Tuesday, October 29, 2019

WHAT ARTIFICIAL INTELLIGENCE CANNOT DO , a grim note to the top 100 intellectuals of this planet , Part 3 - Capt Ajit Vadakayil



THIS POST IS CONTINUED FROM PART 2 , BELOW--




WHEN HINDUS TALK ABOUT ENDLESS REBIRTHS TILL FINAL MOKSHA , THE WHITE MAN RIDICULES..   

THE MOST DIFFICULT HUMAN SKILLS TO REPLICATE BY AI  ARE THE UNCONSCIOUS ONES, THE PRODUCT OF MILLENNIA OF EVOLUTION WHERE THE SOUL PICKS UP EXPERIENCE FROM BACTERIA TO PLANT TO ANIMAL TO CONSCIOUS HUMAN TO MOKSHA HUMAN...   

IN AI THIS IS KNOWN AS MORAVEC'S PARADOX.


MORAVEC'S PARADOX IS THE OBSERVATION BY ARTIFICIAL INTELLIGENCE AND ROBOTICS RESEARCHERS THAT, CONTRARY TO TRADITIONAL ASSUMPTIONS, HIGH-LEVEL REASONING REQUIRES VERY LITTLE COMPUTATION, BUT LOW-LEVEL SENSORIMOTOR SKILLS REQUIRE ENORMOUS COMPUTATIONAL RESOURCES.

MORAVEC WROTE " IT IS COMPARATIVELY EASY TO MAKE COMPUTERS EXHIBIT ADULT LEVEL PERFORMANCE ON INTELLIGENCE TESTS OR PLAYING CHECKERS, AND DIFFICULT OR IMPOSSIBLE TO GIVE THEM THE SKILLS OF A ONE-YEAR-OLD WHEN IT COMES TO PERCEPTION AND MOBILITY"

It seems almost paradoxical to suggest that a technology ruled by logic — such as AI — could fall prey to paradoxes.

"Encoded in the large, highly evolved sensory and motor portions of the human brain is a billion years of experience about the nature of the world and how to survive in it," he wrote in his 1988 book "Mind Children." "The deliberate process we call reasoning is, I believe, the thinnest veneer of human thought, effective only because it is supported by this much older and much more powerful, though usually unconscious, sensorimotor knowledge."

Moravec’s paradox proposes that this distinction has its roots in evolution. As a species, we have spent millions of years in selection, mutation, and retention of specific skills that has allowed us to survive and succeed in this world. Some examples of such skills include learning a language, sensory-motor skills like riding a bicycle, and drawing basic art.

It is comparatively easy to make computers exhibit adult level performance, and difficult or impossible to give them the skills of a one-year-old.


Artificial intelligence can complete tricky logical problems and advanced mathematics. But the ‘simple’ skills and abilities we learn as babies and toddlers — perception, speech, movement, etc. — require far more computation for an AI to replicate.

Minsky emphasized that the most difficult human skills to reverse engineer are those that are unconscious. "In general, we're least aware of what our minds do best", he wrote, and added "we're more aware of simple processes that don't work well than of complex ones that work flawlessly"
A compact way to express this argument would be:

We should expect the difficulty of reverse-engineering any human skill to be roughly proportional to the amount of time that skill has been evolving in animals.

The oldest human skills are largely unconscious and so appear to us to be effortless.

Therefore, we should expect skills that appear effortless to be difficult to reverse-engineer, but skills that require effort may not necessarily be difficult to engineer at all.

It is very difficult to reverse engineer certain human skills that are unconscious. It is easier to reverse engineer motor processes (think factory automation), cognitive skills (think big data analytics), or routinised computations (think predictive/ prescriptive algorithms).

In general, we’re less aware of what our minds do best…. We’re more aware of simple processes that don’t work well than of complex ones that work flawlessly

In other words, for AI the complex is easy, and the easy is complex.

AI ‘learns’ through us telling it how to do things. We’ve consciously learned how to do mathematics, win games and follow logic. We know the steps (computations) needed to complete these tasks. And so, we can teach them to AI.

But how do you tell anything how to see, hear, or move? We don’t consciously know all the computations needed to complete these tasks. These skills are not broken down into logical steps to feed into an AI. As such, teaching them to an AI is extremely difficult.

There are two key implications of this:--

High level reasoning is a very new phenomenon, and so humans haven’t had much time to perfect it. As a result, it still feels “hard” for us to conduct.

“Simple” skills, which took hundreds of millions of years to develop ( soul evolution as per Sanatana Dharma ) , have had plenty of time to be refined, making them seem comparatively easy and natural to us.

As AI hasn’t had the benefit of a hundred million years of evolution, developing sensory motor skills is quite a tall order. Complex calculations, problem solving, and analysis however, are a computer’s strong suit, and were commensurately developed in a fraction of the time by humans.

The explanation of these contradictions, as well as that of Moravec’s paradox, is related to the different functions [and different thinking strategies] of the left and right hemispheres of humans.”

Thus, while the formal logical thinking of the left hemisphere organizes the information in “a strictly ordered monosemantic context and without ambiguities.. Such a thinking strategy makes it possible to construct a pragmatically convenient but simplified reality model”.

In contrast, the function of the right hemisphere is to “simultaneously capture an infinite number of real connections and shape an integral but ambiguous polysemantic context.” This hemisphere plays a key role in creativity … but also “it is especially related to the limbic system, which controls bodily functions.”


While creativity is one of the last skills that appeared in biological evolution (and the area of the brain responsible for it has been the last to mature), “it is very difficult – and, even now even impossible – find an algorithm capable of processing and computerizing creativity.”


ANCIENT 12 STRAND DNA MAHARISHIS COULD ARRIVE AT RESULTS FASTER AND MORE ACCURATE THAN MODERN SUPER COMPUTERS ARMED WITH ARTIFICIAL INTELLIGENCE ..

TODAY WE ARE 2 STRAND DNA ( 97% JUNK ) DEGRADED MACHINES.

WHY SHOULD THE CAPTAIN OF A SHIP , THE GOVERNOR OF A STATE OR THE PRESIDENT OF INDIA, BE AFFORDED  DISCRETIONARY POWERS WITH VETO POWERS,  WHICH IS "SUBJECTIVE" ?

THE REASON IS THE HUMAN BRAIN WORKS DIFFERENT FROM THAT OF A OBJECTIVE COMPUTER

NO COMPUTER CAN TELL THE MORAL OF A STORY EVER .. BECAUSE IT DOES NOT HAVE A SUBJECTIVE CONSCIOUS BRAIN .

ARTIFICIAL INTELLIGENCE IS OBJECTIVE..

ON CHEMICAL TANKERS WE HAVE TEN HOUR VETTING INSPECTIONS BY OIL MAJORS LIKE SHELL/ MOBIL ETC ..

THE WHOLE INQUIRY IS OBJECTIVE.. HUNDREDS OF QUESTIONS..

THE LAST QUESTION IS SUBJECTIVE,   AND THIS IS AIMED AT THE HEART OF THE INSPECTOR  ( NOT HIS LEFT BRAIN LOBE )     " WOULD  YOU SAIL ON THIS SHIP FOR A VOYAGE UNDER THE PRESENT CREW AND CAPTAIN WITHOUT RESERVATION"

THE INSPECTOR COULD HAVE GIVEN 100% MARKS ON THE TEN HOUR OBJECTIVE INQUIRY..    BUT IF HE WRITES THE WORD "NO" FOR THE LAST SUBJECTIVE QUESTION, THE SHIP HAS FAILED..

WE CANNOT HAVE EVERYTHING OBJECTIVE-- WE ARE HUMANS  NOT MONKEYS OR COMPUTERS ..

SUBJECTIVE MUST HOLD THE "VETO POWER".. 

VETO POWER CAN NEVER BE GIVEN TO THE OBJECTIVE..

OBJECTIVE IS FOR MEDIOCRE BRAINS, WHO NEEDS CHECKLISTS TO DRESS UP.. OR HE MIGHT LAND UP LIKE PHANTOM WITH UNDIES OUTSiDE PANTS..

A SUBJECTIVE MORALITY IS ONE ROOTED IN HUMAN FEELINGS AND CONSCIENCE .

NATURAL JUSTICE IS INHERENT. THESE ARE THE THINGS THAT ARE MOST IMPORTANT TO US, INDEED THE ONLY THINGS IMPORTANT TO US!

RELIGION IS OBJECTIVE .

SPIRITUALITY IS SUBJECTIVE.

HINDUISM TELLS ALL TO USE THEIR CONSCIENCE. OBJECTIVE MORALITY BREEDS FALSE EXCUSES

TANGIBLE LAW IS THE OBJECTIVE FORM OF MORALITY. OBJECTIVE IS INDEPENDENT OF PEOPLEs OPINIONS.

OBJECTIVE MORALITY IGNORED CONTEXT .

ATHEIST COMMUNISTS AND SINGLE MESSIAH / HOLY BOOK RELIGIONS ARE OBJECTIVE WITH MORALITY. THEY HAVE FAILED .

RELIGION IS DOING WHAT YOU ARE TOLD REGARDLESS OF WHAT IS RIGHT.

RULES DON’T MAKE US MORAL.

SANATANA DHARMA IS SUBJECTIVE.    

SUBJECTIVE IS STRICTLY WITHIN HUMANS BEINGS –IT DERIVES FROM OUR INTANGIBLE CONSCIENCE ALONE.

IN SUBJECTIVE MORALITY PERCEPTION WITHIN PERIMETER OF CONTEXT IS PARAMOUNT.

SUBJECTIVE IS DEPENDENT ON PEOPLEs OPINIONS.

SANATANA DHARMA IS BASED ON CONSCIOUS HUMAN CONSCIENCE. NO MAN CAN MANIPULATE OR SILENCE HIS CONSCIENCE. WE ARE NOT THE SOURCE OF OUR OWN CONSCIENCE.

BHAGAWAD GITA IS OUR GUIDE NOT ASHTAVAKRA GITA COOKED UP BY JEW ROTHSCHILD WITH DOs AND DONTs

ENEMIES OF HINDUISM USES FAKE GURUS LIKE TRIPLE SRI TO CONVERT SANATANA DHARMA TO AN OBJECTIVE RELIGION. SORRY, IT WONT WORK

SPIRITUALLY SOAKED HINDUS ARE SUBJECTIVE WITH MORALITY. ONLY THIS WORKS

MORALITY IS DOING WHAT IS RIGHT, REGARDLESS OF WHAT YOUR ARE TOLD.
SUBJECTIVE MORALITY HAS NO SCOPE FOR EXCUSES. LOVE , COMPASSION AND FAIRNESS MAKE US MORAL.

MANAGERS CAN ONLY DO OBJECTIVE EVALUATIONS

LEADERS CAN DO SUBJECTIVE EVALUATIONS

THE PERFORMANCE OF A TEAM MEMBER CAN BE EVALUATED ONLY SUBJECTIVELY..
A  TEACHER EVALUATES OBJECTIVELY

A MENTOR EVALUATES SUBJECTIVELY

WHEN I RECOMMEND PROMOTION IT IS NEVER ON OBJECTIVE  PAST/ CURRENT  PERFORMANCE.    IT IS BASED ON SUBJECTIVE EVALUATION OF FUTURE POTENTIAL

I HAVE NEVER EVALUATED A OFFICER ON THE ANSWERS HE GAVE ME,   RATHER I EVALUATED HIM BASED ON THE QUESTIONS HE ASKED ME –   AFTER I ASKED HIM TO READ AN DIGEST A FEW PAGES .

IN OUR HUMAN WORLD, THERE ARE THINGS THAT WE CAN MEASURE OR TEST AND, THEREFORE, VERIFY OR FALSIFY. CONSEQUENTLY, THERE IS NO DIFFICULTY IN DISCOVERING OR DESCRIBING THE FACTS. THESE WE CALL OBJECTIVE JUDGMENTS.

TO SUSTAIN DHARMA YOU CANNOT APPLY NUMBER CRUNCHING OR OBJECTIVE JUDGMENT--YOU HAVE TO  APPLY SUBJECTIVE JUDGMENTS.

THIS IS WHY A GOVERNOR OR PRESIDENT  IS AN EXPERIENCED AND LEVEL HEADED MAN . 

THIS IS WHY THE PRESIDENT IS THE SUPREME COMMANDER OF OUR ARMED FORCES..

THE NEW WORLD ORDER OF KOSHER BIG BROTHER WANTS ONLY OBJECTIVE JUDGMENTS

LEADERSHIP IS SUBJECTIVE:   WE NEED YOUR EXPERIENCE, EXPERTISE, AND JUDGMENT; WE NEED YOUR RELATIONSHIPS, INITIATIVE, AND INNOVATION; WE NEED YOUR THOUGHTS, OPINIONS, AND INSTINCTS.  WE NEED CORE VALUES.  IF WE DIDN’T, YOU WOULD BE REPLACED BY A CALCULATOR.

AI IS BEING USED TO FOOL THE PEOPLE OF THE WORLD BY BIG BROTHER..

GEORGE ORWELL NEVER THOUGHT OF ARTIFICIAL INTELLIGENCE BEING USED BY BIG BROTHER TO PUT FOG ON HIS MALICIOUS DEEDS..



AFTER ALL AN ALGORITHM CANNOT BE TAKEN TO COURT OF INCARCERATED OR HUNG

http://ajitvadakayil.blogspot.com/2019/03/crash-of-boeing-737-max-flight-root.html

POISON INJECTED AI IS THE REASON WHY BEGGAR NATIONS ( WITHOUT INDIA/ CHINA ) SIT AT G6 SUMMITS SIPPING PREMIUM WINE.

BIG BROTHER CAN CRASH THE ECONOMY ( LOWER GDP/ LOWER GROWTH RATE/ RAISE INFLATION )  OF ANY NATION WHOSE RULER DOES NOT ALLOW JEWS TO STEAL ( ZIMBABWE/ VENEZUELA )..

BIG BROTHER CAN CAUSE WORLD RECESSION AT WILL, TO STEAL.

AT SEA I NEVER USED AUTOMATION UNLESS IT WAS CALIBRATED.. I KEPT RECORDS WITH SIGNATURES.

ANYBODY LYING IN THIS CALIBRATION EXERCISE I BLED THEM PHYSICALLY AS THIS COULD MEAN LOSS OF LIVES ..


IF THE SHIP WAS UNMANNED AUTOMATION I LAID OUT STRICT PROCEDURES.



MACHINE-LEARNING CODE, PICKS UP ALL OF ITS PREJUDICES FROM ITS HUMAN CREATORS.   IN ISRAEL PALESTINIANS ARE AT THE RECEIVING END

Machine learning models can only regurgitate what they’ve learned.  Poor model performance is often caused by various kinds of actual bias in the data or algorithm, sometimes deliberate.  Machine learning algorithms do precisely what they are taught to do and are only as good as their mathematical construction and the data they are trained on.

Algorithms that are biased will end up doing things that reflect that bias. Sample bias is a problem with training data. It occurs when the data used to train your model does not accurately represent the environment that the model will operate in.

There is virtually no situation where an algorithm can be trained on the entire universe of data it could interact with.

But there’s a science to choosing a subset of that universe that is both large enough and representative enough to mitigate sample bias. This science is well understood by social scientists, but not all data scientists are trained in sampling techniques.


Sometimes the sampling is deliberately biased like what happens in Israel against Palestinians..The Israeli surveillance operation in the West Bank is undoubtedly among the largest of its kind in the world. 

It includes monitoring the media, social media and the population as a whole — and now it turns out also the biometric signature of West Bank Palestinians. This monitoring op is now competing with the Chinese regime, that intensively uses facial recognition and monitors its civilians' activity on social networks.

AI cannot make moral decisions.

AI, does not have the human faculty of understanding, which makes it incapable of writing software. Software writing is a process requiring deep comprehension of the real world and the ability to transform those intricacies into rules.  Bug detection is the key to delivering useful software. While 

AI can detect patterns it cannot exercise free will. AI can make choices based on the rules of the program. These rules are deterministic, i.e. the resulting behavior is determined by initial inputs. With free will, every decision made is backed by infinite ways of doing it with countless outcomes. In computing, there are only two states – do or do not. 

For AI to have free will, infinite states would have to be present, something that has not been achieved to date. AI cannot question their existence as humans do, nor can AI explain their decisions as humans do. These questions tied to philosophy and free will are not in AI’s zone of reach.

 AI  cannot find bugs.

AI cannot write software, in spite of the advancements nor can it detect  malware

AI cannot do creative writing. While AI has generated content, it cannot create without guidelines. Natural language generation (NLG) is a software process that automatically creates content from data. It is being used by businesses for making data reports, messaging communication, and portfolios. 

NLG creates thousands of more documents than humans. However, all these documents are data-driven and devoid of spontaneous creativity humans are capable of. Writers create stories with nuanced emotions that machines do not have. Fear, joy, love, and anger are some of the emotions that make compelling storytelling.

AI cannot bring inventions. AI can follow rules; it cannot create from scratch like humans. . AI uses past observations to learn a general model or a pattern, that can be used to make predictions about future similar occurrences. AI cannot think out of the box like humans.

While AI can recognize objects in images, translate languages, speak, navigate maps, predict crop yields, use visual data analysis to clarify disease diagnoses, verify user identity, prepare documents, make lending decisions in financial management and scores of related tasks, it cannot do everything. 

AI works best only with human collaboration, as seen from the above examples. We must be realistic about the scope of AI, while we can tickle ourselves and get hajaaar excited about its prospects.

The biggest limitation of artificial intelligence is it’s only as smart as the data sets served
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 inaccuracies in the data will be reflected in the results.

Human resource constraints will be the ultimate limitation for successful AI development.

No matter how smart a machine becomes, it can never replicate a human. Machines are rational but, very inhuman as they don’t possess emotions and moral values. They don’t know what is ethical and what’s legal and because of this, don’t have their own judgment making skills. 

They do what they are told to do and therefore the judgment of right or wrong is nil for them. If they encounter a situation that is unfamiliar to them then they perform incorrectly or else break down in such situations.

Artificial intelligence cannot be improved with experience, they can perform the same function again if no different command is given to them.

AI can’t cope up with the dynamic environment and so they are unable to alter their responses to changing environments.

Machines can’t be creative. They can only do what they are being taught or commanded. Though they help in designing and creating, they can’t match the power of a human brain.

Humans are sensitive and they are very creative . They can generate ideas, can think out of the box. They see, hear, think and feel which machine can’t. Their thoughts are guided by the feelings which completely lacks in machines. No matter how much a machine outgrows, it can’t inherent intuitive abilities of the human brain and can’t replicate it.

Whilst AI can process huge data sets and suggest best possible scenarios, what the technology can’t do is contextualise these findings with data it doesn’t have. For example, some law firms are using AI to identify relevant documents in legal cases but a human judge is still needed to adjudicate a decision.

Electronic calculators are superhuman at arithmetic. Calculators didn’t take over the world; therefore, there is no reason to worry about superhuman AI..




Machine learning is a term describing the feature, function or characteristic of computer systems or machines trying to simulate human-thinking behavior or human intelligence..  It is the science that deals with machine performance tasks that require intelligence based on humans. ..

Machine learning, is where machines can learn by experience and acquire skills without human involvement..   Similarly to how we learn from experience, the deep learning algorithm would perform a task repeatedly, each time tweaking it a little to improve the outcome.

AI algorithms need assistance to unlock the valuable insights lurking in the data your systems generate. You can help by developing a comprehensive data strategy that focuses not only on the technology required to pool data from disparate systems but also on data availability and acquisition, data labeling, and data governance.

Although newer techniques promise to reduce the amount of data required for training AI algorithms, data-hungry supervised learning remains the most prevalent technique today.

And even techniques that aim to minimize the amount of data required still need some data. So a key part of this is fully knowing your own data points and how to leverage them.


Machine Learning can be done in the following ways:--

Supervised Learning
Unsupervised Learning
Reinforcement Learning
Ensemble Learning

In Supervised ML , the outputs are labeled, and the inputs are mapped to corresponding outputs

In Unsupervised ML , the inputs are unlabeled, and the algorithms have to find patterns. In Supervised learning, Algorithms are trained using labelled data while in Unsupervised learning Algorithms are used against data which is not labelled.

Reinforcement ML is similar to supervised ML, but in this case, instead of a labeled output, there are rewards and the algorithm’s goal is to maximize rewards

An ensemble contains a number of hypothesis or learners which are usually generated from training data with the help of a base learning algorithm..  

The idea is to generate a large number of scenarios and train the machine learning model to tell  the answer .   Train the model ahead of time and then get the answer right away


The researchers train the machine by feeding it a set of data that includes the solutions, as if the machine were studying previous “exams” before trying new ones. This is called supervised learning. 

In supervised learning, the training data you feed to the algorithm includes a label. Supervised learning means teaching AI by using huge quantities of data that has already been organized appropriately by humans..Supervised means that you trained the algorithm using labeled data.

Imagine you are meant to build a program that recognizes objects. To train the model, you will use a classifier. A classifier uses the features of an object to try identifying the class it belongs to.

In the example, the classifier will be trained to detect if the image is a:--

Bicycle
Boat
Car
Plane

The four objects above are the class the classifier has to recognize. To construct a classifier, you need to have some data as input and assigns a label to it. The algorithm will take these data, find a pattern and then classify it in the corresponding class.


Algorithms are like an engine: they run, but someone still needs to turn the ignition. The marketer is still very much needed in order to plan, design and run the marketing campaign. They are the ones feeding the AI system with all the new information required for them to learn in the first place. 

This form of ‘supervised learning’ does not mimic the way a human learns naturally and  this is one of the biggest obstacles when it comes to creating a more human-like AI.

Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier (the key difference to many other statistical classification problems is the inherent unbalanced nature of outlier detection). 

Over 82% of the time spent in AI projects are spent dealing with and wrangling data. Even more importantly, and perhaps surprisingly, is how human-intensive much of this data preparation work is. 

In order for supervised forms of machine learning to work, especially the multi-layered deep learning neural network approaches, they must be fed large volumes of examples of correct data that is appropriately annotated, or “labeled”, with the desired output result. 

For example, if you’re trying to get your machine learning algorithm to correctly identify cats inside of images, you need to feed that algorithm thousands of images of cats, appropriately labeled as cats, with the images not having any extraneous or incorrect data that will throw the algorithm off as you build the model

There are many steps required to get data into the right “shape” so that it works for machine learning projects:


In supervised learning, often used when labeled data are available and the preferred output variables are known, training data are used to help a system learn the relationship of given inputs to a given output— for example, to recognize objects in an image or to transcribe human speech.

Most current AI models are trained through “supervised learning.” This means that humans must label and categorize the underlying data, which can be a sizable and error-prone chore. 

For example, companies developing self-driving-car technologies are hiring hundreds of people to manually annotate hours of video feeds from prototype vehicles to help train these systems. 

Supervised learning uses a set of paired inputs and desired outputs. The learning task is to produce the desired output for each input. In this case the cost function is related to eliminating incorrect deductions.

A commonly used cost is the mean-squared error, which tries to minimize the average squared error between the network's output and the desired output.


Tasks suited for supervised learning are pattern recognition (also known as classification) and regression (also known as function approximation). 

Supervised learning is also applicable to sequential data (e.g., for hand writing, speech and gesture recognition). This can be thought of as learning with a "teacher", in the form of a function that provides continuous feedback on the quality of solutions obtained thus far.

Supervised Learning is like teacher-student learning. The relation between the input and the output variable is known. The machine learning algorithms will predict the outcome on the input data which will be compared with the expected outcome.


The error will be corrected and this step will be performed iteratively till an acceptable level of performance is achieved.

In supervised learning, training datasets are provided to the system. Supervised learning algorithms analyse the data and produce an inferred function. The correct solution thus produced can be used for mapping new examples. Credit card fraud detection is one of the examples of Supervised Learning algorithm.

Supervised machine learning excels at examining events, factors, and trends from the past. Historical data trains supervised machine learning models to find patterns not discernable with rules or predictive analytics.

In Supervised ML , the algorithm helps to understand how the system has learned in the past and also at the present and also understand how accurate are the outputs for future analysis. They help in considering a dataset or say a training dataset, and then with the use of this algorithm, we can produce a function that can make predictions for the resulting outputs. 

Later the outputs will be checked for errors for more accurate results comparing it with the already calculated output initially.

Supervised learning: requires a data set and classification of the dataset. The training process attempts to match patterns in the data to the classification.  Can be applied to forecast data.

Supervised machine learning refers to providing input and output to an algorithm. The algorithm then learns the relation between the two and is able to make predictions on the training data. The humans supervising machine learning can correct and adjust until the algorithm reaches acceptable performance when predicting outcomes.

Pattern recognition involves classification and cluster of patterns. In classification, an appropriate class label is assigned to a pattern based on an abstraction that is generated using a set of training patterns or domain knowledge. Classification is used in supervised learning.

Supervised learning allows you to collect data or produce a data output from the previous experience. .
Two most common supervised tasks are classification and regression. The Classification process models a function through which the data is predicted in discrete class labels. On the other hand, regression is the process of creating a model which predict continuous quantity. The classification algorithms involve decision tree, logistic regression, etc

A classification problem requires that examples be classified into one of two or more classes. A classification can have real-valued or discrete input variables. A regression problem requires the prediction of a quantity. A regression can have real valued or discrete input variables.


In Supervised MLAlgorithms, input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time.

In this, a model is prepared through a training process. Also, this required to make predictions. And is corrected when those predictions are wrong. The training process continues until the model achieves the desired level.

Example problems are classification and regression.

Example algorithms include logistic regression and back propagation Neural Network.


In Supervised learning, you train the machine using data which is well "labeled." It means some data is already tagged with the correct answer. It can be compared to learning which takes place in the presence of a supervisor or a teacher.


A supervised learning algorithm learns from labeled training data, helps you to predict outcomes for unforeseen data. Successfully building, scaling, and deploying accurate supervised machine learning Data science model takes time and technical expertise from a team of highly skilled data scientists. Moreover, Data scientist must rebuild models to make sure the insights given remains true until its data changes.

Supervised learning allows you to collect data or produce a data output from the previous experience.
Helps you to optimize performance criteria using experience..Supervised machine learning helps you to solve various types of real-world computation problems.

Supervised machine learning is the simplest way to train an ML algorithm as it produces the simplest algorithms. Supervised ML learns from a small dataset, known as the training dataset. This knowledge is then applied to a bigger dataset, known as the problem dataset, resulting in a solution. The data fed to these machine learning algorithms is labeled and classified to make it understandable, thus requiring a lot of human effort to label the data.


Most current AI models are trained through "supervised learning." It means that humans must label and categorize the underlying data, which can be a sizable and error-prone chore. For example, companies developing self-driving-car technologies are hiring hundreds of people to manually annotate hours of video feeds from prototype vehicles to help train these systems.


Labeling is an indispensable stage of data preprocessing in supervised learning. Historical data with predefined target attributes (values) is used for this model training style. 




Annotation is nothing but labeling or marking of data which could be in various forms like images, videos, audios, text etc. Various entities such as tree, dog, etc are usually labeled or tagged in order to teach (train) other ML systems about those objects.

Sometimes mistakes in annotations can happen due to a language barrier or a work division. Asking workers to pass a qualification test is another strategy to increase annotation accuracy.


Annotation is often the most arduous part of the artificial intelligence (AI) model training process. That’s particularly true in computer vision — traditional labeling tools require human annotators to outline each object in a given image. 


Again, Supervised learning is a technique in which we teach or train the machine using data which is well labeled.  To understand Supervised Learning let’s consider an analogy. As kids we all needed guidance to solve math problems. Our teachers helped us understand what. addition is and how it is done.


Similarly, you can think of supervised learning as a type of Machine Learning that involves a guide. The labeled data set is the teacher that will train you to understand patterns in the data. The labeled data set is nothing but the training data set.

Supervised Learning can be used to solve two types of Machine Learning problems:--
Regression
Classification

Regression algorithm builds a model on the features of training data and using the model to predict value for new data





Classification problems can be solved using the following Classification Algorithms:0- 
Logistic Regression
Decision Tree
Random Forest
Naive Bayes Classifier
Support Vector Machine

K Nearest Neighbour


Supervised Machine Learning applies what it has learnt based on past data, and applies it to produce the desired output. They are usually trained with a specific dataset based on which the algorithm would produce an inferred function. It uses this inferred function to predict the final output and delivers an approximation of it.


This is called supervised learning because the algorithm needs to be taught with a specific dataset to help it form the inferred function. The data set is clearly labelled to help the algorithm ‘understand’ the data better. The algorithm can compare its output with the labelled output to modify its model to be more accurate.


In Supervised Learning an algorithm takes a labelled data set (data that’s been organized and described), deduces key features characterizing each label, and learns to recognize them in new unseen data.  One example of supervised machine learning: having been shown multiple labelled images of cats, an algorithm will learn how to recognize a cat and identify one in other previously unseen pictures


Supervised learning is a machine learning task of learning a function that maps an input to an output based on example input-output pairs.  A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.  In supervised learning, we have labelled training data.

In Supervised Learning  inputs and outputs are identified, and algorithms are trained using labeled examples.  

Approximately 71 percent of Machine Learning is supervised learning, while unsupervised learning ranges from 10 – 20 percent. Other methods that are used less often include semi-supervised and reinforcement learning.

The supervised learning algorithm receives a set of inputs along with the corresponding output to find errors. Based on these inputs, it would modify the model accordingly. This is a form of pattern recognition since supervised learning uses methods like classification, regression, prediction, and gradient boosting. Supervised learning then uses these patterns to predict the values of the label on other unlabeled data.


Supervised learning is typically used in applications with which historical data predicts future events, such as fraudulent credit card transactions.

In supervised learning, the machine observes a set of cases (think of “cases” as scenarios like “The weather is cold and rainy”) and their outcomes (for example, “ Krishnan will go to the beach”) and learns rules with the goal of being able to predict the outcomes of unobserved cases (if, in the past, Krishnan usually has gone to the beach when it was cold and rainy, in the future the machine will predict that Krishnan will very likely go to the beach whenever the weather is cold and rainy).

In Supervised Learning, as the name rightly suggests, it involves making the algorithm learn the data while providing the correct answers or the labels to the data. This essentially means that the classes or the values to be predicted are known and well defined for the algorithm from the very beginning.

Supervised learning trains an algorithm based on example sets of input/output pairs. The goal is to develop new inferences based on patterns inferred from the sample results. Sample data must be available and labeled. For example, designing a spam detection model by learning from samples labeled spam/nonspam is a good application of supervised learning.

Supervised Learning is like teacher-student learning. The relation between the input and the output variable is known. The machine learning algorithms will predict the outcome on the input data which will be compared with the expected outcome.










Unsupervised learning, involves feeding a computer raw data and allowing it to sift out patterns without telling it any “answers.”  Unsupervised learning is a machine learning technique, where you do not need to supervise the model.  Unsupervised learning is the training of a machine learning algorithm to infer structure from unlabeled data.

Unsupervised learning is a set of techniques used without labeled training data—for example, to detect clusters or patterns

In Unsupervised learning you do not need to supervise the model. Instead, you need to allow the model to work on its own to discover information. It mainly deals with the unlabelled data. Unsupervised machine learning helps you to finds all kind of unknown patterns in data.

Unsupervised learning algorithms allows you to perform more complex processing tasks compared to supervised learning. Although, unsupervised learning can be more unpredictable compared with other natural learning methods.

In Supervised learning, Algorithms are trained using labelled data while in Unsupervised learning Algorithms are used against data which is not labelled.

Anomaly detection can discover important data points in your dataset which is useful for finding fraudulent transactions.

Unsupervised learning does not rely on trained data sets to predict the outcomes but it uses direct techniques such as clustering and association in order to predict outcomes. Trained data sets mean the input for which the output is known.

Unsupervised learning is used against data that has no historical labels. The system is not told the "right answer." The algorithm must figure out what is being shown. The goal is to explore the data and find some structure within. 

This algorithm helps to check if the system can actually draw data and inferences from no resulted outputs and no information for the training. Now the system from the hidden structure and from all the relevant and several unused data draws a pattern to actually give details of the hidden structure. Here they give an output but it is not necessary to check whether the given output is accurate or not.

Unsupervised learning works well on transactional data. For example, it can identify segments of customers with similar attributes who can then be treated similarly in marketing campaigns. Or it can find the main attributes that separate customer segments from each other. 

Popular techniques include self-organizing maps, nearest-neighbor mapping, k-means clustering and singular value decomposition. These algorithms are also used to segment text topics, recommend items and identify data outliers.

Unsupervised learning is associated with unclassified data set. It analyzes data without human intervention. The training process allows the algorithm to recognize patterns and structure in the data that is usually not obvious.

Here, are prime reasons for using Unsupervised Learning:--

Unsupervised machine learning finds all kind of unknown patterns in data.
Unsupervised methods help you to find features which can be useful for categorization.
It is taken place in real time, so all the input data to be analyzed and labeled in the presence of learners.
It is easier to get unlabeled data from a computer than labeled data, which needs manual intervention.

Disadvantages of Unsupervised Learning--

You cannot get precise information regarding data sorting, and the output as data used in unsupervised learning is labeled and not known
Less accuracy of the results is because the input data is not known and not labeled by people in advance. This means that the machine requires to do this itself.
The spectral classes do not always correspond to informational classes.
The user needs to spend time interpreting and label the classes which follow that classification.

Spectral properties of classes can also change over time so you can't have the same class information while moving from one image to another.

The biggest drawback of Unsupervised learning is that you cannot get precise information regarding data sorting.

The future of AI-based fraud prevention relies on the combination of supervised and unsupervised machine learning.  Unsupervised machine learning is adept at finding anomalies, interrelationships, and valid links between emerging factors and variables. 

Combining both unsupervised and supervised machine learning defines the future of AI-based fraud prevention and is the foundation of the top nine ways AI prevents fraud..

Combining supervised and unsupervised machine learning as part of a broader Artificial Intelligence (AI) fraud detection strategy enables digital businesses to quickly and accurately detect automated and increasingly complex fraud attempts.

AI is a necessary foundation of online fraud detection, and for platforms built on these technologies to succeed, they must do three things extremely well. First, supervised machine learning algorithms need to be fine-tuned with decades worth of transaction data to minimize false positives and provide extremely fast responses to inquiries. 

Second, unsupervised machine learning is needed to find emerging anomalies that may signal entirely new, more sophisticated forms of online fraud. Finally, for an online fraud platform to scale, it needs to have a large-scale, universal data network of transactions to fine-tune and scale supervised machine learning algorithms that improve the accuracy of fraud prevention scores in the process.

Unsupervised Learning algorithms are much harder because the data to be fed is unclustered instead of datasets. Here the goal is to have the machine learn on its own without any supervision. The correct solution of any problem is not provided. The algorithm itself finds the patterns in the data. .

Unsupervised classification seeks pattern recognition in unlabeled data. This classification finds the hidden structures present in such data using clustering or segmentation strategies.

Clustering is considered unsupervised learning, because there's no labeled target variable in clustering. Clustering algorithms try to, well, cluster data points into similar groups (or… clusters) based on different characteristics of the data

Clustering is an unsupervised machine learning task that automatically divides the data into clusters, or groups of similar items. ... The definition of similarity might vary across applications, but the basic idea is always the same—group the data so that the related elements are placed together

The difference is that classification is based off a previously defined set of classes whereas clustering decides the clusters based on the entire data. . Supervised clustering still clusters based on the entire data and thus would be clustering rather than classification.


Four common unsupervised tasks included clustering, visualization, dimensionality reduction , and association rule learning.

In unsupervised learning, there is no training data set and outcomes are unknown. ... Incredible as it seems, unsupervised machine learning is the ability to solve complex problems using just the input data, and the binary on/off logic mechanisms that all computer systems are built on. No reference data at all.   

The goal of unsupervised learning is to create general systems that can be trained with little data. It models the underlying structure or distribution in the data in order to learn more about the data

The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data.. “Clustering” is the process of grouping similar entities together. The goal of this unsupervised machine learning technique is to find similarities in the data point and group similar data points together. Grouping similar entities together help profile the attributes of different groups

Both Classification and Clustering is used for the categorisation of objects into one or more classes based on the features. They appear to be a similar process as the basic difference is minute. In the case of Classification, there are predefined labels assigned to each input instances according to their properties whereas in clustering those labels are missing.

Classification is used for supervised learning whereas clustering is used for unsupervised learning.

The process of classifying the input instances based on their corresponding class labels is known as classification whereas grouping the instances based on their similarity without the help of class labels is known as clustering.

As Classification have labels so there is need of training and testing dataset for verifying the model created but there is no need for training and testing dataset in clustering.

Classification is more complex as compared to clustering as there are many levels in classification phase whereas only grouping is done in clustering.


Classification examples are Logistic regression, Naive Bayes classifier, Support vector machines etc. Whereas clustering examples are k-means clustering algorithm, Fuzzy c-means clustering algorithm, Gaussian (EM) clustering algorithm etc.




Unsupervised learning is a type of self-organized Hebbian learning that helps find previously unknown patterns in data set without pre-existing labels. It is also known as self-organization and allows modeling probability densities of given inputs. 

The Hebbian Learning Rule is a learning rule that specifies how much the weight of the connection between two units should be increased or decreased in proportion to the product of their activation. .. 

The Hebbian Rule works well as long as all the input patterns are orthogonal or uncorrelated. Two of the main methods used in unsupervised learning are principal component and cluster analysis. 

Hebbian network is a single layer neural network which consists of one input layer with many input units and one output layer with one output unit. This architecture is usually used for pattern classification.

Two of the main methods used in unsupervised learning are principal component and cluster analysis. Cluster analysis is used in unsupervised learning to group, or segment, datasets with shared attributes in order to extrapolate algorithmic relationships. 

Cluster analysis is a branch of machine learning that groups the data that has not been labelled, classified or categorized. Instead of responding to feedback, cluster analysis identifies commonalities in the data and reacts based on the presence or absence of such commonalities in each new piece of data. This approach helps detect anomalous data points that do not fit into either group.






Apriori algorithm is nothing but an algorithm that is used to find out patterns or co-occurrences between items in a data set. Apriori algorithm is called apriori because it uses prior knowledge of frequent item set properties

Apriori algorithm is a classical algorithm in data mining. It is used for mining frequent itemsets and relevant association rules. It is devised to operate on a database containing a lot of transactions, for instance, items brought by customers in a store. 

Given a threshold , the Apriori algorithm identifies the item sets which are subsets of at least transactions in the database. Apriori uses a "bottom up" approach, where frequent subsets are extended one item at a time (a step known as candidate generation), and groups of candidates are tested against the data. 

Apriori is designed to operate on database containing transactions (for example, collections of items bought by customers, or details of a website frequentation)

The algorithm gets terminated when the frequent itemsets cannot be extended further. The advantage is that multiple scans are generated for candidate sets. The disadvantage is that the execution time is more as wasted in producing candidates everytime, it also needs more search space and computational cost is too high.

The primary limitation of this alogirithm is the efficiency, as mentioned above. Apriori algorithm may become really slow especially when there are more candidates to analyze. 1) When the size of the database is very large, the Apriori algorithm will fail. because large database will not fit with memory(RAM)



Unsupervised learning means there is no output variable to guide the learning process (no this or that, no right or wrong) and data is explored by algorithms to find patterns. We only observe the features but have no established measurements of the outcomes since we want to find them out.

As opposed to supervised learning where your existing data is already labeled and you know which behaviour you want to determine in the new data you obtain, unsupervised learning techniques don’t use labelled data and the algorithms are left to themselves to discover structures in the data.

Within the universe of clustering techniques, K-means is probably one of the mostly known and frequently used

The k-means clustering algorithm attempts to split a given anonymous data set (a set containing no information as to class identity) into a fixed number (k) of clusters. Initially k number of so called centroids are chosen. A centroid is a data point (imaginary or real) at the center of a cluster.

K-Means performs division of objects into clusters that share similarities and are dissimilar to the objects belonging to another cluster.

The term ‘K’ is a number. You need to tell the system how many clusters you need to create. For example, K = 2 refers to two clusters. There is a way of finding out what is the best or optimum value of K for a given data.

K-Means clustering is used in a variety of examples or business cases in real life, like:--

Academic performance
Diagnostic systems
Search engines
Wireless sensor networks


In Unsupervised Machine Learning the data given to algorithms is neither labeled nor classified. This means that the ML algorithm is asked to solve the problem with minimal manual training.  These algorithms are given the dataset and left to their own devices, which enables them to create a hidden structure. Hidden structures are essentially patterns of meaning within unlabeled datasets, which the ML algorithm creates for itself to solve the problem statement.

Unsupervised learning algorithms can perform more complex processing tasks than supervised learning systems.


Unlike supervised learning, unsupervised learning works with data sets without historical data. An unsupervised learning algorithm explores collected data to find a structure. 

This works best for transactional data; for instance, it helps identify customer segments and clusters with specific attributes, often used in content personalization. techniques where unsupervised learning is used also include self-organizing maps, nearest-neighbor mapping, singular value decomposition, and k-means clustering. 

In other words: online recommendations, identification of data outliers, and segment text topics are examples of unsupervised learning.


Think of unsupervised learning as a smart kid that learns without any guidance. In this type of Machine Learning, the model is not fed with labeled data, as in the model has no clue that ‘this image is Tom and this is Jerry’, it figures out patterns and the differences between Tom and Jerry on its own by taking in tons of data.


For example, it identifies prominent features of Tom such as pointy ears, bigger size, etc, to understand that this image is of type 1. Similarly, it finds such features in Jerry and knows that this image is of type 2.  Therefore, it classifies the images into two different classes without knowing who Tom is or Jerry is.


Unsupervised machine learning  categorizes entries within datasets by examining similarities or anomalies and then grouping different entries accordingly. For example, an unsupervised learning algorithm might look at many unlabeled images of cats and dogs and would sort images with similar characteristics into different groups without knowing that one contained "cats" and the other "dogs."

Unsupervised Learning can be used to solve Clustering and association problems. One of the famous clustering algorithms is the K-means Clustering algorithm.

K-means is one of the simplest unsupervised learning algorithms that solve the well known clustering problem. 

The procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters) fixed apriori. The main idea is to define K centers, one for each cluster.



With unsupervised learning, the training data is still provided but it would not be labelled. In this model, the algorithm uses the training data to make inferences based on the attributes of the training data by exploring the data to find any patterns or inferences. It forms its logic for describing these patterns and bases its output on this.

Artificial intelligence and machine learning platforms can be designed to combine supervised and unsupervised machine learning. As a result, it can be possible to deliver a weighted score for any activity associated with digital businesses in less than a second.

AI-based fraud prevention is increasingly becoming more dependent on the marriage of supervised and unsupervised machine learning. According to Forbes, artificial intelligence should be “Explainable” and “Understandable.”

Let’s first take the so-called Explainable AI. It’s to do with the fields of data science and AI engineering or the creation and coding of AI algorithms. The goal is to give birth to new algorithms to shed light on intermediate outcomes or their solutions.


As for Understandable AI, the latter brings together the technical expertise of engineers and the design usability knowledge of user interface (UI)/user experience (UX) experts. Besides, it also connects the people-focused design of product developers.

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. ... The technical challenge of explaining AI decisions is sometimes known as the interpretability problem.

They try have an efficient trade-off between accuracy and explainability along with a great human-computer interface which can help translate the model to understandable representation for the end users.

There need to be three steps which should be fulfilled by the system :--
1) Explained the intent behind how the system affects the concerned parties
2) Explain the data sources you use and how you audit outcomes
3) Explain how inputs in a model lead to outputs.

Explainability is motivated due to lacking transparency of the black-box approaches, which do not foster trust and acceptance of AI generally and ML specifically. Rising legal and privacy aspects, e.g. with the new European General Data Protection Regulations will make black-box approaches difficult to use in Business, because they often are not able to explain why a machine decision has been made.

Interpretability is about the extent to which a cause and effect can be observed within a system. Or, to put it another way, it is the extent to which you are able to predict what is going to happen, given a change in input or algorithmic parameters. It’s being able to look at an algorithm and go yep, I can see what’s happening here.

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.  Transparency rarely comes for free and that there are often trade-offs between the accuracy and the explanaibility of a solution..

It is conceivable that a data scientist's version of 'explainable' is indecipherable to most people. Perhaps what people seek is not explainability but understanding. Explainability is a top-down method of speaking at people from the expert's perspective, while understanding seeks to understand how the listener interprets and adjusts the explanation according to the user's needs

By enabling the technology to help humans understand the nature of the algorithmic decision-making and learning as well as allowing humans to apply judgement that is incorporated into the model, trust can be built and models refined in a way that could deliver additional value and shift the perception of AI as a tool that makes decisions that no one can understand.

‘Understandable AI’ is different from ‘explainable AI’. Explainable AI is the domain of data scientists and AI engineers – the individuals who create and code these algorithms.

Understandable AI is the domain of UI/UX designers and product developers in collaboration with AI engineers and data scientists. AI-driven solutions should be developed with similar “user-first” principles in mind. 

Understandable AI combines the technical expertise of engineers with the design usability knowledge of UI/UX experts as well as the people-centric design of product developers.

An understandable AI enables people to be a part of the decision-making process in an AI-driven enterprise.

 Also critical to the Understandable AI process is the integration of non-data scientists to the development and design of AI products, illustrating the imperative of workforce upskilling for the future AI economy.

For example, an algorithm can be used to determine whether a credit card transaction is fraudulent. Given the millions of transactions that occur every day, an algorithm is the obvious solution to this problem. 

There is a risk to incorrectly identifying a transaction as fraudulent (false positive), as you may frustrate and lose a customer. There is also a risk to missing a fraudulent transaction (false negative), as your risk losing a customer’s trust.

 Most fraud can be identified with high certainty. But what do we do about the potentially fraudulent transactions that the AI has low confidence in? Enter understandable AI.

To help businesses and consumers alike better understand AI, Samsung has launched a new initiative called FAIR Future with the aim of involving everyone in AI by making it easier to understand.

In Unsupervised Learning the  model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity.

An unsupervised learning algorithm explores collected data to find a structure. This works best for transactional data; for instance, it helps identify customer segments and clusters with specific attributes, often used in content personalization.

Popular techniques where unsupervised learning is used also include self-organizing maps, nearest-neighbor mapping, singular value decomposition, and k-means clustering. In other words: online recommendations, identification of data outliers, and segment text topics are examples of unsupervised learning.

In unsupervised ML , the algorithm doesn’t have correct answers or any answers at all, it is up to the algorithms discretion to bring together similar data and understand it.

Unsupervised machine learning is good at discovering underlying patterns and data, but is a poor choice for a regression or classification problem. Network anomaly detection is a security problem that fits well in this category

Unsupervised learning happens without the help of a supervisor just like a fish learns to swim by itself. It is an independent learning process.

Unsupervised learning does not rely on trained data sets to predict the outcomes but it uses direct techniques such as clustering and association in order to predict outcomes. Trained data sets mean the input for which the output is known. 

The error will be corrected and this step will be performed iteratively till an acceptable level of performance is achieved.




Unsupervised methods help you to find features which can be useful for categorization.  It is taken place in real time, so all the input data to be analyzed and labeled in the presence of learners.

It is easier to get unlabeled data from a computer than labeled data, which needs manual intervention.





As there are no known output values that can be used to build a logical model between the input and output, some techniques are used to mine data rules, patterns and groups of data with similar types. These groups help the end-users to understand the data better as well as find a meaningful output.

The fed inputs are not in the form of a proper structure just like training data is (in supervised learning). It may contain outliers, noisy data, etc. These inputs are together fed to the system. While training the model, the inputs are organized to form clusters.

When new data is fed to the model, it will predict the outcome as a class label to which the input belongs. If the class label is not present, then a new class will be generated.

While undergoing the process of discovering patterns in the data, the model adjusts its parameters by itself hence it is also called self-organizing. The clusters will be formed by finding out the similarities among the inputs.

Types Of Unsupervised Algorithms--
Clustering Algorithm: The methods of finding the similarities between data items such as the same shape, size, color, price, etc. and grouping them to form a cluster is cluster analysis.
Outlier Detection: In this method, the dataset is the search for any kind of dissimilarities and anomalies in the data. For example, a high-value transaction on credit card is detected by the system for fraud detection.
Association Rule Mining: In this type of mining, it finds out the most frequently occurring itemsets or associations between elements. Associations such as “products often purchased together”, etc.

Autoencoders: The input is compressed into a coded form and is recreated to remove noisy data. This technique is used to improve image, and video quality.



Semi-supervised learning  is a hybridization of supervised and unsupervised techniques. Two of the main methods used in unsupervised learning are principal component and cluster analysis.

Unsupervised or semisupervised approaches reduce the need for large, labeled data sets. Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set, and then test the likelihood of a test instance to be generated by the learnt model.

Semi-supervised learning uses a combination of both labelled and unlabelled data. This solves the problem of having to label large data sets – the programmer can just label and a small subset of the data and let the machine figure the rest out based on this. 

This method is usually used when labelling the data sets is not feasible, either due to large volumes of a lack of skilled resources to label it.

In a typical scenario, the algorithm uses a small amount of labeled data with a large amount of unlabeled data. Semi-supervised type of Machine Learning for classification, regression, and prediction.

Examples of semi-supervised learning are face- and voice-recognition applications. It is primarily used to improve the quality of training sets. For exploit kit identification problems, we can find some known exploit kits to train our model, but there are many variants and unknown kits that can’t be labeled.  Semisupervised learning can address the problem.



Reinforcement machine learning is the science of decision making.

In reinforcement learning, systems are trained by receiving virtual “rewards” or “punishments,” often through a scoring system, essentially learning by trial and error. Through ongoing work, these techniques are evolving.  Here the system is trained through reinforcement; the algorithm receives feedback and the feedback is used to guide users to the best outcomes.

On the one hand it uses a system of feedback and improvement that looks similar to things like supervised learning with gradient descent. On the other hand, datasets are not used in solving reinforcement learning problems.

Reinforcement learning works well in situations where we don’t know whether a specific action is “good” or “bad” ahead of time, but we can measure the outcome of the action and figure that out after the fact. These kinds of problems are surprisingly common, and computers are well suited to learning this kind of pattern. Reinforcement learning is still a learning algorithm


Three major components make up reinforcement learning: the agent, the environment, and the actions. The agent is the learner or decision-maker, the environment includes everything that the agent interacts with, and the actions are what the agent does.

Reinforcement learning occurs when the agent chooses actions that maximize the expected reward over a given time. This is easiest to achieve when the agent is working within a sound policy framework.


The goal of reinforcement learning in this case is to train the dog (agent) to complete a task within an environment, which includes the surroundings of the dog as well as the trainer. First, the trainer issues a command or cue, which the dog observes (observation). The dog then responds by taking an action. 

If the action is close to the desired behavior, the trainer will likely provide a reward, such asa food treat or a toy; otherwise, no reward or a negative reward will be provided. At the beginning of training, the dog will likely take more random actions like rolling over when the command given is “sit,” as it is trying to associate specific observations with actions and rewards. This association, or mapping, between observations and actions is called policy.

From the dog’s perspective, the ideal case would be one in which it would respond correctly to every cue, so that it gets as many treats as possible. So, the whole meaning of reinforcement learning training is to “tune” the dog’s policy so that it learns the desired behaviors that will maximize some reward. 

After training is complete, the dog should be able to observe the owner and take the appropriate action, for example, sitting when commanded to “sit” by using the internal policy it has developed. By this point, treats are welcome but shouldn’t be necessary (theoretically speaking!).


In Reinforcement Learning, there are rewards given to the algorithm upon every correct prediction thus driving the accuracy higher up.

When we look at the core loop of reinforcement learning we have: Make a decision (action), get feedback (scoring), use that feedback to improve the logic. Compare that to supervised learning where we have: Make a decision (prediction), get feedback (error metric), use that feedback to improve the logic.

We have an agent and a reward, with many hurdles in between. The agent is supposed to find the best possible path to reach the reward


A reinforcement learning algorithm, or agent, learns by interacting with its environment. The agent receives rewards by performing correctly and penalties for performing incorrectly. The agent learns without intervention from a human by maximizing its reward and minimizing its penalty

In supervised learning we have a dataset of examples, labeled with the correct outputs. The model uses those examples and labels to find trends and patterns that can be used to predict the response value. 

Everything a supervised learning model “knows” comes from this training dataset. Training is also entirely passive: there is no notion of the model needing to do anything in order to generate or access new training data.

In reinforcement learning that is not the case. With reinforcement learning instead of labeled training data what we get (oftentimes) is a set of rules.  The agent has to explore by choosing an action, transforming the state, and receiving feedback. In other words the learning process requires the agent to be actively doing thing, unlike any of the other learning algorithms we’ve seen so far.

Reinforcement Learning employs the  use of rewarding systems that achieve objectives in order to strengthen (or weaken) specific outcomes. This is frequently used with agent systems.

Reinforcement learning  shows how flexible the mechanism of feedback and improvement can be at generating a logic.

Reinforcement learning is an unsupervised technique allows algorithms to learn tasks simply by trial and error. 

The methodology hearkens to a “carrot and stick” approach: for every attempt an algorithm makes at performing a task, it receives a “reward” (such as a higher score) if the behavior is successful or a “punishment” if it isn’t. With repetition, performance improves, in many cases surpassing human capabilities—so long as the learning environment is representative of the real world.

Reinforcement learning can  help AI transcend the natural and social limitations of human labeling by developing previously unimagined solutions and strategies that even seasoned practitioners might never have considered.

In reinforcement learning, the aim is to weight the network (devise a policy) to perform actions that minimize long-term (expected cumulative) cost.  At each point in time the agent performs an action and the environment generates an observation and an instantaneous cost, according to some (usually unknown) rules. 

The rules and the long-term cost usually only can be estimated. At any juncture, the agent decides whether to explore new actions to uncover their costs or to exploit prior learning to proceed more quickly.

Multi-Agent Reinforcement Learning(MARL) is the deep learning discipline that focuses on models that include multiple agents that learn by dynamically interacting with their environment

There are four types of reinforcement: positive, negative, punishment, and extinction. Positive reinforcement is the delivery of a reinforcer to increase appropriate behaviors whereas negative reinforcement is the removal of an aversive event or condition, which also increases appropriate behavior

Reinforcement learning allows machines and software agents to automatically determine the ideal behaviour within a specific context, in order to maximize its performance.

Extinction is a procedure in which reinforcement of a previously rewarded behavior is stopped. Extinction of positively reinforced behaviors does not allow the learner to access positive reinforcers after a problem behavior.

In reinforcement learning, instead of youtube videos you have an agent which interacts with an environment (an animal in an ecosystem, a robot in a house, an AI player in a videogame, etc.) during an extended time, and we design the problem so that doing some specific actions in some specific states yields a numerical value we call reward. 

We set this value to be positive for states and actions we want the system to do, and negative (in which case we sometimes call it punishment) for states and actions we want it to avoid. What you are searching for in your optimization problem is then a function to tell the agent what to do in a given situation (a policy) that maximizes the long term reward, that is, the sum of the reward it gets over a long period of time.

“Punishment” is just negative terms in that sum; since the goal of your optimization algorithm is to maximize it the solution will avoid them (or make sensible compromises - some good solutions may require to take a bit of punishment before reaching higher reward). 

There is no “understanding” of any kind. You use an optimization algorithm to find a function that maximizes a measurement which is the sum of many terms which can have various positive or negative values; the solution will be a function that tends to favor high, positive terms and avoid negative ones. 

It’s a simple consequence of the way you described the problem and the mathematical and computational machinery you are using to solve it.

Reinforcement learning is a goal-oriented learning approach inspired by behavioral psychology that allows you to take inputs from the environment. As such, reinforcement learning implies that the agent will get better as it is in use: it learns while in usage. 

When we humans learn from our mistakes, we are actually functioning through a reinforcement learning approach. There is no actual training phase; instead the agent learns through trial-and-error using a predetermined reward function that sends back the input about how optimal a specific action it took turned out to be.

 Technically, reinforcement learning does not need to be fed with data, but instead generates its own as it goes.



Reinforcement learning that  requires no mathematical model is Q-learning. Q-learning is reinforcement learning technique which tries to maximize rewards. These rewards are for example  inning a game or when learning to walk any forward movement is a reward.

Basically doing well at the task you are performing is a reward and a Q-learning algorithm tries to maximize these rewards and thus in turn maximise performance. Of course the Q-learning algorithm is just one algorithm among many and has pros and cons in different situations, but the point is that there are today computer programs capable of this ability to at least some extent.



Q-learning is a model-free reinforcement learning algorithm. The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances. It does not require a model (hence the connotation "model-free") of the environment, and it can handle problems with stochastic transitions and rewards, without requiring adaptations.

Q-learning is a model-free reinforcement learning algorithm. The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances. It does not require a model (hence the connotation "model-free") of the environment, and it can handle problems with stochastic transitions and rewards, without requiring adaptations.



Again,Reinforcement learning, is a type of dynamic programming that trains algorithms using a system of reward and punishment. A reinforcement learning algorithm, or agent, learns by interacting with its environment

Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. .



Some of the most popular reinforcement learning training algorithms rely on deep neural network policies. The biggest advantage of neural networks is that they can encode really complex behaviors, which opens up the use of reinforcement learning in applications that are otherwise intractable or very challenging to tackle with alternative methods, including traditional algorithms.

A trained deep neural network policy is often treated as a “blackbox,” meaning that the internal structure of the neural network is so complex, often consisting of millions of parameters, that it is almost impossible to understand, explain, and evaluate the decisions taken by the network .

This makes it hard to establish formal performance guarantees with neural network policies. Think of it this way: Even if you train your pet, there will still be occasions when your commands will go unnoticed.

Reinforcement Learning is a multi-decision process. Unlike the “one instance, one prediction” model of supervised learning, an RL agent's target is to maximize the cumulative rewards of a series of decisions — not simply the immediate reward from one decision..


Unsupervised learning is where you only have input data (X) and no corresponding output variables. The unsupervised learning in convolutional neural networks is employed via autoencoders. The autoencoder structure consists of two layers, an encoding and a decoding layer.







Reinforcement learning is dependent on the algorithms environment. The algorithm learns by interacting with it the data sets it has access to, and through a trial and error process tries to discover ‘rewards’ and ‘penalties’ that are set by the programmer. 

The algorithm tends to move towards maximising these rewards, which in turn provide the desired output. It’s called reinforcement learning because the algorithm receives reinforcement that it is on the right path based on the rewards that it encounters. The reward feedback helps the system model its future behaviour.


Reinforcement learning differs from supervised learning in that labelled input/output pairs need not be presented, and sub-optimal actions need not be explicitly corrected.  Instead the focus is finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge) .  

What makes deep learning and reinforcement learning functions interesting is they enable a computer to develop rules on its own to solve problems. Deep learning is essentially an autonomous, self-teaching system in which you use existing data to train algorithms to find patterns and then use that to make predictions about new data. 

Reinforcement learning is an autonomous, self-teaching system that essentially learns by trial and error. It performs actions with the aim of maximizing rewards, or in other words, it is learning by doing in order to achieve the best outcomes. when computers use reinforcement learning, they try different actions, learn from the feedback whether that action delivered a better result, and then reinforce the actions that worked, i.e. reworking and modifying its algorithms autonomously over many iterations until it makes decisions that deliver the best result.

A good example of using reinforcement learning is a robot learning how to walk. The robot first tries a large step forward and falls. The outcome of a fall with that big step is a data point the reinforcement learning system responds to. Since the feedback was negative, a fall, the system adjusts the action to try a smaller step. The robot is able to move forward. This is an example of reinforcement learning in action. 

Deep learning and reinforcement learning are both systems that learn autonomously. The difference between them is that deep learning is learning from a training set and then applying that learning to a new data set, while reinforcement learning is dynamically learning by adjusting actions based in continuous feedback to maximize a reward.

An example of reinforcement learning is a generative adversarial network (GAN)



Reinforcement learning is defined by characterising a learning problem and not by characterising learning methods. Any method which is well suited to solve the problem, we consider it to be the reinforcement learning method.  

Reinforcement learning assumes that a software agent i.e. a robot, or a computer program or a bot, connect with a dynamic environment to attain a definite goal. This technique selects the action that would give expected output efficiently and rapidly.



Ensemble machine learning is a  technique that combines several base models in order to produce one optimal predictive model.

An ensemble method is a technique that combines the predictions from multiple machine learning algorithms together to make more accurate predictions than any individual model

Voting and averaging are two of the easiest ensemble methods. ... Voting is used for classification and averaging is used for regression. In both methods, the first step is to create multiple classification /regression models using some training dataset.

It is done  to decrease variance (bagging), bias (boosting), or improve predictions (stacking). The main principle behind the ensemble model is that a group of weak learners come together to form a strong learner, thus increasing the accuracy of the model.

When we try to predict the target variable using any machine learning technique, the main causes of difference in actual and predicted values are noise, variance, and bias. Ensemble helps to reduce these factors (except noise, which is irreducible error).

Another way to think about Ensemble learning is Fable of blind men of Hindoostan and the elephant. All of the blind men had their own description of the elephant. Even though each of the description was true, it would have been better to come together and discuss their undertanding before coming to final conclusion. 

This story perfectly describes the Ensemble learning method.







Using several models to predict the final result actually reduces the likelihood of giving weightage to decisions made by a poor models.


 Unlike a statistical ensemble in statistical mechanics, which is usually infinite, a machine learning ensemble consists of only a concrete finite set of alternative models, but typically allows for much more flexible structure to exist among those alternatives.

An ensemble contains a number of hypothesis or learners which are usually generated from training data with the help of a base learning algorithm.. 

Ensemble models in machine learning combine the decisions from multiple models to improve the overall performance. The main causes of error in learning models are due to noise, bias and variance.

Ensemble methods help to minimize these factors. These methods are designed to improve the stability and the accuracy of Machine Learning algorithms.

The more diverse these base learners are, the more powerful will the final model be. In any machine learning model, the generalization error is given by the sum of squares of bias + variance + irreducible error. Irreducible errors are something that is beyond us! We cannot reduce them. 

However, by using ensemble techniques, we can reduce the bias and variance of a model. This reduces the overall generalization error.

The bias-variance trade-off is the most important benchmark that differentiates a robust model from an inferior one. In machine learning, the models which have a high bias tend to have a lower variance and vice-versa.

1. Bias: Bias is an error which arises due to false assumptions made in the learning phase of a model. A high bias can cause a learning algorithm to skip important information and correlations between the independent variables and the class labels, thereby under-fitting the model.

Bias is the difference between the average prediction of our model and the correct value which we are trying to predict. Model with high bias pays very little attention to the training data and oversimplifies the model. It always leads to high error on training and test data.

2. Variance: Variance tells us how sensitive a model is to small changes in the training data. That is by how much the model changes. High variance in a model will make it prone to random noise present in the dataset thereby over-fitting the model.

Variance is the variability of model prediction for a given data point or a value which tells us spread of our data. Model with high variance pays a lot of attention to training data and does not generalize on the data which it hasn’t seen before. As a result, such models perform very well on training data but has high error rates on test data.





In supervised learning, underfitting happens when a model unable to capture the underlying pattern of the data. These models usually have high bias and low variance. It happens when we have very less amount of data to build an accurate model or when we try to build a linear model with a nonlinear data. Also, these kind of models are very simple to capture the complex patterns in data like Linear and logistic regression.

In supervised learning, overfitting happens when our model captures the noise along with the underlying pattern in data. It happens when we train our model a lot over noisy dataset. These models have low bias and high variance. These models are very complex like Decision trees which are prone to overfitting.




You can think of ensemble learning analogous to the board of directors in a company, where the final decision is taken by the CEO.   Instead of taking a decision all by himself, the CEO takes inputs ( brainstorming ) from each of the board members before arriving at a final conclusion.

The CEO, in this case, is the final model and the board members are the base learners which provide independent inputs to the CEO. This drastically reduces the chance of committing an error when the CEO makes his final decision.

We use this approach regularly in our daily lives as well — for example, we ask for the opinions of different experts before arriving at conclusions, we read different product reviews before buying a product, a panel of judges consult among them to declare a winner. 

In each of the above scenarios what we are actually trying to achieve is to minimize the likelihood of an unfortunate decision made by one person (in our case a poor model).


The goal of any machine learning problem is to find a single model that will best predict our wanted outcome. Rather than making one model and hoping this model is the best/most accurate predictor we can make, ensemble methods take a myriad of models into account, and average those models to produce one final model.


Ensemble  learning is used to combine the predictions from multiple separate models. It reduces the model complexity and reduces the errors of each model by taking the strengths of multiple models. Out of multiple ensembling methods, two of the most commonly used are Bagging and Boosting.

Typically, ensemble learning can be categorized into four categories:--

1. Bagging: Bagging is mostly used to reduce the variance in a model. A simple example of bagging is the Random Forest algorithm.

2. Boosting: Boosting is mostly used to reduce the bias in a model. Examples of boosting algorithms are Ada-Boost, XGBoost, Gradient Boosted Decision Trees etc.

3. Stacking: Stacking is mostly used to increase the prediction accuracy of a model. 

4. Cascading: This class of models are very very accurate. Cascading is mostly used in scenarios where you cannot afford to make a mistake. For example, a cascading technique is mostly used to detect fraudulent credit card transactions, or maybe when you want to be absolutely sure that you don’t have cancer.

Decision Trees are not the only form of ensemble methods, just the most popular and relevant in DataScience today.  Voting and averaging are two of the easiest ensemble methods. They are both easy to understand and implement. Voting is used for classification and averaging is used for regression.

Ensemble methods is a machine learning technique that combines several base models in order to produce one optimal predictive model.   

Ensemble Methods allow us to take a sample of Decision Trees into account, calculate which features to use or questions to ask at each split, and make a final predictor based on the aggregated results of the sampled Decision Trees.

A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements.



Decision tree is the most powerful and popular tool for classification and prediction. A Decision tree is a flowchart like tree structure, where each internal node denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (terminal node) holds a class label.



Strengths and Weakness of Decision Tree approach

The strengths of decision tree methods are:--

Decision trees are able to generate understandable rules.
Decision trees perform classification without requiring much computation.
Decision trees are able to handle both continuous and categorical variables.
Decision trees provide a clear indication of which fields are most important for prediction or classification.



The weaknesses of decision tree methods :--

Decision trees are less appropriate for estimation tasks where the goal is to predict the value of a continuous attribute.
Decision trees are prone to errors in classification problems with many class and relatively small number of training examples.

Decision tree can be computationally expensive to train. The process of growing a decision tree is computationally expensive. At each node, each candidate splitting field must be sorted before its best split can be found. In some algorithms, combinations of fields are used and a search must be made for optimal combining weights. Pruning algorithms can also be expensive since many candidate sub-trees must be formed and compared.

Ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone.

Machine learning (ML) refers to algorithms that autonomously improve their performance, without   humans directly encoding their expertise.  Usually, ML algorithms improve by training themselves  , hence 'data-driven' AI.  

The major recent advances in this field are not due to major breakthroughs in the techniques per se but, rather, through massive increases in the availability of  data. In this sense, the tremendous growth of data-driven AI is, itself, data-driven. Usually, ML  algorithms find their own ways of identifying patterns, and apply what they learn to make statements about data.  

Different approaches to ML are suited to different tasks and situations, and have different implications. . Machine learning falls under the umbrella of AI, that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed.


In the field of machine learning there is an incredibly important problem is known as the bias-variance dilemma.  It’s entirely possible to have state-of-the-art algorithms, the fastest computers, and the most recent GPUs, but if your model overfits or underfits to the training data, its predictive powers are going to be terrible no matter how much money or technology you throw at it.



















  1. I MUST EXPOUND ON WHAT I WROTE BELOW--

    #########################################

    KARVA CHAUTH DOES NOT WEAKEN WOMEN, RATHER IT IS A POWERFUL TOOL TO EMPOWER MARRIED WOMEN ...

    NO WIFE WILL FAST ON KARVA CHAUTH FOR HER HUSBAND IF HE IS NOT WORTH IT ( APOLOGIES TO AISHWARYA RAI ).. NO WONDER SHOBHAA DE DOES NOT FAST FOR HER SECOND HUSBAND ..

    THERE IS SOMETHING CALLED HINDU HUSBANDS HAVING STRENGTH AND HONOUR WHEN IT COMES TO PROTECTING HIS WIFE AND HER INTERESTS, AND THUS SECURING HER LOVE , GRATITUDE AND RESPECT...

    #############################

    I MUST MAKE A STATEMENT OF FACT--

    LET GOD STRIKE ME AND MY TWO SONS DEAD IF I LIE BELOW..

    I HAVE BEEN MARRIED FOR 36 YEARS ..

    MY WIFE SAILED ON SHIPS I COMMANDED .. I MARRIED AFTER GETTING COMMAND

    WHILE ON THE SHIP MY WIFE FOLLOWED PROTOCOL AS BEFITTING A CAPTAINs WIFE.. WHO MUST SET AN EXAMPLE.. WE HAD STEWARD SERVICE..

    WHEN MY WIFE DID NOT SAIL WITH ME..

    ( MIND YOU AFTER 30 YEARS AS CAPTAIN , EVERYTHING FREE ON BOARD , EARNING IN DOLLARS AND SPENDING IN RUPEES , I AM NOT EXACTLY A POOR FELLOW.. WE HAVE SERVANTS )

    1) EVERY CUP OF TEA I HAD WAS MADE BY MY WIFE..

    2) MY WIFE HAS NEVER EATEN BEFORE ME OR WITH ME .. ( UNLESS WE ATE AT A RESTAURANT)..

    3) MY WIFE HAS IRONED EVERY SHIRT AND PANT I WORE , AFTER WASHING IT HERSELF..

    4) DESPITE HAVING COOKS -- MY WIFE COOKED FOR HER HUSBAND AND HER CHILDREN.. THE COOK ONLY DID THE PRELIMINARIES

    5) MY WIFE TAUGHT HER TWO SONS .. MY ELDER SON IS A GENIUS , HE NEEDED NO HELP AFTER KINDERGARTEN..

    MY YOUNGER SON IS ARTISTICALLY ORIENTED AND HATES NUMBERS ..THOUGH MY WIFE IS A ECONOMICS GRADUATE ( PODDAR COLLEGE MUMBAI ), SHE BOUGHT GUIDES LEARNT MATH/ PHYSICS/ CHEMISTRY AND TAUGHT HIM.. HE NEVER FAILED AND GOT GOOD GRADES.. YOU MUST KNOW THAT THIS IS A GREAT SACRIFICE..

    INDIAN CULTURE IS ENVIED BY WESTERN TOURISTS.. THEY COME TO INDIA AND WATCH INDIAN FAMILIES IN PARKS , BEACHES AND PUBLIC SPACES.. THEY WATCH THE HARMONY AND COHESION.. THIS IS A TAKE HOME LESSON.. THEY GLEAN THE DIFFERENCE BETWEEN LOVE AND LUST..

    IF I WANT TO MAKE MY WIFE CRY, ALL I NEED IS TO ENTER THE KITCHEN WHEN MY WIFE IS SLEEPING AND MAKE MY OWN CUP OF TEA..

    http://ajitvadakayil.blogspot.com/2013/12/ipc-section-377-love-lust-perversion.html

    MY WIFE IS A VERY HAPPY AND CONTENDED WOMAN.. HER SONS ADORE HER.. SHE DOES UNIVERSAL GOOD BY REIKI..

    THERE IS NOT A SINGLE DAY MY ELDER SON DOES NOT CALL HER FROM ABROAD.. THEY TALK FOR A LONG TIME.. I HAVE NEVER SEEN THEM RUNNING OUT OF CONVERSATION TILL TODAY..

    AS I GET OLD ( 64 YEARS ) - MY WIFE MOTHERS ME.. SHE CLUCKS LIKE MOTHER HEN.. I LOVE IT.. I ALLOW HER TO TUCK ME TO BED..

    MY SONS DO NOT SMOKE OR DRINK.. ( MY ELDER SON SOCIALLY DRINKS WHEN WARRANTED ).. MY SONS WONT BE CAUGHT DEAD IN TATTERED JEANS ..

    http://ajitvadakayil.blogspot.com/2013/11/nagging-unhappiness-at-home-death-of.html

    http://ajitvadakayil.blogspot.com/2010/05/marriage-sans-fights-capt-ajit.html

    MY SONS ARE PROUD OF THEIR DAD.. MY WIFE HATES WOMENs LIB..

    WHAT RIGHT HAS THIS COMMIE BITCH ROHINI CHATTERJEE TO WRITE A LYING POST ABOUT MY WIFE?

    .. HER GRANDFATHER WAS SOMNATH CHATTERJEE, WHO WAS PARLIAMENT SPEAKER FOR 5 YEARS -- A TEN 5 YEAR TERM MP .

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

    HAVING SAILED AROUND THE WORLD FOR 40 YEARS , I KNOW WE ARE LUCKY TO HAVE A PRICELESS CUTURE..

    NURTURE IT !

    MY ELDER SON CAME TO INDIA A FEW DAYS AGO WITH HIS WIFE .. HE SAW THE FIRST T20 BANGLADESH MATCH IN DELHI WITH HIS WIFE .. HE FLEW IN TO ATTEND HIS CLASSMATEs WEDDING.. HE STAYED IN A 7 STAR DELHI HOTEL FOR A WEEK AND SAW TAJ MAHAL THROUGH THE SMOG .

    capt ajit vadakayil
    ..
BELOW" MY SON AND WIFE AT LEELA



please apne bachchon ki kasam mat khayen . thats not good . may god shower all happiness on you and your family !
take care always as you you know so much of the untold history .
  1. THERE ARE BIMBETTES WHO THINK THAT MY WIFE IS A WOMAN WITHOUT SPIRIT , A DEHAATI BEHENJI.. IN REALITY SHE IS MORE WELL READ THAN ME..

    WELL-- MY WIFE CAN KICK THEIR EMPTY HEADS AND PURULENT TWATS .

    IN THE POST BELOW YOU CAN SEE HER BEATING 25 YOUNG SAILORS WHO ARE TALLER THAN HER IN THE "HIGH KICK" CONTEST...

    http://ajitvadakayil.blogspot.com/2012/06/equator-crossing-ceremony-at-sea-capt.html

    THIS WAS A CEREMONY ON THE WAY TO CURACAO..

    capt ajit vadakayil
    ..

BELOW:  THIS IS A CORNY DIALOGUE FROM MALAYALAM MOVIE CHEMMEEN.    

IT IS ABOUT FORBIDDEN LOVE BETWEEN A HINDU MARRIED WOMAN AND A UNMARRIED MUSLIM MAN.  

HE MOANS "KARUTHAMMA IF YOU LEAVE ME , I WILL DIE OF A BROKEN THROAT "



WHEN I WAS IN 6TH STANDARD IN SCHOOL, WE HAD A CORNY CLASSMATE NAMED SARVATHAMANAN WHO GAVE THIS DIALOGUE WELL WITH ALL ITS EMOTIONS AND EVEN TEARS .   

WE USED TO ROLL IN LAUGHTER 

MADHUs SISTERS DAUGHTER IS  A HEADMISTRESS OF AN ELITE BANGALORE SCHOOL..   WHEN I MET HER I REPEATED THIS DIALOGUE.. SHE CORRECTED ME WHEN I MADE A WEE MISTAKE..






BELOW:  THIS POST IS CONTINUED TO PART 4 BELOW--

https://ajitvadakayil.blogspot.com/2019/11/what-artificial-intelligence-cannot-do.html








CAPT AJIT VADAKAYIL
..

196 comments:

  1. Good to see you back Captain After such a long pause.

    ReplyDelete
  2. It is very good to see you Capt. Ajit. Happy Diwali from me Capt. Okay, I am done for now.

    ReplyDelete
  3. Happy Diwali Captain Ajit, I hope you got that as well mate.

    ReplyDelete
  4. happy deepavali to the captain of subjective organic intelligence

    ReplyDelete
  5. Dear Captain,
    Whew!!

    Welcome back!!

    Regards

    Sukanya
    &
    Srinivas

    ReplyDelete
  6. God bless you captain, long live.

    ReplyDelete
  7. Realy I got worried a lot , daily I used to check the blog... after seeing ur new blog I got relief... on some of your relasiation of facts in the blog still some of them my mind not accepted, still I realy thank God I don't want to miss you... Please before taking such long gap inform in advance.

    ReplyDelete
  8. It feels so great to see your posts.
    Alas you are here.
    I can not express my happiness now.
    Unless I read your word, my days are uncomplete.
    You are the true leader and your posts on sanatan dharma is truly a great releif and so is your every word
    God, your posts are more addicted than cocaine.
    God bless you caption.
    Accept my reiki energy.

    ReplyDelete
  9. https://twitter.com/shree1082002/status/1195046668992839681

    ReplyDelete
  10. captain, regarding moravec's paradox

    you once said that your youngest son's ability to take a look at a person and draw a potrait of someone is real iq

    is it because he can unconsciously do it?

    ReplyDelete
  11. Welcome back Ajitji.
    Nothing more to say..

    ReplyDelete
  12. Welcome back captain...
    Had a sigh of relief when saw your new post,it was like a fixed ritual to check for update every other day...
    We get a lot to learn and understand how to analyse things from your blogs....
    Please don't ever stop blogging.
    Thank you for coming back.
    Wish for your good health and long life.

    Gratitude
    Aditya

    ReplyDelete
  13. capt, is it also the reason why adi shankara made a rule that a namboothiri must be attached to the 4 mutts that he created, the reason being the unconscious ability of the namboothiris with vedas due to their ancestors experience in oral transmission of vedas

    ReplyDelete

  14. Soujanya VNovember 14, 2019 at 10:15 PM
    Hi Sir- How are you doing?, I assumed that you took a break and were visiting your son in the USA.

    ReplyDelete

  15. KnoxNovember 14, 2019 at 10:31 PM
    Welcome back Captain :D

    ReplyDelete

  16. RN MurthyNovember 14, 2019 at 10:51 PM
    Dear Capt Ajit sir,

    Watching Arnab's show on #FaithVsEquality, with 6 women against 1 Rahul Easwar...what hypocrisy !!!
    Observing Padma Pillai and Shilpa Nair what will they say...Shilpa said beautifully, we respect SC verdict, but we do not accept SC judgement as it doesn't accept the faith of lakhs of women who came out in Kerala to ban women of ages 10-50 not to visit Sabarimala...SC has made a kichdi of all cases to make it as equality for women in Sabarimala, Haji Ali, Parsi temples...what a farce to put SC on a channe ke jhaad...and make judges God...Bhadralok uplift was visible.
    Padma says it's given both sides something and for us, how much Court should interpret Art 25 with deeper thinking in philosophy....Kasturi pointed very clearly that women are allowed in all Ayyappa temples except only Sabarimala, where even Males have restrictions in terms of clothing, follow certain customs/traditions....so Padma's Art 25 quote is quite shallow...more of neutral and pushing towards equality rather than faith.

    ReplyDelete

  17. AlokeNovember 14, 2019 at 10:57 PM
    With lots of love and support from the eastern frontiers of India

    ReplyDelete

  18. ashuuuNovember 14, 2019 at 11:14 PM
    captain i was really worried i thought you have been threatened or your family is in danger since you never know how invisible enemies you have created,but deep in my heart i checked almost once every two days if you are posting i knew you would post some day.i remember GAME OF DEATH OF MOVIE WHERE Bruce lee stages his fake death just to know his real enemies and then attacks them one by one

    ReplyDelete
    Replies
    1. thanks captain hope your legacy would be alive like bruce leenfor ever

      Delete

  19. Praveen BajiNovember 14, 2019 at 11:14 PM
    Realy I got worried a lot , daily I used to check the blog... after seeing ur new blog I got relief... on some of your relasiation of facts in the blog still some of them my mind not accepted, still I realy thank God I don't want to miss you... Please before taking such long gap inform in advance.

    ReplyDelete

  20. Proud_SanataniNovember 14, 2019 at 11:25 PM
    Thank God you are back captain. we were worried why you were away for so long. Good to see the wheel of dharma going.
    "The Lion takes TWO STEPS back not because it is scared but to create a momentum to ATTACK."

    ReplyDelete
  21. nice to see you safe n sound after many days .i thought govt. has blocked your posts .
    yes , break is very important sometimes . t c .

    ReplyDelete
  22. Dear Ajit Sir,

    Just curious!

    Kindly do share what all you did for these many days being away from blogging.

    Only posting on Times of India or anything else?

    One reader has put in the comment that if you could receive the telepathy of we missing you.

    Did We really reach your 6th Sense?

    It would be interesting to know the secret that what happens to the person to whom anyone or group of people miss

    No one can give an apt answer other than you

    《Do share》

    Every time you took break you informed us before hand.
    For these many years it never happened the ones it happened last month, your undeclared missing away from the scene & we missing you 24×7.
    It was literally painful.

    Only one question was hitting the emotion "Captain कब आएँगे"

    ReplyDelete
  23. https://timesofindia.indiatimes.com/india/260-writers-approach-pm-modi-on-aatish-taseer/articleshow/72063455.cms

    PROFILE ALL THE DESH DROHI BASTARDS IN HIS LIST..

    ReplyDelete
    Replies
    1. Dear Captain,

      Here is the complete list

      https://pen.org/indian-government-review-taseer/

      Delete
  24. wow.. you're in a very good shape Captain
    Healthy, Strong.. Hulk..

    Inspiring

    Touch wood

    God Bless You

    Om Namah Shivaya

    ReplyDelete
  25. https://www.theguardian.com/technology/2019/nov/12/google-medical-data-project-nightingale-secret-transfer-us-health-information

    ReplyDelete
  26. Hi sir,

    And then the real Boss arrived.. All hail.. Happy that you are doing good and back.. :) Thanks to the old gods.. May Lord Arunachaleshwara bless you and your family sir.. Im happy now.. :)

    Thanks
    Maheshwar Singh

    ReplyDelete
  27. Dear Sir
    Please reveal in detail about the international influencial lady MADI SHARMA , who arranged the European countries Parliament members unofficial meet with prime minister and trip to Kashmir.

    ReplyDelete
  28. Dear Captain,

    Extremely happy to see you back. i did search you name on google to see if any news was posted about you going offline. take care.

    Kind Regards,
    SB

    ReplyDelete
  29. Captain, Relieved, good to see you back. Most of your readers care for you, please say a word before you take some time off from blogging. A Humble request.

    Pranaam..

    ReplyDelete
  30. Hi Captain,

    Relieved to see you back.

    Regards,
    Balamurali Shivaram

    ReplyDelete
  31. Welcome back Captain.

    I thought you had taken vacation and would have probably traveled to US to spend time with your Son and daughter in law.

    ReplyDelete
  32. Dear Captain,

    Greetings.
    Great relief to see you back.

    Initially thought you would be on vacation for 2 weeks.. Then for 3 & 4th week, was a bit anxious; then thought you may have a very good news with your grand son / daughter in your family and was keenly following as it was approaching a month on 13th..

    My wife was confident that you'll be back soon; Our prayers for you, your family were always.

    Following you for all these years, was aware of the John Galt back swing you do for your detractors.

    Happy to see you come in full force, after a well deserved break.

    Grace & Peace,
    JTA

    ReplyDelete
  33. https://pen.org/indian-government-review-taseer/

    WHY IS AATISH TAHEER A BLUE EYED BOY WITH SO MANY CRYPTO JEWS ( LIKE CHRISTIANE AMANPOUR) IN THE PAYROLL OF THE KOSHER DEEP STATE SPONSORING HIM ?

    http://ajitvadakayil.blogspot.com/2017/04/the-most-evil-journalist-capt-ajit.html

    BECAUSE AATISH TAHEERs BIOLOGICAL PAKISTANI FATHER SALMAN TASEER IS A JEW, WHO HAD SEX WITH HIS UNMARRIED MOTHER TAVLEEN SINGH..

    WHY DO SO MANY INDIAN LUTYENS JOURNALISTS ( AND AMAAN KI AASHA BOLLYWOOD BIMBETTES ) RUN TO PAKISTAN FOR LITERARY FESTS ?

    BECAUSE THEY LOVE HARD ANAL SEX PROVIDED BY THE HANDSOME PAKISTANI CRYPTO JEW MEN ( SOME ARE ISI AGENTS )..

    THEY DONT GET ANAL SEX FROM THEIR HUSBANDS/ BOYFRIENDS IN INDIA.. MY PAKISTANI OFFICERS HAVE TOLD ME WHO THESE SHAMELESS INDIAN WOMEN ARE..

    SAME WITH SOME FAT UGLY JNU WOMEN COMMIE PROFESSORS IN JNU , WHO NEED THIS FROM HANDSOME KASHMIRI ISLAMIC SEPARATIST STUDENTS..THEY ARE ADDICTED..

    THERE ARE NW PASHTUN AREAS IN PAKISTAN WERE VAGINAL SEX, IS ONLY TO PRODUCE A CHILD.. THE REST OF THE TIME WOMEN GET ONLY ANAL SEX..

    THEIR ASSHOLE ORIFICE GETS TORN AGAIN AND AGAIN AND NON- ELASTIC SCAR TISSUE BUILDS UP, LEAVING A GAPING BUTTON HOLE ANUS ...

    IMRAN KHAN IS A JEW.

    MALALA YOUSAFZAI IS A JEWESS..

    THE MAYOR OF LONDON SADIQ KHAN WHO KEEPS HAVING HEAD ON COLLISIONS WITH DONALD TRUMP GHADI GHADI IS A JEW..

    MIND YOU SADIQ KHAN BEAT HEAVY WEIGHT ZAC GOLDSMITH TO THE MAYORs POST..

    JEW IMRAN KHAN HAD MARRIED A JEWESS JEMIMA GOLDSMITH ( GERMAN JEW LINEAGE GOLDSCHMIDT AND ROTHSCHILD PARTNER ) --. AND TODAY HE PEPPERS EVERY SENTENCE WITH 10 INSHA ALLAHS.

    ZAC GOLDSMITH IS SON OF BILLIONAIRE BUSINESSMAN AND FINANCIER SIR JAMES GOLDSMITH, A PARTNER OF JEW ROTHSCHILD.

    ZAC GOLDSMITH, DIVORCED HIS WIFE AFTER HE WAS ELECTED BRITISH MP AND WAS LIVING WITH ALICE ROTHSCHILD.

    DIANA LOOKS LIKE SIR JAMES GOLDSMITH .

    SIR JAMES GOLDSMITH'S OTHER THREE CHILDREN, ZAK, BEN AND JEMIMA GOLDSMITH.

    ZAC GOLDSMITH IS A SPLITTING IMAGE OF JEWESS JEMIMA— EX-WIFE OF PAKISTANI CRICKETER JEW IMRAN KHAN !

    PRINCE WILLIAM'S WIFE KATE MIDDLETON IS JEWISH WITH HER MOTHER'S NAME BEING CAROLE GOLDSMITH.

    JEW DOCTOR HASNAT KHAN WHO WAS SCREWING JEWESS PRINCESS DIANA IS A COUSIN OF JEW IMRAN KHAN , THE CRICKET PLAYER..

    THE BLOGPOST BELOW HAS BEEN WRITTEN FOR AJIT DOVAL AND MODI. TO UNDERSTAND WORLD INTRIGUE.. TO AVOID FUTURE WARS UNDERSTAND TRUE HISTORY..

    https://ajitvadakayil.blogspot.com/2019/11/paradox-redemption-victory-in-defeat.html

    capt ajit vadakayil
    ..

    ReplyDelete
    Replies
    1. PUT ABOVE COMMENT IN WEBSITES OF--
      MODI
      PMO
      DONALD TRUMP
      PUTIN
      INDIAN AMBASSADORS TO PAKISTAN/ USA/ RUSSIA
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      RAM MADHAV
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      UDDHAV THACKREY
      VIVEK OBEROI

      Delete
    2. LIST CONTINUED--

      GAUTAM GAMBHIR
      ASHOK PANDIT
      ANUPAM KHER
      KANGANA RANAUT
      VIVEK AGNIHOTRI
      KIRON KHER
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      WEBSITES OF DESH BHAKT LEADERS
      SPREAD OF SOCIAL MEDIA

      Delete
    3. Your Registration Number is : PMOPG/E/2019/0659548

      Delete
  34. Welcome back sir.. missed you a lot

    ReplyDelete
  35. https://timesofindia.indiatimes.com/india/ldf-govt-wont-give-security-to-women-visiting-shrine/articleshow/72063437.cms

    PINARAYI VIJAYAN IS NOT AN IDIOT..

    HE WILL SCREW MODI WITHOUT GREASE..

    PINARAYI VIJAYAN KNOWS THE SLIMY GAME OF MODI/ BJP..

    AGAIN GET PILGRIMS AGITATED .. AGAIN ALLOW KERALA CM TO THROWN HUNDREDS INTO JAIL..

    FORCE FRUSTRATED COMMIE HINDUS TO VOTE FOR BJP..

    THIS WONT HAPPEN..

    PINARAYI VIJAYAN NOW ACCEPTS THAT COMMIE VOTERS ARE NOT ATHEISTS BUT IMPOVERISHED GOD FEARING PEOPLE WHOSE ONLY SOLACE IS THEIR LORD AYYAPPA.. HE WENT TO GURUVAYUR TEMPLE TO DELIVER A MESSAGE ..

    POOR MODI/ POOR AMIT SHAH/ POOR BJP/ POOR RSS.

    http://ajitvadakayil.blogspot.com/2019/06/guruvayur-temple-idol-carved-out-of.html

    ReplyDelete
    Replies
    1. Yes his party said women will need a court order to visit the shrine under police protection.

      He knows which side the bread is buttered.

      Supporting Vaman jayanti was a mistake. Until BJP learns to respect and understand King Mahabali they will be nobodies.

      But politicians have put chameleons to shame in the past. So anything is possible.

      Delete
  36. So glad to see you back, Captain! Was hoping you were MIA due to family commitments or taking a break. Good to see you back in action. Missed knowing your views on the events that happened while you were away from this blog.

    ReplyDelete
  37. Any reason why you have not spoken about ayodhya verdict?

    ReplyDelete
    Replies
    1. SUPREME COURT TOLD A LIE " UNINTERRUPTED WORSHIP WS GOING ON IN BABRI MASJID SINCE IT WAS MADE"..

      TRUTH?

      THE MOMENT BABURs GENERAL SAW THAT VARAHA ( PIG ) STATUE WAS UNEARTHED --THE NAMAAZ STOPPED..

      http://ajitvadakayil.blogspot.com/2012/11/babri-masjid-demolition-mughal-emperor.html

      Delete
    2. https://www.instagram.com/p/B4aCi-BlGpR/

      Some of these extinct south american mammals look uncannily similar to Varah avatar.

      Delete
  38. https://timesofindia.indiatimes.com/india/shiv-sena-will-lead-government-in-maharashtra-for-next-25-yrs-sanjay-raut/articleshow/72066674.cms

    SENA IS DEAD

    LONG LIVE SENA

    TEE HEEEEEEE

    ReplyDelete
    Replies
    1. Sanjay Raut had done so much mischief,and literally barking like mad

      Delete
  39. LOT OF INDIANS GO ABROAD ON ECONOMIC TOUR PACKAGE..

    FOR EXAMPLE A TEN DAY EUROPEAN TOUR COSTS JUST 1.5 LAKHS..

    THE WHOLE IDEA IS TO PUT PHOTOS IN FACEBOOK TO MAKE OTHERS JEALOUS..

    THEY WILL NEVER TELL THAT THEY GOT CHEATED AND SCAMMED BY THE TOUR OPERATORS WHO ARE IN CAHOOTS WITH MAFIA AND POLICE.

    MY FRIENDs FAMILY LOST THEIR PASSPORTS IN GREECE.. OF COURSE ONCE THEY REGISTER A POLICE COMPLAINT THEY WILL GET IT BACK FOR A HUGE RANSOM ( THEY WILL NEVER REVEAL THIS )..

    ANOTHER FEMALE LOST HER MONEY AND SIM CARD ( THEY DID NOT STEAL THE MOBILE PHONE ).. STOLEN FROM THE TOURIST BUS WHEN THEY WENT ON FOOT FOR SIGHT SEEING..

    EUROPE IS A POOR AND CRIMINAL AREA..

    OUR BASTARD BENAMI INDIAN MEDIA ARE PAID BY THE DEEP STATE TO RUN DOWN THEIR OWN WATAN..

    I HAVE SEEN THE BACKYARDS OF THIS PLANET FOR 40 YEARS

    I KNOW.

    http://ajitvadakayil.blogspot.com/2013/02/jealousy-growing-ulcer-within-capt-ajit.html

    ReplyDelete
    Replies
    1. What about Russia ? Is it a good place for tourism ?

      Delete
    2. For Honeymoon my wife wanted to go someplace like Singapore, Europe, Mauritius.
      I made it clear to her my Honeymoon memories have to be from India.
      We both unanimously decided Kerala.
      Landed Kozhikode and started our trip to Vythiri and ahead

      Delete
  40. Your Registration Number is : PMOGP/E/2019/0659027

    ReplyDelete
  41. What happened in Karnataka has repeated itself in Maharashtra. After elections with pre poll alliance, partner SS split up and joined hands with NCP n INC.

    Kissa CM ki kursi ka?

    ReplyDelete
  42. Captain,

    Is it true that Karna (son of kunti) was born in Iran ? I did some research and came to this conclusion.

    ReplyDelete
  43. PS Welcome back.

    Even now the nut job in Delhi is blaming burning of crop stubble and wants odd even rule as a thoonk patti environmental saviour.

    Please tell them how to solve their inversion.

    Meanwhile on tv a Kashmiri Hindu made waves in DC about human rights abuses on Kashmiri Hindus that these Congressional enquiry types were conspicuously silent about at that time.

    Please do share your method of catharsis n meditation later.

    ReplyDelete
  44. It was very difficult to not see your post or comment for so long.Thoroughly relieved to see you back. Jaan mein jaan aa gayi firse 🙏

    ReplyDelete
  45. Glad and relieved to see you back Ajit ji! We assumed that you must be visiting your elder son and hence enjoying your much deserved family time. At times we were worried, but I firmly believe that you will always be protected.
    God bless you always !
    amrita

    ReplyDelete
  46. Happy to be back in touch with you sir.

    ReplyDelete
  47. Respected Sir,
    After how many days of the death of a relative
    Can we visit temple sir.
    Thank you

    ReplyDelete
  48. https://twitter.com/shree1082002/status/1195276046930595840

    ReplyDelete
  49. Captain ji very happy to see you back ..i was worried about your absence ,then asked Veeresh malik sir he told you are doing fine and will be back after break.

    ReplyDelete
  50. https://twitter.com/taslimanasreen/status/1194999589079781383

    sir this femenist is pushing New World Order Agenda . Showing her true colours . Its time govt should kick her out of country

    ReplyDelete

  51. VR(northern k)November 15, 2019 at 3:09 PM
    Welcome back captain.
    I thought you went on Svadhyaya mode.

    ReplyDelete
  52. https://timesofindia.indiatimes.com/india/why-keeping-sabarimala-issue-alive-helps-the-bjp-in-kerala/articleshow/72067376.cms

    AFTER LOSING 19 OUT OF 20...

    PINARAYI VIJAYAN WON 3 OUT OF 6 BYPOLLS..

    HIS COMMIE CADRE WENT HOUSE TO HOUSE APOLOGIZING FOR THE HUGE MISTAKE THEY MADE IN LAST SABARIMALA SEASON..

    KERALA COMMUNIST PARTY HAS DECLARED THAT THEY ARE NOT AN ATHEIST PARTY.. BUT A PARTY FOR GOD FEARING HAVE-NOTS..

    ReplyDelete
    Replies
    1. https://www.ndtv.com/kerala-news/no-protection-for-activists-making-sabarimala-pilgrimage-warns-kerala-2133190

      Captain sir,
      guess the communist pinarayi has learnt his lesson and for the time being is trying to rectify his mistake.
      However the decision rests with the 7 bench anti Hindu SC

      Delete
    2. If Pinarayi Vijayan truly regrets, he should drop cases against all innocent people who were arrested under criminal charges for protesting peacefully by lighting lamps and chanting Swamiye Ayyappa.

      Delete
  53. Genius mathematician Vashishth Narayan Singh died in a Patna hospital .. unsung.

    We allowed another Ramanujan to fade away ..

    ReplyDelete
  54. TATTU MEN WHO FAST FOR THEIR BOLLYWOOD BIMBETTE WIVES ON KARVA CHAUTH MUST KNOW THIS..

    KARVA CHAUTH DOES NOT WEAKEN WOMEN, RATHER IT IS A POWERFUL TOOL TO EMPOWER MARRIED WOMEN ...

    https://twitter.com/ashutosh83b/status/392523798756356097

    NO WIFE WILL FAST ON KARVA CHAUTH FOR HER HUSBAND IF HE IS NOT WORTH IT ( APOLOGIES TO AISHWARYA RAI ).. NO WONDER SHOBHAA DE DOES NOT FAST FOR HER SECOND HUSBAND ..

    THERE IS SOMETHING CALLED HINDU HUSBANDS HAVING STRENGTH AND HONOUR WHEN IT COMES TO PROTECTING HIS WIFE AND HER INTERESTS, AND THUS SECURING HER LOVE , GRATITUDE AND RESPECT...

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

    capt ajit vadakayil
    ..

    ReplyDelete
    Replies
    1. PUT ABOVE COMMENT IN WEBSITES OF--
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      GITA GOPINATH
      NOBEL WINNER ABHIJIT BANNERJEE


      WEBSITES OF DESH BHAKT LEADERS
      SPREAD OF SOCIAL MEDIA

      Delete
    2. Tweets:
      https://twitter.com/AghastHere/status/1195354823929016320?s=20
      https://twitter.com/AghastHere/status/1195354950148251648?s=20
      https://twitter.com/AghastHere/status/1195355040472539138?s=20
      https://twitter.com/AghastHere/status/1195355143631491072?s=20
      https://twitter.com/AghastHere/status/1195355207900680192?s=20
      https://twitter.com/AghastHere/status/1195355292827111426?s=20
      https://twitter.com/AghastHere/status/1195355429519400961?s=20
      https://twitter.com/AghastHere/status/1195355520602976256?s=20

      Handles:
      @ashutosh83b @DeShobhaa @PMOIndia @narendramodi @AmitShah @HMOIndia @MEAIndia @DrSJaishankar @NIA_India @dir_ed @BDUTT @RanaAyyub @ReallySwara @prakashraaj @ikamalhaasan @JBrittas @MIB_India @PrakashJavdekar @ADevvrat @SatyadeoNArya @KalrajMishra @KeralaGovernor @BSKoshyari @tathagata2 @jagdishmukhi @GovernorOdisha @vpsbadnore @anandibenpatel @jdhankhar1 @rashtrapatibhvn @MVenkaiahNaidu @VPSecretariat @loksabhaspeaker @BiswabhusanHC @jagdishmukhi @AnusuiyaUikey @RSSorg @VHPDigital @ABVPVoice @rammadhavbjp @rajeev_mp @SwatiJaiHind @sharmarekha @nramind @svaradarajan @ShekharGupta @TVMohandasPai @PritishNandy @authoramish @devduttmyth @chetan_bhagat @RajThackeray @OfficeofUT @vivekoberoi @GautamGambhir @ashokepandit @Ram_Guha @PavanK_Varma @JohnDayal @RahulEaswar @Kapil08618991 @KaranThapar_TTP @virsanghvi @seemay @Raghav_Bahl @vineetjaintimes @aroonpurie @PrannoyRoyNDTV @ravishndtv @fayedsouza @Nidhi @vikramchandra @Sonal_MK @SreenivasanJain @AnchorAnandN @navikakumar @sagarikaghose @sardesairajdeep @ArnabGoswamiRtv @madhutrehan @mkvenu1 @ThePrintIndia @SriSri @SadhguruJV @yogrishiramdev @Swamy39 @sambitswaraj @GeneralBakshi @madhukishwar @sudhirchaudhary @sonal_mansingh @imbhandarkar @prasoonjoshi_ @smritiirani @M_Lekhi @KirronKherBJP @vivekagnihotri @KanganaTeam @AnupamPKher @ashokepandit @GautamGambhir @vivekoberoi @OfficeofUT @RajThackeray @NITIAayog @amitabhk87 @ayesha_kidwai @Mohanlal @ActorSureshGopi @drajoykumar @AkhileshPSingh @BHAKTACHARANDAS @DeependerSHooda @dineshgrao @GouravVallabh @JM_Scindia @JaiveerShergill @rajeevgowda @MYaskhi @Meem_Afzal @PCChackoOffice @plpunia @Pawankhera @RajBabbarMP @adamdangelo @Quora @gshewakr @sundarpichai @jimmy_wales @jack @AudreyTruschke @davidfrawleyved @DalrympleWill @Koenraad_Elst @fgautier26

      Delete
    3. https://twitter.com/shree1082002/status/1195359283908468736

      Delete
    4. With the media brainwashing via movies and ads a day will come when only men will fast.

      Delete
  55. Hello Captain,

    Good to see you are back.

    Thank you again for continuing to lead us.

    Kind regards,

    ReplyDelete
  56. Capt, Welcome back!
    Please write something about the christian conversions going on all over the country. In recent days, there is a huge spike of conversions in states like tamilnadu & andhra nd in other indian cities kolkata, Delhi. In Andhra, the people follishly elected a missionary as CM, so Andhra too is now a gone state like kerala (I meant no offense to your state). Please write a post about population control law and conversions. Modi is only interested in oppurtunistic politics.

    ReplyDelete
    Replies
    1. IF JAGAN TRIES TO CONVERT ANDHRA TELUGUS TO CHRISTIANITY LIKE HIS FATHER YSR --BEEEG MISTAKE-- HE WILL SUFFER

      CANDRA BABU NAIDU NAIDU WILL WIN AND PUT AWAY JAGAN MOHAN REDDY FOR THE REST OF HIS LIFE IN JAIL..

      http://ajitvadakayil.blogspot.com/2012/05/jagan-mohan-reddy-finally-in-jail-for.html

      Delete
    2. Jagan will soon meet his waterloo.
      Watch the below videos.

      https://twitter.com/ExSecular/status/1195129796167995394
      https://www.youtube.com/watch?v=VRiIjoFXe30

      Delete
    3. I jokingly tell everyone,that if Xtian missionaries are given free hand in Andhra and TN,they would have guts to rename it to Andrew Pradesh and Tommy Nadu.

      Delete
    4. ALL TAMIL NADU FISHERMEN WERE CONVERTED FROM HINDUISM TO CHRISTIANITY SO THAT WHITE JEWS CAN STEAL OUR THORIUM..

      THIS HAPPENED WHEN MGR WAS A VEGETABLE , LITERALLY BRAIN DEAD AND PROPPED UP ON A CHAIR WITH DARK GLASSES FOR THE MASSSES..

      LTTE CONVERTED TO CHRISTIANITY AND WENT FROM TOEHOLD TO FOOTHOLD..

      http://ajitvadakayil.blogspot.com/2012/07/scrap-sethusamudram-project-now-capt.html

      http://ajitvadakayil.blogspot.com/2012/08/aidmk-dmk-misplaced-support-for-ltte.html

      http://ajitvadakayil.blogspot.com/2010/03/gold-finger-ajit-vadakayil.html

      JAYALALITHAA AND MGR WANTED TO BE CREMATED.. BOTH WERE BURIED BY VESTED TAMIL CHRISTIAN FORCES.

      http://ajitvadakayil.blogspot.com/2017/09/inquiry-into-death-of-cm-of-tamil-nadu.html

      Delete
    5. Hi DJ,
      Missionaries already started this name changing game.
      See in below link.

      https://twitter.com/shriramviswa/status/1059099905732591617?s=19

      India becomes Samariya.
      Tamilnadu becomes Yudeya.
      Kanyakumari district (Highest percentage of christians in Tamilnadu) becomes Yerusalem.

      When I tried to explain some sense to tamil christians, I came to know of sinister plot of hijacking entire Tamil heritage as christian thus severing Tamils from hinduism.

      Delete
  57. Namaskaara_/\_
    Glad so much to hear from you this early morning!!!
    May you be blessed with blissful years ahead!!!
    Lots of Love and Gratitude!
    _/\_Alupa Raaja

    ReplyDelete
  58. U r right sir,sena is dead.ground level shivsainik who was with sena for decades witj sena is unhappy with uddhav decision to go with congress.if elections are held nw sena is going to drains.reporţs are also coming that sonia has asked uddav to soften its stand on hindutva,ram mandir,ucc.

    ReplyDelete
  59. https://www.ndtv.com/india-news/centre-must-read-extremely-important-order-on-sabarimala-justice-nariman-2132931

    CAPT AJIT VADAKAYIL WILL WRITE THE LEGACIES OF "LIBERAL MELORDS" CHANDRACHUD AND NARIMAN..

    BOTH ARE DARLINGS OF THE JEWISH DEEP STATE

    ReplyDelete
  60. so much bad karma has been done by jews and by a particular famaily.Even after All this sanchit bad karma why they are still in power and will they get moksha.

    ReplyDelete
    Replies
    1. WHO KNOWS ?

      IN YOUR LAST BIRTH YOU MIGHT HAVE BEEN A ROTHSCHILD !

      Delete
    2. Zero karmic baggage leads to moksha to whosoever understands this concept.

      Delete
    3. MOKSHA TIED TO KARMA IS ADVANCED QUANTUM PHYSICS..

      Delete
  61. https://timesofindia.indiatimes.com/india/cbi-raids-amnesty-international-bengaluru-delhi-offices/articleshow/72074121.cms

    INCARCERATE THESE FOREIGN PAYROLL DESH DROHIS

    ReplyDelete
  62. https://ajitvadakayil.blogspot.com/2019/11/paradox-redemption-victory-in-defeat.html

    PUT ABOVE COMMENT IN WEBSITES OF--

    INDIAN AMBASSADORS TO ALL SOUTH AND CENTRAL AMERICAN NATIONS..

    AMBASSADORS OF ABOVE NATIONS TO INDIA

    EXTERNAL AFFAIRS MINISTER/ MINISTRY

    AJIT DOVAL

    RAW

    PMO

    PM MODI

    ReplyDelete
    Replies
    1. https://twitter.com/shree1082002/status/1195392525827100672

      Delete
  63. not at all sir..thanks for answering sir i am very luck today that you have answered my question i am reading your blogs from 2012 you have answered my 2nd post in all these days i am delighted .Sir from that day till today i try to educate everyone who comes in my friend circle about what ever i can remember from your posts be it about politics or about history.Always wants your blessing.The first blog i read was about tipu sultan was not consistent in between but back track now.

    ReplyDelete
  64. Hi captain. Welcome back. Today i managed to get a2 milk powder from somewhere. Had a cup of tea with that and right now i feel ghee all inside my body.

    ReplyDelete
  65. This comment has been removed by the author.

    ReplyDelete
  66. Dear sir,
    Please watch this video(in hindi), past life regression where this woman is taken into spiritual realms, where she talks directly to god and discusses coming of lord kalki, among other things,
    https://youtu.be/3Y_M53wft9Q
    Please do watch..

    ReplyDelete
  67. I MUST EXPOUND ON WHAT I WROTE BELOW--

    #########################################

    KARVA CHAUTH DOES NOT WEAKEN WOMEN, RATHER IT IS A POWERFUL TOOL TO EMPOWER MARRIED WOMEN ...

    NO WIFE WILL FAST ON KARVA CHAUTH FOR HER HUSBAND IF HE IS NOT WORTH IT ( APOLOGIES TO AISHWARYA RAI ).. NO WONDER SHOBHAA DE DOES NOT FAST FOR HER SECOND HUSBAND ..

    THERE IS SOMETHING CALLED HINDU HUSBANDS HAVING STRENGTH AND HONOUR WHEN IT COMES TO PROTECTING HIS WIFE AND HER INTERESTS, AND THUS SECURING HER LOVE , GRATITUDE AND RESPECT...

    #############################

    I MUST MAKE A STATEMENT OF FACT--

    LET GOD STRIKE ME AND MY TWO SONS DEAD IF I LIE BELOW..

    I HAVE BEEN MARRIED FOR 36 YEARS ..

    MY WIFE SAILED ON SHIPS I COMMANDED .. I MARRIED AFTER GETTING COMMAND

    WHILE ON THE SHIP MY WIFE FOLLOWED PROTOCOL AS BEFITTING A CAPTAINs WIFE.. WHO MUST SET AN EXAMPLE.. WE HAD STEWARD SERVICE..

    WHEN MY WIFE DID NOT SAIL WITH ME..

    ( MIND YOU AFTER 30 YEARS AS CAPTAIN , EVERYTHING FREE ON BOARD , EARNING IN DOLLARS AND SPENDING IN RUPEES , I AM NOT EXACTLY A POOR FELLOW.. WE HAVE SERVANTS )

    1) EVERY CUP OF TEA I HAD WAS MADE BY MY WIFE..

    2) MY WIFE HAS NEVER EATEN BEFORE ME OR WITH ME .. ( UNLESS WE ATE AT A RESTAURANT)..

    3) MY WIFE HAS IRONED EVERY SHIRT AND PANT I WORE , AFTER WASHING IT HERSELF..

    4) DESPITE HAVING COOKS -- MY WIFE COOKED FOR HER HUSBAND AND HER CHILDREN.. THE COOK ONLY DID THE PRELIMINARIES

    5) MY WIFE TAUGHT HER TWO SONS .. MY ELDER SON IS A GENIUS , HE NEEDED NO HELP AFTER KINDERGARTEN..

    MY YOUNGER SON IS ARTISTICALLY ORIENTED AND HATES NUMBERS ..THOUGH MY WIFE IS A ECONOMICS GRADUATE ( PODDAR COLLEGE MUMBAI ), SHE BOUGHT GUIDES LEARNT MATH/ PHYSICS/ CHEMISTRY AND TAUGHT HIM.. HE NEVER FAILED AND GOT GOOD GRADES.. YOU MUST KNOW THAT THIS IS A GREAT SACRIFICE..

    INDIAN CULTURE IS ENVIED BY WESTERN TOURISTS.. THEY COME TO INDIA AND WATCH INDIAN FAMILIES IN PARKS , BEACHES AND PUBLIC SPACES.. THEY WATCH THE HARMONY AND COHESION.. THIS IS A TAKE HOME LESSON.. THEY GLEAN THE DIFFERENCE BETWEEN LOVE AND LUST..

    IF I WANT TO MAKE MY WIFE CRY, ALL I NEED IS TO ENTER THE KITCHEN WHEN MY WIFE IS SLEEPING AND MAKE MY OWN CUP OF TEA..

    http://ajitvadakayil.blogspot.com/2013/12/ipc-section-377-love-lust-perversion.html

    MY WIFE IS A VERY HAPPY AND CONTENDED WOMAN.. HER SONS ADORE HER.. SHE DOES UNIVERSAL GOOD BY REIKI..

    THERE IS NOT A SINGLE DAY MY ELDER SON DOES NOT CALL HER FROM ABROAD.. THEY TALK FOR A LONG TIME.. I HAVE NEVER SEEN THEM RUNNING OUT OF CONVERSATION TILL TODAY..

    AS I GET OLD ( 64 YEARS ) - MY WIFE MOTHERS ME.. SHE CLUCKS LIKE MOTHER HEN.. I LOVE IT.. I ALLOW HER TO TUCK ME TO BED..

    MY SONS DO NOT SMOKE OR DRINK.. ( MY ELDER SON SOCIALLY DRINKS WHEN WARRANTED ).. MY SONS WONT BE CAUGHT DEAD IN TATTERED JEANS ..

    http://ajitvadakayil.blogspot.com/2013/11/nagging-unhappiness-at-home-death-of.html

    http://ajitvadakayil.blogspot.com/2010/05/marriage-sans-fights-capt-ajit.html

    MY SONS ARE PROUD OF THEIR DAD.. MY WIFE HATES WOMENs LIB..

    WHAT RIGHT HAS THIS COMMIE BITCH ROHINI CHATTERJEE TO WRITE A LYING POST ABOUT MY WIFE?

    .. HER GRANDFATHER WAS SOMNATH CHATTERJEE, WHO WAS PARLIAMENT SPEAKER FOR 5 YEARS -- A TEN 5 YEAR TERM MP .

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

    HAVING SAILED AROUND THE WORLD FOR 40 YEARS , I KNOW WE ARE LUCKY TO HAVE A PRICELESS CUTURE..

    NURTURE IT !

    MY ELDER SON CAME TO INDIA A FEW DAYS AGO WITH HIS WIFE .. HE SAW THE FIRST T20 BANGLADESH MATCH IN DELHI WITH HIS WIFE .. HE FLEW IN TO ATTEND HIS CLASSMATEs WEDDING.. HE STAYED IN A 7 STAR DELHI HOTEL FOR A WEEK AND SAW TAJ MAHAL THROUGH THE SMOG .

    capt ajit vadakayil
    ..

    ReplyDelete
    Replies
    1. Dear sir,

      In Bihar woman keep fast on teej , almost similar but they don’t t wait to see the moon. pandit ji comes to our home and do puja reciting Vedic mantra. When we grew up watching the movies we got to know about karwa chauth.

      Delete
    2. Same as my parents.

      Didnot surprise me reading the above, neither did it do before when I read the Karva chauth post.

      What surprises me is separation, I get headache when I read someone's ordeal about domestic violence or the story of divorce(inauspicious word)

      Tasleema Nasreen had tweeted yesterday that why do one needs to marry
      Didnot feel she was doing any NLP.
      She is a lost soul.
      Many of her tweets are directly or indirectly about loneliness.
      Possibly split personality, conflicting emotions, inside the feeling of being lonely while outside pretending to be unfazed.

      Delete
    3. Dear captain,
      You are a wonderful husband,wonderful father,and a exceptional human being....your blog awakened and moulded me.No wonder you have a moksha jatakam.Had I not found your blog,I would have missed a trove of treasure in this birth,which can uplift the soul..you are a guru and fatherly figure to me...your family is a perfect example of ideal indian family where home is filled with happiness,care,love...May god shower his blessing always on your family and may you reach moksha in this birth..

      With lots of love and gratitude
      Charishma

      Delete
  68. Dear captain,
    Great relief to have you back. Great photo can we have a smiling photo for a change.
    Sudhir.

    ReplyDelete
  69. https://www.indiatoday.in/india/story/iit-madras-suicide-case-in-note-student-blames-professor-for-harassing-her-1618535-2019-11-13

    IITs ARE ELITE TECHNICAL COLLEGES

    WHAT IS THIS BULLSHIT HUMANITIES STUDIES ?

    ReplyDelete
    Replies
    1. Neither are IIMs of any prestige.
      A fellow in one of PPT presentation to a topic started by uttering "Kind of" ; used it more than the contents of his PPT & ended with "Kind of"

      The "kind of" was ridiculous to hear from that duffer.
      He was from IIM Calcutta.

      I was in the audience listening to the PPT.

      I kept wondering if these are the products from IIM.

      Totally crap(sorry for the language)

      Delete


  70. Buddha ZorbaNovember 15, 2019 at 10:28 PM
    Hello Sir,
    This woman is literally speaking from your blog. Really. Its a long audio please go through it randomly if possible. Some really sick rituals of jews is mentioned.
    After being posted here this audio might get deleted, so we might save it.

    Pornography: Weaponized Degeneracy

    https://redice.tv/red-ice-radio/pornography-weaponized-degeneracy

    Jeanice Barcelo, M.A., is a birth doula and independent childbirth educator, specializing in the prevention and healing of birth trauma. She is the author of Birth Trauma and the Dark Side of Modern Medicine. Through her radio and television shows, Jeanice has worked tirelessly to expose the evils of pornography, pedophile rings, Jewish supremacism, and more.

    We begin by discussing Trump’s recent victory. Jeanice tells us that she believes this is not only a sign that the elite’s power is waning, but that there is a positive, spiritual force propelling Trump. We talk about the fact that many within the conspiracy community still think Trump is controlled opposition; this, according to Jeanice, is a poor, defeatist attitude. Transitioning to our main topic, pornography and sexuality, we discuss the ways in which sexual deviancy are used to undermine the positive, life-affirming values traditionally found in the West. Jeanice explains to us that this is merely part of a greater, Satanic attack on life itself. The first hour also touches on Jewish sexual mores, Sigmund Freud, and the question of transexuality.

    In the members’ hour, we delve deeper into the topic of pornography. Jeanice urges us to see pornography as a weapon, rather than an idle amusement. We discuss the fact that sexuality exists to bring new life into world, and that fornication, being rooted in carnality, therefore lies outside of the natural order. Jeanice then outlines what she believes to be the truth behind pornography, which includes occult rituals and demonic forces. We discuss how pornography and sexual degeneracy have been snuck into the mainstream with very minimal pushback from conservative organizations. The members’ hour also explores the various ways in which sexual degeneracy is pushed on children.

    ReplyDelete
  71. guruji, what is the last animal body your soul takes before taking a first ever birth as a human ? is it elephant ?

    ReplyDelete
    Replies
    1. Captain, does the concept of evolution apply to physical-&-mental attributes as well ?

      Say in the first-life as a human, the soul is given a dull-mind but normal body so that it can pick up basic human-functions and do physical-activity (blue-collar-jobs) to earn a living, since it is easy transition because all previous lives too were physical activity based rather than mental. But with every next-life it gets more intelligent-bodies.

      So does this mean that a smart person could be a soul which has had many previous human lives ? Is increased-intelligence-&-consciousness an evolution/promotion based on seniority of a soul ? Or does Karma have the right to overrule such evolution based on the soul's actions over it's multiple previous lives?

      Delete
    2. YOUR KARMA DECIDES INTO WHICH WOMB YOU ARE BORN IN YOUR NEXT LIFE..

      IF YOU DONT CREMATE AFTER DEATH, THE SOUL IS TRAPPED ON EARTH--LONELY AND UNHAPPY FOREVER, HANGING AROUND THE BURIED GRAVE..

      WHAT IS THE CONCEPT OF DARGAH?

      CHRISTIANS ALL OVER THE PLANET HAVE STOPPED BURIALS AND STARTED CREMATION..

      http://ajitvadakayil.blogspot.com/2012/11/rip-impossible-with-burial-world-is.html

      Delete
  72. Happy to see you back Ajit sir.

    ReplyDelete
  73. Captain now the latest craze is of canada PR,every tom dick and harry is going to canada through express entry and then posting pictures on fb and instaa,in real time they are living hand to mouth in extreme cold weather conditions.i think even IELTS exam is a money minting scheme where hundreds of pounds are lost just to score perfect bands,kindly tell everyone about about IELTS EXAM fraud.

    ReplyDelete
    Replies
    1. I HAVE MANY BATCHMATES ( AMONG MY 125 ON THE TRAINING SHIP) WHO HAVE US AND CANADIAN PASSPORTS..

      THEY COME TO INDIA FOR OUR GET TOGETHERS AND PRETEND THEY ARE WELL OFF.

      IN REALITY-- ALL ARE HAND TO MOUTH DESPITE BEING SHIP CAPTAINS... I HAVE DONE EXTENSIVE RESEARCH USING MY US AND CANADIAN SOURCES...

      Delete
    2. Captain,

      speking of canada...

      i remember when YOU tried to contact a certain calicut u-toober based in canada with awkward mouth expressions and four eyed goggles on his face....then later you said he is not goood...

      well he is scoring some big interviews with BIIGIES discussin NEFARI0US propagaanda....(ther is interview with da wife-k1ller thaar00r on his front page)


      and oh yes there is this video which u might want to check out----->> https://www.youtube.com/watch?v=LcLRWb4Ofn0

      what is he upto captain ??

      Delete
    3. the only good thing abt canADA IS CLEAN AIR AND WATER

      Delete
  74. Captain, is it possible that the words Secular and Socialist were introduced by Indira-Gandhi as a preventive-measure for future ?

    Maybe she feared that in State-Govt or even Central-Govt a day may come where the majority is non-Hindu and hence she introduced the word secular so that Hindus do not face sufferings again ? And for Socialist maybe she did so that no future Govt focuses on USA style economics where poor are neglected ?

    Is this a plausible theory ? I ask because you have written that she was a desh-bhakt.

    ReplyDelete
    Replies
    1. http://ajitvadakayil.blogspot.com/2014/12/remove-word-secular-from-indian.html

      Delete
  75. please apne bachchon ki kasam mat khayen . thats not good . may god shower all happiness on you and your family !
    take care always as you you know so much of the untold history .

    ReplyDelete
    Replies
    1. THERE ARE BIMBETTES WHO THINK THAT MY WIFE IS A WOMAN WITHOUT SPIRIT , A DEHAATI BEHENJI.. IN REALITY SHE IS MORE WELL READ THAN ME..

      WELL-- MY WIFE CAN KICK THEIR EMPTY HEADS AND PURULENT TWATS .

      IN THE POST BELOW YOU CAN SEE HER BEATING 25 YOUNG SAILORS WHO ARE TALLER THAN HER IN THE "HIGH KICK" CONTEST...

      http://ajitvadakayil.blogspot.com/2012/06/equator-crossing-ceremony-at-sea-capt.html

      THIS WAS A CEREMONY ON THE WAY TO CURACAO..

      capt ajit vadakayil
      ..

      Delete
  76. Captain, please read this.

    QUOTE=== On Section 4, Chandrachud wrote, "Since the Constitution had conferred a limited amending power on the Parliament, the Parliament cannot under the exercise of that limited power enlarge that very power into an absolute power. Indeed, a limited amending power is one of the basic features of our Constitution and therefore, the limitations on that power can not be destroyed. In other words, Parliament can not, under Article 368, expand its amending power so as to acquire for itself the right to repeal or abrogate the Constitution or to destroy its basic and essential features. The donee of a limited power cannot by the exercise of that power convert the limited power into an unlimited one." The ruling was widely welcomed in India, and Gandhi did not challenge the verdict. The Supreme Court's position on constitutional amendments laid out in its judgements in Golak Nath v. State of Punjab, Kesavananda Bharati v. State of Kerala and the Minvera Mills case, is that Parliament can amend the Constitution but cannot destroy its "basic structure". ===UNQUOTE

    https://en.wikipedia.org/wiki/Forty-second_Amendment_of_the_Constitution_of_India

    Does this mean India is forever stuck with this Constitution ? Any major reform in the Constitution to improve governance of India can be declared "unconstitutional" and be cancelled by the above claims. Is this a bluff on part of the courts or can the Govt of any Nation overcome their Constitutions, maybe even discard the old ones and introduce a new one ?

    I ask because constitutions in all countries is being treated with a dogmatic-attitude as if it were some holy-book which can never be changed/replaced (unless there is a sponsored-revolution). Isn't it more desirable for it to be more like a manual/rulebook which should be easy to amend, upgrade and even replaced if required ?

    ReplyDelete
    Replies
    1. CONSTITUTION IS A DYNAMIC ARTICLE.. IT MUST BE AMENDED TO MOVE WITH THE TIMES.. LIKE HOW TECHNICAL BOOKS HAVE NEW EDITIONS EVERY YEAR ..

      WHAT IS THE USE OF STUDYING A NCERT SCIENCE BOOK IN THIS DNA AGE, WHICH BRAINWASHES US THAT MAN EVOLVED FROM MONKEY?

      EVOLUTION IS "SOUL EVOLUTION"..

      BALLS TO FARHAN AKTHAR " ZINDAGI NA MILEGI DOOBAARA"..

      FOR HINDUS ZINDAGI ZAROOR MILEGI DOOBAARA ( UNLESS YOU HAVE A MOKSHA JATAKAM LIKE YOURS TRULY )..

      FOR SUBJECTS LIKE ARTIFICIAL INTELLIGENCE -- THE CORE CHANGES EVERY THREE YEARS .. THIS IS WHY I AM WRITING A TEN PART POST ON AI..

      WE HAVE HALWAI DOCTOR CONMAN WHO IS UNEMPLOYED FOR SEVEN YEARS TEACHING DATA MINING AND ML / AI BY CONDUCTING HIS BULLSHIT PRIVATE SEMINARS..

      CHANDRACHUD / NARIMAN ARE DARLINGS OF THE JEWISH DEEP STATE..LIKE OUR KAYASTHA MINISTERS PRASAD AND JAVEDEKAR..

      Delete
    2. Dear Ajit Sir,

      You mentioned: "Zindegi zaroor milegi doobara"

      What a profound statement!!!

      यही बात आपकी है जो हमारा दिल जीत लिया है

      It's such a bliss when we read all these from you.
      So crystal clear

      Wisdom Wisdom Wisdom

      Your replies are as if you are not typing but talking to the person infront of you.

      So much realistic & apt.

      Wowww...!!!

      Delete
    3. @abcindiagogo
      Art 368 of constitution empowers parliament to amend any part of the constitution with(depending on which part of constitution is being amended)
      Simple majority,
      2/3rds majority,
      2/3rds majority with consent of half of the states
      "The basic structure doctrine is laid out by the supreme court".Again what constitutes the basic structure is interpreted by SC itself.
      Indian constitution is the largest constitution in the world.
      Collegium system is not part of the constitution. It was laid out by SC in two judges case.
      Recently SC has dismissed review plea of second judges case that created collegium system

      https://www.thehindu.com/news/national/plea-to-review-second-judges-case-order-dismissed/article29902091.ece

      The above opinion by chandrachud is his own interpretation based on the previous rulings..we know where his interests lie..

      Delete
  77. INDIA CANNOT HAVE A CAPITAL WITH SMOG ( TEMPERATURE INVERSION PROBLEMS ) IF WE HAVE TO THIS PLANETs NO 1 SUPERPOWER IN 14 YEARS..

    SHIFT THE CAPITAL OF INDIA AWAY FROM DELHI AND PAKISTAN ..

    IT IS A LIE THAT INDRAPRASTHA WAS DELHI 6000 YEARS AGO..

    THE SLUMS OF DELHI ARE THE WORST ON THE PLANET.

    A FEW DAYS AGO MY ELDER SON AND WIFE TRAVELLED FROM DELHI TO AGRA BY TRAIN, TO SEE THE TAJ MAHAL..

    HE COULD NOT HAVE A REGULAR TAJ MAHAL PHOTO FROM FRONT FACADE SITTING ON THE USUAL BENCH , AS SMOG CAUSES TAJ TO BE INVISIBLE..

    ON THE RAIL TRIP-- WESTERN TOURISTS INSIDE THE TRAIN WERE TAKING VIDEOS OF THE FILTH ON EITHER SIDE OF THE RAILWAY TRACKS ..

    THE WHOLE RAILWAY TRACKS ON EITHER SIDE WERE LITTERED WITH PLASTIC.. WHEREVER THERE WAS WATER, IT WAS EITHER YELLOW/ GREEN OR BLACK ( DEPENDING ON HOW OLD THE SHIT IS )..

    MY SON AND WIFE WENT TO THE FAMOUS STREET FOR STREET FOOD IN DELHI AT MIDNIGHT ( IT WAS RUSH HOUR ) --AT KABABS AND HAD DYSENTRY FOR THREE DAYS..

    THE BEST PART WAS NO OLA / UBER CAB WOULD COME..

    FINALLY AFTER WALKING ON THE DESERTED STREETS THEY MANAGED TO BRIBE AN AUTO DRIVER.. THE FELLOW HAD NO IDEA WHERE CHANAKYA PURI AND LEELA HOTEL IS, AND MY SON HAS TO GUIDE HIM USING GOOGLE MAPS..

    ON THE WAY FROM USA TO DELHI, THEY TOURED SCOTLAND AND CAMPED AT LOCHNESS LAKE SHORE AT NIGHT.. I GUESS THE MONSTER STORY IS BULLSHIT.. SCOTLAND WAS CLEAN --

    MY SON SAID , ONCE THEY SAT OUTDOORS WITHIN LEELA HOTEL COMPOUND-- WITHIN TEN MINUTES THE EYES BECAME RED AND THROAT BURNED AND THEY RAN BACK INSIDE THE HOTEL..

    HE SENT ME VIDEOS OF THE SUITE THEY STAYED -- SHEER OPULENCE..

    MY SON HAS STAYED IN THE BEST HOTELS , ALL OVER USA ON COMPANY ACCOUNT.. COMPARED TO LEELA THEY ARE SHIT..

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

    ReplyDelete
    Replies
    1. https://www.youtube.com/watch?v=4lRsqxQbAKQ

      MY SON SAID THAT IN HIS LEELA PALACE SUITE THE TOILET SHOWER WAS LIKE RAIN FROM TOP ..

      AT THE AIRPORT THERE IS ONE PERSON TO RECEIVE YOU, THEN HE HANDS YOU OVER TO ANOTHER UNIFORMED MAN TO TAKE YOU TO THE PARKING LOT-- THEN ANOTHER MAN TO ACCOMPANY YOU ON THE BNW LUXURY RIDE ..

      AS SOON AS YOU ENTER THE HOTEL, DOZENS OF EMPLOYEES FAWN OVER YOU, TREATING YOU LIKE ROYALTY..

      IN HIS SEVEN DAY STAY AT LEELA HE HAD COMPLIMENTARY BREAKFAST ONLY ONCE. THE HALL HAD THE CHOICEST SPREAD --WHICH YOU DONT GET IN USA.. EVEN IN THE TOP THREE HOTELS AT LAS VEGAS

      Delete
    2. Sad to see Nairs selling this iconic brand to Brookfield (Kneeda)

      Delete
    3. Capt Ajitji

      Never knew Hotel leela in the video above has a copy of 'Bhagavad Gita as it is' and ' Bible' in the drawer.

      Video timestamp 3:00

      Delete

  78. https://timesofindia.indiatimes.com/business/india-business/govt-mulls-raising-insurance-cover-on-bank-deposits-to-above-rs-1-lakh-fm/articleshow/72074060.cms

    BY THE TIME MODI FINISHES HIS TENURE AS PM, BHARATMATA WILL AGAIN BE ENSLAVED BY ROTHSCHILD..

    ALREADY JEWISH FOREIGN BANKS AND INSURANCE COMPANIES ( FRONTS OF ROTHSCHILD ) ARE ON THE DRIVERS SEAT..

    MODIs SOLE AIM IS TO FORCE INDIANS TO LOSE TRUST IN INDIAN BANKS..SO THAT THEY SHIFT THEIR HARD EARNED DEPOSITS TO ROTHSCHILDs BANKS..

    https://www.tribuneindia.com/news/punjab/pmc-bank-scam-maharashtra-sikhs-unable-to-go-on-kartarpur-pilgrimage/859447.html

    WHEN AN INDIAN PUTS HIS MONEY IN A GOVERNMENT BANK-- IT IS AKIN TO LENDING MONEY TO THE ELECTED PM OF INDIA...

    ROTHSCHILD MURDERED INDIRA GANDHI FOR NATIONALIZING HIS BANKS -- USING CRYPTO JEW KHALISTANIS WITH PALE EYES..

    ROTHSCHILD USED INDIANS ( WEARING SIKH TURBAN FANCY DRESS IN 1976 ) TO KICK OUT INDIRA GANDHI..

    EVEN I HAVE LOST TRUST IN INDIAN BANKS..

    THIS SAYS IT ALL.

    capt ajit vadakayil
    ..

    ReplyDelete
    Replies
    1. But Guruji,if this is a propaganda,should we fall prey to it.Or we should still keep our money in our Indian Banks only.Because I remember ,that your Russian crew didnt trust their home banks.Please comment on this.

      Delete


    2. Mediclaim insurance Claims are being arbitrary deducted by insurance companies based on a clause "Reasonable and Customary Charges".

      No definition has been clearly defined of same.

      Definition of reasonable and customary is left to insurance companies imagination.

      Patients intimate the insurance company within 24 hours of admission with the possible diagnosis.
      Then, Why are the citizens not informed of such reasonable charges payable at time of admission?

      Till now insurance companies would target hospitals, now they are targeting citizens: knowing that most citizens would not go to consumer courts against such deductions.
      The squeeze is on. We are being slowly harvested.

      Delete
    3. I WAS HEART BROKEN WHEN MY SERBIAN CHIEF ENGINEER AND ELECTRICAL OFFICER DID OT SEND MONTHLY ALLOTMENT BY BANKING ROUTE ..

      INSTEAD THEY PREFERRED TO COLLECT ALL THEIR HARD EARNED MONEY IN 100 DOLLAR NOTES AND PUT IT IN A HIDDEN MONEY BELT AROUND THEIR WAIST WHILE SIGNING OFF

      CHIEF ENGINEER TOLD ME THAT ALL SERBIAN BANKS ARE JEWISH AND NONE CAN BE TRUSTED..

      Delete
    4. ROTHSCHILD IS THRILLED WITH MODI

      HIS MEDICAL INSURANCE COMPANIES IN INDIA HAVE GONE FROM TOE HOLD TO FOOT HOLD TO DRIVERs SEAT..

      Delete
  79. https://timesofindia.indiatimes.com/india/ldf-govt-cpm-do-u-turn-on-entry-of-women-at-sabarimala/articleshow/72079244.cms

    JEWISH DEEP STATE DARLINGS JUDGES CHANDRACHUD AND NARIMAN MUST KNOW THIS--

    SABARIMALA IS A PILGRIMAGE ..THE PLANETs LARGEST ..

    DONT EVER TRY TO COMPARE THIS WITH BOHRA / PARSI / MUSLIM/ CHRISTIAN PLACES OF WORSHIP AND PUT FOG ...

    http://ajitvadakayil.blogspot.com/2018/11/5900-year-old-sabarimala-this-planets.html

    ReplyDelete
    Replies
    1. PUT ABOVE COMMENT IN WEBSITES OF--
      PUT ABOVE COMMENT IN WEBSITES OF--
      JANAM TV
      MARUNADAN TV
      CM PINARAYI VIJAYAN
      ALL KERALA MINISTERS
      ALL KERALA MLAs
      ALL KERALA COLLECTORS
      DGP BEHERA
      RAMAN SRIVASTAVA
      HIGH COURT CHIEF JUSTICE KERALA
      PMO
      PM MODI
      AJIT DOVAL
      CBI
      NIA
      ED
      IB
      HOME MINISTRY
      AMIT SHAH
      LAW MINISTER
      LAW MINISTRY
      NEW CJI
      INDU MALHOTRA
      ROHINGTON NARIMAN
      CHANDRACHUD
      KHANWILKAR
      ALL SUPREME COURT JUDGES
      TOM VADAKKAN
      GOVERNOR OF KERALA
      PRESIDENT OF INDIA
      VP OF INDIA
      SPEAKER LOK SABHA
      SPEAKER RAJYA SABHA
      PC GEORGE MLA
      RAHUL EASHWAR
      SRIDHARAN PILLAI
      PARASARAN
      SAI DEEPAK
      VIDYASAGAR GURUMURTHY
      I&B DEPT/ MINISTER
      AMITABH KANT
      NITI AYOG
      RSS
      VHP
      AVBP
      SPREAD ON SOCIAL MEDIA

      Delete
    2. Sir, Posted screenshot on twitter to almost all of the above handles.

      Thank you very much for coming back.
      Gratitude.

      Delete
    3. https://twitter.com/kkarthikeyan09/status/1195576487719473152
      https://twitter.com/kkarthikeyan09/status/1195577448227692544

      Delete
    4. Tweets:
      https://twitter.com/AghastHere/status/1195580359863394304?s=20
      https://twitter.com/AghastHere/status/1195580450749763584?s=20

      Handles:
      @tvjanam @marunadannews @vijayanpinarayi @NIA_India @dir_ed @AmitShah @HMOIndia @OfficeOfRSP @rsprasad @RSSorg @VHPDigital @ABVPVoice @NITIAayog @amitabhk87 @MIB_India @PrakashJavdekar @jsaideepak @parasaran @RahulEaswar @rashtrapatibhvn @MVenkaiahNaidu @VPSecretariat @loksabhaspeaker @KeralaGovernor @TomVadakkan2

      Delete
    5. sent to all the major news handles on twitter, but most of the tweets were blocked by twitter

      only these two tweets could be verified:

      https://twitter.com/tvjanam/status/1193048610360840192

      https://twitter.com/surendranbjp/status/1195639941864153090

      Delete
  80. Yin and Yang.

    Make yin more like yang under guise of empowerment.

    Make yang more like yin under brolly if gender equality .

    Both become neutral fertility drops automatically along with other mental traits creating a bunch of servile people. Good depopulation strategy also works towards destroying traditional family values ..

    Suicide of IIT Madras student is sailing with political winds..
    Reminds me of a similar case of a doctor who was allegedly ragged to death by her seniors in Maharashtra.
    The doctors were jailed for over a month without bail .

    Meanwhile the distressed telecom sector has got public sector banks to pressure the govt saying loan default of 100000 crores is possible due to this sector in distress.

    ReplyDelete
    Replies
    1. IIT FEMALE STUDENT SUICIDE
      ##############################

      THE MALAYALI MOTHER OF THIS FEMALE MUSLIM STUDENT LIED ON TV THAT HER DAUGHTER WAS AFRAID OF HINDU PROFESSORS IN IIT MADRAS AND SHE DID NOT EVEN DARE TO PUT THE FACE SCARF AND REVEAL THAT SHE IS A MUSLIM..

      THIS STUDENTS NAME IS FATHIMA LATHEEF ... IS THIS NAME OPAQUE?

      WE KNOW THAT IT IS NATURAL FOR A DISTRAUGHT MOTHER TO LASH OUT WHEN HER DAUGHTER COMMITS SUICIDE--

      BUT WHY DRIVE COMMUNAL WEDGES?..

      LOT OF FOREIGN FUNDED NGOs HANG AROUND IIT MADRAS , TO HELP COMMIE ATHEIST AND MUSLIM STUDENTS COOK UP STORIES AGAINST THEIR ELITE HINDU PROFESSORS WHO REJECT THEIR PROJECTS / THESIS BECAUSE IT HAS BEEN PLAGIARIZED FROM THE INTERNET ..

      WE ASK PM MODI TO DEAL WITH THIS PROBLEM..

      IN CORNELL IF THE STUDENT PLAGIARIZES THE PROJECTS -- HE/ SHE IS CHUCKED OUT IMMEDIATELY AFTER A SUMMARY INQUIRY BY A BOARD..

      THIS INQUIRY ABOUT STUDENT MENTAL INTEGRITY IS VERY FAIR..

      SOMETIMES CORNELL STUDENTS COPY FROM OTHERS.. THE INTELLIGENT STUDENT WHO ALLOWS A WEAK STUDENT TO COPY IS ALSO PENALIZED EQUALLY..

      http://ajitvadakayil.blogspot.com/2010/11/my-son-at-cornell-university-capt-ajit_25.html

      capt ajit vadakayil
      ..

      Delete
    2. PUT ABOVE COMMENT IN WEBSITES OF--
      PMO
      PM MODI
      EDUCATION MINISTER MINISTRY CENTRE / STATES
      ALL IIT DEANS
      AJIT DOVAL
      CBI
      NIA
      ED
      RAW
      AMIT SHAH
      HOME MINISTRY
      NITI AYOG
      AMITABH KANT
      NEW CJI
      ATTORNEY GENERAL

      Delete
    3. Posted on twitter to central ministries and IITs..

      Delete
    4. koshy@iitm.ac.in
      contact@amitshah.co.in
      PMOPG/E/2019/0660958

      Informed above people and PMO
      Visvanathan

      Delete
  81. Is kosher vinegar the slow death kryptonite of Jews? R can kill for money while most ppl cannot that is the advantage he has. But his family's power over so many centuries means that he has been granted so sort of boon (wish); but like any other boon there must be something that can counter it. What is it? who will do it and when?

    ReplyDelete
  82. https://www.latimes.com/projects/la-me-oxycontin-part3/

    They have never stopped dealing in opium. Still maximum earning potential.

    ReplyDelete
    Replies
    1. MOST OF THE BOLLYWOOD KHANS ARE DESCENDANTS OF OPIUM RETAIL STREET DRUG RUNNERS..

      RABINDRANATH TAGOREs KABILLIWALAH STORY IS BASED ON SUCH A PASHTUN KHAN JEW..

      BISHNOIS ( WHO ARE AFTER SALMAN KHANs ASS ) WERE KILLER HITMEN OF OPIUM DRUG RUNNING MARWARIS --AGENTS OF ROTHSCHILD..

      http://ajitvadakayil.blogspot.com/2018/04/salman-khan-chinkara-poaching-jail-term.html

      Delete
  83. hail Captain!

    You're back with a bang!

    Pranamams,
    Sriganesh

    ReplyDelete
  84. Dear Ajit Sir,

    Sunanda Vasisth has spoken for the cause of Kashmiri Pandits in tandem with the cause of Jews of Israel as their right to have their homeland.

    In Washington DC for Congressional hearing on Human Rights, the short video clips of Sunanda making the round ups has very cleverly & conveniently captured the statement where she has mentioned the rights of Jews in sync with Kasmiri Pandits & their ethnic cleansings.

    Jews will never come out of their mischiefs.
    Every opportunity they find, they just tag themselves exclusively.

    On Social Media, having exchanged hints, the Western World is thoroughly aware of Jews but they don't have the balls to go against them.

    How come Israel couldn't gather information regarding massive Rocket strikes from Gaza which happened 3 days back?

    With such huge catche of Rockets, it takes time to assemble them & upon that the Canister Launcher itself is more than enough to guess that there would be a massive Rocket strike.

    Is it a False Flag attack?

    Having been searching, couldn't get any answer...

    ReplyDelete
  85. Captain sir,

    https://thewire.in/government/andhra-pradesh-bureaucracy-accountability-cmo

    AP Chief Secretary LV Subramanyam was transferred to an obscure post by CM YS Jagan because the CS took strict action against non-hindu TTD[Tirumala Tirupathi Devasthanam] employees and even threatened them with surprise inspection at their houses who submitted a wrong declaration to conceal their religion for the sake of getting a job in the TTD.

    As soon as he was transferred Christian organizations celebrated it as their victory
    https://twitter.com/PVSIVAKUMAR1/status/1191584974098403328/photo/1

    Jagan seems to be giving in too much leeway to 'Dalit' Christians just like his papa.

    Regards,
    Sriganesh

    ReplyDelete
  86. NETFLIX IS JUST ANOTHER TOOL OF THE JEWISH DEEP STATE TO DO PRO-JEWISH PROPAGANDA LIKE HOLLYWOOD/ HISTORY CHANNEL/ WIKIPEDIA/ QUORA ETC..

    THE NETFLIX SERIES "NARCOS" IS GENUINE..

    THE NETFLIX SERIES " PABLO ESCOBAR" IS JUST A JEWISH PROPAGANDA MELODRAMA .., EVEN JEETENDRAs DAUGHTER EKTA KAPOOR CANNOT MATCH THE SOAP MELODRAMA..

    THIS SERIES WAS ORIGINALLY 115 EPISODES--LATER REDUCED TO 75..

    AS SOON AS I STARTED SEEING THE SERIES, I TOLD MY WIFE THAT THIS IS JEWISH PROPAGANDA OF THE EL SPECTATOR NEWSPAPER WHO DID EXTREME YELLOW JOURNALISM TO MAKE PABLO ESCOBAR MENTALLY DISINTEGRATE..

    https://ajitvadakayil.blogspot.com/2019/11/paradox-redemption-victory-in-defeat.html

    EVERY FALSE FLAG ATTACK OF BOMBING PUBLIC SPACES WAS ATTRIBUTED WRONGLY TO PABLO ESCOBAR MAKING HIM LOSE PUBLIC SUPPORT.. IMAGINE IN THE END PABLO WAS HIDING LIKE A LAK EVEN IN HIS MEDELLIN WHERE ONE HE WAS "ROBIN HOOD"..

    TODAY MY WIFE LET OUT A WHOOP.. SHE DID RESEARCH ON HER OWN.

    SHE SAID I AM RIGHT.. THIS NETFLIX SERIES "PABLO ESCOBAR" WAS PRODUCED BY THE DESCENDANTS OF THREE ANTI-PABLO JEWISH FORCES.

    THE SERIES WAS CREATED BY CAMILO CANO AND JUANA URIBE WHO ARE BOTH CLOSELY TIED WITH PABLO ESCOBAR.

    1) CAMILO CANO IS THE SON OF GUILLERMO CANO WHO WAS THE PUBLISHER OF NEWSPAPER EL ESPECTADOR AND WHO WAS MURDERED BY ESCOBAR IN DECEMBER 1986.

    2) JUANA URIBE IS THE VICE PRESIDENT OF CARACOL TV AND ALSO THE SERIES' PRODUCER. SHE IS THE DAUGHTER TO MARUJA PACHÓN WHO WAS KIDNAPPED BY PABLO ESCOBAR ON 7 NOVEMBER 1990 AND LATER RELEASED.

    3) JUANA IS ALSO THE NIECE TO PRESIDENTIAL CANDIDATE LUIS CARLOS GALÁN WHO WAS KILLED BY ESCOBAR IN AUGUST 1989.

    WE ARE MADE TO BELIEVE THAT THESE JEWISH FAMILIES ARE THE SALT NAY BOUNTY OF THE PLANET..

    https://en.wikipedia.org/wiki/Pablo_Escobar,_The_Drug_Lord

    capt ajit vadakayil
    ..

    ReplyDelete
  87. Namaste Captain,
    Glad to see you're back.
    Charanasparsham
    - Biju
    Pranaam

    ReplyDelete
  88. SOMEBODY CALLED ME UP AND SAID --

    IN THE NETFLIX SERIAL "PABLO ESCOBAR ", PABLO IS SHOWN NAKED WITH A FEMALE VOLLEYBALL PLAYER MARIA ON THE BED..

    WELL YOU WILL NOTICE THAT HE DID NOT HAVE HIS PRICK INSIDE HER CUNT..

    SHE WAS IN TWO PIECE BIKINI LYING ON TOP OF HIM HEART TO HEART ..

    YOU WILL NOTICE THAT PABLO WAS SPEAKING TO HIS WIFEs BROTHER AND GUNMEN WHO BROUGHT HER TO HIS HIDEOUT..

    PABLO WAS ILL AND WEAK.. WHEN THE POLICE HELICOPTERS STARTED SHOOTING, HE TELLS MARIA TO RUN, BUT HE CONTINUES LYING DOWN..

    HIS WIFEs BROTHER DIES IN THIS SHOOTOUT..

    JET A FEW EPISODES BEFORE THIS INCIDENT , PABLOs SMALL DAUGHTER WAS VERY ILL... HER EAR WAS DAMAGED BY A BOMB BLAST BY CALI CARTEL..

    PABLO TELL HIS WIFE.. REMOVER MY DAUGHTERS CLOTHES AND PUT HER ON TOP OF ME HEART TO HEART..

    HE EXPLAINS TO HIS WIFE THAT HE IS GIVING HER HIS OWN PRANA.. ( LIFE FORCE )..

    ##########

    ONCE I WAS NEARLY BRANDED AS A DIRTY OLD MAN.

    ME AND MY WIFE WERE CLIMBING THE DARK WINDING STAIRS OF CHARMINAR IN HYDERABAD..

    MY WIFE BEING A REIKI CHANNEL IS VERY SENSITIVE TO NEGATIVE ENERGY..

    AT A PARTICULAR SPOT SHE WAS OVERWHELMED .. SHE CRIED OUT TO ME " I AM FEELING FAINT"

    I CARRIED HER TO THE NEXT STAIR LANDING JUST TWO METRES AWAY.

    THEN I DID BODY TO BODY REIKI.. JUST HUGGING..

    YOU SHOULD HAVE SEEN THE FACES OF SOME WOMEN WHO SAW THIS -- DIRTY OLD SEXUAL PREDATOR DOING IT IN A PUBLIC SPACE.. THEY DID NOT KNOW SHE IS MY WIFE..

    IN THREE MINUTES FLAT MY WIFE WAS OK.. I PUMPED PRANA INTO HER..

    WESTERN CHILDLESS WOMEN UNDERSTAND THIS .. SPERM IS ELECTRICALLY CHARGED .. IT WAS TO SWIM UPWARDS TO CONCEIVE.. ( A healthy adult male can release 1.2 billion sperm cells in a single ejaculation. only one sperm is required for a baby ).

    WHEN YOU HAVE SEX , HEART CHAKRA TO HEART CHAKRA MAKES THE SPERM SWIM STRONG UPWARDS..

    https://www.youtube.com/watch?v=R-lrEBevJ60

    POOR ATHEISTS

    WHAT DO THEY KNOW !

    http://ajitvadakayil.blogspot.com/2017/02/my-visit-to-bhagyalaxmi-nagar-or-modern.html

    capt ajit vadakayil
    ..

    ReplyDelete
    Replies
    1. Do you think sylicon implants create an aditional blockage in this regard?

      btw I read this book *the female brain* and it seems to be prooven fact that as female babies (unlike boys) have an enhanced activity in the brain zones connected wirh facial recognition mothers who have lip implant of any sort wind up affecting them at a psycho-neurological level in the long run.

      Delete
  89. https://ajitvadakayil.blogspot.com/2018/11/5900-year-old-sabarimala-this-planets.html

    https://ajitvadakayil.blogspot.com/2018/11/save-5900-year-old-sabarimala.html

    http://ajitvadakayil.blogspot.com/2018/07/menstruating-women-and-sabarimala.html

    POOR MODI/ BJP/ RSS/ AMIT SHAH

    THEIR DESIGNS FOR SABARIMALA FAILED..

    THESE IMMORAL PEOPLE/ ORGS WANTED TO USE SABARIMALA TO GRAB POWER IN KERALA..

    PINARAYI VIJAYAN UPSET THE BJP / RSS APPLE CART..

    HE HAS GIVEN STRICT ORDERS THAT POLICE MUST RESPECT PILGRIMS AND ADDRESS THEM AS "SWAMY"..

    JEWISH DEEP STATE DARLINGS CHANDRACHUD / NARIMAN AND BENAMI MEDIA MUST KNOW THIS..

    SABARIMALA IS A PILGRIMAGE--NOT SOME WEE BOHRA OR PARSI TEMPLE..

    capt ajit vadakayil
    ..

    ReplyDelete
    Replies
    1. PUT ABOVE COMMENT IN WEBSITES OF--
      JANAM TV
      MARUNADAN TV
      CM PINARAYI VIJAYAN
      ALL KERALA MINISTERS
      ALL KERALA MLAs
      ALL KERALA COLLECTORS
      DGP BEHERA
      RAMAN SRIVASTAVA
      HIGH COURT CHIEF JUSTICE KERALA
      PMO
      PM MODI
      AJIT DOVAL
      CBI
      NIA
      ED
      IB
      HOME MINISTRY
      AMIT SHAH
      LAW MINISTER
      LAW MINISTRY
      NEW CJI
      INDU MALHOTRA
      ROHINGTON NARIMAN
      CHANDRACHUD
      KHANWILKAR
      ALL SUPREME COURT JUDGES
      TOM VADAKKAN
      GOVERNOR OF KERALA
      PRESIDENT OF INDIA
      VP OF INDIA
      SPEAKER LOK SABHA
      SPEAKER RAJYA SABHA
      PC GEORGE MLA
      RAHUL EASHWAR
      SRIDHARAN PILLAI
      PARASARAN
      SAI DEEPAK
      VIDYASAGAR GURUMURTHY
      I&B DEPT/ MINISTER
      AMITABH KANT
      NITI AYOG
      RSS
      VHP
      AVBP
      SPREAD ON SOCIAL MEDIA

      Delete
    2. The women/activists who try and enter do they do the 41 day vrat? Is it applicable to women even? Pilgrimage rules must be followed. All temples have their specific practices. If u don't like then don't go. No one is forcing u to enter a temple nor is it mandatory.

      Delete
    3. BJP version 2 is going all out in Hindu extinction. From NRC is Assam identifying mainly Hindu illegals, to AP and Northeast doing mass conversions - there is no stopping the juggernaut (Jagannath).

      Delete
  90. Have u heard of this pre-Incan Goddess called Pachamama? There must be some Hindu/Vedic connection.

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

    ReplyDelete
  91. JEWISH DEEP STATE DARLINGS MODI/ AMIT SHAH/ RSS/ BJP / CHANDRACHUD/ NARIMAN WANT INVADERS RELIGION STANDARDS TO BE APPLIED TO A HINDU PILGRIMAGE, SABARIMALA.. THIS PLANETs LARGEST ..

    IT WONT WORK..

    KERALA CM PINARAYI VIJAYAN HAS SCREWED ALL OF THEM WITHOUT GREASE..

    https://ajitvadakayil.blogspot.com/2018/11/5900-year-old-sabarimala-this-planets.html


    capt ajit vadakayil
    ..

    PUT ABOVE COMMENT IN WEBSITES OF--
    JANAM TV
    MARUNADAN TV
    CM PINARAYI VIJAYAN
    ALL KERALA MINISTERS
    ALL KERALA MLAs
    ALL KERALA COLLECTORS
    DGP BEHERA
    RAMAN SRIVASTAVA
    HIGH COURT CHIEF JUSTICE KERALA
    PMO
    PM MODI
    AJIT DOVAL
    CBI
    NIA
    ED
    IB
    HOME MINISTRY
    AMIT SHAH
    LAW MINISTER
    LAW MINISTRY
    NEW CJI
    INDU MALHOTRA
    ROHINGTON NARIMAN
    CHANDRACHUD
    KHANWILKAR
    ALL SUPREME COURT JUDGES
    TOM VADAKKAN
    GOVERNOR OF KERALA
    PRESIDENT OF INDIA
    VP OF INDIA
    SPEAKER LOK SABHA
    SPEAKER RAJYA SABHA
    PC GEORGE MLA
    RAHUL EASHWAR
    SRIDHARAN PILLAI
    PARASARAN
    SAI DEEPAK
    VIDYASAGAR GURUMURTHY
    I&B DEPT/ MINISTER
    AMITABH KANT
    NITI AYOG
    RSS
    VHP
    AVBP
    SPREAD ON SOCIAL MEDIA

    ReplyDelete
    Replies
    1. That's why they try an equate Islam, Christianity and Judaism to Sanatana Dharma. It's not the same; they are intentionally categorizing it as such - DECEPTION.

      Delete
  92. Welcome Back, Captain! Missed the daily dose of strength n valor. Great to have you back again!! After years in IT, it all seems a sheet waste. Need your guidance on a way out.

    ReplyDelete
  93. https://twitter.com/shree1082002/status/1195726946740523013

    ReplyDelete
  94. Welcome back Captain _/\_. It was a great relief for us to see you and your blogs.

    Regards,

    K N Rao

    ReplyDelete
  95. Namaste captain,
    You had told you will be writing about karl marx and what he actually meant in his book Das Kapital.
    Plz if you could in your next post.

    Gratitude
    Aditya

    ReplyDelete
  96. Dear captain,

    Please tell us about recent brics summit and new development bank.

    By any means this bank is beneficial to india.

    Please clarify.

    Thanks
    Radhakrishnan kundayil

    Jai hind.

    ReplyDelete
  97. My Dear Captain,
    Welcome back and many congratulations on your Catharsis journey and the flashpoints. Was concerned and your last post Captain is all alone also had got me worried but then again got to know you are fine..
    :) and was waiting for your come back.Now we have a Reninvented and Rejuvenated Captain. Pranams.

    ReplyDelete
  98. The bloodline of Chatrapati Shivaji ended with Emperor Shahu, because the Chitpavan Peshwas indirectly helped the enemy and betrayed Maratha people. Now the Shav Sena, controlled by "The Gang of Four", is repeating the same mistake by betraying the popular mandate.

    ReplyDelete
    Replies
    1. for all their hoopla around being a maratha party the thackerays are all ckps. it seems only fair that a party which was created to unleash a reign of strong armed thuggery style terror against the labour unions and immigrant communities in Mumbai meets an opportunists fate who can't see the big picture and overplayed his hand. Being pro hindu was just a facade for them.

      Delete
  99. India resumes buying Malaysian palm oil as Kuala Lumpur offers discount:

    Today most of savouries packed and sold are made from palmolein as they have long shelf life.

    ReplyDelete
  100. very good question ! need to stop this evil

    ReplyDelete
  101. Captain,

    What is your opinion on the recent spat between cristiano ronaldo and msurizio sarri of Juventus.

    https://www.google.com/amp/s/www.express.co.uk/sport/football/1204852/cristiano-ronaldo-juventus-sarri-portugal-hat-trick/amp

    ReplyDelete
  102. today in navi mumbai katy perry concert happened. all chutney mary with cellulite filled were roaming nanga punga after the concert.

    Katy Perry and Lipa Dua's Mumbai concert: Katy Perry and Dua Lipa enthrall Mumbai with their mesmerising performances

    https://timesofindia.indiatimes.com/entertainment/english/music/news/katy-perry-and-lipa-duas-mumbai-concert-katy-perry-and-dua-lipa-enthrall-mumbai-with-their-mesmerising-performances/articleshow/72088180.cms

    ReplyDelete
  103. Welcome back captain! The path is lit again.

    ReplyDelete
  104. Captain Sahib,
    Can you kindly throw some light on this so called Kushwaha caste. Seems bit like a Kayastha thingy.
    Sincerely,
    G. Rawat

    ReplyDelete
  105. https://timesofindia.indiatimes.com/india/karma-tends-to-explain-everything-justice-nariman/articleshow/72091029.cms

    PARSI PRIEST , AND LIBERAL DEEP STATE DARLING SUPREME COURT JUDGE ROHINTON NARIMAN KNOWS NOTHING ABOUT KARMA..

    THE WORD KARMA APPEARS IN THE RIG VEDA , ATHARVA VEDA AND THE BRHADARANYAKA UPANISAD PENNED 7000 YEARS AGO .. THE CONCEPT OF KARMA APPEARS STRONGLY IN THE BHAGAVAD GITA PENNED 6000 YEARS AGO .

    KARMA IS NOT RELIGION OR EVEN SPIRITUALITY..THIS IS ABOUT CONSCIOUSNESS AND ADVANCED QUANTUM PHYSICS. THERE IS NO DOGMA OR SUPERSTITION HERE..

    THE UNIVERSE IS WOVEN FROM CONSCIOUSNESS .. IT KNOWS WHAT GOES ON IN YOUR MIND..

    IDIOTS LIKE NARIMAN , MUST NOT TALK ABOUT KARMA ( BUTTERFLY EFFECT ) , WHICH IS ADVANCED QUANTUM PHYSICS.. A HUMAN ACTION CREATES AN INVISIBLE QUANTUM MOTION OF FORCE AT THE SUBATOMIC LEVEL.

    KARMA IS "INTENTION BASED " NOT ACTION BASED.. IF YOU DO A BAD DEED WITH GOOD INTENTION, YOU DONT GET BAD KARMA-- AND VICE VERSA. .

    YOU CAN HIDE YOUR INTENTIONS FROM OTHERS BUT NOT FROM YOURSELF OR THE UNIVERSE. YOU ARE A SMALL COG WITHIN MANY OTHER COGS. WHEN YOU MOVE, YOU MOVE ALL THE OTHER COGS.

    KARMA IS THAT CATALYST THAT CONNECTS ACTIONS AND THOUGHTS WITH THE QUANTUM RIPPLES OF ENERGY THAT IN ESSENCE CREATES THIS DYNAMISM IN LIFE HERE AND HEREAFTER.

    THIS IS WHY INTENTION IS SUCH A POWERFUL THING. IT MOVES THE COSMOS.

    KARMA CATCHES UP BEHIND CLOSED DOORS..KARMA IS A UNIVERSAL LAW; IT CANNOT BE BROKEN.

    THE DOCTRINE OF KARMA TEACHES: "DO NOT BLAME ANYBODY WHEN YOU SUFFER. DO NOT ACCUSE GOD. BLAME YOURSELF FIRST. YOU WILL HAVE TO REAP WHAT YOU HAVE SOWN IN YOUR PREVIOUS BIRTH.

    AN INDIVIDUAL’S KARMA IS BASED ON THEIR THOUGHTS, WORDS, AND INTENTIONAL ACTIONS AND THE CHOICES THEY MAKE.

    WE HINDUS WERE THE FIRST TO PRAY ON THIS PLANET.. WE WERE THE FIRST TO UNDERSTAND THE MEANING OF INTENTION AND CONCSIOUSNESS

    KARMA IS NOT PUNISHMENT OR RETRIBUTION BUT SIMPLY AN EXPRESSION OR CONSEQUENCE OF NATURAL ACTS.

    KARMA IS LIKE A SEED, MOST OF THE TIMES KARMA DOES NOT FRUCTIFY IMMEDIATELY AFTER THE SEED IS SOWN.

    “LAW OF KARMA” IS LIKE A UNIVERSAL LAW SAY “LAW OF GRAVITY” - IT APPLIES WHETHER YOU ACCEPT OR REJECT THE LAW.

    KARMA MEANS YOU CREATE YOUR OWN LIFE. KARMA IS AN UNBREAKABLE LAW OF THE COSMOS. YOU DESERVE EVERYTHING THAT HAPPENS TO YOU, GOOD OR BAD. YOU CREATED YOUR HAPPINESS AND YOUR MISERY.,

    THE WHOLE UNIVERSE IS OBSERVING YOU..

    KARMA TREATS EVERYONE EQUALLY. YOU WON’T GET SPECIAL TREATMENT.

    KARMA PLANTS A SEED. OVER TIME IT WILL GROW. AT JUST THE RIGHT MOMENT, THE EXACT MOMENT, YOU WILL RECEIVE YOUR KARMA. KARMA MAKES YOU EXPERIENCE WHAT YOU HAVE DONE TO OTHERS.

    THE DOCTRINE OF KARMA ONLY CAN BRING SOLACE, CONTENTMENT, PEACE AND STRENGTH TO THE AFFLICTED AND THE DESPERATE. IT SOLVES OUR DIFFICULTIES AND PROBLEMS OF LIFE.

    IT GIVES ENCOURAGEMENT TO THE HOPELESS AND THE FORLORN. IT PUSHES A MAN TO RIGHT THINKING, RIGHT SPEECH AND RIGHT ACTION

    THE MOST IMPORTANT PART OF YOUR LIFE WILL BE THE UNSEEN RANDOM ACTS OF KINDNESS YOU PERFORMED.

    THE THING WITH KARMA IS THAT IT DOESN’T ALWAYS HAPPEN IMMEDIATELY. SOMETIMES WE HAVE TO WAIT FOR YEARS. SOMETIMES OUR KARMA ARRIVES WITHOUT US EVEN KNOWING IT, OR KNOWING WHAT FORM IT HAS TAKEN.

    REMEMBER THE PURPOSE OF KARMA IS NOT REVENGE. THE UNIVERSE IS NOT VENGEFUL. THE PURPOSE OF KARMA IS TO HELP YOU BECOME A BETTER PERSON..

    THERE CAN BE NO MOKSHA EVEN IF YOU HAVE AN IOTA OF EGO WITHIN YOU.

    AS EGO REDUCES , KARMIC BAGGAGE LIGHTENS, AND YOUR SOUL FREQUENCY INCREASES..

    KARMA IS SELF-BALANCING DIVINE JUSTICE; IT IS 100% FAIR AND ABSOLUTELY INFALLIBLE. IT DOESN'T MATTER IF A CRIMINAL SEEMS TO "GET AWAY WITH IT", BECAUSE THERE IS NO GETTING AWAY WITH IT – KARMA WILL EVENTUALLY CATCH UP WITH HIM.

    KARMA IS SIMPLY AN OPPORTUNITY TO MAKE GOOD – IT NEITHER PUNISHES NOR REWARDS; IT SIMPLY GUIDES.

    LAWS OF KARMA GIVE HOPE..

    CONTINUED TO 2---

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    1. CONTINUED FROM 1--

      THE AIM OF EVERY RELIGION IS TO GIVE HOPE.. BUT THE SINGLE MESSSIAH / SINGLE HOLY BOOK RELIGIONS PUT YOU UNDER SERVILITY. THE MULLAHS AND POPES WANT TO SAVE YOU--BECAUSE THEY HAVE A HOT LINE WITH GOD

      IN SANATANA DHARMA THERE IS NO MIDDLE MAN BETWEEN YOU AND GOD..

      THE PURPORT OF THE TERM KARMA IS TO MAKE INDIVIDUALS TAKE OWNERSHIP FOR THEIR CONSCIOUS ACTIONS

      THE CONSEQUENCES OF YOUR ACTIONS GOOD OR BAD MAY WILL FOLLOW YOU INTO THE NEXT LIFE. . ZINDAGI NA MILEGI DOOBARA HEDONISM IS A CONCEPT OF SINGLE MESSIAH BURIAL RELIGIONS..

      THE DOCTRINE OF KARMA BRINGS HOPE TO THE HOPELESS, HELP TO THE HELPLESS, JOY TO THE CHEERLESS AND NEW STRENGTH TO THE WEAK. IT BRACES UP A SUNKEN MAN. IT IS AN IDEAL "PICK-ME-UP" FOR THE DEPRESSED AND GLOOMY.

      THE DOCTRINE OF KARMA TEACHES: "DO NOT BLAME ANYBODY WHEN YOU SUFFER. DO NOT ACCUSE GOD. BLAME YOURSELF FIRST. YOU WILL HAVE TO REAP WHAT YOU HAVE SOWN IN YOUR PREVIOUS BIRTH.

      YOUR PRESENT SUFFERINGS ARE DUE TO YOUR OWN BAD KARMA IN YOUR PAST LIFE. YOU ARE YOURSELF THE AUTHOR OF THE PRESENT STATE.

      GUILT IS A FORM OF PUNISHMENT. GUILT DEPRIVES MAN OF REM SLEEP. PAINFUL RESULTS IN YOUR LIFE COME FROM ACTIONS YOU TOOK THAT CLASHED WITH THE LAWS OF THE UNIVERSE. THE PAIN DRIVES HOME THE LESSON.

      KARMA IS THE PHYSICAL MANIFESTATION OF THE LAW OF BALANCE AND HARMONY, AS IT APPLIES TO THE RESULTS OF DECISIONS REACHED AND ATTITUDES HELD BY BEINGS CAPABLE OF FREE WILL AND CHOICE

      YOUR INTENTIONAL ACTIONS CREATE YOUR FUTURE. WHAT YOU ARE EXPERIENCING RIGHT NOW IS WHAT KARMA WANTS YOU TO EXPERIENCE

      YOU CAN’T ESCAPE FROM YOUR PAST, BUT LEARNING FROM IT WILL CHANGE YOUR FUTURE.

      KARMA IS REAL. KARMA IS YOUR BOND WITH THE PAST. KARMA TEACHES YOU AND MAKES YOUR KNOWLEDGE OF THE WORLD MORE COMPLETE.

      YOUR PRESENT SUFFERINGS ARE DUE TO YOUR OWN BAD KARMA IN YOUR PAST LIFE. YOU ARE YOURSELF THE AUTHOR OF THE PRESENT STATE.

      OUR INDIAN JUDICIARY HAS NEVER FOLLOWED SANATANA DHARMA WHICH IS ENMESHED WITH KARMA.. KARMA GIVES US HOPE, IN THIS LIFETIME..

      DHARMA IS ABOVE WE THE PEOPLE, THE WATAN AND THE CONSTITUTION..

      http://ajitvadakayil.blogspot.com/2018/01/sanatana-dharma-hinduism-exhumed-and_21.html

      capt ajit vadakayil
      ..

      Delete
    2. Suprabhat Capt Ajitji

      Once a king was performing a grand offering of food to people in his kingdom. While the king was serving food to a brahmin, an eagle went above them holding a snake which was its prey. Accidentally, a little poison from the snake spilt in the food the king was serving and the Brahmin died.
      So whom should the laws of karma be applied for the death of Brahmin. The king, the eagle or the snake?.
      The karma went to a lady who was making spiteful comments to the people about the king that all this offering is a farce and show off.

      I think that ladys name could be Rohini Chatterjee.

      Delete
    3. Superbly explained.
      Karma is cause and effect where intention generated on base of inherent beliefs generates cause which is never visible.
      Effect and it's consequences are.

      Laws of physics govern this.
      Good or bad is ego intellect complex at work w R t societal mores prevailing at that time.

      Delete
    4. Masterpiece...thanks, Capt.

      Sharing on Social Media.

      Delete
    5. POISON ON FOOD WONT KILL THE BRAHMIN -- UNLESS HE HAS LOT OF ALKALINE CHOONA.

      Delete
    6. @mohit bhai,
      As i understand what is written above, the intention of neither party is set towards what conspired. The law hence applies to the past life karma of the brahmin, animals are bereft of karma as guruji has explained earlier. Hence eagle and snake are out of scope. Only king and brahmin remain in karmic scope, the king with the intention of offering food to the people is a good deed with a good intention, hence he is also not afflicted. As guruji points out "we are the architects of our own state of existence". Thus, the karma of the brahmin delivered his fruit to him at the right time.....

      Delete
    7. Captain Ajitji's info on choona is too good.

      One more story. This is just a story for conversation.

      Once Yudhistra was very saddened and felt unbelievable to see Pitahmah Bhism on a bed of arrows as he had performed many great deeds in his life and why should he bear through this pain.
      So he performed meditation and went through past 100 life's of Pitamah Bhism. He couldn't find any misdeed that Bhism would have done. He was very furious and called his God Father Yaksha and demanded explanation.
      On this Yaksha God appeared and said why did you stop only till 100 previous lives. In one of the earlier life he caught a snake and threw it in bushes full of thorn which injected the snake all over. That is why he is facing this pain in this life.
      On hearing this Yudhistra got annoyed and asked Yaksha God that couldn't he get any better time to punish Pitamah Bhism than this life.
      He replied that his deeds were so good that I couldn't punish him.
      So Yudhistra asked that why did you choose this life. To which Yakhsa God replied that this is Pitamah Bhism's last life after which he will attain MOKSHA. I had to settle all karma before he attains Moksha.
      Finally Yudhistra understands and pays his respects to Yaksha GOD.

      Delete
    8. Respected Sir,
      Kindly pardon me if am wrong.
      It is said that free will is directly proportional to awareness.and also that one's vasanas and karma determine our present environment and experiences.with so many factors influencing our current life how much of free will do we really have percentage wise to shape our future.is not life and events predetermined in such a case.
      Is it that only when one is self-realized do we have 100% free will and not bound by karma and it's effects.
      Also when Lord Krishna says consider me as the doer of all your actions and not the ego what are we to understand.
      Thank you

      Delete
    9. Hello Sir,
      What you have done for me and all the readers which can't be described in words.
      Today i shape my life on my terms. But i feel i can't help my near and dear ones.
      This question is related to karma. Can we at all help our near and dear ones ?
      It is like they are possessed and can't even take the best advice even from this blog.
      Do we leave them to their own karma and not think too much into it ?
      Now I truely understand why a mightly warrior like Arjuna faced depression during Mahabharata war.
      Earlier i used to wonder is Arjuna so weak to go under depression. Raising weapon against own blood and gurus was really hard on him.

      Did war of Mahabharata reduce arjuna's karma ? even with Krishna on his side he does not get Moksha ??????

      Makes me sometimes wonder what else simple people like us have to do ?

      Also i understand when u say karmically women can't achive moksha.

      Does following dharma totally alienate you ? or changes your company with the ones who follow your path ?
      Do we leave our friends who are still stuck in the old rut ( and we know they have chosen not to get out of it even though they suffer ) ?

      Delete
  106. Glad to see you back Kaptaan Sahib, hope you are recharged. Was W.D Gann a genius or did he do partial ripoff of Vedic Astrology and was still one of the greatest stock market predictors.

    ReplyDelete
    Replies
    1. All Gann Theory, Elliot Wave analysis is based on astrology and fractal theory respectively which is lifted from India.
      Captain has already mentioned about Fibonacci series and the golden ratio which was lifted from India is used in stock markets and is a critical element of Elliot wave theory.
      I know this because I perform regular analysis with these.

      Delete
  107. It may be a tough decision to shift capital Dilli out of inversion layer.

    Lots of vested interests will try n abort it.
    No other solution in sight? What else will disruptor the inversion layer.

    High voltage electron beams???

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  108. It is good to see you blogging again captain

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  109. namaste captain ji,

    thanks & welcome for coming back.

    om

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  110. Nobody can explain KARMA as you have done! Thanks a TON dear Captain.
    Grateful to you always!

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  111. https://timesofindia.indiatimes.com/home/sunday-times/jnu-its-not-about-freebies-its-about-freedom/articleshow/72092659.cms

    JEW FAIZ AHMED FAIZ WAS A ROTHSCHILD COMMIE.. HIS WIFE WAS A WHITE SKINNED JEWISH HONEY TRAP ALYS ...

    NEHRUs HAF BROTHER JEW SHEIKH ABDULLAH WHOSE WIFE IS THE DAUGHTER OF FRENCH JEW MICHAEL HARRY NEDOU MARRIED OFF FAIZ..

    ALYS WAS THE AUNT OF JEW SALMAN TASEER WHOSE HAS SEX WITH TAVLEEN SINGH AND PRODUCED JEW AATISH TASEER....

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  112. SOMEBODY ASKED ME --

    CAPTAIN, ARE THE GERMANS IN SOUTH AMERICA , CHRISTIANS?

    SORRY--

    GERMAN IN SOUTH AMERICA ARE ALL JEWS-- OR GERMAN CHRISTIANS WITH JEWISH WIVES..

    AFTER WW2 JEW HITLER AND HIS JEWISH THUGS ESCAPED TO SOUTH AMERICA BY SUBMARINES..

    http://ajitvadakayil.blogspot.com/2015/10/if-zionist-jews-created-isis-who.html

    THEY WERE WARNED BY THE ZIONISTS -- PRETEND TO BE CHRISTIANS AND CHANGE YOUR NAMES.. KOSHER WORSHIP MUST BE DONE ONLYBIN SECRET AT HOME .. NEVER ATTEND ANY PUBLIC SYNAGOGUES..

    ONE JEW ADOLF EICHMANN IGNORED THIS STRICT INSTRUCTION AND MOSSAD MADE AN EXAMPLE OUT OF HIM..

    THE ENTIRE MEDIA OF SOUTH AND CENTRAL AMERICA IS JEWISH..( LIKE THE REST OF THE PLANET )..

    ENEMY OF JEWS IS ENEMY OF THE NATION..

    THESE JEWISH MEDIA BARONS HAVE KILLER ORGS TO CREATE NEWS, PLANT BOMBS IN PUBLIC SPACES, ASSASSINATE POLICE..

    THESE JEWISH MEDIA ARE CONTROLLED BY JEWISH OLIGARCHS WHOSE TENTACLES ARE IN EVERY GOVT INSTITUTIONS..

    WHEN IT CAME TO PABLO ESCOBAR, THE JEWISH DEEP STATE CONTROLLED THE LEFT AND RIGHT WING GUERRILLAS..

    THIS IS BASED ON ROTHSCHILDs DIKTAT- " IF YOU WANT TO CONTROL THE OPPOSITION LEAD THEM YOURSELF"..

    GANDHI AND INDIAN NATIONAL CONGRESS WAS CONTROLLED BY JEW ROTHSCHILD..

    TILL TRUMP CAME ON SCENE NOBODY BELIEVED ME..

    ROTHSCHILDs CANDIDATE HILLARY CLINTON WAS GIVEN PRESIDENTIAL DEBATE QUESTIONS IN ADVANCE..

    HAVE YOU SEEN HOW THE JEWISH MEDIA HAS LAID SIEGE ON TRUMP TODAY? PEOPLE NOW SMELL A RAT..

    THE US JUDICIARY WAS CONTROLLED BY THE JEWISH DEEP STATE STILL TRUMP MANAGED TO TILT IT A WEE BIT AWAY FROM JEWS..

    http://ajitvadakayil.blogspot.com/2018/10/second-defeat-for-deep-state-capt-ajit.html

    THE RULING FAMILIES OF AMERICA ARE ALL JEWISH DESCENDANTS OF ROTHSCHILDs DRUG RUNNERS..

    http://ajitvadakayil.blogspot.com/2010/12/dirty-secrets-of-boston-tea-party-capt.html

    MODI / AJIT DOVAL / RAW KNOW SHIT ABOUT WORLD INTRIGUE..

    BHARATMATA IS RACING TO BE THIS PLANETs NO 1 SUPERPOWER IN 14 YEARS.. WITH PEOPLE LIKE JEW DARLING MODI IN CHARGE WHO KNOWS?

    HESS ( NO 2 RANK IN GERMANY ) PARACHUTED TO ENGLAND ONE DAY BEFORE START OF WW2 TO TELL THE BRITISH KING THAT HITLER IS A JEW..

    HE GOT A SHOCK OF HIS LIFE WHEN HE CAME TO KNOW THE HARD WAY, THAT THE BRITISH ROYALTY ARE OF GERMAN JEW BLOOD-- THAT CHURCHILL/ ROOSEVELT/ STALIN/ EISENHOWER ARE ALL JEWS.

    http://ajitvadakayil.blogspot.com/2011/11/rudolf-hess-honourable-man-who-was.html

    capt ajit vadakayil
    ..

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  113. COMMENTS IN THIS BLOG POST ARE NOW CLOSED..

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  114. https://m.timesofindia.com/business/india-business/air-india-bharat-petroleum-corporation-to-be-sold-by-march-fm/articleshow/72090771.cms

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  115. Dear Captainji,

    So happy today, Sir. Welcome back.

    Regards,
    Bunga

    ReplyDelete