..
ReplyDeleteDear Capt Ajit sir,
ReplyDeleteThis 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
sent via mails sir, are you travelling ??
ReplyDeleteNamaste Captain Ajitji,
ReplyDeleteNo 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.
Dear Capt Ajit sir,
ReplyDeleteIt'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
Dear Sir, are you busy ? No comments published.
ReplyDeleteGratitude.
Pranaam Guruji,
ReplyDeletewe 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
Namastae Master Ji,
ReplyDeleteNamaskaram - Hope all is well, could not see your comments & post for last 2 days.
Grace us with your blessings always,
Thanks with highest gratitude.
Mahodaya,
ReplyDeleteStrength, 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.
:-)
Dear sir,
ReplyDeleteI 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 🙏🙏🙏
Dear Capt Ajit sir,
ReplyDeleteThis 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
Dear Capt Ajit sir,
ReplyDeleteHow 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 ?
Dear Capt Ajit sir,
ReplyDeleteKalki 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 !! "
Dear Ajit Sir,
ReplyDeleteक्या आपने अल्प अवधि के लिए खुद पर विराम लगाया हुआ है!
I am missing you please!
Hope everything is fine!
Namaste Captain ,
ReplyDeleteNot heard yet from you. We are really worried. Please response through you blog.
Capt. hope everything is fine...
ReplyDeleteGurunatha!
ReplyDeleteHope all is well and my prayers!!!
Vinith
Respected Sir, Hope you are doing fine..
ReplyDeleteAjit garu, no comment from u since 2 days. Hope u nd ur family is safe. Love u sir.
ReplyDeleteHope you are keeping well Captain. If on vacation, wishing you a wonderful time. Spread the message about Karva Chauth.
ReplyDeleteRegards,
Prapulla
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.
ReplyDeleteCapt 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.
Dear Sir,
ReplyDeleteIt 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.
Hi Sir,
ReplyDeleteHope you are doing good.
Thanks
Maheshwar
Respected captain
ReplyDeletePranaam,
Best wishes for you and your family .Hope all of you are keeping fine and having good time by Almighty's blessings.
Regards
What r you upto Sir No update.
ReplyDeleteI do my part though don't post it very often
https://twitter.com/Vaibhav34632584/status/1186344915820638208?s=19
Captain,
ReplyDeleteI am worried. Hope you are well. Please publish the comments when you are back.
Regards,
Muthu Swamynathan.
Dear Capt Ajit sir,
ReplyDeletePls 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 :-)
Sir.
ReplyDeleteAre you in travel?
Hope everything is fine.
Namaskaara_/\_
ReplyDeleteYou are touring i suppose.
_/\_Alupa Raaja
Capt, where are you?
ReplyDeleteIn 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.
Sir, hope everything is ok!
ReplyDeleteThankyou.
Hello Captain,
ReplyDeleteI 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,
Dear Ajit Sir,
ReplyDeletePlease 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!!!
Captain, Hope you are doing well and ok.
ReplyDeleteNamaskar Guruji, Please come back..... waiting for you ......... love you always:-)
ReplyDeleteDear Capt Ajit sir,
ReplyDeleteWe 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 ?
Respected Captain
ReplyDeletePranaam
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
Dear Ajit Sir,
ReplyDeleteYou 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!
where is Captain? It is so long he is not present on blog.
ReplyDeleteCaptain did absolutely right
ReplyDeleteI 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.
dear captain,
ReplyDeleteIt's been a while since you stopped blogging.
Hope everything is going well with you and your family.
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,
ReplyDeleteDear Capt Ajit ji
ReplyDeleteSir.. 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
Dear Capt Ajit sir,
ReplyDeleteWhat'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 :-)
Dear Captain,
ReplyDeleteI hope you are doing well. I will pray for the same. Please take care.
Regards,
Hari
Namaste Captain Ajitji,
ReplyDeleteHAPPY 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
Namaste Captain,
ReplyDeleteSharing 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.
Capt, how are you? Hope you're doing well.
ReplyDeleteRespected sir
ReplyDeleteWhere 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
Dear Captain
ReplyDeletePeace 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
Captain, I hope you are fine and just taking a break. Just worried to see no updates. Take care.
ReplyDeleteApologies Capt. Pls respond.
ReplyDeleteThx
Saurin
Captain Sir,
ReplyDeleteWish 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
Dear Captain
ReplyDeleteHappy Diwali to you and your family...
Where are you Sir...?? Hope you are doing well...
Namaste Capt Ajitji,
ReplyDeleteHAPPY 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
Dear sir,
ReplyDeleteHope you have taken break to relax and spend time with your family.
Wishing you and your beautiful family a very happy Deepavali
Dear Captain,
ReplyDeleteWish 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..
Ajit Sir where are you?? Missing you badly. Hope all is well with you and family. Wish you happy Diwali
ReplyDeleteRespected 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.
ReplyDeleteDear 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.
ReplyDeleteSincerely,
KP
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.
ReplyDeleteRegards
Dear Capt Ajit sir,
ReplyDeleteAre 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 ?