Monday, November 25, 2019

WHAT ARTIFICIAL INTELLIGENCE CANNOT DO , a grim note to the top 100 intellectuals of this planet , Part 6 - Capt Ajit Vadakayil



THIS POST IS CONTINUED FROM PART 5, BELOW--

https://ajitvadakayil.blogspot.com/2019/11/what-artificial-intelligence-cannot-do_4.html




Generative adversarial networks (GANs) are deep neural net architectures comprised of two nets, pitting one against the other (thus the “adversarial”).




GANs’ potential is huge, because they can learn to mimic any distribution of data. That is, GANs can be taught to create worlds eerily similar to our own in any domain: images, music, speech, prose. They are robot artists in a sense, and their output is impressive

In a surreal turn, Christie’s sold a portrait for $432,000 that had been generated by a GAN, based on open-source code written by Robbie Barrat of Stanford.

In 2019, DeepMind showed that variational autoencoders (VAEs) could outperform GANs on face generation.




Autoencoders encode input data as vectors. They create a hidden, or compressed, representation of the raw data. They are useful in dimensionality reduction; that is, the vector serving as a hidden representation compresses the raw data into a smaller number of salient dimensions. 

Autoencoders can be paired with a so-called decoder, which allows you to reconstruct input data based on its hidden representation, much as you would with a restricted Boltzmann machine.





To understand GANs, you should know how generative algorithms work, and for that, contrasting them with discriminative algorithms is instructive. Discriminative algorithms try to classify input data; that is, given the features of an instance of data, they predict a label or category to which that data belongs.

Meanwhile, the generator is creating new, synthetic images that it passes to the discriminator. It does so in the hopes that they, too, will be deemed authentic, even though they are fake. The goal of the generator is to generate passable hand-written digits: to lie without being caught. The goal of the discriminator is to identify images coming from the generator as fake.

Here are the steps a GAN takes:--- 
The generator takes in random numbers and returns an image.
This generated image is fed into the discriminator alongside a stream of images taken from the actual, ground-truth dataset.
The discriminator takes in both real and fake images and returns probabilities, a number between 0 and 1, with 1 representing a prediction of authenticity and 0 representing fake.

So you have a double feedback loop:--

The discriminator is in a feedback loop with the ground truth of the images, which we know.
The generator is in a feedback loop with the discriminator.

You can think of a GAN as the opposition of a counterfeiter and a cop in a game of cat and mouse, where the counterfeiter is learning to pass false notes, and the cop is learning to detect them. Both are dynamic; i.e. the cop is in training, too (to extend the analogy, maybe the central bank is flagging bills that slipped through), and each side comes to learn the other’s methods in a constant escalation.

One neural network, called the generator, generates new data instances, while the other, the discriminator, evaluates them for authenticity; i.e. the discriminator decides whether each instance of data that it reviews belongs to the actual training dataset or not.

When you train the discriminator, hold the generator values constant; and when you train the generator, hold the discriminator constant. Each should train against a static adversary. For example, this gives the generator a better read on the gradient it must learn by.

Each side of the GAN can overpower the other. If the discriminator is too good, it will return values so close to 0 or 1 that the generator will struggle to read the gradient. If the generator is too good, it will persistently exploit weaknesses in the discriminator that lead to false negatives. This may be mitigated by the nets’ respective learning rates. The two neural networks must have a similar “skill level.”

GANs take a long time to train. On a single GPU a GAN might take hours, and on a single CPU more than a day.


Generative Adversarial Networks – describe pairs of alternately trained models using competing deep learning algorithms. Here, the first model is trained using a second model, to discriminate between actual data and synthetic data. This ability to capture and copy variations within a dataset can be applied for uses such as understanding risk and recovery in healthcare and pharmacology.

Adversarial machine learning is a technique employed in the field of machine learning which attempts to fool models through malicious input. This technique can be applied for a variety of reasons, the most common being to attack or cause a malfunction in standard machine learning models.



Machine learning techniques were originally designed for stationary and benign environments in which the training and test data are assumed to be generated from the same statistical distribution. 

However, when those models are implemented in the real world, the presence of intelligent and adaptive adversaries may violate that statistical assumption to some degree, depending on the adversary. 

This technique shows how a malicious adversary can surreptitiously manipulate the input data so as to exploit specific vulnerabilities of learning algorithms and compromise the security of the machine learning system

In the old days, the voice generated by computers did not sound human, and the creation of a voice model required hundreds of hours of coding and tweaking. Now, with the help of neural networks, synthesizing human voice has become less cumbersome.

The process involves using generative adversarial networks (GAN), an AI technique that pits neural networks against each other to create new data.

GANs are an exciting and rapidly changing field, delivering on the promise of generative models in their ability to generate realistic examples across a range of problem domains, most notably in image-to-image translation tasks such as translating photos of summer to winter or day to night, and in generating photorealistic photos of objects, scenes, and people that even humans cannot tell are fake.

First, a neural network ingests numerous samples of a person’s voice until it can tell whether a new voice sample belongs to the same person. Then, a second neural network generates audio data and runs it through the first one to see if validates it as belonging to the subject. 

If it doesn’t, the generator corrects its sample and re-runs it through the classifier. The two networks repeat the process until they are able to generate samples that sound natural.

 For instance, companies are using AI-powered voice synthesis to enhance their customer experience and give their brand its own unique voice. In the field of medicine, AI is helping ALS patients to regain their true voice instead of using a computerized voice. 




AI speech synthesis also has its evil uses. Namely, it can be used for forgery, to place calls with the voice of a targeted person, or to spread fake news by imitating the voice of a head of state or high-profile politician.

GANs can generate realistic images by training a  'detective ANN' to recognise whether a picture was produced by a human or a computer, then  training a 'forger ANN' to produce images, which are tested by the detective ANN. 



In the process,  the pair of ANNs both get better, the detective at identifying fake images and the forger at  producing realistic images. The forger can be used to develop various image modification and production tools, for example to create aged versions of real faces, or to generate completely novel imagined faces.

The same principles have been applied to train ANNs to produce realistic sounds, such as voice impersonation, or videos, adding (imagined) movements to photographs.  Such techniques can be used to generate extremely realistic AI-generated videos known as  'deepfakes', which have been used to produce fake pornographic videos featuring celebrities, and  videos that appear to show politicians making statements

The problem with neural networks, however, is that the way they develop their pattern recognition behavior is very complex and opaque. And despite their name, neural networks work in ways that are very different from the human brain. That’s why they can be fooled in ways that will be unnoticed by humans.



Adversarial examples are input data manipulated in ways that will force a neural network to change its behavior while maintaining the same meaning to a human observer. For instance, in the case of an image classifier neural network, adding a special layer of noise to an image will cause the AI to assign a different classification to it.

During adversarial training, engineers expand neural networks. For each layer, they add more neurons to memorize the mistakes and make the AI model more robust

Hackers have been tricking AI systems using what is termed “adversarial attacks” where there is an added layer (the adversary) onto data, such as an extra layer of noise on an image


Companies like FedEx and Sprint are also using predictive analytics to pinpoint customers who are “flight risk” factors and may defect to a competitor. This can trick the AI’s algorithms and make it misclassify the image, which can then let malicious code enter.


Pre-empting criminals attempting to hijack artificial intelligence by tampering with datasets or the physical environment, researchers have turned to adversarial machine learning. This is where data has been tweaked to trick a neural network and fool systems into seeing something that isn't there, ignoring what is, or misclassifying objects entirely.

Currently, there is not a concrete way for defending against adversarial machine learning; however, there are a few techniques which can help prevent an attack of this type from happening. Such techniques include adversarial training, defensive distillation.

Adversarial training is a process where examples adversarial instances are introduced to the model and labeled as threatening. This process can be useful in preventing further adversarial machine learning attacks from occurring, but require large amounts of maintenance.

Defensive distillation aims to make a machine learning algorithm more flexible by having one model predict the outputs of another model which was trained earlier.  This approach can identify unknown threats. 

It is similar in thought to generative adversarial networks (GAN), which sets up two neural networks together to speed up machine learning processes—in the idea that two machine learning models are used together.  This happens very frequently – some of the most advanced spammer groups try to throw the Gmail filter off-track by reporting massive amounts of spam emails as not spam

Adversarial data describes a situation in which human users intentionally supply an algorithm with corrupted information. The corrupted data throws off the machine learning process, tricking the algorithm into reaching fake conclusions or incorrect predictions.


UC Berkeley professor Dawn Song notably tricked a self-driving car into thinking that a stop sign says the speed limit is 45 miles per hour.

A malicious attack of this nature could easily result in a fatal accident. Similarly, compromised algorithms could lead to faulty biomedical research, endangering lives or delaying life-saving innovations.

Adversarial data has only recently begun to be recognized for the threat it is — and it can’t go overlooked any longer.

Interestingly, adversarial data output can occur even without malicious intent. This is largely because of the way algorithms can “see” things in the data that we humans are unable to discern. Because of that “visibility,” a recent case study from MIT describes adversarial examples as “features” rather than bugs.

In the study, researchers separated “robust” and “non-robust” characteristics during AI learning. Robust features are what humans typically perceive, while non-robust features are only detected by AI. An attempt at having an algorithm recognize pictures of cats revealed that the system was looking at real patterns present in the images to draw incorrect conclusions.

The misidentification occurred because the AI was looking at an apparently imperceivable set of pixels that led it to improperly identify photos. This caused the system to be inadvertently trained to use misleading patterns in its identification algorithm.

These non-robust characteristics served as a type of interfering “noise” that led to flawed results from the algorithm. As a result, for hackers to interfere with AI, they often simply need to introduce a few non-robust characteristics — things that aren’t easily identified by human eyes, but that can dramatically change AI output.

Adversarial attacks could induce an algorithm to incorrectly label harmful or contaminated samples as clean and benign. This can lead to misguided research results or incorrect medical diagnoses.

Despite these issues, adversarial data can also be used for good. Indeed, many developers have begun using adversarial data to uncover system vulnerabilities on their own, allowing them to implement security upgrades before hackers can take advantage of the weakness. Developers are using machine learning to create AI systems that are more adept at identifying and eliminating potential digital threats.

At a high level, attacks against classifiers can be broken down into three types:

Adversarial inputs, which are specially crafted inputs that have been developed with the aim of being reliably misclassified in order to evade detection. Adversarial inputs include malicious documents designed to evade antivirus, and emails attempting to evade spam filters.

Data poisoning attacks, which involve feeding training adversarial data to the classifier. The most common attack type we observe is model skewing, where the attacker attempts to pollute training data in such a way that the boundary between what the classifier categorizes as good data, and what the classifier categorizes as bad, shifts in his favor. 

The second type of attack we observe in the wild is feedback weaponization, which attempts to abuse feedback mechanisms in an effort to manipulate the system toward misclassifying good content as abusive (e.g., competitor content or as part of revenge attacks).

Model stealing techniques, which are used to “steal” (i.e., duplicate) models or recover training data membership via blackbox probing. This can be used, for example, to steal stock market prediction models and spam filtering models, in order to use them or be able to optimize more efficiently against such models.

The creation of adversarial samples often involves first building a ‘mask’ that can be applied to an existing input, such that it tricks the model into producing the wrong output. In the case of adversarially created image inputs, the images themselves appear unchanged to the human eye. .

Adversarial samples created in this way can even be used to fool a classifier when the image is printed out, and a photo is taken of the printout. Even simpler methods have been found to create adversarial images. 

The ease, and the number of ways in which adversarial samples designed to fool image recognition models can be created, illustrates that should these models be used to make important decisions (such as in content filtering systems), mitigations (described in the fourth article in this eries) should be carefully considered before production deployment.

An attacker submits adversarially altered pornographic ad banners to a popular, well-reputed ad provider service. The submitted images bypass their machine learning-based content filtering system. 

The pornographic ad banner is displayed on frequently visited high-profile websites. As a result, minors are exposed to images that would usually have been blocked by parental control software. This is an availability attack.

Researchers have recently demonstrated that adversarial samples can be crafted for areas other than image classification.

 In August 2018, a group of researchers at the Horst Görtz Institute for IT Security in Bochum, Germany, crafted psychoacoustic attacks against speech recognition systems, allowing them to hide voice commands in audio of birds chirping. The hidden commands were not perceivable to the human ear, so the audio tracks were perceived differently by humans and machine-learning-based systems.

An attacker embeds hidden voice commands into video content, uploads it to a popular video sharing service, and artificially promotes the video (using a Sybil attack).



Sybil Attack is a type of attack seen in peer-to-peer networks in which a node in the network operates multiple identities actively at the same time and undermines the authority/power in reputation systems. In a Sybil attack, the attacker subverts the reputation system of a peer-to-peer network by creating a large number of pseudonymous identities and uses them to gain a disproportionately large influence.


When the video is played on the victim’s system, the hidden voice commands successfully instruct a digital home assistant device to purchase a product without the owner knowing, instruct smart home appliances to alter settings (e.g. turn up the heat, turn off the lights, or unlock the front door), or to instruct a nearby computing device to perform searches for incriminating content (such as drugs or child pornography) without the owner’s knowledge (allowing the attacker to subsequently blackmail the victim). This is an availability attack.


An attacker forges a ‘leaked’ phone call depicting plausible scandalous interaction involving high-ranking politicians and business people. The forged audio contains embedded hidden voice commands. 

The message is broadcast during the evening news on national and international TV channels. The attacker gains the ability to issue voice commands to home assistants or other voice recognition control systems (such as Siri) on a potentially massive scale. This is an availability attack.


Availability attacks against natural language processing systems--


Natural language processing (NLP) models are used to parse and understand human language. Common uses of NLP include sentiment analysis, text summarization, question/answer systems, and the suggestions you might be familiar with in web search services.    

It is a piece of cake to use adversarial samples to fool natural language processing models by replacing words with synonyms  to bypass spam filtering, change the outcome of sentiment analysis, and fool a fake news detection model. 

English is a stupid language..

http://ajitvadakayil.blogspot.com/2010/11/sanskrit-digital-language-versus-versus.html


Scenario: evade fake news detection systems to alter political discourse

Fake news detection is a relatively difficult problem to solve with automation, and hence, fake news detection solutions are still in their infancy. As these techniques improve and people start to rely on verdicts from trusted fake news detection services, tricking such services infrequently, and at strategic moments would be an ideal way to inject false narratives into political or social discourse. In such a scenario, an attacker would create a fictional news article based on current events, and adversarially alter it to evade known respected fake news detection systems. The article would then find its way into social media, where it would likely spread virally before it can be manually fact-checked. This is an availability attack.

Scenario: trick automated trading algorithms that rely on sentiment analysis
Over an extended period of time, an attacker publishes and promotes a series of adversarially created social media messages designed to trick sentiment analysis classifiers used by automated trading algorithms. One or more high-profile trading algorithms trade incorrectly over the course of the attack, leading to losses for the parties involved, and a possible downturn in the market. This is an availability attack.


Availability attacks – reinforcement learning

Reinforcement learning is the process of training an agent to perform actions in an environment. Reinforcement learning models are commonly used by recommendation systems, self-driving vehicles, robotics, and games. Reinforcement learning models receive the current environment’s state (e.g. a screenshot of the game) as an input, and output an action (e.g. move joystick left). It is a piece of cake to use adversarial attacks to trick reinforcement learning models into performing incorrect actions. 


Two distinct types of attacks can be performed against reinforcement learning models.

A strategically timed attack modifies a single or small number of input states at a key moment, causing the agent to malfunction. For instance, in the game of pong, if a strategic attack is performed as the ball approaches the agent’s paddle, the agent will move its paddle in the wrong direction and miss the ball.

An enchanting attack modifies a number of input states in an attempt to “lure” the agent away from a goal. For instance, an enchanting attack against an agent playing Super Mario could lure the agent into running on the spot, or moving backwards instead of forwards.

Scenario: hijack autonomous military drones
By use of an adversarial attack against a reinforcement learning model, autonomous military drones are coerced into attacking a series of unintended targets, causing destruction of property, loss of life, and the escalation of a military conflict. This is an availability attack.

Scenario: hijack an autonomous delivery drone
By use of a strategically timed policy attack, an attacker fools an autonomous delivery drone to alter course and fly into traffic, fly through the window of a building, or land (such that the attacker can steal its cargo, and perhaps the drone itself). This is an availability attack.

The processes used to craft attacks against classifiers, NLP systems, and reinforcement learning agents are similar. As of writing, all attacks crafted in these domains have been purely academic in nature, and we have not read about or heard of any such attacks being used in the real world. 

However, tooling around these types of attacks is getting better, and easier to use. During the last few years, machine learning robustness toolkits have appeared on github. These toolkits are designed for developers to test their machine learning implementations against a variety of common adversarial attack techniques. 

IBM Adversarial Robustness Toolbox, developed by IBM, contains implementations of a wide variety of common evasion attacks and defence methods, and is freely available on github. Cleverhans, a tool developed by Ian Goodfellow and Nicolas Papernot, is a Python library to benchmark machine learning systems’ vulnerability to adversarial examples. It is also freely available on github.


Replication attacks: transferability attacks
Transferability attacks are used to create a copy of a machine learning model (a substitute model), thus allowing an attacker to “steal” the victim’s intellectual property, or craft attacks against the substitute model that work against the original model. Transferability attacks are straightforward to carry out, assuming the attacker has unlimited ability to query a target model.

In order to perform a transferability attack, a set of inputs are crafted, and fed into a target model. The model’s outputs are then recorded, and that combination of inputs and outputs are used to train a new model. It is worth noting that this attack will work, within reason, even if the substitute model is not of absolutely identical architecture to the target model.


It is possible to create a ‘self-learning’ attack to efficiently map the decision boundaries of a target model with relatively few queries. This works by using a machine learning model to craft samples that are fed as input to the target model. The target model’s outputs are then used to guide the training of the sample crafting model. As the process continues, the sample crafting model learns to generate samples that more accurately map the target model’s decision boundaries.


Confidentiality attacks: inference attacks
Inference attacks are designed to determine the data used during the training of a model. Some machine learning models are trained against confidential data such as medical records, purchasing history, or computer usage history. An adversary’s motive for performing an inference attack might be out of curiosity – to simply study the types of samples that were used to train a model – or malicious intent – to gather confidential data, for instance, for blackmail purposes.

A black box inference attack follows a two-stage process. The first stage is similar to the transferability attacks described earlier. The target model is iteratively queried with crafted input data, and all outputs are recorded. This recorded input/output data is then used to train a set of binary classifier ‘shadow’ models – one for each possible output class the target model can produce. For instance, an inference attack against an image classifier than can identify ten different types of images (cat, dog, bird, car, etc.) would create ten shadow models – one for cat, one for dog, one for bird, and so on. All inputs that resulted in the target model outputting “cat” would be used to train the “cat” shadow model, and all inputs that resulted in the target model outputting “dog” would be used to train the “dog” shadow model, etc.

The second stage uses the shadow models trained in the first step to create the final inference model. Each separate shadow model is fed a set of inputs consisting of a 50-50 mixture of samples that are known to trigger positive and negative outputs. The outputs produced by each shadow model are recorded. For instance, for the “cat” shadow model, half of the samples in this set would be inputs that the original target model classified as “cat”, and the other half would be a selection of inputs that the original target model did not classify as “cat”. 

All inputs and outputs from this process, across all shadow models, are then used to train a binary classifier that can identify whether a sample it is shown was “in” the original training set or “out” of it. So, for instance, the data we recorded while feeding the “cat” shadow model different inputs, would consist of inputs known to produce a “cat” verdict with the label “in”, and inputs known not to produce a “cat” verdict with the label “out”. A similar process is repeated for the “dog” shadow model, and so on. All of these inputs and outputs are used to train a single classifier that can determine whether an input was part of the original training set (“in”) or not (“out”).

This black box inference technique works very well against models generated by online machine-learning-as-a-service offerings, such as those available from Google and Amazon. Machine learning experts are in low supply and high demand. Many companies are unable to attract machine learning experts to their organizations, and many are unwilling to fund in-house teams with these skills. Such companies will turn to machine-learning-as-a-service’s simple turnkey solutions for their needs, likely without the knowledge that these systems are vulnerable to such attacks.


Poisoning attacks against anomaly detection systems

Anomaly detection algorithms are employed in areas such as credit card fraud prevention, network intrusion detection, spam filtering, medical diagnostics, and fault detection. Anomaly detection algorithms flag anomalies when they encounter data points occurring far enough away from the ‘centers of mass’ of clusters of points seen so far. These systems are retrained with newly collected data on a periodic basis. As time goes by, it can become too expensive to train models against all historical data, so a sliding window (based on sample count or date) may be used to select new training data.



Attacks against recommenders
Recommender systems are widely deployed by web services (e.g., YouTube, Amazon, and Google News) to recommend relevant items to users, such as products, videos, and news. Some examples of recommender systems include:----

YouTube recommendations that pop up after you watch a video
Amazon “people who bought this also bought…”
Twitter “you might also want to follow” recommendations that pop up when you engage with a tweet, perform a search, follow an account, etc.
Social media curated timelines
Netflix movie recommendations

App store purchase recommendations


Recommenders are implemented in various ways:---

Recommendation based on user similarity
This technique finds users most similar to a target user, based on items they’ve interacted with. They then predict the target user’s rating scores for other items based on the rating scores of those similar users. For instance, if user A and user B both interacted with item 1, and user B also interacted with item 2, recommend item 2 to user A.

Recommendation based on item similarity
This technique finds common interactions between items and then recommends a target user items based on those interactions. For instance, if many users have interacted with both items A and B, then if a target user interacts with item A, recommended B.

Recommendation based on both user and item similarity
These techniques use a combination of both user and item similarity-matching logic. This can be done in a variety of ways. For instance, rankings for items a target user has not interacted with yet are predicted via a ranking matrix generated from interactions between users and items that the target already interacted with.

An underlying mechanism in many recommendation systems is the co-visitation graph. It consists of a set of nodes and edges, where nodes represent items (products, videos, users, posts) and edge weights represent the number of times a combination of items were visited by the same user.
  
 The most widely used attacks against recommender systems are Sybil attacks (which are integrity attacks, see above). The attack process is simple – an adversary creates several fake users or accounts, and has them engage with items in patterns designed to change how that item is recommended to other users. Here, the term ‘engage’ is dependent on the system being attacked, and could include rating an item, reviewing a product, browsing a number of items, following a user, or liking a post. 

Attackers may probe the system using ‘throw-away’ accounts in order to determine underlying mechanisms, and to test detection capabilities. Once an understanding of the system’s underlying mechanisms has been acquired, the attacker can leverage that knowledge to perform efficient attacks on the system (for instance, based on knowledge of whether the system is using co-visitation graphs). Skilled attackers carefully automate their fake users to behave like normal users in order to avoid Sybil attack detection techniques.


Motives include:--

promotion attacks – trick a recommender system into promoting a product, piece of content, or user to as many people as possible

demotion attacks – cause a product, piece of content, or user to not be promoted as much as it should
social engineering – in theory, if an adversary already has knowledge on how a specific user has interacted with items in the system, an attack can be crafted to target that user with a recommendation such as a YouTube video, malicious app, or imposter account to follow.

Numerous attacks are already being performed against recommenders, search engines, and other similar online services. In fact, an entire industry exists to support these attacks. With a simple web search, it is possible to find inexpensive purchasable services to poison app store ratings, restaurant rating systems, and comments sections on websites and YouTube, inflate online polls, and engagement (and thus visibility) of content or accounts, and manipulate autocomplete and search results.

The prevalence and cost of such services indicates that they are probably widely used. Maintainers of social networks, e-commerce sites, crowd-sourced review sites, and search engines must be able to deal with the existence of these malicious services on a daily basis. Detecting attacks on this scale is non-trivial and takes more than rules, filters, and algorithms. Even though plenty of manual human labour goes into detecting and stopping these attacks, many of them go unnoticed.

From celebrities inflating their social media profiles by purchasing followers, to Cambridge Analytica’s reported involvement in meddling with several international elections, to a non-existent restaurant becoming London’s number one rated eatery on TripAdvisor, to coordinated review brigading ensuring that conspiratorial literature about vaccinations and cancer were highly recommended on Amazon , to a plethora of psy-ops attacks launched by the alt-right, high profile examples of attacks on social networks are becoming more prevalent, interesting, and perhaps disturbing. 

These attacks are often discovered long after the fact, when the damage is already done. Identifying even simple attacks while they are ongoing is extremely difficult, and there is no doubt many attacks are ongoing at this very moment.


Attacks against federated learning systems--
Federated learning is a machine learning setting where the goal is to train a high-quality centralized model based on models locally trained in a potentially large number of clients, thus, avoiding the need to transmit client data to the central location. A common application of federated learning is text prediction in mobile phones. 

Each phone contains a local machine learning model that learns from its user (for instance, which recommended word they clicked on). The phone transmits its learning (the phone’s model’s weights) to an aggregator system, and periodically receives a new model trained on the learning from all of the other phones participating.



Attacks against federated learning can be viewed as poisoning or supply chain (integrity) attacks. A number of Sybils, designed to poison the main model, are inserted into a federated learning network. These Sybils collude to transmit incorrectly trained model weights back to the aggregator which, in turn, pushes poisoned models back to the rest of the participants. 

For a federated text prediction system, a number of Sybils could be used to perform an attack that causes all participants’ phones to suggest incorrect words in certain situations. The ultimate solution to preventing attacks in federated learning environments is to find a concrete method of establishing and maintaining trust amongst the participants of the network, which is clearly very challenging.


The understanding of flaws and vulnerabilities inherent in the design and implementation of systems built on machine learning and the means to validate those systems and to mitigate attacks against them are still in their infancy, complicated – in comparison with traditional systems –  by the lack of explainability to the user, heavy dependence on training data, and oftentimes frequent model updating. 

This field is attracting the attention of researchers, and is likely to grow in the coming years. As understanding in this area improves, so too will the availability and ease-of-use of tools and services designed for attacking these systems.

As artificial-intelligence-powered systems become more prevalent, it is natural to assume that adversaries will learn how to attack them. Indeed, some machine-learning-based systems in the real world have been under attack for years already. As we witness today in conventional cyber security, complex attack methodologies and tools initially developed by highly resourced threat actors, such as nation states, eventually fall into the hands of criminal organizations and then common cyber criminals. 

This same trend can be expected for attacks developed against machine learning models.
Data Poisoning: Owing to the large volume of structured and unstructured data, BFSI companies become a prime target for cyber crooks to perpetrate data attacks. As deployment of AI-enabled models in financial services sees an uptick, there is a risk of manipulating the data used to train these models by hackers. Known as Data Poisoning, such an attack results in generation of erroneous output.



Adversarial AI: As organizations deploy intelligent systems, there are untrusted infrastructure tools such as open source data analysts and ML frameworks which can be compromised by criminals to extract data. Hackers use adversarial machine learning to detect patterns and identify vulnerabilities and fraud controls in the network. This enables then to commission malware in the systems which sits undetected on the network, and slowly exfiltrates confidential data passing through the system.

AI-systems are susceptible to adversarial attacks, and thus input sanitization should be on the security agenda of BFSI companies. IT systems should be trained to identify potential adversarial attacks by implementing a weaker version of the same such as distorted images. To prevent data leakage, security infrastructure should exhaustively cover all network endpoints. 

Humans are often the weakest link in security; business leaders should take conscious steps such as regular trainings and awareness initiatives to develop a common understanding of company’s security procedures among the employees. It is important to note that security is a system design, therefore security features should be baked in the design stages of an AI-based application and updated overtime to tackle the expanding threat landscape.

Trojans in Artificial Intelligence (TrojAI)
The U.S. Army Research Office, in partnership with the Intelligence Advanced Research Projects Activity, seeks research and development of technology and techniques for detection of Trojans in Artificial Intelligence. TrojAI is envisioned to be a two-year effort with multiple awardees coming together as a group of performers who will work together to achieve the common program goals set forth in the Broad Agency Announcement.



Current State
Using current machine learning methods, an Artificial Intelligence is trained on data, learns relationships in that data, and then is deployed to the world to operate on new data. For example, an AI can be trained on images of traffic signs, learn what stop signs and speed limit signs look like, and then be deployed as part of an autonomous car. The problem is that an adversary that can disrupt the training pipeline can insert Trojan behaviors into the AI. 

For example, an AI learning to distinguish traffic signs can be given potentially just a few additional examples of stop signs with yellow squares on them, each labeled “speed limit sign”. If the AI were deployed in a self-driving car, an adversary could cause the car to run through the stop sign just by putting a sticky note on it, since the AI would incorrectly see it as a speed limit sign. The goal of the TrojAI Program is to combat such Trojan attacks by inspecting AIs for Trojans.

Defending Against Trojan Attacks
Trojan attacks, also called backdoor or trapdoor attacks, involve modifying an AI to attend to a specific trigger in its inputs, which if present will cause the AI to give a specific incorrect response. In the traffic sign case, the trigger is a sticky note. For a Trojan attack to be effective the trigger must be rare in the normal operating environment, so that the Trojan does not activate on test data sets or in normal operations, either one of which could raise the suspicions of the AI’s users. 



Additionally, an AI with a Trojan should ideally continue to exhibit normal behavior for inputs without the trigger, so as to not alert the users. Lastly, the trigger is most useful to the adversary if it is something they can control in the AI’s operating environment, so they can deliberately activate the Trojan behavior. Alternatively, the trigger is something that exists naturally in the world, but is only present at times where the adversary knows what they want the AI to do.



Obvious defenses against Trojan attacks include securing the training data (to protect data from manipulation), cleaning the training data (to make sure the training data is accurate), and protecting the integrity of a trained model (prevent further malicious manipulation of a trained clean model). 

Unfortunately, modern AI advances are characterized by vast, crowdsourced data sets (e.g., 109 data points) that are impractical to clean or monitor. Additionally, many bespoke AIs are created by transfer learning: take an existing, public AI published online and modify it a little for the new use case. 

Trojans can persist in an AI even after such transfer learning. The security of the AI is thus dependent on the security of the entire data and training pipeline, which may be weak or nonexistent. Furthermore, the user may not be the one doing the training. Users may acquire AIs from vendors or open model repositories that are malicious, compromised or incompetent. 

Acquiring an AI from elsewhere brings all of the problems with the data pipeline, as well as the possibility of the AI being modified directly while stored at a vendor or in transit to the user. Given the diffuse and unmanageable supply chain security, the focus for the TrojAI Program is on the operational use case where the complete AI is already in the would-be users’ hands: detect if an AI has a Trojan, to determine if it can be safely deployed.

Evidently, the arm’s race between defenders and attackers favors the attackers. The rise of fake news and ‘data poisoning’ attacks aimed at machine learning inspired cyber threat intelligence systems is the result of a new  strategy adopted by attackers that adds complexity to an already complex and ever changing cyber threat and scape.

Attackers are now exploiting a vulnerability in the data training process of AI and ML inspired cyber threat  intelligence systems by injecting ‘poisoned data’ in training datasets to allow their malicious code to evade  detection. The ‘poisoned’ corpus is specifically tailored and targeted to AI and ML cyber threat intelligence  defense systems, especially those based on supervised and semi-supervised learning algorithms to make them  misclassify malicious code as legitimate data.


The input data itself is validated by using a mix of related indicators to determine its reliability. Based  on the validation of input data sources, the authors make an assumption that the corpus is trustworthy and then  add a security feature that prevents ‘data poisoning’ attacks. .. Our solution is based on working with trusted sources of input raw data. The dynamics of a solution  changes completely if the input raw data comes with ‘poisoned data’ that mimic trusted data. .


The arm’s race between cyberspace attackers and defender continues. Attackers’ tools, tactics and  procedures (TTPs) evolves so quickly that cyber defence, legislation and law enforcement lag behind.
On the one hand, new technology developments like cloud computing, social media, big data, machine  learning, internet of things and others are continually disrupting existing business models in a global  scale.

Hence, there is a mad rush to adopt new business models that open up new risks. It is no coincidence that today’s cyber threat landscape reflects that the attackers are gaining more grounds than the defenders. 

For example, attackers are very quick to adopt the latest technologies like artificial  intelligence (AI) and machine learning (ML) to detect and exploit defence systems’ vulnerabilities and,  evade detection asserts that attackers are using AI and ML to analyse  features of cyber threat intelligence systems on how they flag malware.


They then remove or conceal  through encryption the code snippet from their malware that could raise the red flags so that the classification algorithms cannot catch it.


The gap between attackers and defenders seems to be widening even more. This is no coincidence as  today’s cyber attackers are well funded and organized; they have vast resources at their disposal; operates in a well-structured, coordinated and highly incentivized underground economy

Today’s cyber attackers are patient and do their nefarious deeds with sophistication and targeting vulnerabilities in people, process and technology right across the globe with no respect for national boundaries. Attackers are now deploying advanced malware that leverages on cutting edge technology to not only circumvent advanced security defences but also to widen their scope and scale of their attacks

Attackers using autonomous and self-learning malware with catastrophic implications.  The anonymity or plausible deniability of cyber threats adds to the already complex threat landscape Cyber threats often emanate behind a veil of Internet anonymity that hides details of the attackers   

This anonymity is one of the biggest challenges of deterring any defence mechanism against or retaliating to cyber threats. Hence, the difficulty to determine who exactly is behind today’s cyber threats. This considerably challenges the field of cyberspace and has raised the intractable issue of  cybersecurity attribution. It must be noted that not knowing the enemy is one way to lose a battle

Chanakya’s theory onto the cyberspace battlefield and asserts that defenders who know their defence systems, the terrain of the cyberspace battlefield and  cybercriminals and their modus operandi have no reason to fear the result of a hundred cyberspace battles. However, it is also argued therein that defenders that know their defence systems and the  cyberspace battlefield’s terrain but not their enemy, for every victory gained, they also suffer a defeat.

This means that all efforts to understand the battlefield and own defence systems cannot guarantee  victory without an effort to understand the tactics of the adversary. It is also argued that defenders  who know neither their enemy nor their defence systems nor the terrain of the battlefield, will always  succumb in every battle that they engage in . 

Today’s threat landscape reflects that  cybersecurity defenders might know something about their defence systems, but they have a partial view of the cyberspace battlefield and have almost zero knowledge of the attackers and their modus  operandi.

Furthermore, a reactive approach to defending systems also adds to the already complex threat landscape. Most organisations only act after a breach has already occurred. In the ever changing cyber  threat landscape of rapid zero-days, advanced persistent threats (APTs), botnets, ransomware and state-sponsored espionage activities; a secure company today would be vulnerable by tomorrow.

A reactive approach to defence is totally insufficient to address today’s ever changing threats of fake news and ‘data poisoning’  Given the increasing use of predictive cybersecurity analytics and cyber threat intelligence platforms  which gives system defenders a capability to somehow anticipate signatures of new malware this is  inevitable. 

Attackers have since realised that new malware gets detected at first appearance by AI and  ML inspired malware classifiers and detectors in current cyber threat intelligence systems. Hence,  instead of concentrating their efforts on developing new malware, they are now investing their  resources into finding ways to breach AI and ML inspired cyber threat intelligence defences.


There is a huge need for proactive defence  efforts that make use of cyber threat intelligence systems. This is to help organizations build a better  situational awareness, recommend resilient cyber security controls, and learn from breaches in order  to adapt and re-shape existing controls to improve cyber threat detection and system resilience. 

Most  existing cyber threat intelligence (CTI) systems are leveraging on the current data-driven economy to  collect and collate massive cyber threat data from different source feeds. Attackers on the other hand  have since realized an opportunity to ‘poison’ cyber threat data sources to try and circumvent  detection systems.


Hence, it is  important that our solution make plausible means to validate, curate and secure input data to prevent  ‘data poisoning’... A robust cyber threat intelligence system can  provide highly technical metrics, countermeasures and corrective actions (.). 

The goal is to use  AI and ML algorithms in cyber threat intelligence platforms to transform threat data into actionable  intelligence to help thwart cyber-attacks. This has also caused attackers to focus on targeting datadriven cyber threat intelligence systems.

For example, some of the existing solutions are based on supervised learning algorithms that classify code as either clean (trustworthy) or malicious (deceptive) based  upon known signatures or patterns. Therefore, all that it takes for an attacker to foil such systems is to access the training data and tag malware as clean code. Such a scenario is possible, mainly because existing systems are concerned about the analysis of the data and have turned a blind eye to its protection.


At times attackers do not even have to corrupt the training  data sets, but alter the underlying algorithms that process the data…some existing threat intelligence systems are built based on ‘black box’ type  deep learning algorithms. This is to try and solve the problems of supervised learning algorithms.

However, with deep learning algorithms and the multiple layers in a neural network, it means that there is no way to know how system’ algorithms do their classification of threats in the different levels  of abstraction. This is a big cause for concern because it is not completely clear how the algorithms  make their decisions

Black box deep learning algorithms must be understandable to their creators and accountable to their users.

The layered approach  ensures that even if malware can circumvent one layer, it can still be detected by the other layers.

Within each layer, there is a number of individual ML models trained to recognize new and emerging  threats.

Their solution provides a robust multi-faceted view of new generation of threats that no single algorithm can achieve. The solution therein also uses stacked  ensemble models that take predictions from the base classifiers of ML models and combine them to create even stronger malware predictions.


Data protection is required to ensure that malicious entities cannot  tamper with the training and test data.

An attacker may use obfuscated attack samples in existing clusters  to prevent clustering algorithms from detecting their malicious code. This has raised serious concerns  around the integrity of AI and ML datasets and clustering algorithms thereof. .   cyber threat intelligence systems must adopt cybersecurity countermeasure that embrace the secure by design principle to effectively thwart ‘data poisoning‘ attacks (.) recognize the difficulty of identifying and separating ‘poisoned data’ samples from the legitimate corpus. 

Instead they choose to model their solution based on a small sample of training data, choosing only the points that are ‘close’ to legitimate points. Moreover, this work also made some interesting discoveries on the potential impact of ‘data poisoning’ in healthcare applications. For example, this study shows the effects that a small sample of ‘poisoned data’ points can have on the dosage of patients.

The results therein show that patient dosage can increase to an estimate of 350% . This basically mean that if patient health data is ‘poisoned’, patients would be made to take more than required drugs. This can potentially cause drug overdose and result in deaths. .. 

This is also based on supervised learning algorithms which basically mean that is suffers the same fate as other supervised learning algorithms, i.e. garbage-in, garbage-out. An attack to the clustering algorithms would reverse the results to make sources that are classified ‘not credible’ to be credible


Three ways that an attacker could use to evade detection by carefully adding moderate ‘poisoned data’ at different intervals. Though, this approach is  argued not to be worth it therein, but it performs well to throw off false-positives and negative-positive balance and hamper the efficacy of the detector 

The ANTIDOTE solution  therein was designed to prevent ‘data poisoning’ attacks from shifting false positives and false-negatives. Hence, the solution is argued to reject contaminated data. . on detecting ‘poisoned data’ samples using an anomaly detection system. The . al.’s methods work best if the chosen small training data sample is trustworthy. Should the contrary be true, both propositions fail dismally and can even result in ‘poisoned’ outlier detectors.



This would basically means that they will both work in reverse i.e. flagging malicious data as legitimate and legitimate data as malicious... The ‘black-box’ approach of unsupervised classifiers raises trust issues because it is not so clear how the algorithms make their decisions in classifying data points. Hence, the rise of research that attempts to solve the serious ‘data poisoning’ issues of AI and ML based cyber threat intelligence systems. .‘poisoned data’ which stands to poison even the data classifiers. 

Data cleansing of the corpus includes removing duplicate entries and null values. This exercise does not compromise the completeness and/or integrity of the data as obtained from the original source.
However, the removal of duplicate entries ensures that there is only one record for each and every  entry. This also helps in reducing the size of the corpus, more especially for storage purposes. 

DHCP protocol that changes IP addresses every time a machine is rebooted. .. Should an attacker attempt to inject ‘poisoned data’ the hash value of the changed data object would reflect that the data has been changed. This action raises a flag and sends the administrator and alert. Once the data objects have  been hashed and indexed, they are then encrypted with AES-124 for secure storage. The encryption is per data object as compared to the entire corpus. 

The security on the crypto comes with a performance cost in that each of the data object has to be individually decrypted before the data can be processed. The encryption process is not necessarily a problem because by the time the data is put into the database, there is not real-time requirement for it to be processed. So, the encryption is not necessarily time-bound as is the case with the decryption process. The decryption performance cost is balanced by the hash-based quick retrieval of data objects.


The hashed, indexed and encrypted data objects are stored in an encrypted database. This just adds another layer of security to prevent unauthorized access to the database. So an attacker would have to go through the database encryption before they can get to the encrypted data objects. So it takes  to layers to get to the plain-text data. The system uses a need-to-know principle to restrict access to  database. 

Hence, database access is restricted to the module of the system that does the processing  and analysing of the data and the administrators only. This also has the pipelining feature to monitor incomplete processes and alert the administrators in case of incomplete processes.. , the data includes IP address, reliability score, a unique object ID, date of verification and others. The contents of each of the data objects vary depending on the data source..

Attackers are now exploiting a vulnerability in the data training process of AI and ML inspired cyber  threat intelligence systems to allow their malicious code to evade detection.

One of the major safety strategies that has arisen from this research is an approach called model hardening, which has advanced techniques that combat adversarial attacks by  strengthening the architectural components of the systems. Model hardening techniques  may include adversarial training, where training data is methodically enlarged to include  adversarial examples.

Other model hardening methods involve architectural modification, regularisation, and data pre-processing manipulation. A second notable safety strategy is runtime detection, where the system is augmented with a discovery apparatus that can identify  and trace in real-time the existence of adversarial examples. 

You should consult with  members of your technical team to ensure that the risks of adversarial attack have been  taken into account and mitigated throughout the AI lifecycle. This  threat to safe and reliable AI involves a malicious compromise of data sources at the point of  collection and pre-processing. 

Data poisoning occurs when an adversary modifies or  manipulates part of the dataset upon which a model will be trained, validated, and tested. By altering a selected subset of training inputs, a poisoning attack can induce a trained AI system  into curated misclassification, systemic malfunction, and poor performance. 

An especially  concerning dimension of targeted data poisoning is that an adversary may introduce a  ‘backdoor’ into the infected model whereby the trained system functions normally until it  processes maliciously selected inputs that trigger error or failure.

In order to combat data poisoning, your technical team should become familiar with the  state of the art in filtering and detecting poisoned data. However, such technical solutions  are not enough. Data poisoning is possible because data collection and procurement often  involves potentially unreliable or questionable sources. 

When data originates in  uncontrollable environments like the internet, social media, or the Internet of Things, many  opportunities present themselves to ill-intentioned attackers, who aim to manipulate training  examples. Likewise, in third- party data curation processes (such as ‘crowdsourced’ labelling, annotation, and content identification), attackers may simply handcraft malicious inputs.

Data poisoning is a field of  cybersecurity. The goal of such attacks is to pervert a learning system by manipulating a small subset  of the training data. Data poisoning could be a serious threat to deep learning-based malware detection.


Indeed, the system will be trained on a large and uncontrolled set of software produced by other  people (some of who may be hackers). Thus, it will be important not to forget that one could have  introduced a few data-poisoned samples to the training data.

 In particular, training processes for deep  learning do not form hypotheses about the integrity of the training data. In addition,  shows that deep learning is sensitive to such attacks: on MNIST, reports the ability to make the error jump  from 1.3% to 2% and 4% just by manipulating 3% and 6% of the training dataset.

So, deep networks are sensitive both to adversarial examples and data poisoning data poisoning  that is invisible to human eyes can be generated by adding adversarial noise to the training data. 

The most classical data poisoning task consists of manipulating freely a small subset of the training  data; as such, when trained on these data, the targeted system will have a low testing accuracy Adversarial attacks are very impressive, especially in computer vision where small perturbations  of images are usually undetected to human eyes.

Concern can be broken down to specific areas:---

The ability of hackers to steal data used to train the algorithms.
The manipulation of data to provide incorrect results.
The use of AI to impersonate authorized users to access a network.
The ability of AI to automate cyberattacks.
If you think about the ability to reverse engineer an algorithm … you’re effectively stealing that application and you’re displacing the competitive advantage that company [that developed it] may have in the marketplace



Augmented intelligence is an alternative conceptualization of artificial intelligence that focuses on AI's assistive role, emphasizing the fact that cognitive technology is designed to enhance human intelligence rather than replace it.. The choice of the word augmented, which means "to improve," reinforces the role human intelligence plays when using machine learning and deep learning algorithms to discover relationships and solve problems. 

While a sophisticated AI program is certainly capable of making a decision after analyzing patterns in large data sets, that decision is only as good as the data that human beings gave the programming to use.

Deep Learning goes much further and attempts to analyze the nature of the phenomena that the data represents, including discovery of rules of behavior, interactions, and strategy.

Artificial intelligence and machine learning are often used in lieu of each other. However, they mean different things altogether, with machine learning algorithms simply being a subset of AI where the algorithms can undergo improvement after being deployed. This is known as self-improvement and is one of the most important parts of creating AI of the future.

Computer vision relies on pattern recognition and deep learning to recognize what’s in a picture or video. When machines can process, analyze and understand images, they can capture images or videos in real time and interpret their surroundings.

The financial technology sector has already started using AI to save time, reduce costs, and add value. Deep learning is changing the lending industry by using more robust credit scoring. Credit decision-makers can use AI for robust credit lending applications to achieve faster, more accurate risk assessment, using machine intelligence to factor in the character and capacity of applicants.

Deep-learning methods required thousands of observation for models to become relatively good at classification tasks and, in some cases, millions for them to perform at the level of humans. Deep learning is famous in giant tech companies; they are using big data to accumulate petabytes of data. It allows them to create an impressive and highly accurate deep learning model.

Neural architecture search (NAS) is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine learning. NAS has been used to design networks that are on par or outperform hand-designed architectures. Methods for NAS can be categorized according to the search space, search strategy and performance estimation strategy used

As impressive as NAS algorithms are, they come with a caveat. Their main limiting factor is computational power. The more you have, the faster the controller algorithm would iterate through architectures before reaching the best fit solution. And since most machine learning problems require a fair bit of ‘searching,’ using NAS turns into an arms race for computing power.

Thus, being resource-heavy makes the NAS algorithms relatively inaccessible to everyone, but a few technological giants with vast access to computing power and data.

However, a project called ScyNet is going down a path that could substantially lower the barrier to the access of NAS algorithms. To do that, we create a decentralized open network where people are rewarded for sharing computational resources and data.

. Data is all around us. The Internet of Things (IoT) and sensors have the ability to harness large volumes of data, while artificial intelligence (AI) can learn patterns in the data to automate tasks for a variety of business benefits.
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Artificial Intelligence enhances the speed, precision and effectiveness of human efforts. In financial institutions, AI techniques can be used to identify which transactions are likely to be fraudulent, adopt fast and accurate credit scoring, as well as automate manually intense data management tasks.
Not all features are meaningful for the algorithm. A crucial part of machine learning is to find a relevant set of features to make the system learns something.

One way to perform this part in machine learning is to use feature extraction. Feature extraction combines existing features to create a more relevant set of features. It can be done with PCA, T-SNE or any other dimensionality reduction algorithms.

For example, an image processing, the practitioner needs to extract the feature manually in the image like the eyes, the nose, lips and so on. Those extracted features are feed to the classification model.
When there is enough data to train on, deep learning achieves impressive results, especially for image recognition and text translation. The main reason is the feature extraction is done automatically in the different layers of the network.

One of the earliest examples of AI in security was in the filtering of spam email. Instead of applying crude filters to email, probabilities were applied using Bayesian filters. In this approach, the user would identify whether an email was spam. 

The algorithm would adjust the priorities of all of the words based on whether it was spam. In the context of malware classification, deep learning has been found effective in this domain. In this context, an executable application consists of a series of bytes that have a defined structure along with numeric instructions that are run on the given processor architecture. 

Rather than use the executable’s numerical instructions directly, these instructions are encoded using an embedded encoding. This entails taking an instruction numeric and translating it into a higher dimensional space (similar to the way words are encoded into vectors with word2vec for use by deep learning). The embedded encodings can then be applied to the deep neural network (DNN) through the convolutional and pooling layers to yield a classification. 

The DNN was trained on a set of executables that represented normal programs and those that were malware. The DNN could successfully isolate the features that make up a malware program versus a typical program. Fireeye demonstrated this approach with upwards of 96-percent accuracy in detecting malware in the wild. .With deep learning, algorithms can operate on relatively raw data and extract features without human intervention. 

Deep learning methods significantly improve detection of threats. Development and productization of deep learning systems for cyber defense require large volumes of data, computations, resources, and engineering effort. Stronger detection of malicious PowerShell scripts and other threats on endpoints using deep learning mean richer and better-informed security

Deep learning is successful in a wide range of tasks but no one knows exactly why it works and when it fails.Because deep learning models will be used more and more in the future, we should not put blind faith in those that are not well understood and that can put lives at risk.

Very powerful AI tools can fail badly in seemingly trivial scenarios. They developed a method to analyze these scenarios and establish "risk" maps that identify these risks in real applications.

AI models are commonly tested for robustness using pixel perturbations, that is, adding noise to an image to try to fool the deep-trained network,   However, artificial pixel perturbations are actually unlikely to occur in real-life applications . It is much more likely that semantic or contextual scenarios occur that the networks have not been trained on.

Algorithmic models can be punitive, discriminatory and, in some instances, they can even be illegal. Many add an extra layer of harm to already vulnerable population groups. Our own values and desires influence our choices: from the data we choose to collect, to the questions we ask. Models are opinions embedded in mathematics.

For example, rather than being faced with a manipulated image of an object, an autonomous driving system is more likely to be faced with an object orientation or a lighting scenario that it has not learned or encountered before; as a result, the system may not recognize what could be another vehicle or nearby pedestrian. This is the type of failure that has occurred in a number of high-profile incidents involving self-driving vehicles.

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

With machine learning, you need fewer data to train the algorithm than deep learning. Deep learning requires an extensive and diverse set of data to identify the underlying structure. Besides, machine learning provides a faster-trained model. 

Most advanced deep learning architecture can take days to a week to train. The advantage of deep learning over machine learning is it is highly accurate. You do not need to understand what features are the best representation of the data; the neural network learned how to select critical features. In machine learning, you need to choose for yourself what features to include in the model.

When there is enough data to train on, deep learning achieves impressive results, especially for image recognition and text translation. The main reason is the feature extraction is done automatically in the different layers of the network.

ML/DL are creating significant impact on our lives. They have vastly improved the recognition techniques e.g. face, speech, handwriting etc. They help online companies recommend product to users, understand the responses, filter the emails etc. They help predict outages in IT systems and telecom networks, diseases in patients etc. They are replacing analysts in predicting financial market movements, asset allocations, preparing tax returns etc. 

Deep Learning is particular has changed the course of Natural Language Processing (NLP) as computers understand, generate and translate written text as well as speech. The rise of autonomous robots and vehicles is directly linked to advances in Artificial Intelligence. Affective computing focuses on systems which can even decipher emotions. These computers would use facial expressions, postures, gestures, speech, temperature of body etc to understand emotional state of user and adapt its response accordingly. The list is endless.

Data Efficient Learning - is the ability to learn complex task domains without requiring massive amounts of data. Although supervised deep learning can address the problem of learning from larger datasets, for many real-world examples the amount of available training data is not sufficient to utilize such systems

In deep learning, data is typically pushed through a "deep" stack of activation layers. Each layer builds a representation of the data, with subsequent layers using the features of the previous layer to build more complex representations. The output of the final layer is mapped to a category to which the data should belong. Getting this final mapping correct is the objective of a deep learning algorithm.

At first, the computer program is provided with training data, for example images that have been labeled with meta tags. The algorithm uses this information to build a progressively more accurate predictive capability. In contrast, shallow machine learning approaches rely on a substantial amount of feature engineering processes carried out by humans before a model can learn the relationship between features; in deep learning, however, the system acquires the features and their relationships simultaneously.



Evolutionary algorithms (EA) are a subset of evolutionary computation—algorithms that mimic biological evolution to solve complex problems.




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

In addition to more data creation, deep learning algorithms benefit from the stronger computing power that’s available today as well as the proliferation of Artificial Intelligence (AI) as a Service. AI as a Service has given smaller organizations access to artificial intelligence technology and specifically the AI algorithms required for deep learning without a large initial investment. 

Deep learning allows machines to solve complex problems even when using a data set that is very diverse, unstructured and inter-connected. The more deep learning algorithms learn, the better they perform.

8 practical examples of deep learning--

Now that we’re in a time when machines can learn to solve complex problems without human intervention, what exactly are the problems they are tackling? Here are just a few of the tasks that deep learning supports today and the list will just continue to grow as the algorithms continue to learn via the infusion of data.

      Virtual assistants
Whether it’s Alexa or Siri or Cortana, the virtual assistants of online service providers use deep learning to help understand your speech and the language humans use when they interact with them.

      Translations
In a similar way, deep learning algorithms can automatically translate between languages. This can be powerful for travelers, business people and those in government.

      Vision for driverless delivery trucks, drones and autonomous cars
The way an autonomous vehicle understands the realities of the road and how to respond to them whether it’s a stop sign, a ball in the street or another vehicle is through deep learning algorithms. The more data the algorithms receive, the better they are able to act human-like in their information processing—knowing a stop sign covered with snow is still a stop sign.

      Chatbots and service bots
Chatbots and service bots that provide customer service for a lot of companies are able to respond in an intelligent and helpful way to an increasing amount of auditory and text questions thanks to deep learning.

      Image colorization
Transforming black-and-white images into color was formerly a task done meticulously by human hand. Today, deep learning algorithms are able to use the context and objects in the images to color them to basically recreate the black-and-white image in color. The results are impressive and accurate.

      Facial recognition
Deep learning is being used for facial recognition not only for security purposes but for tagging people on Facebook posts and we might be able to pay for items in a store just by using our faces in the near future. The challenges for deep-learning algorithms for facial recognition is knowing it’s the same person even when they have changed hairstyles, grown or shaved off a beard or if the image taken is poor due to bad lighting or an obstruction.

      Medicine and pharmaceuticals
From disease and tumor diagnoses to personalized medicines created specifically for an individual’s genome, deep learning in the medical field has the attention of many of the largest pharmaceutical and medical companies.

      Personalized shopping and entertainment
Ever wonder how Netflix comes up with suggestions for what you should watch next? Or where Amazon comes up with ideas for what you should buy next and those suggestions are exactly what you need but just never knew it before? Yep, it’s deep-learning algorithms at work.

The more experience deep-learning algorithms get, the better they become. It should be an extraordinary few years as the technology continues to mature.

Though based loosely on the way neurons communicate in the brain, these “deep learning” systems remain incapable of many basic functions that would be essential for primates and other organisms. In artificial neural networks, “catastrophic forgetting” refers to the difficulty in teaching the system to perform new skills without losing previously learned functions. 

For example, if a network initially trained to distinguish between photos of dogs and cats is then re-trained to distinguish between dogs and horses, it will lose its earlier ability. By contrast, the brain is capable of “continual learning,” acquiring new knowledge without eliminating old memories, even when the same neurons are used for multiple tasks. 

One strategy the brain uses for this learning challenge is the selective activation of cells or cellular components for different tasks—essentially turning on smaller, overlapping sub-networks for each individual skill, or under different contexts.

Deep learning techniques are based on artificial neural networks arranged in different layers, each of which calculates the values for the next one so that the information is processed more and more completely.

Usually, a set of known answers to the problem is used to “train” the network, but when these are not known, another technique called “reinforcement learning” can be used.



In this approach two neural networks are used: an “actor” has the task of finding new solutions, and a “critic” must assess the quality of these solution. Provided a reliable way to judge the respective results can be given by the researchers, these two networks can examine the problem independently.

Reinforcement learning differs from the supervised learning in a way that in supervised learning the training data has the answer key with it so the model is trained with the correct answer itself whereas in reinforcement learning, there is no answer but the reinforcement agent decides what to do to perform the given task. In the absence of training dataset, it is bound to learn from its experience.
Main points in Reinforcement learning –

Input: The input should be an initial state from which the model will start
Output: There are many possible output as there are variety of solution to a particular problem
Training: The training is based upon the input, The model will return a state and the user will decide to reward or punish the model based on its output.
The model keeps continues to learn.
The best solution is decided based on the maximum reward.

Types of Reinforcement: There are two types of Reinforcement:

Positive –
Positive Reinforcement is defined as when an event, occurs due to a particular behavior, increases the strength and the frequency of the behavior. In other words it has a positive effect on the behavior.
Advantages of reinforcement learning are:

Maximizes Performance
Sustain Change for a long period of time
Disadvantages of reinforcement learning:

Too much Reinforcement can lead to overload of states which can diminish the results
Negative –
Negative Reinforcement is defined as strengthening of a behavior because a negative condition is stopped or avoided.
Advantages of reinforcement learning:

Increases Behavior
Provide defiance to minimum standard of performance
Disadvantages of reinforcement learning:

It Only provides enough to meet up the minimum behavior
Various Practical applications of Reinforcement Learning –

RL can be used in robotics for industrial automation.
RL can be used in machine learning and data processing
RL can be used to create training systems that provide custom instruction and materials according to the requirement of students.
RL can be used in large environments in the following situations:

A model of the environment is known, but an analytic solution is not available;
Only a simulation model of the environment is given (the subject of simulation-based optimization)

The only way to collect information about the environment is to interact with it.



Constraint programming, differential programming (i.e. Deep Learning) and generative programming share a common trait. The program or algorithm that discovers the solution is fixed. In other words, a programmer does not need to write the program that translates the specification into a solution. Unfortunately though, the fixed program is applicable only in narrow domains

This is known as the “No Free Lunch” theorem in machine learning. You can’t use a linear programming algorithm to solve an integer programming problem. Deep Learning however has a unique kind of general capability that the same kind of algorithm (i.e. stochastic gradient descent) appears to be applicable to many problems.

While some machine learning models – like decision trees – are transparent, the majority of models used today – like deep neural networks, random forests, gradient boosting machines, and ensemble models – are black-box models. 

Large and complex models can be hard to explain, in human terms. For instance, why a particular decision was obtained. It is one reason that acceptance of some AI tools are slow in application areas where interpretability is useful or indeed required.

Furthermore, as the application of AI expands, regulatory requirements could also drive the need for more explainable AI models.

There will be no runaway AIs, there will be no self-developing AIs out of our  control. There will be no singularities. AI will only be as intelligent as we encourage (or force) it to be, under  uress.
 But AIs will never have human intelligence.  Amore advanced AI will fit us so closely that it will become integrated within us and our  societies.

Today all AIs are extremely limited in their intelligences that we cannot create general purpose intelligences using a single approach. There is no single AI on the planet (not even the fashionable “Deep Learning”) that can use the same method  to process speech, drive a car, learn how to play a complex video game, control a robot to run along a  busy city street, wash dishes in a sink, and plan a strategy to achieve investment for a company.

The military is also developing and testing many other kinds of autonomous aircraft and ground vehicles.

However, there is still some doubt that soldiers would take some time in feeling comfortable inside a robotic tank.

Especially if that tank does not explain its decisions to soldiers.


It is also true that intelligence analysts would show some reluctance in acting on information that does not come with proper reasoning.

Existing machine learning computer systems  produce a good amount of false alarms.

Because of that, an intelligence analyst would really require a good bit of help in order to understand why the new machine learning system made a recommendation that it made.

Artificial intelligence community still had a long way to go if it truly wanted to have interpretable artificial intelligence..

Knowing the reasoning behind artificial intelligence’s decisions is going to become crucial if this type of technology evolves to something very common and very useful part of the people’s daily lives.

Deep learning is fundamentally blind to cause and effect. Unlike a real doctor, a deep learning algorithm cannot explain why a particular image may suggest disease. This means deep learning must be used cautiously in critical situations.

A robot that understands that dropping things causes them to break would not need to toss dozens of vases onto the floor to see what happens to them.

Humans don't need to live through many examples of accidents to drive prudently,They can just imagine accidents

But deep learning algorithms aren’t good at generalizing, or taking what they’ve learned from one context and applying it to another. They also capture phenomena that are correlated—like the rooster crowing and the sun coming up—without regard to which causes the other
.
The “black box” complexity of deep learning techniques creates the challenge of “explainability,” or showing which factors led to a decision or prediction, and how. This is particularly important in applications where trust matters and predictions carry societal implications, as in criminal justice applications or financial lending. Some nascent approaches, including local interpretable model-agnostic explanations (LIME), aim to increase model transparency.

The types of AI being deployed are still limited. Almost all of AI’s recent progress is through one type, in which some input data X is used to generate some output response Y—where the algorithms identify complex input and output relationships. 

The most common deep learning networks (containing millions of simulated “neurons" structured in layers) are convolutional neural networks (CNNs) and recurrent neural networks (RNNs).


Then there are combinations of these networks like the Generative adversarial networks where two networks compete against each other and square off to improve their understanding. The X,Y systems have been improving rapidly with these neural networks. 

There will be breakthroughs that make higher levels of intelligence possible but currently it is far from the science fiction AI and fall short of answering queries 

Often, artificial intelligence (AI) applications employ neural networks that produce results using algorithms with a complexity level that only computers can make sense of. In other instances, AI vendors will not reveal how their AI works. In either case, when conventional AI produces a decision, human end users don’t know how it arrived at its conclusions.

This black box can pose a significant obstacle. Even though a computer is processing the information, and the computer is making a recommendation, the computer does not have the final say. That responsibility falls on a human decision maker, and this person is held responsible for any negative consequences.

As AI applications have expanded, machines are being tasked with making decisions where millions of dollars -- or even human health and safety -- are on the line. In highly regulated, high-risk/high-value industries, there's simply too much at stake to trust the decisions of a machine at face value, with no understanding of a machine’s reasoning or the potential risks associated with a machine’s recommendations. These enterprises are increasingly demanding explainable AI (XAI)

Many of the algorithms used for machine learning are not able to be examined after the fact to understand specifically how and why a decision has been made. This is especially true of the most popular algorithms currently in use – specifically, deep learning neural network approaches. 

As humans, we must be able to fully understand how decisions are being made so that we can trust the decisions of AI systems. The lack of explainability and trust hampers our ability to fully trust AI systems. We want computer systems to work as expected and produce transparent explanations and reasons for decisions they make. This is known as Explainable AI (XAI).



One way to gain explainability in AI systems is to use machine learning algorithms that are inherently explainable. For example, simpler forms of machine learning such as decision trees, Bayesian classifiers, and other algorithms that have certain amounts of traceability and transparency in their decision making can provide the visibility needed for critical AI systems without sacrificing too much performance or accuracy. 

More complicated, but also potentially more powerful algorithms such as neural networks, ensemble methods including random forests, and other similar algorithms sacrifice transparency and explainability for power, performance, and accuracy.

Traceability will enable humans to get into AI decision loops and have the ability to stop or control its tasks whenever need arises. An AI system is not only expected to perform a certain task or impose decisions but also have a model with the ability to give a transparent report of why it took specific conclusions.

Local Interpretable Model-Agnostic Explanations LIME is an actual method developed to gain greater transparency on what’s happening inside an algorithm.


LIME incorporates interpretability both in the optimization and the notion of interpretable representation, such that domain and task specific interpretability criteria can be accommodated.




Cognitive Computing is the individual technologies that perform specific tasks that facilitate human intelligence. These are smart decision support systems that we have been working with since the beginning of the internet boom.


With recent breakthroughs in technology, these decision support systems simply use better data, better algorithms to come up with a better analysis of vast stores of information.

Therefore, cognitive computing refers to:--
understanding and simulating reasoning
understanding and simulating human behavior


Using cognitive computing systems, every day, we make better human decisions at work.



Cognitive, bio-inspired AI solutions that employ human-like reasoning and problem-solving let users look inside the black box. In contrast to conventional AI approaches, cognitive AI solutions pursue knowledge using symbolic logic on top of numerical data processing techniques like machine learning, neural networks and deep learning.

The neural networks employed by conventional AI must be trained on data, but they don’t have to understand it the way humans do. They “see” data as a series of numbers, label those numbers based on how they were trained and solve problems using pattern recognition. When presented with data, a neural net asks itself if it has seen it before and, if so, how it was labeled it previously.

In contrast, cognitive AI is based on concepts. A concept can be described at the strict relational level, or natural language components can be added that allow the AI to explain itself. A cognitive AI says to itself: “I have been educated to understand this kind of problem. You're presenting me with a set of features, so I need to manipulate those features relative to my education.”

Cognitive systems do not do away with neural nets, but they do interpret the outputs of neural nets and provide a narrative annotation. Decisions made by cognitive AI are delivered in clear audit trails that can be understood by humans and queried for more detail. These audit trails explain the reasoning behind the AI’s recommendations, along with the evidence, risk, confidence and uncertainty.

A robust cognitive AI system can automatically adjust the depth and detail of its explanations based on who is viewing the information and on the context of how the information will be used.

In most cases, the easiest way for humans to visualize decision processes is by the use of decision trees, with the top of the tree containing the least amount of information and the bottom containing the most. With this in mind, explainability can generally be categorized as either top-down or bottom-up.

The top-down approach is for end users who are not interested in the nitty-gritty details; they just want to know if an answer is correct or not. A cognitive AI might generate a prediction of what the equipment will produce in its current condition. 

More technical users can then look at the detail, determine the cause of the issue and then hand it off to engineers to fix. The bottom-up approach is useful to the engineers who must diagnose and fix the problem. These users can query the cognitive AI to go all the way to the bottom of the decision tree and look at the details that explain the AI’s conclusion at the top.

Explainable AI begins with people. AI engineers can work with subject matter experts and learn about their domains, studying their work from an algorithm/process/detective perspective. What the engineers learn is encoded into a knowledge base that enables the cognitive AI to verify its recommendations and explain its reasoning in a way that humans can understand.

A cognitive AI is future-proof. Although governments have been slow to regulate AI, legislatures are catching up. The European Union’s General Data Protection Regulation (GDPR), a data governance and privacy law that went into effect this past May, grants consumers the right to know when automated decisions are being made about them, the right to have these decisions explained and the right to opt out of automated decision-making completely. Enterprises that adopt XAI now will be prepared for future compliance mandates.


AI is not supposed to replace human decision making; it is supposed to help humans make better decisions. If people do not trust the decision-making capabilities of an AI system, these systems will never achieve wide adoption. For humans to trust AI, systems must not lock all of their secrets inside a black box. XAI provides that explanation.

Cognitive Computing focuses on mimicking human behavior and reasoning to solve complex problems.

Cognitive Computing tries to replicate how humans would solve problems while AI seeks to create new ways to solve problems that can potentially be better than humans.

AI is not intended to mimic human thoughts and processes but to solve a problem through the best possible algorithm.

Cognitive Computing is not responsible for making the decision for humans. They simply supplement information for humans to make decisions.


AI is responsible for making decisions on their own minimizing the role of humans.



BELOW:  THESE TWO VIDEOS,   IS JUST CHAATNE KE VAASTE..    

I WILL WRITE ABOUT CLOUD FROM A CONDORs EYE VIEW (  NOT TUNNEL VISION EARTH WORMs )  LATER..





ALL MAJOR CLOUD PROVIDERS ARE AGENTS OF THE JEWISH DEEP STATE


https://www.guru99.com/cloud-computing-service-provider.html


BELOW: KILL THE INTERNET AND YOU KILL ACCESS TO YOUR CLOUD DATA .. THE DEEP STATE CAN KILL THE INTERNET..





If your internet access goes down, then it will take your vendor’s cloud service with it. If you need the cloud service to process customer payments or access important data, too bad – you have to wait until the internet is back up.

An Internet kill switch is a countermeasure concept of activating a single shut off mechanism for all Internet traffic.

The concept behind having a kill switch is based on creating a single point of control (i.e. a switch) for a single authority to control or shut down the Internet for whatever reasons 






Jack Dorsey’s Twitter account getting hacked by having his telephone number transferred to another account without his knowledge is a wake-up call to everyone of how vulnerable mobile devices are. 

The hackers relied on SIM swapping and convincing Dorsey’s telecom provider to bypass requiring a passcode to modify his account. With the telephone number transferred, the hackers accessed the Twitter founder’s account. If the telecom provider had adopted zero trust at the customer’s mobile device level, the hack would have never happened.

The Twitter CEO’s account getting hacked is the latest in a series of incidents that reflect how easy it is for hackers to gain access to cloud-based enterprise networks using mobile devices. 

Verizon’s Mobile Security Index 2019 revealed that the majority of enterprises, 67%, are the least confident in the security of their mobile assets than any other device. Mobile devices are one of the most porous threat surfaces a business has. 

They’re also the fastest-growing threat surface, as every employee now relies on their smartphones as their ID. IDG’s recent survey completed in collaboration with MobileIron, titled Say Goodbye to Passwords found that 89% of security leaders believe that mobile devices will soon serve as your digital ID to access enterprise services and data.

Because they’re porous, proliferating and turning into primary forms of digital IDs, mobile devices and their passwords are a favorite onramp for hackers wanting access to companies’ systems and data in the cloud. It’s time to kill passwords and shut down the many breach attempts aimed at cloud platforms and the valuable data they contain.

Killing passwords improve cloud security by:--

Eliminating privileged access credential abuse. Privileged access credentials are best sellers on the Dark Web, where hackers bid for credentials to the world’s leading banking, credit card, and financial management systems.

Killing passwords improve cloud security by:--

Eliminating privileged access credential abuse. Privileged access credentials are best sellers on the Dark Web, where hackers bid for credentials to the world’s leading banking, credit card, and financial management systems.

74% of all breaches involved privileged access abuse. Killing passwords shuts down the most common technique hackers use to access cloud systems.

Acquiring privileged access credentials and launching breach attempts from mobile devices is the most common hacker strategy today. By killing passwords and replacing them with a zero-trust framework, breach attempts launched from any mobile device using pirated privileged access credentials can be thwarted.

When every mobile device is secured through a zero-trust platform built on a foundation of unified endpoint management (UEM) capabilities, zero sign-on from managed and unmanaged services become achievable for the first time.

Identities are the new security perimeter, and mobile devices are their fastest-growing threat surface. Long-standing traditional approaches to network security, including “trust but verify” have proven ineffective in stopping breaches. They’ve also shown a lack of scale when it comes to protecting a perimeter-less enterprise. 

What’s needed is a zero-trust network that validates each mobile device, establishes user context, checks app authorization, verifies the network, and detects and remediates threats before granting secure access to any device or user. If Jack Dorsey’s telecom provider had this in place, his and thousands of other people’s telephone numbers would be safe today.


The sooner organizations move away from being so dependent on passwords, the better.




  1. https://www.gadgetsnow.com/tech-news/google-may-have-a-new-problem-and-its-called-employees/articleshow/72236471.cms?utm_source=toiweb&utm_medium=referral&utm_campaign=toiweb_hptopnews

    HEY, WHAT ABOUT PROJECT MAVEN?

    PROJECT MAVEN IS A PENTAGON PROJECT INTENDED TO USE MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE IN ORDER TO DIFFERENTIATE PEOPLE AND OBJECTS IN THOUSANDS OF HOURS OF DRONE FOOTAGE.

    GOOGLE EMPLOYEES WERE “OUTRAGED” THE COMPANY WAS USING THEM AND THE SOFTWARE THEY HELPED DEVELOP TO AID A GOVERNMENT PROGRAM THAT INVOLVES THE KILLING OF HUMAN BEINGS OVERSEAS USING UNMANNED AERIAL VEHICLES.

    GOOGLE WAS WORKING CLANDESTINELY WITH THE US DEFENSE DEPARTMENT TO DEVELOP ARTIFICIAL INTELLIGENCE FOR ANALYZING DRONE FOOTAGE AS PART OF AN INITIATIVE KNOWN AS PROJECT MAVEN.

    GOOGLE'S INVOLVEMENT SPARKED ETHICAL CONCERN AND ANGER AMONG EMPLOYEES, 3000 OF THEM RESIGNED ENMASSE.

    MORE THAN 3,000 GOOGLE EMPLOYEES SIGNED AN OPEN LETTER TO GOOGLE CEO SUNDAR PICHAI, A DEEP STATE TATTU , ADVISING THE COMPANY TO PULL OUT OF THE PROGRAM, WRITING, “GOOGLE SHOULD NOT BE IN THE BUSINESS OF WAR.”

    “THIS CONTRACT PUTS GOOGLE’S REPUTATION AT RISK AND STANDS IN DIRECT OPPOSITION TO OUR CORE VALUES,” THE LETTER READ. “BUILDING THIS TECHNOLOGY TO ASSIST THE US GOVERNMENT IN MILITARY SURVEILLANCE — AND POTENTIALLY LETHAL OUTCOMES — IS NOT ACCEPTABLE.”

    ON MAY 18TH, GOOGLE UPDATED ITS CODE OF CONDUCT TO REMOVE PROMINENT REFERENCES TO THE COMPANY’S OLD MOTTO, “DON’T BE EVIL,” WHICH HAS SINCE BEEN REPLACED WITH, “DO THE RIGHT THING.”

    TEE HEEEEEEEEE

    capt ajit vadakayil
    ..
WITHOUT INTERNET , THERE IS NO CLOUD .

https://www.wired.com/story/google-cloud-outage-catch-22/


HONEY POTS ON CLOUD?


https://www.sophos.com/en-us/medialibrary/PDFs/Whitepaper/sophos-exposed-cyberattacks-on-cloud-honeypots-wp.pdf



BELOW: I WILL WRITE ABOUT SHADOW IT LATER.. THIS IS CHAATNE KE VAASTE


SHADOW IT, ALSO CALLED STEALTH IT OR PHANTOM IT, REFERS TO ANY TECH UTILIZED WITHIN AN ORGANIZATION WITHOUT THE KNOWLEDGE OR APPROVAL OF THE IT DEPARTMENT — AND TODAY IT ACCOUNTS FOR ABOUT 40 PERCENT OF ALL IT SPENDING.


BELOW : I WILL EXPLAIN EDGE COMPUTING LATER... THIS IS CHAATNE KE VAASTE..

EDGE COMPUTING IS THE FUTURE

IN EDGE AI, THE AI ALGORITHMS ARE PROCESSED LOCALLY ON A HARDWARE DEVICE, WITHOUT REQUIRING ANY CONNECTION. IT USES DATA THAT IS GENERATED FROM THE DEVICE AND PROCESSES IT TO GIVE REAL-TIME INSIGHTS IN LESS THAN FEW MILLISECONDS.










THIS POST IS NOW CONTINUED TO PART 7 BELOW--




CAPT AJIT VADAKAYIL
..

154 comments:

  1. THE SECRET OF THEERTHAM IS THAT IT IS H 1.5 O.. ( NOT H2 O )… WATER MOLECULE CHANGES PARTNERS A HUNDRED BILLION TIMES A SECOND.

    IT IS SCALAR ENERGY SCIENCE .. THIS IS QUANTUM SCIENCE NOT CLASSICAL SCIENCE.. THEERTHAM WATER STRUCTURE IS AFFECTED BY THE EMOTIONS OF PEOPLE.

    VEDAS ARE INFALLIBLE..

    MOBIUS COIL FLOW IN GANGES WATER ( KASHI GHATS ) EMIT A GIANT SCALAR FIELD.. THE FIELD OF BRAHMAN IS SCALAR..

    WATER IS A TINY BENT MOLECULE WITH THE MOLECULAR FORMULA H2O, CONSISTING OF TWO LIGHT HYDROGEN ATOMS ATTACHED TO EACH 16-FOLD HEAVIER OXYGEN ATOM. EACH MOLECULE IS ELECTRICALLY NEUTRAL BUT POLAR, WITH THE CENTER OF POSITIVE AND NEGATIVE CHARGES LOCATED IN DIFFERENT PLACES..

    THE REASON WATER HAS A BENT SHAPE IS THAT THE TWO LONE PAIR OF ELECTRONS ARE ON THE SAME SIDE OF THE MOLECULE.

    THE TWO HYDROGEN ATOMS AND THE TWO LONE ELECTRON PAIRS ARE AS FAR APART AS POSSIBLE AT NEARLY 108 DEGREES BOND ANGLE. 108 IS THE DIGITAL VALUE OF HINDU KING MANTRA OM..

    THE WATER MOLECULE IS BENT MOLECULAR GEOMETRY BECAUSE THE LONE ELECTRON PAIRS, ALTHOUGH STILL EXERTING INFLUENCE ON THE SHAPE, ARE INVISIBLE WHEN LOOKING AT MOLECULAR GEOMETRY.

    QUANTUM COMPUTERS WILL TAKE OFF ONLY WHEN SILICON IS REPLACED WITH LIVING GANGES WATER , AND WIRING IS ORGANIC LIKE DNA..

    WATER IS A POLAR MOLECULE AND ALSO ACTS AS A POLAR SOLVENT. WHEN A CHEMICAL SPECIES IS SAID TO BE "POLAR," THIS MEANS THAT THE POSITIVE AND NEGATIVE ELECTRICAL CHARGES ARE UNEVENLY DISTRIBUTED.

    THE POSITIVE CHARGE COMES FROM THE ATOMIC NUCLEUS, WHILE THE ELECTRONS SUPPLY THE NEGATIVE CHARGE. IT'S THE MOVEMENT OF ELECTRONS THAT DETERMINES POLARITY.

    WATER (H2O) IS POLAR BECAUSE OF THE BENT SHAPE OF THE MOLECULE. THE SHAPE MEANS MOST OF THE NEGATIVE CHARGE FROM THE OXYGEN ON SIDE OF THE MOLECULE AND THE POSITIVE CHARGE OF THE HYDROGEN ATOMS IS ON THE OTHER SIDE OF THE MOLECULE. THIS IS AN EXAMPLE OF POLAR COVALENT CHEMICAL BONDING.

    THE ELECTRONEGATIVITY VALUE OF HYDROGEN IS 2.1, WHILE THE ELECTRONEGATIVITY OF OXYGEN IS 3.5. THE SMALLER THE DIFFERENCE BETWEEN ELECTRONEGATIVITY VALUES, THE MORE LIKELY ATOMS WILL FORM A COVALENT BOND. A LARGE DIFFERENCE BETWEEN ELECTRONEGATIVITY VALUES IS SEEN WITH IONIC BONDS

    BOTH HYDROGEN ATOMS ARE ATTRACTED TO THE SAME SIDE OF THE OXYGEN ATOM, BUT THEY ARE AS FAR APART FROM EACH OTHER AS THEY CAN BE BECAUSE THE HYDROGEN ATOMS BOTH CARRY A POSITIVE CHARGE. THE BENT CONFORMATION IS A BALANCE BETWEEN ATTRACTION AND REPULSION.

    REMEMBER THAT EVEN THOUGH THE COVALENT BOND BETWEEN EACH HYDROGEN AND OXYGEN IN WATER IS POLAR, A WATER MOLECULE IS AN ELECTRICALLY NEUTRAL MOLECULE OVERALL. EACH WATER MOLECULE HAS 10 PROTONS AND 10 ELECTRONS, FOR A NET CHARGE OF 0.

    WATER ACTS AS A POLAR SOLVENT BECAUSE IT CAN BE ATTRACTED TO EITHER THE POSITIVE OR NEGATIVE ELECTRICAL CHARGE ON A SOLUTE. THE SLIGHT NEGATIVE CHARGE NEAR THE OXYGEN ATOM ATTRACTS NEARBY HYDROGEN ATOMS FROM WATER OR POSITIVE-CHARGED REGIONS OF OTHER MOLECULES.

    I HAVE CHEMICAL TANK CLEANING SECRETS WHICH WILL BE REVEALED ONLY WHEN MY REVELATIONS REACH 98%.

    http://ajitvadakayil.blogspot.com/2010/11/water-valley-and-walking-on-water-capt.html

    HOMEOPATHY ALL OVER THE WORLD HAS BEEN HIJACKED BY JEW ROTHSCHILD.. IN INDIA THEY USE ALCOHOL .. SORRY, THE PROPERTY OF “ WATER HOLDING MEMORY” IS THE BASE OF HOMEOPATHY.

    WATER CAN RETAIN A "MEMORY" OF SOLUTE PARTICLES AFTER ARBITRARILY LARGE DILUTION. .. EVEN WHEN THEY ARE DILUTED TO THE POINT THAT NO MOLECULE OF THE ORIGINAL SUBSTANCE REMAINS.

    CAPT AJIT VADAKAYIL DEMANDS OF INDIAS HEALTH MINISTER.. USE WATER IN HOMEOPATHY—NEVER ALCOHOL..

    http://ajitvadakayil.blogspot.com/2011/01/living-water-capt-ajit-vadakayil.html

    Capt ajit vadakayil
    ..

    ReplyDelete
    Replies
    1. PUT ABOVE COMMENT IN WEBSITES OF--
      HEALTH MINISTER
      HEALTH MINISTRY
      PMO
      PM MODI
      AJIT DOVAL
      RAW
      IB
      NIA
      ED
      CBI
      AMIT SHAH
      HOME MINISTRY
      NEW CJI
      ALL SUPREME COURT JUDGES
      ATTORNEY GENERAL
      ALL HIGH COURT CHIEF JUSTICES
      CMs OF ALL INDIAN STATES
      DGPs OF ALL STATES
      GOVERNORS OF ALL STATES
      PRESIDENT OF INDIA
      VP OF INDIA
      SPEAKER LOK SABHA
      SPEAKER RAJYA SABHA
      DEFENCE MINISTER - MINISTRY
      ALL THREE ARMED FORCE CHIEFS.
      RBI
      RBI GOVERNOR
      FINANCE MINISTER/ MINISTRY
      NCERT
      EDUCATION MINISTER/ MINISTRY
      ROMILA THAPAR
      RAJEEV CHANDRASHEKHAR
      MOHANDAS PAI
      NITI AYOG
      AMITABH KANT
      RAM MADHAV
      RAJ THACKREY
      UDDHAV THACKREY
      VIVEK OBEROI
      GAUTAM GAMBHIR
      ASHOK PANDIT
      ANUPAM KHER
      KANGANA RANAUT
      VIVEK AGNIHOTRI
      KIRON KHER
      MEENAKSHI LEKHI
      SMRITI IRANI
      PRASOON JOSHI
      MADHUR BHANDARKAR
      SWAPAN DASGUPTA
      SONAL MANSINGH
      MADHU KISHWAR
      SUDHIR CHAUDHARY
      GEN GD BAKSHI
      SAMBIT PATRA
      RSN SINGH
      SWAMY
      RAJIV MALHOTRA
      THE QUINT
      THE SCROLL
      THE WIRE
      THE PRINT
      MK VENU
      MADHU TREHAN
      CLOSET COMMIE ARNAB GOSWMI
      RAJDEEP SARDESAI
      PAAGALIKA GHOSE
      NAVIKA KUMAR
      ANAND NARASIMHAN
      SRINIVASAN JAIN
      SONAL MEHROTRA KAPOOR
      VIKRAM CHANDRA
      NIDHI RAZDAN
      FAYE DSOUZA
      RAVISH KUMAR
      PRANNOY JAMES ROY
      AROON PURIE
      VINEET JAIN
      RAGHAV BAHL
      SEEMA CHISTI
      DILEEP PADGOANKAR
      VIR SANGHVI
      KARAN THAPAR
      PRITISH NANDI
      SHEKHAR GUPTA
      SIDHARTH VARADARAJAN
      ARUN SHOURIE
      N RAM
      NCW
      REKHA SHARMA
      SWATI MALLIWAL
      CHETAN BHAGAT
      I&B DEPT/ MINISTER
      LAW MINISTER/ MINISTRY
      ALL CONGRESS SPOKESMEN
      RAHUL GANDHI
      SONIA GANDHI
      PRIYANKA VADRA
      JACK DORSEY
      MARK ZUCKERBERG
      THAMBI SUNDAR PICHAI
      CEO OF WIKIPEDIA
      QUORA CEO ANGELO D ADAMS
      QUORA MODERATION TEAM
      KURT OF QUORA
      GAUTAM SHEWAKRAMANI
      DAVID FRAWLEY
      STEPHEN KNAPP
      WILLIAM DALRYMPLE
      KONRAED ELST
      FRANCOIS GAUTIER
      RANA AYYUB
      SWARA BHASKAR
      BRINDA KARAT
      PRAKASH RAJ
      KAMALA HASSAN
      ANNIE RAJA
      JOHN BRITTAS
      ADOOR GOPALAKRISHNAN
      ROMILA THAPAR
      SADGURU JAGGI VASUDEV
      SRI SRI RAVISHANKAR
      BABA RAMDEV
      PAVAN VARMA
      RAMACHANDRA GUHA
      JOHN DAYAL
      KANCHA ILIAH
      FATHER CEDRIC PERIERA
      ANNA VETTICKAD
      FAZAL GHAFOOR ( MES KERALA)
      MAMMOOTY
      DULQER SALMAN
      IRFAN HABIB
      NIVEDITA MEMON
      AYESHA KIDWAI
      VC OF JNU/ DU/ JU / TISS / FTII
      ALL SOCIAL SCIENCES PROFESSORS OF JNU/ DU/ JU / TISS
      SWARA BHASKAR
      IRA BHASKAR
      ROHINI CHATTERJEE
      PINARAYI VIJAYAN
      KODIYERI BALAKRISHNAN
      PRAKASH KARAT
      BRINDA KARAT
      SITARAM YECHURY
      SUMEET CHOPRA
      DINESH VARSHNEY
      BINAYAK SEN
      SUDHEENDRA KULKARN
      D RAJA
      ANNIE RAJA
      NCERT
      EDUCATION MINISTER/ MINISTRY
      WEBSITES OF DESH BHAKT LEADERS
      SPREAD OF SOCIAL MEDIA

      Delete
    2. Sent mails to-
      shripad.naik@sansad.nic.in
      minister-ayush@nic.in
      dckatoch@rediffmail.com
      webmanager-ayush@gov.in
      secy-ayush@nic.in
      swapnil.naik@gov.in

      Delete
    3. List of email ids can be found here..

      https://mohfw.gov.in/department-health-and-family-welfare-directory

      https://mohfw.gov.in/sites/default/files/MoHFW%20Directory_7.pdf

      Delete
    4. homeopathy solvent should be ganges type living water or copper charged/gold charged or agnihotra ash infused water

      Delete
    5. Received an Email from the Health Minister saying he has Noted the details.

      Delete
    6. It's better to send mails for such lengthy information.
      There on Twitter, there's a Gang which has formed, no one is caring about others.
      No one.
      The Gang has literally dominated the space.
      They don't allow anyone inside.
      Twitter can be used as a platform to guess how INDIA operates as a whole.
      Except few who are working out genuinely, most operators of the accounts are for RT & LIKES.
      They think by Like & RT they have done some handsome job means they want patting on the back that yeah they did a fantastic job of RT & LIKE.

      Indian Democracy is some how the same.

      Delete
    7. Dear Captain, Mind blowing explanation. How to energize the water at home? The crystal structure of the water changes with Mantra and Prayer. Is there any method to prepare the water for drinking such that it acts as medicine and attacks antibodies?

      Delete
  2. Captain, these two article claims that one of the reasons Indira-Gandhi was assassinated was because she Nationalized the Oil-industries in 1971. Is this true ? The two quotes are from the two separate articles:---

    QUOTE === (A1) During 1971 war there was only private oil business in India. During the war the private oil companies refused to supply oil to Indian Army stating that there should be no war as war was bad to business. During that time oil companies were nationalized in India by Indira Gandhi.

    (A2) Even after Dominion Independence the British had a tight control over India and Pakistan’s oil resource via Burma Shell and ESSO. During 1971 war with Pakistan they denied to supply oil to Indian Army, Navy and Air Force. Irritated by this, the then prime minister Indira Gandhi nationalized the oil industry to protect our country’s mineral wealth, indigenously develop self-sufficiency (swadeshi swaraj) and reduce dependency on foreign (videshi) raw material. === UNQUOTE

    http://greatgameindia.com/energy-geopolitics-an-overview/
    http://greatgameindia.com/truth-reliance-rothschild-jio-india-pakistan-war/

    ReplyDelete
  3. Captain, see this interesting painting titled -- "A sale of English-beauties, in the East Indies" -- in the British-Museum, it shows White-British courtesans being sold in Calcutta-Docks in the year 1786. How is this possible Captain especially since, as you have revealed, the Devadasi culture was created by the white-invaders to satisfy their sexual-desires ? Were these white-courtesans meant for the stooge-native-royals or the higher-ranking people of East-India-Company ?

    https://tinyurl.com/slmqm7o
    https://tinyurl.com/qmacuoq

    ReplyDelete
  4. https://www.alivewater.com/viktor-schauberger

    This guy has made a lot of work on water , vortices, river flow.

    ReplyDelete
  5. Captain,
    that is a cool picture. you made the picture perfect! the turquoise waters are inviting even on a winter day like this.
    what caught my attention as much as the waters is that silver dates tree. what a sight!
    that reminded of the days people used to get get a kind of wine (toddy) even from that silver date (others are coconut and palm). who killed that industry and tradition boss? the liquor lobby come to mind but there could be more. I hope there comes a day when I have choice of toddies; silver date, coconut and palm.
    please write about the toddy from silver date tree

    ReplyDelete
  6. Dear Captainji,

    Sorry Sir, a digression from your article.

    My mom had a dream yesterday and when she woke up, she said to me..i think i dreamt of Ajit. I was confused & then she said, Capt Ajit. Can you show me Capt's photo? So i showed her and she said yes, its Capt. The dream was that, you had come to our house on my invitation to do a puja. And that you kept calling me to assist you.

    Although it was my mom's dream, I felt so happy and honoured, Sir. Never expected it but i do tell my mom about you and your articles.

    Thank you Sir. Love you very much.

    Regards,
    Bunga

    ReplyDelete
  7. https://timesofindia.indiatimes.com/world/us/battle-of-billionaires-2020-us-polls-could-see-trump-bloomberg-face-off/articleshow/72213908.cms

    MICHAEL BLOOMBERG LIKE SOROS AND HILLARY IS A JEWISH DEEP STATE AGENT..

    BLOOMBERG IS WORTH 58 BILLION USD, NINTH RICHEST PERSON ON THE PLANET.

    ROTHSCHILD PLANS TO USE BLOOMBERG TO TAKE AWAY GUNS FROM AMERICANS AND MAKE THE SECOND AMENDMENT REDUNDANT..

    MIND YOU, HILLARY WOULD HAVE BECOME PRESIDENT BY RIGGING US ELECTIONS IF DONALD TRUMP HAD NOT THREATENEDNEDEDED TO INVOKE THE SECOND AMENDMENT .. JEWS FEEL UNSAFE WITH PATRIOTIC CHRISTIANS HOLDING GUNS..

    UNDER BLOOMBERG ROTHSCHILD HOPES TO DRIVE HIS CLIMATE CHANGE AGENDA WHERE COAL WILL BE BANNED ... INDIA WILL BE ARM TWISTED AND FORCED TO SHUT DOWN OUR COAL PLANTS .. JEWS MUST HAVE MONOPOLY OVER THE PLANETs ENERGY SOURCES..

    UNLIKE DONALD TRUMP, BLOOMBERG WILL BE A JEWISH DEEP STATE MAIN STREAM MEDIA DARLING..

    BLOOMBERG IS PART OF THE SWAMP WHO FLOODED USA WITH COCAINE AND MEXICAN ILLEGAL IMMIGRANT SMURFS FOR LAUNDERING DRUG MONEY AND ENHANCING THE DEMOCRAT VOTE BANK..

    JEW MICHAEL BLOOMBERG HAS CRITICIZED DONALD TRUMP FOR ADVOCATING THE WALL AND MASS DEPORTATION OF ILLEGAL IMMIGRANTS: "WE'RE NOT GOING TO DEPORT 12 MILLION PEOPLE, SO LET'S STOP THIS FICTION. LET'S GIVE THEM PERMANENT STATUS"..

    REGARDING MEXICAN BORDER WALL AND SECURITY FOR KEEPING AWAY ILLEGAL IMMIGRANTS , DEEP STATE AGENT BLOOMBERG SPAKE "IT'S AS IF WE EXPECT BORDER CONTROL AGENTS TO DO WHAT A CENTURY OF COMMUNISM COULD NOT: DEFEAT THE NATURAL MARKET FORCES OF SUPPLY AND DEMAND ... AND DEFEAT THE NATURAL HUMAN DESIRE FOR FREEDOM AND OPPORTUNITY. YOU MIGHT AS WELL AS SIT IN YOUR BEACH CHAIR AND TELL THE TIDE NOT TO COME IN. AS LONG AS AMERICA REMAINS A NATION DEDICATED TO THE PROPOSITION THAT 'ALL MEN ARE CREATED EQUAL, ENDOWED BY THEIR CREATOR WITH CERTAIN UNALIENABLE RIGHTS, THAT AMONG THESE ARE LIFE, LIBERTY AND THE PURSUIT OF HAPPINESS', PEOPLE FROM NEAR AND FAR WILL CONTINUE TO SEEK ENTRY INTO OUR COUNTRY."

    HEY, HOW ABOUT TAKING IN ISLAMIC ROHINGYA REFUGEES ?..

    http://ajitvadakayil.blogspot.com/2019/06/record-heat-wave-europe-2019-root-cause.html

    http://ajitvadakayil.blogspot.com/2019/02/america-caused-global-warming-with.html

    http://ajitvadakayil.blogspot.com/2018/12/poland-katowice-cop-24-global-warming.html

    http://ajitvadakayil.blogspot.com/2018/12/global-warming-climate-change-fear.html

    capt ajit vadakayil
    ..

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      Delete
    2. Tweeted to Trump, Putin, embassy USA / RUSSIA,Mea, Armed forces,NIA and central ministers.

      Delete

  8. RishabNovember 25, 2019 at 4:18 AM
    Mahoday! What is ephedra? Is it really dangerous as in is it being used to poison someone?

    ReplyDelete
    Replies

    Capt. Ajit VadakayilNovember 25, 2019 at 8:39 AM
    MORE THAN 800 DANGEROUS REACTIONS HAVE BEEN REPORTED WITH USE OF THE HERB. THESE INCLUDE HEART ATTACKS, STROKES, SEIZURES, AND SUDDEN DEATHS..

    EPHEDRINE USED AS A PRECURSOR FOR METH , IS A CHEMICAL CONTAINED IN THE EPHEDRA HERB

    ReplyDelete
  9. https://twitter.com/ShekharGupta/status/1198462553833132033

    WE KNOW THE INDIAN MAIN STREAM MEDIA IN PAKISTANI ISI PAYROLL..

    ReplyDelete
  10. LOT OF JEWISH BILLIONAIRES LIKE SOROS / BLOOMBERG DO PROPAGANDA THAT THEY WILL DONATE SO MUCH PERCENTAGE OF THEIR MONEY FOR PHILANTHROPY.. AND ROTHSCHILDs MEDIA SINGS THIS FROM THE TREE TOPS..

    WHAT THEY DO NOT SAY IS THAT --ALL THIS DONATION IS TO PUSH THE EVIL JEWISH DEEP STATE AGENDA..

    ReplyDelete
  11. https://www.sakaltimes.com/business/bill-gates-presents-award-dr-cyrus-poonawalla-43154

    POONAWALA IS A JEWISH DEEP STATE DARLING ..

    http://ajitvadakayil.blogspot.com/2015/10/autism-in-india-and-triple-vaccine-mmr.html

    ReplyDelete
  12. JEWISH DEEP STATE DARLING AND KOSHER EVIL PHARMA DARLING PM MODI, WILL CELEBRATE DESH DROHI VERGHESE KURIEN, HUMPLESS JERSEY COWS , A1 TOXIC MILK TOMORROW -- 26TH NOV ( NATIONAL MILK DAY )..

    EVIL PHARMA LOVES TOXIC A1 MILI-- IT BOOSTS THEIR SALES BY 95%..

    A1 AMUL MILK FROM HUMPLESS JERSEY COWS IS TOXIC . . . . OUR SCHOOL CHILDRENs BRAINS HAVE BECOME FOGGY . . . . 85 % DISEASES ORIGINATE IN THE HUMAN GUT..

    OUR PRICELESS VEDIC HUMPED COWS WERE SWITCHED BY DESH DROHI VERGHESE KURIEN , THE FATHER OF WHITE REVOLUTION WITH USELESS WESTERN HUMPLESS COWS.. . . .

    BEFORE TOXIC A1 MILK OF FOREIGN JERSEY HUMPLESS COWS WERE INTRODUCED IN INDIA, WE HAD VERY FEW WESTERN MEDICAL SHOPS IN INDIA. . . .

    ONE GLASS OF NUTRITIOUS A2 MILK OF HUMPED VEDIC COW IS BETTER THAN 1000 BUCKETS OF TOXIC A1 MILK OF HUMPLESS FOREIGN JERSEY/ HOLSTEN COWS .. .

    WE HINDUS DO NOT CARE FOR USELESS HUMPLESS WESTERN JERSEY/ HOLSTEN COWS WHICH GIVE TOXIC A1 MILK . . KILL ALL OF THEM FOR BEEF . . . . .

    WE HINDUS ARE SENSITIVE ONLY ABOUT VEDIC HUMPED COW , WHICH GIVES NUTRITIOUS A2 MILK..

    TRAITOR VERGHESE KURIEN DECIMATED OUR VEDIC HUMPED COWS . ..

    OUR VEDIC HUMPED COWS F A R T ONLY 5 % OF THEIR HUMPLESS COW COUSINS DUE TO AN EFFICIENT DIGESTIVE SYSTEM .. METHANE CAUSES GLOBAL WARMING .

    EVEN TODAY GUJARAT MILK MARKETING FEDERATION DOES NOT HAVE A SINGLE VEDIC HUMPED COW. . .

    NANO GOLD COLLOIDS OF GOMUTRA ( VEDIC HUMPED COW COWS UR1NE ) CAN INVADE THE BLOOD BRAIN BARRIER AND ATTACK TUMORS AND CANCER………

    THE DOOMED WEST MUST KNOW THAT ONLY HUMPED COW UR1NE CAN SAVE THEM FROM VARIANT CREUTZFELDT JACOB DISEASE- A DEADLY BRAIN DISEASE WHICH AS ALREADY STARTED AND IS BEING COVERED UP AS ALZHEIMERS …

    BEEF EXPORTS ( HUMPED VEDIC COW ) HAS INCREASED SHARPLY AFTER MODI BECAME PM . . INDIA IS NOW THE WORLDs NO 1 BEEF EXPORTER . . .. MODI CANT FOOL HINDUS ANY MORE WITH HIS BULLSH1T ..

    THE IMPORTANCE OF JALLIKKATTU IS PRIMARILY ABOUT PRESERVING OUR PRICELESS HUMPED VEDIC BULLS WHICH WERE EXTERMINATED BY DESH DROHIS WHO CONDUCTED THE “WHITE REVOLUTION “……

    JEWISH EVIL PHARMA HAS PROSPERED BY FORCING INDIANS TO CONSUME TOXIC A1 MILK OF JERSEY HUMPLESS COWS WITH MODIs CONNIVANCE …

    INDIA BECAME THE LARGEST EXPORTER OF BEEF ---AS THE WHITE MAN WANTS TO EAT SAFE MEAT OF HUMPED COWS--NOT TOXIC MEAT OF HUMPLESS COWS...

    THE KERALA ASSEMBLY INFLUENCED BY DESH DROHI VERGHESE KURIEN PASSED THE LIVESTOCK IMPROVEMENT ACT IN 1961 WHICH PROHIBITED THE MAINTAINING OF PRODUCTIVE, DESI BULLS............ THE LAW WAS SUBSEQUENTLY AMENDED IN 1968 WITH MORE SEVERE PENALTIES...

    THE ABOVE LAW WITH THREATS OF PUNISHMENT LIKE FINES & IMPRISONMENT UPTO ONE MONTH, INSTILLED FEAR IN THE ORDINARY PEOPLE- NOT TO REAR LOCAL PURE HUMPED BREEDS OF VEDIC COWS AND BULLS...

    HUMPLESS COWs URINE AND DUNG EMIT NITROUS OXIDE WHICH CAUSES GLOBAL WARMING ...

    PARIS COP21 – BAS.TARDS, THEY SHOVED METHANE AND NITROUS OXIDE UNDER THE KOSHER CARPET.. .. CO2 IS A GOOD LIFE SAVING GAS.. IF THE GLOBAL WARMING POTENTIAL ( GWP ) IS 1 FOR CARBON DIOXIDE, IT IS 302 FOR NITROUS OXIDE AND 104 FOR METHANE OVER A 20 YEAR PERIOD…

    EVIL PROPAGANDA DONE BY BOLLYWOOD MOVIE AND CHITRAPUR MUTT MEN SHYAM BENEGAL AND GIRISH KARNAD ( BOLLYWOOD MOVIE MATHAN ) TO PROPAGATE HUMPLESS COWS WHICH ARE WORSE THAN PIGS..

    GOMUTRA OF HUMPED VEDIC COW CAN GET RID OF SEVERAL DISEASES WHICH ORIGINATE IN THE HUMAN GUT --IT MELTS THE PROTECTIVE ARMOUR OF VIRUSES / BACTERIA / FUNGI BY ENZYMES ... ONE TEA SPOON FRESH UR1NE IS ENOUGH...

    ROTHSCHILD HAS MADE MODI CHAMPION OF EARTH FOR DECLARING CO2 AS A BAD GAS .'

    HISTORY WILL BEAR OUT THAT GUJJU NO 2 MODI HAS INFLICTED MORE CUTS ON BHARATMATA THAN GUJJU NO 1 KATHIAWARI JEW GANDHI

    http://ajitvadakayil.blogspot.com/2013/07/nutritious-a1-milk-of-vedic-cows-with.html

    http://ajitvadakayil.blogspot.com/2013/02/gomutra-drinking-cows-urine-as-elexir.html

    http://ajitvadakayil.blogspot.com/2013/12/shocking-legacy-of-mad-cow-disease-capt.html

    capt ajit vadakayil
    ..

    ReplyDelete
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    2. Dear Captain,
      I was shocked to read about state sponsored destruction of humped bulls. I went online and read the entire Livestock Improvement ACT of 1961 and it’s amendment of 1968. (5 min read)

      Briefly put, it says that the owner of every bull in the state of Kerala has to obtain a licence to hold it, provided free of cost. In case licence is not provided, the owner has to arrange for castration of the bull. The act says that licence could be denied for -

      “...the licensing officer may refuse to grant or may revoke a licence in respect of any bull if in his opinion the bull appears to be -
      (a)of defective or inferior conformation and consequently likely to beget defective or inferior progeny, or
      (b) suffering from an incurable contagious or infectious disease or from any other disease rending the bull unsuitable for breeding purposes, or
      (c) of a breed which it is undesirable to propagate in the State of Kerala”

      In 1961 the penalty for first offence was rs. 25, and 50 for second.
      In 1968 it’s changed to 100 and 500. And jail term.

      Question:
      Where does it mention that Desi humped bulls are undesirable?

      (I am sharing this important comment)
      Gratefully,
      Anish.

      Delete
    3. Sent emails..Asked for ack.

      sent DM on Facebook- ministry of health.

      Delete
    4. Your Grievance is registered successfully.
      Registration Number : DHLTH/E/2019/05701

      Delete
    5. @Anish bhandarkar

      https://www.naturalfarmerskerala.com/cock-and-bull-story-of-keralas-cattle-development-policy/

      Local Breeds neglected after Industrial Revolution-
      Then came the industrial revolution, it’s effects were global and it was only a matter of time before it was thrust on Kerala too. Lack of vision, imagination and brains made the policy makers ape the west blindly. The conventional wisdom on cattle was rubbished. Suddenly the local breeds were projected as ‘inferior’ and ‘useless’ animals, as they provided only about 2-3 litres compared to the 20-30 litres that their foreign counterparts produced. Simultaneously, the false notion that milk was needed to consumed in large quantities for health, was spread. Previously, in the Desi-Cow phase, only children and pregnant women were asked to consume milk, for the rest half a spoon of ghee or 1-2 spoons of curd would suffice.

      Armed with the new propaganda the state Govt quickly established how essential it was increase milk production in the interest of public health. The Indo-Swiss project was initiated shortly. The Kerala Govt sought the collaboration of the Swiss Govt to bring in the Swiss Browns that yielded 30 litres of milk a day. Suddenly the local breeds were viewed as a threat to milk production. One of the conditions that the Swiss side insisted on was a total elimination of the local breeds. According to them surviving desi-breeds posed two problems, they would not only reduce the milk production but also posed the danger of adulterating the foreign breeds. Thus born was the draconian law The Kerala Livestock Improvement Act 1961
      which was subsequently amended in 1968 with more severe penalties severe penalties

      This act forbids anyone from rearing of bulls capable of reproduction. It effectively brings about killing of bulls as no one would want a bull that could not reproduce.

      A state sponsored crackdown on bulls followed. If anyone was rearing a bull the Live-stock inspector authorized by the AH Dept would castrate it forcibly. Thus the only way to breed cattle was by Artificial Insemination (AI). For the past 52 years cattle from Kerala have been denied the rights to mate, can you believe?

      Within no time the native breed population dwindled. Only very few bulls survived, they were the ones in remote inaccessible places or the Temple Bulls.

      Delete
    6. Captain, message sent to the following people in the environment ministry:

      menong@cag.gov.in (joint secretary)
      ravis.prasad@nic.in (additional secretary)
      secy-moef@nic.in (Secretary)
      mefcc@gov.in (minister)

      The emails went through and I have asked for an acknowledgement

      Delete
    7. pranam captain,

      Your Registration Number is : PMOPG/E/2019/0675193

      https://twitter.com/prashantjani777/status/1198945620280692736

      Delete
    8. @Charishma,
      This is utmost disturbing. It’s true the 1 or 2 surviving bulls must be temple bulls, as the Act exempts them.

      The positive side to this situation is the EXACT same Act can be used to decimate foreign Humpless bulls if the officer can be convinced that it’s the humpless bull that is inferior and undesirable, not the humped bull.

      Delete
    9. Your Registration Number is : PMOPG/E/2019/0675771

      Delete
  13. After what happened in Maharashtra and Karnataka, one thing is sure. Voter may vote. But parties will cobble together a Frankenstein alliance to rule all in the guise if giving good governance.

    Why have seperate parties manifesto etc when all is a means to fool the voter and do opportunistic politics....

    The next Loksabha election or any other election in this country will be selling this trend .

    ReplyDelete
  14. DONALD TRUMP , LISTEN UP, AND LISTEN GOOD..

    IN YOUR NEXT RALLY, INTRODUCE YOURSELF TO THE CROWD THAT YOUR NEW NAME IS DONALD IMPEACHED TRUMP..

    THERE IS ONE IMPORTANT LESSON OF PARADOX I LEARNT AFTER 30 YEARS IN COMMAND OF SHIPS AT SEA..

    WHEN YOU TRY TOO HARD TO SCREW A GOOD MAN BY A WITCH HUNT , EVEN YOUR ENEMIES RALLY AROUND YOU..

    CRY WOLF TOO OFTEN , AND YOU FALL PHUTTT ON YOUR FACE..

    NEVER MAKE THE MISTAKE OF TRUSTING THE KOSHER MEDIA POLLS AND RESIGNING AS GULLIBLE NIXON DID..

    http://ajitvadakayil.blogspot.com/2017/02/richard-m-nixon-greatest-american.html

    TRUMP, START ATTACKING DEEP STATE CANDIDATE AND SWAMP DENIZEN BLOOMBERG NOW ITSELF.. GIVE AMERICANS SOMETHING TO TALK ABOUT ON THANKSGIVING DAY HOLIDAY..

    BRAND BLOOMBERG AS " MICHAEL SWAMP BLOOMBERG".. BLOOMBERG WANTS MEXICAN DRUG SMURFS TO FLOOD AMERICA FOR LAUNDERING DRUG MONEY..

    EL CHAPO WAS PROTECTED BY CIA/ DEA AND THE US PRESIDENT..

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

    capt ajit vadakayil
    ..



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      RAJDEEP SARDESAI
      PAAGALIKA GHOSE
      NAVIKA KUMAR
      ANAND NARASIMHAN
      SRINIVASAN JAIN
      SONAL MEHROTRA KAPOOR
      VIKRAM CHANDRA
      NIDHI RAZDAN
      FAYE DSOUZA
      RAVISH KUMAR
      PRANNOY JAMES ROY
      AROON PURIE
      VINEET JAIN
      RAGHAV BAHL
      SEEMA CHISTI
      DILEEP PADGOANKAR
      VIR SANGHVI
      KARAN THAPAR
      PRITISH NANDI
      SHEKHAR GUPTA
      SIDHARTH VARADARAJAN
      ARUN SHOURIE
      N RAM
      I&B DEPT/ MINISTER
      LAW MINISTER/ MINISTRY
      ALL CONGRESS SPOKESMEN
      RAHUL GANDHI
      SONIA GANDHI
      PRIYANKA VADRA
      JACK DORSEY
      MARK ZUCKERBERG
      THAMBI SUNDAR PICHAI
      CEO OF WIKIPEDIA
      QUORA CEO ANGELO D ADAMS
      QUORA MODERATION TEAM
      KURT OF QUORA
      GAUTAM SHEWAKRAMANI
      DAVID FRAWLEY
      STEPHEN KNAPP
      WILLIAM DALRYMPLE
      KONRAED ELST
      FRANCOIS GAUTIER
      SADGURU JAGGI VASUDEV
      SRI SRI RAVISHANKAR
      BABA RAMDEV
      PAVAN VARMA
      RAMACHANDRA GUHA
      WEBSITES OF DESH BHAKT LEADERS
      SPREAD OF SOCIAL MEDIA

      Delete
    2. Sent to trump by filling up the contact form.

      Delete
    3. Have sent to President Trump via https://www.whitehouse.gov/contact/

      Delete
  15. https://timesofindia.indiatimes.com/city/ahmedabad/yoginis-speak-only-english-cops-clueless/articleshow/72214370.cms

    THIS BLOGSITE WAS ASKED TO FILE FIR IN LOCAL POLICE STATION IF WE HAVE PROBLEMS WITH QUORA PROMOTING PEDOPHILIA , INCEST AND BESTIALITY..

    LIKE VAJPAYEE , MODI SUPPORTS HOMOSEXUALITY AND ANAL SEX .. WE KNOW WHY !

    NOT ONE RSS CHIEF HAS MARRIED TILL TODAY !!

    THIS IS HOW SHELL COMPANIES THRIVES IN INDIA FOR SO LONG..

    THIS BLOGSITE WAS THE FIRST TO EXPOSE SHELL COMPANIES BY A 33 PART BLOG SERIES..

    EVEN CHAMPION SUBRAMANIAN SWAMY WAS QUIET ON SHELL COMPANIES -- WHY ?

    ReplyDelete
    Replies
    1. India's parallel economy is HUGE. Basically Gujarati Mafia act as proxy to R for ruling India. BJP is their tool to accomplish their agenda.

      Delete
  16. https://m.economictimes.com/industry/energy/power/coal-fired-plants-may-have-to-scale-down-utilisation-to-35-by-2022-kpmg/amp_articleshow/72200926.cms

    One of the Big Four, KPMG, has predicted that capacity utilisation for many coal-fired power plants in India will drop to 35-40% by 2022 as renewable power generation sources rise. Average capacity utilisation of coal fired power plants are around 51% at present and some plants may have to be seasonally shut or mothballed, KPMG has predicted.

    Already closing bells for coal

    ReplyDelete
  17. When you sailed down the Amazon that year you must have used water vortices if the receding tide to sense the deepest channel for you ship avoiding sand bars.

    Ditto when you moved over icy seas??

    ReplyDelete
  18. Dear Capt Ajit sir,

    After Modi praised NCP(Maha tussle is tuss now) and BJD (see the kinnars rising)....Why are these transgenders appointed in Security dept in hospitals...slowly they will be in police, armed forces...then India will be put down....
    https://sambadenglish.com/transgenders-appointed-as-security-guards-in-odisha-hospital/?fbclid=IwAR2aSXJZYN4DkWuy01yAY1bOR74zp9TPec2dr56isI5DnJGFPtignevs_74

    ReplyDelete
    Replies
    1. Guruji mentioned there is no problem with transgender,the pests are LGBTQ+ faggots
      http://ajitvadakayil.blogspot.com/2015/08/homosexuality-pedophilia-deviant-porn.html?m=1

      Delete
    2. I have shared this before.
      Again sharing it.
      Transgender have become a menace in Odisha.
      They are literally a bunch of goons.
      TG beat a Naga Sadhu brutally, a year ago.

      https://youtu.be/1PU1LLw5NCs

      This fellow Achyuta Samanta who owns the KIIT University in Bhubaneswar, is single & absolutely horrible.
      Samanta is a BJD MP to Rajya Sabha.
      He runs a satellite channel Kalinga TV which has special episode of why a fellow(boy/girl) choose to a gender orientation though they are born as something different.
      Once a TG answered to the same question of why he chose to be a Girl though being born as guy.
      His reply was that from childhood he always dreamt of being a Mother.
      He cried & that was rocking as video clip to be aired on the scheduled date.
      Kya baat hai!!!
      Teenagers see such programmes.
      My thirteen year old Nephew saw one episode & kept asking some of the most headache giving question.
      I had to trick him.
      Wonder what questions brews up with others.
      Teenage is a time where the blood rushes to a very sensitive part of the body.
      It's been rocked by Captain many a time.

      Till date one thing which Captain has hold back & never replied or didnot publish as my question, is that what is so different about TG that Captain supports them?
      What is the secret?

      In Bhubaneswar Cuttack circle, TG have made guys become Homosexuals.
      I have seen guys fondling eachother.
      Straight guys but turned as such.
      There are lot many varied cases.
      Married Men go to TG cheating their wife.
      These scenes have hit me very hard.

      Delete
  19. Captain,

    You mentioned parashuram accompanying namboothiris to kerala was actually home coming. Why did namboothiris go and settle near saraswati river? To create the vedic civilization on the banks of saraswati?

    Regards,
    Muthu Swamynathan.

    ReplyDelete
    Replies
    1. ONLY KERALA NAMBOODIRIS KNEW VEDAS ON ORAL ROUTE -- STARTING 400 CENTURIES AGO ( DANAVA CIVILIZATION )..

      https://www.youtube.com/watch?v=zl7E00fIHbM

      EVEN AFTER VEDAS WERE PENNED DOWN 70 CENTURIES AGO, KERALA NAMBOODIRIS HAS TO ACCOMPANY VEDAS LIKE AUDIO CDs..

      POWER OF VEDA SRUTI IS IN THE SOUND, NOT THE WORDS..

      Delete
    2. Captain,

      I heard that Ved vyas segregated vedas into four parts. Was ved vyas namboothiri? Were parashara, ved vyas and sukhacharya namboothiris? Ved vyas lived in Krishna's time period. Was ved vyas one of the nambothiris travelled to kerala along with parashurama? Was Arjuna(with guruvayoor idol) also joined with parashuram? Did ved vyas compile mahabharat in kerala after pandavas rule end in Bharata?

      Regards,
      Muthu Swamynathan.

      Delete
    3. Captain,

      When Adi sankaracharya distributed vedas and upanishads to the mutts he created in four corners of india, he should have distributed the ithihasas ramayana and mahabharata too. These mutts are the reason people from all regions of India are aware about ramayana and mahabharata for millenniums.

      Regards,
      Muthu Swamynathan.

      Delete
    4. 400 CENTURIES AGO, THE MAHARISHIS WHO DOWNLOADED THE VEDAS FROM AKASHA WERE KERALA NAMBOODIRIS.

      Delete
    5. The maharishis from the time of matsya avatar to adi sankaracharya were kerala namboothiris!!!!!!!

      Namboothiris deserve the title "Aryan". Only clan in the entire human race breached the veil of Maya. Only maharishis can write down extremely advanced math, astronomy, astrology and quantum science. Namboothiris understood the need to preserve it till the end of human race. There can be no meaningful purpose for human race without akashik records.

      Sadly, namboothiris became the worst clan after 3000bc approx.

      Delete
  20. Dear Capt Ajit sir,

    Now, we have google brain implants instead of school... An expert believes that within the next 20 years, our heads will be boosted with special implants so 'you won’t need to memorize anything'

    https://www.thesun.co.uk/tech/8710836/google-brain-implants-could-mean-end-of-school-as-anyone-will-be-able-to-learn-anything-instantly/

    ReplyDelete
    Replies
    1. a high frequency pulse is enough to earse everything, it is same as EMP

      Delete
    2. These useless concpets can come from GORAS who rely on artificial parts,unlike Indians who believe in enhancing natural parts like Brain,cell memory.

      Delete
  21. WE THE PEOPLE ASK THE MODI GOVT TO CREATE A MILITARY COURT TO PUNISH/ IMPRISON JUDGES RETIRED AND PRESENT TO HAVE BEEN IN THE PAYROLL OF FOREIGN DESH DROHI FORCES AND BLED BHARATMATA.

    ALL THE JUDICIARY IS EMPOWERED TO DO IS TO INTERPRET THE CONSTITUTION, BUT IN INDIA THEY PLAY GOD..

    ON THE DAY CJI DIPAK MISRA RETIRED HE GAVE A SPEECH “ SUPREME COURT IS SUPREME “.. THIS IS AS STUPID AS SAYING CHIEF COOK IS CHIEF OF THE SHIP , NOT THE CAPTAIN..

    DIPAK MISRA WILL TAKE A FLIGHT TO THE CAPITAL OF INDIA, IF A FORM IS GIVEN TO HIM WHICH STATES “FILL UP IN CAPITAL” ( UPPER CASE )..TEE HEEE.

    TO STAR OFF IMPRISON THE JUDGES AND CJI WHO SAT ON THE BENCH WHICH STRUCK DOWN NJAC..

    THE NJAC WOULD HAVE REPLACED THE ILLEGAL COLLEGIUM SYSTEM FOR THE APPOINTMENT OF JUDGES AS INVOKED BY THE SUPREME COURT VIA JUDICIAL FIAT ..

    ALONG WITH THE CONSTITUTION AMENDMENT ACT, THE NATIONAL JUDICIAL APPOINTMENTS COMMISSION ACT, 2014, WAS PASSED BY THE PARLIAMENT OF INDIA..

    THE NJAC BILL AND THE CONSTITUTIONAL AMENDMENT BILL, WAS RATIFIED BY 20 OF THE STATE LEGISLATURES IN INDIA, AND SUBSEQUENTLY ASSENTED BY THE PRESIDENT OF INDIA PRANAB MUKHERJEE ON 31 DECEMBER 2014.

    THE GOVERNMENT HAD SOUGHT TO REPLACE THE EXISTING SYSTEM, WHICH CRITICS SAID WAS OPAQUE, WITH A SIX-MEMBER NJAC COMPRISING THE CHIEF JUSTICE OF INDIA (CJI), TWO SENIOR-MOST SUPREME COURT JUDGES, THE LAW MINISTER AND TWO EMINENT PERSONS.

    THE EMINENT PERSONS WOULD BE CHOSEN BY A SELECTION COMMITTEE INCLUDING THE CHIEF JUSTICE OF INDIA, THE PRIME MINISTER AND THE LEADER OF THE OPPOSITION.. IN NJAC, MEMBERS HAVE VETO POWER. IF TWO MEMBERS VETO A NOMINATION OR DECISION, THE MATTER IS DROPPED..

    THE NJAC ACT AND THE CONSTITUTIONAL AMENDMENT ACT IS IN FORCE FROM 13 APRIL 2015 AND IS STILL IN FORCE..

    ON 16 OCTOBER 2015, THE CONSTITUTION BENCH OF SUPREME COURT BY 4:1 MAJORITY UPHELD THE COLLEGIUM SYSTEM BY A JUDICIAL REVIEW AND STRUCK DOWN THE NJAC AS UNCONSTITUTIONAL..

    JUDICIARY HAS NO SUCH POWERS OF JUDICIAL REVIEW..

    http://ajitvadakayil.blogspot.com/2019/08/we-people-who-are-above-constitution.html

    THE NJAC BILL WAS PASSED UNANIMOUSLY BY BOTH LOK SABHA AND RAJYA SABHA WITH PRESIDENT SIGNING THE LAW.. YET ILLEGAL COLLEGIUM JUDICIARY USED A JUDICIAL REVIEW PROCESS AND STRUCK IT DOWN..

    JUSTICES J. S. KHEHAR, MADAN LOKUR, KURIAN JOSEPH AND ADARSH KUMAR GOEL HAD DECLARED THE 99TH AMENDMENT AND NJAC ACT UNCONSTITUTIONAL .. THESE FOUR JUDGES AND EX-CJI CL DATTU MUST BE TRIED BY THE MILITARY COURT FOR SEDITION…

    WE THE PEOPLE WARN THE ELECTED EXECUTIVE— JUDICIAL REVIEW IS AN UNDEMOCRATIC SYSTEM AND IT IS DANGEROUS TO THE WATAN WHEN COLLEGIUM JUDGES ARE CONTROLLED BY THE DEEP STATE..

    THE SUPREME COURT IS ITSELF BOUND BY THE CONSTITUTION OF INDIA AND THE PARLIAMENT CAN AMEND THE CONSTITUTION ANY TIME THEY WANT..

    JUDICIAL REVIEW EMPOWERS THE ILLEGAL COLLEGIUM JUDICIARY TO DECIDE THE FATE OF LAWS PASSED BY THE ELECTED AND ACCOUNTABLE LEGISTLATURE WHICH REPRESENTS THE SOVEREIGN WILL OF THE PEOPLE.

    PARAMOUNT CLAUSE : WE THE PEOPLE ARE ABOVE THE CONSTITUTION.. THE CONSTITUTION CANNOT BE USED AS A FRANKENSTEIN TO STRAIT-JACKET "WE THE PEOPLE" BY STUPID JUDICIARY ..

    JUDGES DO NOT KNOW THAT THE CONSTITUTION EMPOWERS PRESIDENT AND STATE GOVERNORS WITH ENORMOUS SUBJECTIVE AAND DISCRETIONARY POWERS TO SUSTAIN DHARMA ( NOT BLIND JUSTICE )…

    PRESIDENT AND STATE GOVERNORS ARE NOT RUBBER STAMPS AS CONTENDED BY BENAMI MEDIA AND ILLEGAL COLLEGIUM JUDICIARY..

    INDIA IS NOT A DEMOCRACY.. INDIA IS A BANANA REPUBLIC CONTROLLED BY DEEP STATE WHITE JEWS FROM ABROAD….

    http://ajitvadakayil.blogspot.com/2018/11/the-indian-constitution-does-not-allow.html

    http://ajitvadakayil.blogspot.com/2018/10/we-people-declare-that-all-laws-created.html

    http://ajitvadakayil.blogspot.com/2018/10/judiciary-in-contempt-of-we-people-capt.html

    Read all 8 parts of the post below--
    http://ajitvadakayil.blogspot.com/2019/01/justice-be-damned-enforce-law-not-any.html

    capt ajit vadakayil
    ..

    ReplyDelete
    Replies
    1. PUT ABOVE COMMENT IN WEBSITES OF--
      LAW MINISTER
      LAW MINISTRY
      CJI BOBDE
      ALL SUPREME COURT JUDGES
      ATTORNEY GENERAL
      ALL STATE LAW MINISTERS AND LAW MINISTRIES
      ALL STATE HIGH COURT CHIEF JUSTICES
      PMO
      PM MODI
      RANJAN GOGOI
      DIPAK MISRA
      JS KHEHAR
      TS THAKUR
      RM LODHA
      P SATHASIVAM
      SOLI BABY
      FALI BABY
      KATJU BABY
      SALVE BABY
      KK VENUGOPAL
      M ROHTAGI
      GE VAHANVATI
      MK BANNERJEE
      ASHOK DESAI
      TUSHAR MEHTA
      RANJIT KUMAR
      M PARASARAN
      ROHINTON NARIMAN
      CHANDRACHUD
      ALL SUPREME COURT LAWYERS
      AJIT DOVAL
      RAW
      IB
      NIA
      ED
      CBI
      AMIT SHAH
      HOME MINISTRY
      CMs OF ALL INDIAN STATES
      DGPs OF ALL STATES
      GOVERNORS OF ALL STATES
      PRESIDENT OF INDIA
      VP OF INDIA
      SPEAKER LOK SABHA
      SPEAKER RAJYA SABHA
      DEFENCE MINISTER - MINISTRY
      ALL THREE ARMED FORCE CHIEFS.
      RAJEEV CHANDRASHEKHAR
      MOHANDAS PAI
      NITI AYOG
      AMITABH KANT
      VIVEK OBEROI
      GAUTAM GAMBHIR
      ASHOK PANDIT
      ANUPAM KHER
      KANGANA RANAUT
      VIVEK AGNIHOTRI
      KIRON KHER
      MEENAKSHI LEKHI
      SMRITI IRANI
      PRASOON JOSHI
      MADHUR BHANDARKAR
      SWAPAN DASGUPTA
      SONAL MANSINGH
      MADHU KISHWAR
      SUDHIR CHAUDHARY
      GEN GD BAKSHI
      SAMBIT PATRA
      RSN SINGH
      SWAMY
      RAJIV MALHOTRA
      THE QUINT
      THE SCROLL
      THE WIRE
      THE PRINT
      MK VENU
      MADHU TREHAN
      CLOSET COMMIE ARNAB GOSWMI
      RAJDEEP SARDESAI
      PAAGALIKA GHOSE
      NAVIKA KUMAR
      ANAND NARASIMHAN
      SRINIVASAN JAIN
      SONAL MEHROTRA KAPOOR
      VIKRAM CHANDRA
      NIDHI RAZDAN
      FAYE DSOUZA
      RAVISH KUMAR
      PRANNOY JAMES ROY
      AROON PURIE
      VINEET JAIN
      RAGHAV BAHL
      SEEMA CHISTI
      DILEEP PADGOANKAR
      VIR SANGHVI
      KARAN THAPAR
      PRITISH NANDI
      SHEKHAR GUPTA
      SIDHARTH VARADARAJAN
      ARUN SHOURIE
      N RAM
      I&B DEPT/ MINISTER
      LAW MINISTER/ MINISTRY
      ALL CONGRESS SPOKESMEN
      RAHUL GANDHI
      SONIA GANDHI
      PRIYANKA VADRA
      JACK DORSEY
      MARK ZUCKERBERG
      THAMBI SUNDAR PICHAI
      CEO OF WIKIPEDIA
      QUORA CEO ANGELO D ADAMS
      QUORA MODERATION TEAM
      KURT OF QUORA
      GAUTAM SHEWAKRAMANI
      DAVID FRAWLEY
      STEPHEN KNAPP
      WILLIAM DALRYMPLE
      KONRAED ELST
      FRANCOIS GAUTIER
      SADGURU JAGGI VASUDEV
      SRI SRI RAVISHANKAR
      BABA RAMDEV
      PAVAN VARMA
      RAMACHANDRA GUHA
      WEBSITES OF DESH BHAKT LEADERS
      SPREAD OF SOCIAL MEDIA

      Delete
    2. ASK LAW MINISTRY/ MINISTER , PMO/ PM MODI , FOR AN ACK..

      ALL READERS MUST PARTICIPATE--

      IT IS NOW OR NEVER..

      Delete
    3. Namastae Master Ji

      https://mobile.twitter.com/kannanlp/status/1198925701224656896

      Thanks with highest gratitude

      Delete
    4. Captain, have sent your message to law minister at ravis@sansad.nic.in, secretary of Department of Legal Affairs at secylaw-dla@nic.in, Secretary of Legislative Department at gn.raju@nic.in and Secretary of Department of Justice at secy-jus@gov.in. The emails went through and I have asked for an acknowledgement

      Delete
    5. https://twitter.com/asapexchange/status/1198932005645365248

      Delete
    6. Your Registration Number is : PMOPG/E/2019/0675152

      Delete
    7. Sent to law ministry, legal affairs, pmo and others

      Thanks And Regards

      Delete
    8. Guruji,apologize but most of the people'sname you have given are useless.Pleaee atleast give us the names who are intelligent and wise.

      Delete
    9. PMOPG/E/2019/0675255
      Emailed it to law minister email IDs shared by Dinesh Kumar

      Delete
    10. Sent mails to-
      ravis@sansad.nic.in
      secylaw-dla@nic.in
      gn.raju@nic.in
      secy-jus@gov.in
      narendramodi1234@gmail.com
      contact@amitshah.co.in
      https://mobile.twitter.com/Chn_123/status/1198952561241255936

      Delete
    11. https://twitter.com/KshitijGundale1/status/1198954422958743553?s=19

      Delete
    12. : sent (asked for ack)

      PM Modi
      Amit Shah
      Ravi Shankar Prasad

      Delete
    13. Plzzz no one is listening.
      I have kept this information alive from the time I have read it.
      One can check in my tweets.
      I keep tweeting an information till it is taken up.
      Once I read from Captain, I keep it rocking on every possible avenue.
      This is not the first time those government officials or ministers will come across this piece of information.

      Ravi Sankar Prasad has been asked to resign.
      I have participated enough number of times to ask this fellow's resignation as well as dismissal from BJP.
      Inbetween a whistle blower from Nagpur exposed Ravi Sankar Prasad & Prasad asked police to trace the corresponding account's mobile number.
      Prasad threatened that fellow but those Whistleblowers put the conversation on Twitter for everyone to listen.

      BJP is no more working for this country.
      BJP has cheated every Nationalist wholesale.

      Delete
    14. Captain, back in January-2018 the then CJI Dipak-Misra refused to meet the Principal-Secretary to the PM, Nripendra-Misra. The Secretary's car was made to wait outside the gate for 5-10 minutes and then it had to reverse and go away. Does the CJI (or any member of Judiciary/Media/Military/etc) have the right to refuse meeting with a high-ranking official of the Central-Govt that too when it is known by all involved that the person (in this case CJI) is free and not doing any important-work ? Shouldn't the Government have the right to meet the concerned-officials whenever they want to unless the person they want to meet is actually doing some important work ?

      https://youtu.be/kTJOwWak_dM?t=32

      Delete
    15. Tweeted and verified,https://mobile.twitter.com/PadNaren/status/1198938333956771841

      Delete
    16. Tweets:
      https://twitter.com/AghastHere/status/1199006833857482753?s=20
      https://twitter.com/AghastHere/status/1199007088514605061?s=20
      https://twitter.com/AghastHere/status/1199007255020101634?s=20
      https://twitter.com/AghastHere/status/1199007349819822093?s=20
      https://twitter.com/AghastHere/status/1199007454761308160?s=20
      https://twitter.com/AghastHere/status/1199007516111331334?s=20

      Handles:
      @OfficeOfRSP @rsprasad @PMOIndia @narendramodi @NIA_India @dir_ed @AmitShah @HMOIndia @rashtrapatibhvn @MVenkaiahNaidu @VPSecretariat @loksabhaspeaker @rajnathsingh @DefenceMinIndia @NITIAayog @amitabhk87 @rajeev_mp @TVMohandasPai @vivekoberoi @GautamGambhir @ashokepandit @AnupamPKher @vivekagnihotri @KanganaTeam @M_Lekhi @KirronKherBJP @smritiirani @imbhandarkar @prasoonjoshi_ @sambitswaraj @GeneralBakshi @madhukishwar @RajivMessage @TheQuint @scroll_in @thewire_in @ThePrintIndia @madhutrehan @mkvenu1 @ArnabGoswamiRtv @sagarikaghose @sardesairajdeep @navikakumar @Sonal_MK @SreenivasanJain @AnchorAnandN @sonal_mansingh @vikramchandra @Nidhi @fayedsouza @ravishndtv @PrannoyRoyNDTV @aroonpurie @vineetjaintimes @Raghav_Bahl @seemay @virsanghvi @KaranThapar_TTP @PritishNandy @ShekharGupta @svaradarajan @nramind @MIB_India @PrakashJavdekar @drajoykumar @AkhileshPSingh @BHAKTACHARANDAS @DeependerSHooda @dineshgrao @GouravVallabh @JM_Scindia @JaiveerShergill @rajeevgowda @MYaskhi @Meem_Afzal @PCChackoOffice @plpunia @Pawankhera @RajBabbarMP @RahulGandhi @priyankagandhi @adamdangelo @Quora @gshewakr @sundarpichai @jimmy_wales @jack @davidfrawleyved @DalrympleWill @Koenraad_Elst @fgautier26 @SriSri @SadhguruJV @yogrishiramdev @PavanK_Varma @Ram_Guha

      MyGov IdeaBox Post:
      https://www.mygov.in//comment/107670871/%23comment-107670871/

      Requested acknowledgement from PMO and Law Ministry.

      Delete
    17. https://twitter.com/shree1082002/status/1199017580494643204

      Delete
    18. Your Grievance is registered successfully.
      Registration Number : DLGLA/E/2019/01415

      Delete
    19. PMOPG/E/2019/0675259
      DEPOJ/E/2019/04324
      Have mentioned in replies for all these people
      https://twitter.com/rsprasad/status/1198880657990025216
      https://twitter.com/fskulaste/status/1198929108538974208
      https://twitter.com/barandbench/status/1198503941954101248
      https://twitter.com/pbhushan1/status/1197046997460537344

      For reference for others
      https://ajitvadakayil.blogspot.com/2015/10/extreme-judicial-overreach-in-india.html

      I wasnt aware of this NJAC. I have started to spread among friends. Totally understand the gravity of this and why Capt is asking all readers to participate.

      Request others to understand that this not a one time activity to post on social media. Please keep posting your opinions on NJAC and collegium judiciary. Best type of people to spread this is one who have any sort of cases ongoing. This will resonate best with them.
      Question each and every lawyer you meet on this. It should hit their conscious.

      I am amazed that this bill was ratified by 20 state assemblies and the President of India and still got overturned by a simple PIL. That is the pull these people have through DEEP STATE.

      Raising voice on this has to be a continuous effort and not onetime.

      Delete
    20. Your Registration Number is : PMOPG/E/2019/0682693

      https://twitter.com/prashantjani777/status/1200397416576569345
      https://twitter.com/prashantjani777/status/1200397841883127809

      there is also a video from 2015 by Rahul Mehta of Right to Recall Group discussing NJAC and surrender to judiciary by Modi Govt on this and possible helplessness of politicians against influence of foreign forces/finances/media etc

      https://www.youtube.com/watch?v=VVXyrr_NhFk&t=19s

      Delete
    21. https://timesofindia.indiatimes.com/world/europe/thieves-grab-jewels-treasures-worth-up-to-a-billion-euros-in-dresden/articleshow/72226431.cms

      Seems like someone watching ur Internet

      Delete
  22. Your Registration Number is : PMOPG/E/2019/0675249

    ReplyDelete
  23. Guruji,i am very thankful to you, it is because of you i can see the real truth of the world, guruji your blog changed my life, i am a very long time reader of your blog, and i wanted to say thank you for a long time, i am very thankful to you guruji, keep writing sir

    ReplyDelete
  24. by the way i am bengali, and a Brahmin

    ReplyDelete
  25. https://timesofindia.indiatimes.com/world/europe/thieves-grab-jewels-treasures-worth-up-to-a-billion-euros-in-dresden/articleshow/72226431.cms

    GREEN DIAMOND WAS STOLEN BY JEW ROTHSCHILD FROM INDIA..

    DRESDEN GREEN IS THE LARGEST NATURAL GREEN DIAMOND FOUND, WEIGHING 40.70 CARATS IN ITS PEAR-SHAPED CUT. THIS BEAUTIFUL DIAMOND A PERFECT CLARITY GRADE..

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

    ReplyDelete
  26. You look younger than Me, Love your unpretentious style and attitude, you are a true rock star \m/

    Regards

    ReplyDelete
    Replies
    1. I HAVE NO NEED TO IMPRESS ANYBODY.. WHEN YOU REACH THIS LEVEL, HAPPINESS ALIGHTS ON YOU..

      http://ajitvadakayil.blogspot.com/2010/12/happiness-from-within-capt-ajit.html

      Delete
    2. Thank you very much for your advice, Guruji _/\_

      Delete
    3. MAN IS HAPPY , WHEN THERE IS HARMONY AND PEACE AT HOME.. HIS CHILDREN DO WELL .. AND HIS WIFE LOOKS UP TO HIM..

      Delete
  27. hello captain why do punjabi girls and women marry late and have maximum divorce rates?i mean maybe because of attitude problem.what i noticed that they think they are very pretty,or is it due to hormonal changes.i have seen many punjabi single mothers as compared to other communities

    ReplyDelete
  28. https://timesofindia.indiatimes.com/sports/hockey/top-stories/watch-shocking-on-field-fight-in-nehru-cup-hockey-final/articleshow/72227500.cms

    PUNISH PLAYERS WHO DREW FIRST BLOOD AND PRO-ACTIVELY HIT ANOTHER PLAYER WITH HOCKEY STICK..

    DO NOT PUNISH PLAYERS WHO REACTED..

    DELIVER DHARMA --NOT JUSTICE

    DHARMA ALLOWS MAN NO COWARDICE..

    ReplyDelete
  29. Captain, why are States allowed to have self-decided tax-rates for things like road-tax, alcohol-tax ? Wouldn't it be easier to implement a Nationwide flat-rate, say 10% road-tax, 20% alcohol-tax, etc. ? These discrepancies are creating a lot of chaos, corruption and bureaucratic-headache for both people and officials alike.

    And why are the States allowed to decide their own laws ? What is a considered a crime or violation in one-state is perfectly legal in the neighbouring-state. Is this not another nuisance which can be remedied by having same laws across the Nation ?

    ReplyDelete
  30. Captain, why does the Consciousness have a desire to create other conscious-beings ?

    If we see, BrahmAn the Supreme-Morphogenetic-Consciousness-Field, created the various life-forms with varying levels of consciousness with human-beings having the highest-consciousness-level. To facilitate these life-forms the Universe is created as an environment for these life-forms to live their lives & evolve as per their karma. For BrahmAn, the Universe is like a Lab-Test where it watches the performance of it's creations.

    Now we see Human-Beings doing similar things by trying to play God. Humans try to create conscious-beings of their own by creating artificial-intelligence, neural-networks, etc. Humans try to play God by trying to create environments like say Lab-Tests or Video-Games or even real-life-environments where they place these semi-conscious-beings so that they can study the evolution of these beings and intervene occasionally to take what they think is appropriate-action. Then we see the same fear about AI is basically the fear that the AI will become conscious enough to start creating it's own versions of lower-conscious beings (more robots) which it can use to destroy the original higher-conscious beings, human-beings, like how we see in Terminator and other movies.

    Also, we see Human-Beings again trying to play God by using mediums such as Movies, TV-Serials, Video-Games, etc. where they create the environment, the characters, the "fate" of the characters (a.k.a script-role), etc. This, although made with an intention to entertain, ends up showing the subtle-desire of the conscious-Human to play the roles of God & Karma.

    Why does a (relatively) Higher-Level-of-Consciousness always try to create a bunch of "junior" Consciousnesses ? BrahmAn makes Humans, Humans make AI/Robots, Smart-AI/robot then makes dumb/average-robots/AI, etc.

    Why is this so ? Is this a desire of the Conscious-beings to make something similar but junior to them, just like how parents create children ? If yes, then is this desire to create due to effect of Maya or is it just an independent streak demonstrating the higher levels of thought & willpower in Higher-Consciousness-Beings ?

    ReplyDelete
    Replies
    1. CONSCIOUSNESS ARISES WHEN THE SOUL CROSSES A PARTICULAR FREQUENCY ( SOUL EVOLUTION )

      AFTER THAT KARMA KICKS IN .. KARMA AFFECTS ONLY HUMANS ..

      KARMA THEN POWERS CONSCIOUSNESS LEVEL

      Delete
    2. Captain, The expansion of consciousness is by desire and what is that underlying force that create this desire. Even in Hindu cosmology Brahma also has life and dissolution. Can we say that Shiva is beyond the purview of this consciousness?

      Delete
    3. Very profound reply Captain. It sounds similar to driving a manual-transmission car.

      1) Clutch initiates movement starting from zero till it's max speed
      -(akin to Soul-Evolution and attaining Consciousness)

      2) Clutch is fully released, then Accelerator takes over
      -(akin to Karma kicking in)

      3) Accelerator now decides speed of car
      -(akin to Karma deciding further evolution of consciousness)

      Delete
  31. Hi Sir - What was the need / circumstances for the Pallava kings to develop the Telugu language / script?

    Thank you

    ReplyDelete
    Replies
    1. Telugu was there even millenniums before Pallavs reigned in South.There were historical records of Telugus participating in Mahabharat battle on the side of Kauravas.

      Regards,
      Sriganesh

      Delete
    2. Pallavas have not developed language itself, all Dravidian languages are much much older and influenced by Samskritham. The literature such as Padhya is unique to Telugu cannot be found on other languages, as such the dating is questionable like Captain commented on Malayalam several times. The original name Andhram is totally forgotten now.

      Delete
  32. Guruji,
    Sorry for off topic question. There is a milk company A2 selling milk saying they are producing milk from A2 cows. I have seen their cows, they are humpless. Would humpless western cows give A2 milk or it is scam.
    Thanks in advnace
    Sai

    ReplyDelete
    Replies
    1. SCAM

      HUMPED INDIAN COWS GIVE NUTRITIOUS A2 MILK

      HUMPLESS WESTERN COWS GIVE TOXIC A1 MILK

      Delete
  33. There is a message going around saying Supriya Sule's mother in law is non other than Bal Thakrey 's sister Sudha Sule!

    ReplyDelete
  34. Your Registration Number is : PMOPG/E/2019/0675897

    ReplyDelete
  35. Sir

    As usual you are correct once again

    Cauvery Calling, a campaign by Isha Foundation that has proposed to plant 242 crore trees along the entire length of the Cauvery river bed, cannot possibly achieve its aim of revitalising the river with its unscientific approach, said experts and activists at a seminar organised by the Asian College of Journalism and the Coalition for Environmental Justice in Chennai on Saturday.

    ReplyDelete
  36. Hi Ajit,

    Once karma kicks in.

    Isn't there an inverse relationship between consciousness level and karma? As a rise in consciousness level reduces karmic debt.

    Regards

    Aneesh

    ReplyDelete
    Replies
    1. THE LESSER THE KARMIC BAGGAGE , THE HIGHER THE SOUL FREQUENCY.

      WHEN SOUL FREQUENCY IS SAME AS THE FIELD OF BRAHMAN, YOU BECOME A JIVAN MUKT..

      Delete
    2. Dear Capt Ajit sir,

      I see your soul frequency is same as the field of Brahman....irrespective what drama of life is around you or in your blog....what's the secret, how do you maintain equanimity with brahman ?
      I have an analogy, where Baba Ramdev does heavy breathing but soon after in secs, he's talking calmly, how he's able to control his body is evident and am sure his mind is also very balanced in the same way.
      I did attend this yoga conference as well but missed his talk, here's Ramdev demonstrating his skills, especially watch...from 33:00 to 35:00
      https://www.facebook.com/ayushtv/videos/1135599633304736/UzpfSTcxODE2MzA2MjoxMDE1NjgyODAwMDQwMzA2Mw/

      Delete
  37. Do u think R will extract further revenge on Bengalis? Assam NRC seems to be the first step.

    ReplyDelete
  38. Congratulations, ISRO and fellow Patriots!
    PSLV-C47 successfully launched Cartosat-3 and 13 other nano satellites.

    Mission: https://www.isro.gov.in/launcher/pslv-c47-cartosat-3-mission
    DD Live: https://www.youtube.com/watch?v=LjFK5Ggwpis

    ReplyDelete
  39. Dear captain,
    It is said that vijaynagara king krishnadev rai conquered whole of kerala?
    Is it true?

    Gratitude
    Aditya

    ReplyDelete
  40. Russian President calls Shale oil and gas production technologies barbaric and bad for the environment. Capt. had always been critical about our move to buy Shale oil from half way around the globe, and how it spoils our storage tanks and machinery life.

    YouTube Video (Seek 3:12): https://www.youtube.com/watch?v=Y8LmYTiooic

    Also, it is refreshing to see Kerala CM mending his ways with respect to Sabarimala issue.

    Congratulations for the guidance, Capt.! Heart-warming to see smart leaders heeding to your advise.

    ReplyDelete
    Replies
    1. FOR BUYING SHIT SHALE FRACKED OIL, MODI WILL WIN THE NOBEL PRIZE..

      ALREADY HE HAS GIVEN PADMA VIBHUSHAN TO VERGHESE KURIAN AND MS SWAMINATHAN..

      VERGHESE KURIAN ALLOWED EVIL PHARMA TO GET A FOOTHOLD IN INDIA..WITH TOXIC A1 MILK OF HUMPLESS WESTERN COWS/

      MS SWAMINATHAN KILLED INDIAs LAKES IN 55 YEARS FLAT.. THESE LAKES WERE IN PRISTINE CONDITION FOR MILLIONS OF YEARS OUR HOLY TOP SOIL OF 6 FEET DEEP ( WHICH THRIVED FOR MILLION OF YEARS ) THICKNESS WAS REDUCES TO 10 INCHES ..

      GUJJU NO 2 HAS BLED BHARATMATA MORE THAN GUJJU NO 1 ..

      MODI IS STILL WONDERING WHY THE YAMUNA RIVER IS FOAMING .

      capt ajit vadakayil
      ..

      Delete
    2. Captain, in northern-China the authorities are forcing residents to burn briquettes instead of coal for heating in attempts to reduce smog/pollution. I came across on this discussion-thread on a Chinese-site where a man is complaining about this. Are briquettes really better than coal for heating-purposes ?

      QUOTE == My mother bought 900 tons of coal for one ton, and now she exchange it for briquettes, 600 tons for briquettes. Now the big loudspeakers in the village are broadcasting. They are not allowed to burn coal, they must burn coal balls, and some people come to check at home. I don’t know where to hide the coal. I remember there is a background, that is, the rural village of Tangshan, like Beijing's rural areas, will be converted from coal to electricity and use electricity for heating. Then the climax came. It is said that this matter has not been implemented. The coal-to-electricity conversion in rural Tangshan was unsuccessful. Remove the installed equipment. It will not work. It may be the circuit, etc. So it’s said that coal will continue to be burned this winter, so my parents also bought coal. However, these days they say "Don’t let the coals burn, you have to replace them with briquettes!". Every house is searched ! == UNQUOTE

      http://bbs.tianya.cn/post-develop-2431664-1.shtml

      Delete
    3. Guruji,
      Why doesn't a cow suffer from a snake bite as such I have never seen or heard a desi cow being bitten by a snake and dying...... Is it due it's suryaketu naadi ? Or its ability to absorb toxins? When you pointed to the allegory of lord Shiva drinking polymerised poison and Nandi licking away few dropped drops.... This comes to mind. There is something cosmic about gaumatha....

      Delete
  41. https://timesofindia.indiatimes.com/india/lok-sabha-passes-bill-to-ban-e-cigarettes/articleshow/72268596.cms

    http://ajitvadakayil.blogspot.com/2019/09/untold-danger-of-e-cigarettes-breach-of.html

    ReplyDelete
  42. https://ajitvadakayil.blogspot.com/2011/08/those-were-days-capt-ajit-vadakayil.html

    ReplyDelete

  43. DEEP STATE AGENTS ALL OVER THIS PLANETS ARE WORRIED THAT INDIA WILL BECOME THIS PLANETs NO 1 SUPERPOWER IN 14 YEARS..

    THEY ARE IN A TEARING HURRY TO KILL INDIA .. THE WHITE MAN LOATHES TO BE RULED BY THE BROWN MAN..

    IN 1947 KOSHER BIG BROTHER JEW ROTHSCHILD DIVIDED INDIA BY AMPUTATING PAKISTAN AND BANGLADESH..

    NOW THEY WANT TO AMPUTATE KASHMIR..

    THESE DEEP STATE AGENTS IN- US CONGRESS/ UK-EU PARLIAMENT/ UN/ AMNESTY / INDIAN POLITICAL PARTIES LIKE CONGRESS- DMK- TMC -AIMIM CLAMOUR FOR HUMAN RIGHTS OF KASHMIRI MUSLIMS..

    JUST WHO ARE THESE KASHMIRI MUSLIMS?

    THESE MUSLIM BASTARDS HAVE DONE GENOCIDE OF KASHMIRI HINDU PANDITS, ETHNICALLY CLEANSED KASHMIR , DESTROYED THOUSANDS OF TEMPLES .. DEEP STATE AGENTS IN OUR ILLEGAL INDIAN COLLEGIUM JUDICIARY WERE IN CAHOOTS..

    NONE OF THE DEEP STATE AGENTS WILL UTTER A WORD ABOUT GENOCIDE OF ROMA GYPSIES DURING WW2..

    THEY WONT TALK ABOUT HOW ROMA GYPSIES HAVE BEEN PERSECUTED FOR A THOUSAND YEARS ..

    INDIA HAS WOKEN UP..

    WE KNOW THE TRAITORS FROM WITHIN AND OUR ENEMIES FROM OUTSIDE..

    WE WILL DEAL WITH THEM..

    LET THE JEWISH DEEP STATE AND THEIR AGENTS GET THIS CRYSTAL CLEAR .. KASHMIR IS THE SOURCE OF OUR RIVERS WHICH ORIGINATE IN THE HIMALAYAS.. WE KNOW HOW TO PROTECT OUR ANCIENT MOTHERLAND..

    INSTEAD OF HAVING PIPE DREAMS THAT INDIA CAN BE BULLIED INTO GIVING AWAY KASHMIR TO PAKISTAN-- GET THIS CLEAR IN YOUR SLIME FILLED HEADS-- PAKISTAN BELONGS TO INDIA..

    WE AWAIT PAKISTAN TO DRAW FIRST BLOOD AND NUKE INDIA FIRST. AFTER THAT WE WILL TAKE BACK PAKISTAN , ALBEIT AS RADIOACTIVE WASTELAND..

    WE KNOW THE WEE TACTICAL NUKES IN PAKISTANs ARSENAL.. PAKISTAN AND THEIR ALLIES DO NOT KNOW THE MASSIVE STRATEGIC NUKES INDIA HAS IN HER ARMORY ..

    https://ajitvadakayil.blogspot.com/2019/11/history-of-romani-gypsies-capt-ajit.html

    SUCK ON THIS COMMENT !

    capt ajit vadakayil
    ..

    ReplyDelete
    Replies

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      SPREAD ON SOCIAL MEDIA EVERY WHICH WAY.

      Delete
  44. somwhere i missed you saying that not to play with cat whiskers as they are their super antennas.
    Can cats sense the dangers of high voltage current eg transformers..with their whiskers?
    I loved caressing my cats whiskers...
    Do you think my playing with my cats whiskers affected the whiskers effficiency and he could not sense the danger of transformer current. Just asking for future care..

    ReplyDelete
  45. Blood and Soil in Narendra Modi’s India
    The Prime Minister’s Hindu-nationalist government has cast two hundred million Muslims as internal enemies.
    By Dexter Filkins

    https://www.newyorker.com/magazine/2019/12/09/blood-and-soil-in-narendra-modis-india

    In August 2019 Modi’s government had announced that it was suspending Article 370 of the constitution, which grants autonomy to Kashmir, India’s only Muslim-majority state. The provision, written to help preserve the state’s religious and ethnic identity, largely prohibits members of India’s Hindu majority from settling there.
    Modi, who rose to power trailed by allegations of encouraging anti-Muslim bigotry, said that the decision would help Kashmiris, by spurring development and discouraging a long-standing guerrilla insurgency.

    The change in Kashmir upended more than half a century of careful politics, but the Indian press reacted with nearly uniform approval.

    Ever since Modi was first elected Prime Minister, in 2014, he has been recasting the story of India, from that of a secular democracy accommodating a uniquely diverse population to that of a Hindu nation that dominates its minorities, especially the country’s two hundred million Muslims. Modi and his allies have squeezed, bullied, and smothered the press into endorsing what they call the “New India.”

    ReplyDelete
    Replies
    1. https://www.newyorker.com/magazine/2019/12/09/blood-and-soil-in-narendra-modis-india

      MUSLIMS OF INDIA HAVE BEEN PAMPERED..

      THERE ARE MORE MUSLIMS IN INDIA THAN IN PAKISTAN,, NOT A SINGLE INDIAN MUSLIM WOULD LIKE TO GO TO PAKISTAN..

      NAC OF THE ITALIAN WAITRESS TRIED TO MAKE THEM PROTECTED SPECIES-- LIKE PENGUINS..

      CAPT AJIT VADAKAYIL SAYS THAT DEXTER FILKINS IS A LIAR... YOU GET PULITZER PRIZE FOR LYING....

      PULITZER IS A PRIZE AWARDED BY COLUMBIA UNIVERSITY USA, WHICH IS THE MENTOR OF INDIA’S COMMIE UNIVERSITY JNU. COLUMBIA UNIVERSITY WAS MADE WITH OPIUM DRUG MONEY BY ROTHSCHILD.

      ROTHCHILD WAS THE OWNER OF BRITISH EAST INDIA COMPANY, WHO GREW OPIUM IN INDIA AND SOLD IT IN CHINA.

      http://ajitvadakayil.blogspot.com/2019/07/how-gandhi-converted-opium-to-indigo-in.html

      MIND YOU THIS LYING BASTARD DEXTER FILKINS ENJOYED OUR HOSPITALITY FOR 3 YEARS IN DELHI.

      QUOTE: Rana Ayyub told me over the phone that she was heading to Kashmir. Ayyub, thirty-six years old, is one of India’s best-known investigative reporters :UNQUOTE

      TEE HEEEEEEEE ...THIS MAKES MY DAY !

      LIAR PRAISING LIAR..

      CAPT AJIT VADAKAYIL ASKS PM MODI-- WE THE PEOPLE WANTS THIS LYING BASTARD DEXTER TO BE BANNED FROM INDIA..

      capt ajit vadakayil
      ..

      Delete
  46. https://timesofindia.indiatimes.com/city/hyderabad/hyderabad-encounter-live-updates-all-four-accused-gunned-down-people-hail-police-action/liveblog/72393239.cms

    All four accused — who were allegedly involved in the gang rape and murder of a veterinary doctor — were gunned down by Cyberabad police in an encounter in the wee hours of Friday at Chatanpally in Mahabubnagar district, where the victim was earlier set ablaze by the accused.
    Cops claimed all four accused tried to escape while the crime scene was being reconstructed and were killed in the encounter

    ReplyDelete
  47. SOMEBODY ASKED ME

    CAPTAIN, WHY DID DOUGLAS COE ( FAMILY/ FELLOWSHIP/ PRAYER BREAKFAST ) COME TO INDIA, WHEN HE TOOK HIS PHOTO IN FRONT OF THE TAJ..

    HE CAME TO GIVE FODDER TO THE DRAFT OF THE "COMMUNAL VIOLENCE BILL " WHICH WAS BEING SPONSORED BY THE NAC.. TO SCREW HINDUS AND THROW THEM INTO JAIL WITHOUT BAIL..

    DOUG COE HAS ALREADY APPLIED THIS ALL OVER EUROPE TO PROTECT THE JEWS AND DECLARE THEM AS PROTECTED SPECIES..

    I WILL PUT A SEPARATE PORT ON THE FOUL AGENDA OF NAC , WHERE A ITALIAN MAINO WAITRESS BECAME EMPRESS...

    WE NEED ALL ANTI-HINDU MEMBERS OF NAC TO BE TRIED FOR SEDITION..AND THIS INCLUDES FOREIGN PAYROLL COLLEGIUM JUDICIARY WHO SUPPORTED IT FROM THE SHADOWS ..

    https://ajitvadakayil.blogspot.com/2019/12/national-prayer-breakfast-where-jesus.html

    capt ajit vadakayil
    ..

    ReplyDelete
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      MADHU KISHWAR
      SUDHIR CHAUDHARY
      GEN GD BAKSHI
      SAMBIT PATRA
      RSN SINGH
      GVL NARASIMHA RAO
      PIYUSH GOEL
      CJI BOBDE
      ATTORNEY GENERAL
      ALL SUPREME COURT JUDGES
      SOLI BABY
      FALI BABY
      KATJU BABY
      SALVE BABY
      THE QUINT
      THE SCROLL
      THE WIRE
      THE PRINT
      MK VENU
      MADHU TREHAN
      CLOSET COMMIE ARNAB GOSWMI
      RAJDEEP SARDESAI
      PAAGALIKA GHOSE
      NAVIKA KUMAR
      ANAND NARASIMHAN
      SRINIVASAN JAIN
      SONAL MEHROTRA KAPOOR
      VIKRAM CHANDRA
      NIDHI RAZDAN
      FAYE DSOUZA
      RAVISH KUMAR
      PRANNOY JAMES ROY
      AROON PURIE
      VINEET JAIN
      RAGHAV BAHL
      SEEMA CHISTI
      DILEEP PADGOANKAR
      VIR SANGHVI
      KARAN THAPAR
      PRITISH NANDI
      SHEKHAR GUPTA
      SIDHARTH VARADARAJAN
      ARUN SHOURIE
      N RAM
      I&B DEPT/ MINISTER
      LAW MINISTER/ MINISTRY
      ALL CONGRESS SPOKESMEN
      ANGREZ KA AULAAD- SUHEL SETH
      HISTORY TV CHANNEL
      DAVID HATCHER CHILDRESS
      GIORGIO A TSOUKALOS
      WENDY DONIGER
      SHELDON POLLOCK
      AUDREY TRUSCHKE
      ENTIRE BBC GANG
      MATA AMRITANANDAMAYI
      MOHANLAL
      SURESH GOPI
      EVERY HINDU ORGANISTAION
      E SREEDHARAN
      DULQUER SALMAN
      PGURUS
      WEBSITES OF DESH BHAKTS
      SPREAD ON SOCIAL MEDIA EVERY WHICH WAY.

      Delete


  48. Capt. Ajit VadakayilDecember 6, 2019 at 6:09 PM
    SOMEBODY CALLED ME UP AND CRIED..

    CAPTAIN, YOU ARE THE FIRST AND ONLY MAN ON THIS PLANET TO WRITE THAT THE SOLAR ECLIPSE DURING MAHABHARATA WAR WAS CAUSED BY KRISHNAs SUDARSHAN CHAKRA..

    I WANT ALL MY READERS TO WATCH THE VIDEO BELOW

    https://www.youtube.com/watch?time_continue=3&v=R-Lacu0VG3Y&feature=emb_logo

    I HAVE SEEN FLYING SHIPS TOP UP, AND TOP DOWN IN RED SEA.. FIRST TIME IT WAS HARD TO BELIEVE..

    IN THE POST BELOW --READ THE PASSAGE "JAYADRATA SLAIN"..

    http://ajitvadakayil.blogspot.com/2011/11/mahabharata-and-bhagawat-gita-4000-bc.html

    STUPID CUNTS HAVE BEEN TRYING TO DATE THE MAHABHARATA WAR OF 4000 BC, BY A WILD GOOSE CHASE OF A NON-EXISTENT SOLAR ECLIPSE IN THE EVENING OF THE WAR.

    IT WAS NOT A SOLAR ECLIPSE BUT A SUDDEN TEMPERATURE INVERSION ( SUDARSHANA CHAKRA ON SCALAR INTERFEROMETRY MODE ) WHICH CAUSED THE ATMOSPHERIC REFRACTION TO REVERSE, CAUSING THE SUN TO DIP BELOW THE HORIZON, AND CAUSE SUDDEN DARKNESS ON THE EVENING OF THE 13TH DAY OF THE WAR. THIS WAS AN OPTICAL ILLUSION..

    AFTER BEHEADING JAYADRATA, HIS HEAD WAS DELIVERED TO THE LAP OF HIS FATHER MEDITATING THOUSANDS OF MILES AWAY, IN THE CRUISE MISSILE DRONE MODE.

    AS SOON AS JAYADRATA WAS KILLED KRISHNA USED REVERSE INTERFEROMETRY AND CAUSED THE SUN TO GO BACK ABOVE THE HORIZON.

    ARJUNAs ASTRAS WERE NOT ARROWS BY CRUISE MISSILES WHICH COULD LOITER AND STRIKE..

    KARNA HAD BETTER ASTRAS THAN ARJUNA..

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

    AFTER SAMUDRA MANTHAN BY VISHNU’S KURMA AVATAR, LORD DHANWANTAI ROSE FROM THE COSMIC OCEAN WITH SCALAR FIELD GENERATORS IN THREE OF HIS FOUR HANDS.. 1) SUDARSHANA CHAKRA INTERFEROMETRY DISC WITH WHICH KRISHNA CAUSES SUN TO DRIP DOWN THE HORIZON TO FOOL JAYADRATA..2) A CONCH 3) A AMRIT KUMBAM POT INFUSED WITH IRIDIUM..

    THE FOURTH ITEM WAS A LEECH ( VACCINATION )

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

    TITANIC WATCHKEEPERS WERE LOOKING AT A LARGE ICEBERG IN THE SKY-- AN OPTICAL ILLUSION, WHEN THEY SLAMMED INTO A REAL ONE..

    https://www.youtube.com/watch?v=fiNZcV0TjNc&feature=emb_logo

    MY REVELATIONS NOW JUMP TO 60.12%

    capt ajit vadakayil
    ..

    ReplyDelete
  49. MEN WITHOUT INTEGRITY LIKE DHARMAPADA ARE THE REASON WHY BHARATMATA WAS ENSLAVED FOR 800 YEARS..

    DHARMAPADA WAS CASTIGATING MY READERS FOR NOT DOING ENOUGH TO SPREAD MY CAMPAIGNS.

    I DECIDED TO CHECK WHEN HE THREW SHIT ON SRIRAM HEGDE, WHO IN ONE SITTING SENDS 1600 EMAILS..

    WHEN I FOUND OUT DHARMAPADAs TWITTER SITE-- I NOTICED THAT HE HAS NOT MENTIONED THE WORD "VADAKAYIL" EVEN ONCE.. HE HAS NOT SPREAD OANY OF MY LINKS OR MESSAGES..

    INSTEAD HE WAS STEALING GOLDEN NUGGETS AND SHINING HIMSELF AND ATTRACTING FOLLOWERS FOR HIMSELF..

    DHARMAPADA WILL ONE DAY FALL PHUTTTT ON HIS FACE FROM WHERE THERE WILL BE NO RECOVERY..

    AND THIS TRAITOR FELLOW GAURAV SHARMA-- SHE STARTED A VADAKAYIL FANS WHATS APP GROUP

    AND WHAT WAS HE REALLY DOING? CONTACTING MY FANS AND POISONING THEIR MINDS AND RUNNING DOWN MY WIFE AND SON IN A MOST FOUL MANNER.. THIS BASTARD , I WILL DEAL WITH.. HE KNOWS !

    capt ajit vadakayil
    ..



    ReplyDelete
  50. Dear Capt Ajit sir,
    Is it true that Barabar caves are 2400 year old bunkers and nuclear shelters...who built them and why ?
    http://trueblog.net/2400-year-old-ancient-bunkers-and-nuclear-war-shelters-found-in-india-2458/

    ReplyDelete
    Replies
    1. NEVER MIND WHAT HISTORY BANDIT AND LIAR OF THE FIRST ORDER E.M.FORSTER SAYS IN HIS BOOK "A PASSAGE TO INDIA" THAT THE BARABAR CAVE WAS DUG IN 300 BC .

      THIS CAVE IS MENTIONED IN MAHABHARATA OF 4000 BC—AS GORATHGIRI CAVES.

      Delete
  51. https://youtu.be/WtXvXlIolEw
    Bhusandeswar temple balasore odisha. Shiv ling is
    12 feet tall and 15 feet in circumference. Pls validate if it is genuine. I have been there.
    Story is that Ravan was transporting it from kailash to Lanka. For a reason he had to abandon it at the current spot.
    It is just too huge. Is it real meteroite stone or just some ordinary stone?

    ReplyDelete
    Replies
    1. IT LOOKS REAL..

      https://www.youtube.com/watch?v=aGS51haP94I

      THE REAL TEST OF A SHIVA LINGAM IS ITS WEIGHT-- IT IS BLACK AND AS HEAVY AS STEEL..

      THE BLACK STONE AT KAABA MECCA IS FAKE.. THERE ARE SEVEN PIECES OF PUMICE STONE , CEMENTED ... PUMICE STONE FLOATS ON WATER..

      MUSLIMS HAVE STOPPED GOING TO MECCA.. THEY WOULD RATHER WORSHIP A REAL BLACK SHIVA LINGAM STONE AT KEDARNATH OR HAMPI..

      http://ajitvadakayil.blogspot.com/2013/06/6000-year-old-kedarnath-temple-highest.html

      http://ajitvadakayil.blogspot.com/2011/01/ruined-city-of-hampi-capt-ajit.html

      SHIVA LINGAM IS THE COSMIC PHALLUS,, PROVIDER OF THE SEED ( DNA )..

      http://ajitvadakayil.blogspot.com/2019/07/shiva-lingam-meterorite-hit-65-million.html

      ENERGY SENSITIVE PEOPLE LIKE MY WIFE CAN SENSE SCALAR ENERGY EMANATING FROM A SHIVA LINGAM..

      capt ajit vadakayil
      ..

      Delete
    2. Yes, it complies with the checklist you wrote about shivlingam.
      It is heavy. During British times the british tried to uproot it through a rich Marwari ( most probably crypto jew ) of Balasore town. He brought all the resources he could gather.. But failed multiple times.
      Also the temple lies not more than 15 km from Bengal bay and about 5 km from the Subarna river and its delta. Locals say in earlier times when the sea coast was nearer, the temple used to get submerged in sea water eveyday during tide.(cooling through sea water )
      The storyline is, it was Maa Parvati's personal Shivlingam. When Ravan was allowed by Shiva to take it to Lanka, the condition was that he would not rest the lingam on ground at any point during transit( ofcourse shiv parvati and the story is allegory for intelligent to understand). Ravan was tricked by Devas. He stopped at the temple point for some urgency and asked a local boy to hold the Lingam while he did his business. The boy put the lingam on the ground. When Ravan tried to lift it again he failed. The more he tried to lift it the longer the Lingam grew.
      Even Ravan failed to lift it and he abandoned it at the present site.
      The lingam is a less worshipped one, because it was only discovered popularly by the portugese when they landed at Bengal bay.
      All throughout since Ravan's time the lingam lay in the marshy waters of the Delta. It was a breeding grounds of Crocodiles. No local ventured in that dangerous crocodile infested marshland. Only a few fisherman families uses to take care of the Lingam.
      With advent of modern times a few hundred years back, the Portugese who landed in the area brought it to modern notice.
      The place is still quiet interiors and the lingam still less known.

      Delete
    3. this is interesting !

      some day i really wanna go there and worship the pure shiv lingam and willsip the water dripping from that lingam _/\_

      thanks ramas for sharing with captain and us readers

      Delete
    4. captain,

      we have heard so much about devas who always tricked danavas in every which way. i wonder how much danavas have suffered !! (both by humans and vishnu avatars)

      Delete
  52. https://indianexpress.com/article/india/gujarat-govt-brings-parts-of-anand-town-under-disturbed-areas-act-6155018/

    As per the provisions of the Act, the sale of a property to a Muslim in a Hindu dominated area and vice-versa cannot be done until each immediate neighbour of such property gives their consent to the deal. The parties have to seek an approval from the district collector and also a no objection certificate from the local police.

    Generally it has been observed that the first seller gets a premium for selling .

    The rest don't even get anywhere near property value.
    The whole local building population changes.

    ReplyDelete
  53. The fact that Uddhav Thakrey has stalled Aarey metro crashed is in complete variance with the Royal Palms resort and golf course built under the Shivsena s blessings when in power over the very same Aarey land by cutting more trees than the car shed and the city of Lavasa built by blessings of Sharad Pawar by cutting forests and taking agricultural land from farmers.

    This double standards is obvious when they cry wolf citing green reasons and posing as green warriors...

    It is very likely soon one of their cronies will be alotted Aarey land after all brouhaha dies down as trees have already been cut.

    ReplyDelete
  54. Dear Capt Ajit Sir,
    Guv of Kerala Mr Arif Mohd Khan seems to be righteous in a different way, explaining necessity of religion and the way ahead of moderate Islam. Is he a crypto Jew or a true Muslim ?
    https://www.facebook.com/1465655513484804/posts/2745052752211734/

    ReplyDelete
  55. Dear Capt Ajit sir,

    Yday, Akshay Kumar was speaking about Infertility clinic and asked by his BIL whether is son with blue eyes is his own...or someone else...so thereby he's prompted to do certain films to spoil our culture... He should be shown his place and gheraoed.

    https://www.opindia.com/2019/12/akshay-kumar-apply-indian-citizenship-gives-up-canadian-passport/

    ReplyDelete
    Replies
    1. Could it have anything to do with his father in laws inheritance??

      Delete
  56. There seems to be immense job satisfaction in betraying ones motherland .
    Their employer generously promotes the future generations as long as they are cut from the same cloth.
    Jobs scholarships positions of power and influence and a membership of that exclusive club of power brokers is a given.
    No worries about government increasing reservation etc etc ...

    But ones conscience must agree or be brain washed into believing it's for the better . There is loyalty amongst thieves. Apna ich aadmi hai.....

    ReplyDelete
    Replies
    1. 95% OF PEOPLE IN RAW/ IB/ BIA/ ED/ CBI/ COLLEGIUM JUDICIARY/ THINK TANKS/ BABUS OF VARIOUS CRITICAL MINISTRIES/ IIT PROFESSORS/ SOCIAL SCIENCE PROFESSORS/ ECONOMICS PROFESSORS OF ELITE BUSINESS SCHOOLS NEED TO BE SACKED FOR INCOMPETENCE..

      OUT OF THE 95% , A LOT OF THEM ARE IN FOREIGN PAYROLL .. FOR A FEW PIECES OF SILVER THESE TRAITORS BLEED BHARATMATA..

      WHY DOES IT TAKE A BLOGGER TO EXPOSE THE " PRAYER BREAKFAST" MAFIA WHO WERE INSTRUMENTAL IN CREATING NAC AND CVB ?

      AN ITALIAN MAINO JEWESS WAITRESS BECAME EMPRESS VIA NAC !

      https://ajitvadakayil.blogspot.com/2019/12/national-prayer-breakfast-where-jesus.html

      MODI ALLOWED THE FOREIGN FRENCH CONTINGENT TO LEAD OUR REPUBLIC DAY PARADE.. YOU SEE HE WAS HELL BENT TO MOTHBALL INDIAs COAL WITH THE JEWISH FRENCH PRESIDENT AND ALSO BUY USELESS JEWISH RAFALE JET FIGHTERS

      WE KNOW THE FOREIGN PAYROLL OFFICERS OF INDIAN AIRFORCE WHO PRAISED RAFALE TO THE SKIES

      CHINA IS SECRETLY BUYING SU 57 RUSSIAN JET FIGHTERS.. JUST 4 OF THESE SUPER PLANES WITH BATTLE MANAGEMENT SYSTEM CAN KILL ALL 36 RAFALE PLANES..

      AND SUBRAMANIAN SWAMY WHO WORE A MOSSAD SPONSORED SIKH TURBAN IN 1976 WANTS INDIA TO BUT JEWISH DASSAULT WHO PRODUCES THESE USELESS RAFALE JET FIGHTERS..

      https://twitter.com/swamy39/status/1171250468166619136

      WHEN RAJIV GANDI WAS MURDERED THERE WAS A CALL THE VERY NEXT MINUTE FROM CHANDRASWAMI AT SINDHURI HOTEL CHENNAI AND A TAMIL BRAHMIN POLITICIAN..

      MOSSAD DARLING SWAMY EVEN WANTED INDIA TO CANCEL THE S400 RUSSIAN MISSILES..S400 HAS 100% STRIKE RATE.. THE BEST NATO ANTI-MISSILE SYSTEMS HAVE ZERO PERCENTAGE STRIKE RATE AGAINST INCOMING HYPERSONIC MISSILES

      https://twitter.com/swamy39/status/1124814793762717697?lang=en

      https://twitter.com/Swamy39/status/1126701367119060993

      DASSAULT WAS SINKING WITH ORDERS ONLY FROM CRYPTO JEW RULERS OF EGYPT AND QATAR ( FOR MASSIVE KICKBACKS ). MODI SAVED RAFALE..

      https://timesofindia.indiatimes.com/india/rafales-must-fly-in-with-meteor-air-to-air-missiles-india-tells-france/articleshow/72375495.cms

      CAPT AJIT VADAKAYIL WILL NOT ALLOW SWAMY TO BE INDIAs FINANCE MINISTER.. I WILL WRITE THIS TAMILIAN FELLOWs LEGACY..

      capt ajit vadakayil
      ..

      Delete
  57. I have often wondered ,if you could turn the time back like that jelly fish keeping your experience intact, would you join the same profession all over again?

    Would you do something else?

    ReplyDelete
    Replies
    1. AS AN INDIVIDUAL I LEFT THE SEA A BETTER AND SAFER PLACE..

      I KEPT MY HEAD ON THE CHOPPING BLOCK - DARING POWERS THAT BE TO CHOP IT OFF.

      I MARVEL TODAY-- WHO IS IT THAT AFTER 30 YEARS IN COMMAND OF SHIPS -KICKING THE COLLECTIVE BALLS OF MY SHORE BOSSES, I AM STILL IN ONE PIECE..

      Delete
  58. Railways launch an enquiry into "procurement irregularities" for the
    Vande Bharat trains. Train 18.

    It seems 25000 crores of foreign contracts are at stakebto buy trains made abroad and by harassing these go getter employees, the project can be sabotaged easily.

    Who will use their initiative any more? And why?
    First rule of any government job they broke "cover your ass and pass the buck"

    This is indeed a serious crime worthy of strictest censures.

    Can't have the rest of the baboos {baboons} growing growing a brain can we now?

    ReplyDelete
  59. I google searched for impact of Syrian Muslims in Germany
    Most articles are favoring them as saviours, skilled labour for industries etc.

    ReplyDelete
    Replies
    1. IT IS JEWISH DEEP STATE MEDIA WHO IS CALLING SYRIAN REFUGEES AS MESSIAHS OF EUROPE..

      THE DROWNED SYRIAN BABY HAD ROTHSCHILDs SIGNATURE..

      SAME WAY GRETA THUNGBERG HAD ROTHSCHILDs SIGNATURE..

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

      SO SO SO--

      SHALL WE TAKE CRIMINAL DNA ROHINGYA MUSLIMS IN? BY INVOKING ARTICLE 14 , LIKE OUR STUPID ILLEGAL COLLEGIUM JUDICIARY ( ALWAYS IN CAHOOTS WITH BENAMI MEDIA )?

      SWAMY VIVEKANANDA WITH 33 MAJOR DISEASES WAS TOUTED BY ROTHSCHILD AS INDIAs HEALTHIEST MAN, BECAUSE HE WITHHELD HIS SEMEN..

      BRAHMACHARI IF YOU PLEAAAAJJJJE ..

      TEE HEEEEEEEEEE..

      VERY SOON THESE STUPID JUDGES WILL MAKE RATS AND COCKROACHES INDIAN CITIZENS , BY INVOKING THE CONSTITUTION.. NOT A SINGLE JUDGE OF INDIA SINCE INDEPENDENCE HAS UNDERSTOOD THE CONSTITUTION OR WAS ABLE TO APPLY BODMAS ..

      https://www.thebetterindia.com/185543/animal-rights-india-court-judgement-cruelty-prevention/

      SORRY, INDIAN CONSTITUTION IS FOR LAW ABIDING INDIAN CITIZENS ALONE .. NOT FOR PAKISTANI ISI PAYROLL CRIMINAL LIKE SOHRABUDDIN OR PARIAH STREET DOGS --

      HOME MINISTER AMIT SHAH WAS THROWN INTO JAIL ON THE BASIS OF A PIL FILED BY PAKISTANI ISI SPONSORED NGO..

      -- INDIAN CONSTITUTION DOES NOT PROTECT ILLEGAL ROHINGYA MUSLIM REFUGEES ..

      WE ASK MODI TO FORM MILITARY COURTS TO TRY AND HAND TRAITOR JUDGES ( IN DEEP STATE PAYROLL ) WHO CREATED THE RED NAXAL CORRIDOR AND CAUSED ETHNIC CLEANSING OF KASHMIRI PANDITS..

      WE KNOW WHO THESE TRAITOR JUDGES ( AND MEDIA WHO SUPPORTED THEM ) ARE ..

      INDIA WILL NOT TAKE MUSLIM IMMIGRANTS FROM PAKISTAN/ BANGLADESH.. LET USA AND EU PUT ANY AMOUNT OF PRESSURE.. INDIA HAS STOPPED CARING FOR THESE JEALOUS ELITE NATIONS ( ALL BEGGAR NATIONS PRETENDING TO BE RICH )..

      capt ajit vadakayil
      ..

      Delete
  60. https://tcf.org/content/report/germanys-syrian-refugee-integration-experiment/?session=1

    Merkel had a shortage of labour, this time she spent money on integrating them into German society this article claims by teaching them German etc
    These reasons are not valid for us in India . We have plenty of labour.

    Germany took them in solely for labour as she needed blue collar workers to run her mills. Humanitarian ground is pure hogwash.

    ReplyDelete
    Replies
    1. ANGELA MERKEL IS A JEWESS WITH HITLERs EYES AND CHIN..

      TILL TODAY GERMANS HAVE NOT FIGURED OUT HOW AND HOW MUCH JEWS RAVAGED THEIR NATION..

      I HAVE SEEN EAST GERMANY WHEN THE BERLIN WALL WAS STANDING..

      WHEN THE BERLIN WALL FELL DOWN I WAS IN HAMBURG..

      GERMANY DOES NOT KNOW THAT HITLER/ STALIN/ CHURCHILL / EISENHOWER WERE ALL JEWS..

      EISENHOWER KILLED MORE GERMAN CHRISTIAN POWs AFTER WW2 THAN HITLER KILLED ANAL SEX RECEIVING JEWS DURING WW2..

      EISENHOWER IMPORTED CONDOMS FROM USA FOR ALLIED SOLDIERS TO RAPE GERMAN CHRISTIAN WOMEN.. AFTER WW2

      IN 1975 I HAVE SEEN THE WORLDs LARGEST WHOREHOUSE REEPERBAHN/ ST PAULI WITH WOMEN OF AGE GROUP 28.. ALL WOMEN ATTRACTIVE AND LOOKING AS IF THEY ARE FROM DECENT FAMILIES..

      http://ajitvadakayil.blogspot.com/2011/11/general-eisenhowers-secret-holocaust-of.html

      DURING WW1 AND WW2 ROTHSCHILD CONTROLLED BOTH SIDES AS WELL AS THE NEUTRAL NATIONS.. THE WHOLE IDEA WAS TO CARVE OUT THE STATE OF ISRAEL..

      EVEN THE BALFOUR DECLARATION WAS A FORGED BULLSHIT LETTER WRITTEN BY JEW ROTHSCHILD TO HIMSELF..

      https://www.youtube.com/watch?v=DsRuB-Qw1b0

      PIL ALI MUSLIM DWARAKNATH TAGORE, ( THE OPIUM DRUG RUNNING PARTNER OF JEWS ROTHSCHILD/ SASSOON ) OWNED THE PLANETs BIGGEST WHOREHOUSE SONAGACHI IN HIS TIMES - TO CATER FOR HOMOSEXUAL AND PERVERTED PEDOPHILE SEX TASTES OF THE WHITE ELITE..

      http://ajitvadakayil.blogspot.com/2011/08/opium-drug-running-tagore-family-capt.html

      NCERT TEACHES ROTHSCHILDs HISTORY..

      capt ajit vadakayil
      ..

      Delete
  61. CAB sails through both houses.
    Now it has to run the gauntlet of our unelected unaccountable rulers ..The judiciary....

    It's a foregone conclusion....

    ReplyDelete