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.
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.
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.
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.
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.
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.
.
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.
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.
- 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
..
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
..
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.
ReplyDeleteIT 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
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Deleteshripad.naik@sansad.nic.in
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List of email ids can be found here..
Deletehttps://mohfw.gov.in/department-health-and-family-welfare-directory
https://mohfw.gov.in/sites/default/files/MoHFW%20Directory_7.pdf
homeopathy solvent should be ganges type living water or copper charged/gold charged or agnihotra ash infused water
DeleteGANGES LIVING WATER
DeleteReceived an Email from the Health Minister saying he has Noted the details.
DeleteIt's better to send mails for such lengthy information.
DeleteThere 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.
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?
DeleteCaptain, 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:---
ReplyDeleteQUOTE === (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/
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 ?
ReplyDeletehttps://tinyurl.com/slmqm7o
https://tinyurl.com/qmacuoq
JEWISH WHORES WHO INFILTRATED THE MOGHUL EMPIRE
Deletehttps://www.alivewater.com/viktor-schauberger
ReplyDeleteThis guy has made a lot of work on water , vortices, river flow.
Captain,
ReplyDeletethat 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
Dear Captainji,
ReplyDeleteSorry 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
https://timesofindia.indiatimes.com/world/us/battle-of-billionaires-2020-us-polls-could-see-trump-bloomberg-face-off/articleshow/72213908.cms
ReplyDeleteMICHAEL 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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Sent to trump and putin.
DeleteTweeted to Trump, Putin, embassy USA / RUSSIA,Mea, Armed forces,NIA and central ministers.
Delete
ReplyDeleteRishabNovember 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
https://twitter.com/ShekharGupta/status/1198462553833132033
ReplyDeleteWE KNOW THE INDIAN MAIN STREAM MEDIA IN PAKISTANI ISI PAYROLL..
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..
ReplyDeleteWHAT THEY DO NOT SAY IS THAT --ALL THIS DONATION IS TO PUSH THE EVIL JEWISH DEEP STATE AGENDA..
https://www.sakaltimes.com/business/bill-gates-presents-award-dr-cyrus-poonawalla-43154
ReplyDeletePOONAWALA IS A JEWISH DEEP STATE DARLING ..
http://ajitvadakayil.blogspot.com/2015/10/autism-in-india-and-triple-vaccine-mmr.html
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 )..
ReplyDeleteEVIL 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
..
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SPREAD OF SOCIAL MEDIA
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DeleteDear Captain,
DeleteI 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.
Sent emails..Asked for ack.
Deletesent DM on Facebook- ministry of health.
Your Grievance is registered successfully.
DeleteRegistration Number : DHLTH/E/2019/05701
@Anish bhandarkar
Deletehttps://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.
Captain, message sent to the following people in the environment ministry:
Deletemenong@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
pranam captain,
DeleteYour Registration Number is : PMOPG/E/2019/0675193
https://twitter.com/prashantjani777/status/1198945620280692736
@Charishma,
DeleteThis 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.
Your Registration Number is : PMOPG/E/2019/0675771
DeleteAfter 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.
ReplyDeleteWhy 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 .
DONALD TRUMP , LISTEN UP, AND LISTEN GOOD..
ReplyDeleteIN 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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THE QUINT
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SPREAD OF SOCIAL MEDIA
Sent to trump by filling up the contact form.
DeleteHave sent to President Trump via https://www.whitehouse.gov/contact/
DeleteDone on social media captain.
Deletehttps://timesofindia.indiatimes.com/city/ahmedabad/yoginis-speak-only-english-cops-clueless/articleshow/72214370.cms
ReplyDeleteTHIS 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 ?
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.
Deletehttps://m.economictimes.com/industry/energy/power/coal-fired-plants-may-have-to-scale-down-utilisation-to-35-by-2022-kpmg/amp_articleshow/72200926.cms
ReplyDeleteOne 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
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.
ReplyDeleteDitto when you moved over icy seas??
Dear Capt Ajit sir,
ReplyDeleteAfter 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
Guruji mentioned there is no problem with transgender,the pests are LGBTQ+ faggots
Deletehttp://ajitvadakayil.blogspot.com/2015/08/homosexuality-pedophilia-deviant-porn.html?m=1
I have shared this before.
DeleteAgain 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.
Captain,
ReplyDeleteYou 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.
ONLY KERALA NAMBOODIRIS KNEW VEDAS ON ORAL ROUTE -- STARTING 400 CENTURIES AGO ( DANAVA CIVILIZATION )..
Deletehttps://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..
Captain,
DeleteI 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.
Captain,
DeleteWhen 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.
400 CENTURIES AGO, THE MAHARISHIS WHO DOWNLOADED THE VEDAS FROM AKASHA WERE KERALA NAMBOODIRIS.
DeleteThe maharishis from the time of matsya avatar to adi sankaracharya were kerala namboothiris!!!!!!!
DeleteNamboothiris 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.
Thank you for the reply Captain.
DeleteDear Capt Ajit sir,
ReplyDeleteNow, 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/
a high frequency pulse is enough to earse everything, it is same as EMP
DeleteThese useless concpets can come from GORAS who rely on artificial parts,unlike Indians who believe in enhancing natural parts like Brain,cell memory.
DeleteWE 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.
ReplyDeleteALL 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
..
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THE QUINT
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ASK LAW MINISTRY/ MINISTER , PMO/ PM MODI , FOR AN ACK..
DeleteALL READERS MUST PARTICIPATE--
IT IS NOW OR NEVER..
Namastae Master Ji
Deletehttps://mobile.twitter.com/kannanlp/status/1198925701224656896
Thanks with highest gratitude
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
Deletehttps://twitter.com/asapexchange/status/1198932005645365248
DeleteYour Registration Number is : PMOPG/E/2019/0675152
DeleteSent to law ministry, legal affairs, pmo and others
DeleteThanks And Regards
Re tweeted above comments.
DeleteGuruji,apologize but most of the people'sname you have given are useless.Pleaee atleast give us the names who are intelligent and wise.
DeletePMOPG/E/2019/0675255
DeleteEmailed it to law minister email IDs shared by Dinesh Kumar
Sent mails to-
Deleteravis@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
https://twitter.com/KshitijGundale1/status/1198954422958743553?s=19
Delete: sent (asked for ack)
DeletePM Modi
Amit Shah
Ravi Shankar Prasad
Plzzz no one is listening.
DeleteI 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.
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 ?
Deletehttps://youtu.be/kTJOwWak_dM?t=32
Tweeted and verified,https://mobile.twitter.com/PadNaren/status/1198938333956771841
DeleteTweets:
Deletehttps://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:
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MyGov IdeaBox Post:
https://www.mygov.in//comment/107670871/%23comment-107670871/
Requested acknowledgement from PMO and Law Ministry.
https://twitter.com/shree1082002/status/1199017580494643204
Deletetweeted above comments
DeleteDone on Social media Captain
DeleteYour Grievance is registered successfully.
DeleteRegistration Number : DLGLA/E/2019/01415
PMOPG/E/2019/0675259
DeleteDEPOJ/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.
Your Registration Number is : PMOPG/E/2019/0682693
Deletehttps://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
https://timesofindia.indiatimes.com/world/europe/thieves-grab-jewels-treasures-worth-up-to-a-billion-euros-in-dresden/articleshow/72226431.cms
DeleteSeems like someone watching ur Internet
Your Registration Number is : PMOPG/E/2019/0675249
ReplyDeleteGuruji,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
ReplyDeleteby the way i am bengali, and a Brahmin
ReplyDeletehttps://timesofindia.indiatimes.com/world/europe/thieves-grab-jewels-treasures-worth-up-to-a-billion-euros-in-dresden/articleshow/72226431.cms
ReplyDeleteGREEN 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
You look younger than Me, Love your unpretentious style and attitude, you are a true rock star \m/
ReplyDeleteRegards
I HAVE NO NEED TO IMPRESS ANYBODY.. WHEN YOU REACH THIS LEVEL, HAPPINESS ALIGHTS ON YOU..
Deletehttp://ajitvadakayil.blogspot.com/2010/12/happiness-from-within-capt-ajit.html
Thank you very much for your advice, Guruji _/\_
DeleteMAN IS HAPPY , WHEN THERE IS HARMONY AND PEACE AT HOME.. HIS CHILDREN DO WELL .. AND HIS WIFE LOOKS UP TO HIM..
Deletehello 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
ReplyDeletehttps://timesofindia.indiatimes.com/sports/hockey/top-stories/watch-shocking-on-field-fight-in-nehru-cup-hockey-final/articleshow/72227500.cms
ReplyDeletePUNISH 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..
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.
ReplyDeleteAnd 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 ?
Captain, why does the Consciousness have a desire to create other conscious-beings ?
ReplyDeleteIf 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 ?
CONSCIOUSNESS ARISES WHEN THE SOUL CROSSES A PARTICULAR FREQUENCY ( SOUL EVOLUTION )
DeleteAFTER THAT KARMA KICKS IN .. KARMA AFFECTS ONLY HUMANS ..
KARMA THEN POWERS CONSCIOUSNESS LEVEL
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?
DeleteVery profound reply Captain. It sounds similar to driving a manual-transmission car.
Delete1) 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)
Hi Sir - What was the need / circumstances for the Pallava kings to develop the Telugu language / script?
ReplyDeleteThank you
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.
DeleteRegards,
Sriganesh
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.
DeleteGuruji,
ReplyDeleteSorry 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
SCAM
DeleteHUMPED INDIAN COWS GIVE NUTRITIOUS A2 MILK
HUMPLESS WESTERN COWS GIVE TOXIC A1 MILK
Good morning Captain sir!
DeleteThere is a message going around saying Supriya Sule's mother in law is non other than Bal Thakrey 's sister Sudha Sule!
ReplyDeleteYour Registration Number is : PMOPG/E/2019/0675897
ReplyDeleteSir
ReplyDeleteAs 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.
Hi Ajit,
ReplyDeleteOnce 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
THE LESSER THE KARMIC BAGGAGE , THE HIGHER THE SOUL FREQUENCY.
DeleteWHEN SOUL FREQUENCY IS SAME AS THE FIELD OF BRAHMAN, YOU BECOME A JIVAN MUKT..
Dear Capt Ajit sir,
DeleteI 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/
Do u think R will extract further revenge on Bengalis? Assam NRC seems to be the first step.
ReplyDeleteCongratulations, ISRO and fellow Patriots!
ReplyDeletePSLV-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
Dear captain,
ReplyDeleteIt is said that vijaynagara king krishnadev rai conquered whole of kerala?
Is it true?
Gratitude
Aditya
NOPE
DeleteRussian 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.
ReplyDeleteYouTube 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.
FOR BUYING SHIT SHALE FRACKED OIL, MODI WILL WIN THE NOBEL PRIZE..
DeleteALREADY 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
..
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 ?
DeleteQUOTE == 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
Guruji,
DeleteWhy 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....
https://timesofindia.indiatimes.com/india/lok-sabha-passes-bill-to-ban-e-cigarettes/articleshow/72268596.cms
ReplyDeletehttp://ajitvadakayil.blogspot.com/2019/09/untold-danger-of-e-cigarettes-breach-of.html
Hats off to you Sir. Many thanks!
DeleteCongratulations, Capt. Thanks for your advise.
Deletehttps://ajitvadakayil.blogspot.com/2011/08/those-were-days-capt-ajit-vadakayil.html
ReplyDelete
ReplyDeleteDEEP 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
..
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somwhere i missed you saying that not to play with cat whiskers as they are their super antennas.
ReplyDeleteCan 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..
CAT CAN SENSE FLUX WITH ITS WHISKERS
DeleteBlood and Soil in Narendra Modi’s India
ReplyDeleteThe 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.”
https://www.newyorker.com/magazine/2019/12/09/blood-and-soil-in-narendra-modis-india
DeleteMUSLIMS 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
..
https://timesofindia.indiatimes.com/city/hyderabad/hyderabad-encounter-live-updates-all-four-accused-gunned-down-people-hail-police-action/liveblog/72393239.cms
ReplyDeleteAll 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
SOMEBODY ASKED ME
ReplyDeleteCAPTAIN, 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
..
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DR. N. C. SAXENA
ANU AGA
A. K. SHIVA KUMAR
MIRAI CHATTERJEE
VIRGINIUS XAXA
ARUNA ROY
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DR. RAM DAYAL MUNDA
JEAN DREZE
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ReplyDeleteCapt. 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
..
MEN WITHOUT INTEGRITY LIKE DHARMAPADA ARE THE REASON WHY BHARATMATA WAS ENSLAVED FOR 800 YEARS..
ReplyDeleteDHARMAPADA 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
..
Dear Capt Ajit sir,
ReplyDeleteIs 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/
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 .
DeleteTHIS CAVE IS MENTIONED IN MAHABHARATA OF 4000 BC—AS GORATHGIRI CAVES.
https://youtu.be/WtXvXlIolEw
ReplyDeleteBhusandeswar 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?
IT LOOKS REAL..
Deletehttps://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
..
Yes, it complies with the checklist you wrote about shivlingam.
DeleteIt 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.
this is interesting !
Deletesome 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
captain,
Deletewe 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)
https://indianexpress.com/article/india/gujarat-govt-brings-parts-of-anand-town-under-disturbed-areas-act-6155018/
ReplyDeleteAs 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.
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.
ReplyDeleteThis 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.
Dear Capt Ajit Sir,
ReplyDeleteGuv 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/
Dear Capt Ajit sir,
ReplyDeleteYday, 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/
Could it have anything to do with his father in laws inheritance??
DeleteThere seems to be immense job satisfaction in betraying ones motherland .
ReplyDeleteTheir 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.....
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..
DeleteOUT 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
..
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?
ReplyDeleteWould you do something else?
AS AN INDIVIDUAL I LEFT THE SEA A BETTER AND SAFER PLACE..
DeleteI 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..
Railways launch an enquiry into "procurement irregularities" for the
ReplyDeleteVande 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?
I google searched for impact of Syrian Muslims in Germany
ReplyDeleteMost articles are favoring them as saviours, skilled labour for industries etc.
IT IS JEWISH DEEP STATE MEDIA WHO IS CALLING SYRIAN REFUGEES AS MESSIAHS OF EUROPE..
DeleteTHE 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
..
https://tcf.org/content/report/germanys-syrian-refugee-integration-experiment/?session=1
ReplyDeleteMerkel 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.
ANGELA MERKEL IS A JEWESS WITH HITLERs EYES AND CHIN..
DeleteTILL 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
..
CAB sails through both houses.
ReplyDeleteNow it has to run the gauntlet of our unelected unaccountable rulers ..The judiciary....
It's a foregone conclusion....