THIS POST IS CONTINUED FROM PART 15, BELOW--
CAPT AJIT VADAKAYIL SAYS AI MUST MEAN “INTELLIGENCE AUGUMENTATION “ IN FUTURE ..
Let this be IA
Let this be IA
OBJECTIVE AI CANNOT HAVE A VISION,
IT CANNOT PRIORITIZE,
IT CANT GLEAN CONTEXT,
IT CANT TELL THE MORAL OF A STORY ,
IT CANT RECOGNIZE A JOKE, OR BE A JUDGE IN A JOKE CONTEST
IT CANT DRIVE CHANGE,
IT CANNOT INNOVATE,
IT CANNOT DO ROOT CAUSE ANALYSIS ,
IT CANNOT MULTI-TASK,
IT CANNOT DETECT SARCASM,
IT CANNOT DO DYNAMIC RISK ASSESSMENT ,
IT IS UNABLE TO REFINE OWN KNOWLEDGE TO WISDOM,
IT IS BLIND TO SUBJECTIVITY,
IT CANNOT EVALUATE POTENTIAL,
IT CANNOT SELF IMPROVE WITH EXPERIENCE,
IT CANNOT UNLEARN
IT IS PRONE TO CATASTROPHIC FORGETTING
IT DOES NOT UNDERSTAND BASICS OF CAUSE AND EFFECT,
IT CANNOT JUDGE SUBJECTIVELY TO VETO/ ABORT,
IT CANNOT FOSTER TEAMWORK DUE TO RESTRICTED SCOPE,
IT CANNOT MENTOR,
IT CANNOT BE CREATIVE,
IT CANNOT THINK FOR ITSELF,
IT CANNOT TEACH OR ANSWER STUDENTs QUESTIONS,
IT CANNOT PATENT AN INVENTION,
IT CANNOT SEE THE BIG PICTURE ,
IT CANNOT FIGURE OUT WHAT IS MORALLY WRONG,
IT CANNOT PROVIDE NATURAL JUSTICE,
IT CANNOT FORMULATE LAWS
IT CANNOT FIGURE OUT WHAT GOES AGAINST HUMAN DIGNITY
IT CAN BE FOOLED EASILY USING DECOYS WHICH CANT FOOL A CHILD,
IT CANNOT BE A SELF STARTER,
IT CANNOT UNDERSTAND APT TIMING,
IT CANNOT FEEL
IT CANNOT GET INSPIRED
IT CANNOT USE PAIN AS FEEDBACK,
IT CANNOT GET EXCITED BY ANYTHING
IT HAS NO SPONTANEITY TO MAKE THE BEST OUT OF SITUATION
IT CAN BE CONFOUNDED BY NEW SITUATIONS
IT CANNOT FIGURE OUT GREY AREAS,
IT CANNOT GLEAN WORTH OR VALUE
IT CANNOT UNDERSTAND TEAMWORK DYNAMICS
IT HAS NO INTENTION
IT HAS NO INTUITION,
IT HAS NO FREE WILL
IT HAS NO DESIRE
IT CANNOT SET A GOAL
IT CANNOT BE SUBJECTED TO THE LAWS OF KARMA
ON THE CONTRARY IT CAN SPAWN FOUL AND RUTHLESS GLOBAL FRAUD ( CLIMATE CHANGE DUE TO CO2 ) WITH DELIBERATE BLACK BOX ALGORITHMS, JUST FEW AMONG MORE THAN 60 CRITICAL INHERENT DEFICIENCIES.
HUMANS HAVE THINGS A COMPUTER CAN NEVER HAVE.. A SUBCONSCIOUS BRAIN LOBE, REM SLEEP WHICH BACKS UP BETWEEN RIGHT/ LEFT BRAIN LOBES AND FROM AAKASHA BANK, A GUT WHICH INTUITS, 30 TRILLION BODY CELLS WHICH HOLD MEMORY, A VAGUS NERVE , AN AMYGDALA , 73% WATER IN BRAIN FOR MEMORY, 10 BILLION MILES ORGANIC DNA MOBIUS WIRING ETC.
SINGULARITY , MY ASS !
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########## SUBJECT -- JUDICIARY WANTS TO KNOW BY WHICH LAW YOGI ADITYANATH NAMED AND SHAMED DESH DROHIS WHO VANDALIZED DURING CAA RIOTS #############
HEY MELORD, BY WHICH LAW DID YOU GO FOR A SHIT IN THE MORNING ? WHICH PART OF THE CONSTITUTION ALLOWS YOU TO CLEAR YOUR BOWELS ?...
EVERYTHING IN LIFE IS NOT COVERED BY THE CONSTITUTION... COMMONSENSE CANNOT BE ABANDONED..
WHAT IS MORE IMPORTANT-- THE SECURITY OF BHARATMATA OR THE PRIVACY OF PAKISTANI ISIS FUNDED VANDALISERS?,,,
BY WHICH LAW DID YO SEND GUJARAT HOME MINISTER INTO JAIL, WHEN SOME PAKISTANI ISI FUNDED NGO COMPLAINED ON BEHALF OF DREADED TERRORIST SOHRABUDDIN? .. CAN THIS HAPPEN ANYWHERE ELSE ON THE PLANET?..
THIS IS WHY THE WHOLE WORLD KNOWS THAT INDIAN JUDICIARY IS THE MOST STUPID ON THE PLANET.. BOTTOM DREGS OF THE SCHOOL CEREBRAL BARREL AND DISCARDS OF THE LOSER LAWYER POOL..
ABOVE THE CONSTITUTION LIES "WE THE PEOPLE"...
ABOVE THIS LIES "THE WATAN"..
ABOVE ALL LIES "THE RULE OF DHARMA".. THE WEST CALLS THIS NATURAL LAW..
PM AND LAW MINISTER ARE NAPUNSAKS OF THE FIRST ORDER.. THEY ALLOW STUPID JUDGES TO PLAY GOD..
IN THEIR WATCH SUPREME COURT STRUCK DOWN NJAC, WHICH WAS PASSED WITH 100% UNANIMITY IN BOTH LOK / RAJYA SABHA-- AND SIGNED BY THE PRESIDENT.. THERE IS NO SUCH PROVISION FOR JUDICIAL REVIEW IN OUR CONSTITUTION..
WE KNOW HOW MODI/ PRASAD STOOD IN THE SHADOWS AND ALLOWED DEEP STATE PAYROLL JUDICIARY TO KICK BHARATMATA INTO THE KOSHER ADULTERY/ HOM0SEXUAL1TY MANDI.. INDIA IS NO LONGER A MORAL NATION..
OUR TRAITOR JUDICIARY CREATED THE NAXAL RED CORRIDOR AND CAUSES ETHNIC CLEANSING OF KASHMIRI PANDITS..
JUDICIARY HAS NO POWERS TO STOP ELECTED EXECUTIVE FROM FOLLOWING THE RULE OF DHARMA OR PROVIDING NATURAL JUSTICE..
CJI WHO CANNOT GO BEYOND THE OBJECTIVE HAS NO POWERS TO STOP PRESIDENT AND STATE GOVERNORS WHO HAVE BEEN EMPOWERED WITH ENORMOUS SUBJECTIVE POWERS INCLUDING VETO POWERS ..
SO SO SO -- UNDER WHICH LAW HAVE YOU PREVENTED THE FOREIGN BLACK MAMBA FROM BITING YOUR BABY?..BATAAOH NAH.. PLEAJJE…
MANY INDIAN JOURNALISTS , COLLEGIUM JUDGES , PROFESSORS OF SOCIAL SCIENCES IN ELITE INDIAN COLLEGES ARE IN DEEP STATE PAYROLL…
WHY HAS JUDICIARY LEGALIZED BITCOIN WHICH IS USED TO FUND ISLAMIC MERCENARIES IN KASHMIR AND DESH DROHIS IN INDIA?..
THE WHITE JEW KNOWS THAN IN 13 YEARS INDIA WILL BE THIS PLANETs NO 1 SUPERPOWER AND IT PLANS TO MAKE INDIA IMPLODE FROM WITHIN..
HARSH MANDER WHO TRIGGERED THE DELHI ANTI-CAA MUSLIM RIGHTS RIOTS IS AN AGENT OF JEW SOR0S WHO HAS DONATED ONE BILLION USD TO FIGHT HINDUS AND CREATE DISCORD IN INDIA....
WE ASK MODI , SUMMON CJI BOBDE -- ASK HIM WHY HE HAS NOT DECLARED THAT UNHCHR HAS NO POWERS TO FILE A PETITION AGAINST CAA..
BHARATMATA WILL NOT SURVIVE THIS DECADE IF WE DO NOT CLEANSE THE ILLEGAL COLLEGIUM JUDICIARY OF TRAITORS IN FOREIGN PAYROLL...
ALL THE JUDICIARY CAN DO IS TO INTERPRET THE CONSTITUTION.. THEY CANNOT RULE THE NATION...
STARE DECISIS IN NOT ALLOWED BY THE INDIAN CONSTITUTION WHERE JUDGES TREAT THEIR PAST STUPID JUDGEMENTS SANS CONTEXT AND SUBJECTIVITY AS LAWS..
WE THE PEOPLE WILL NOT ALLOW TYRANNY OF THE UNELECTED, THESE COLLEGIUM JUDGES CHOSEN BY FOREIGN FORCES..
https://ajitvadakayil.blogspot.com/2020/01/we-people-are-done-with-illegal.html
WE THE PEOPLE WILL NOT STAND IDLE IF THE JUDICIARY BLEEDS THE WATAN...
capt ajit vadakayil
..
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https://www.livemint.com/news/india/sc-quashes-rbi-ban-on-cryprocurrency-11583300795934.html
WHENEVER BHARATMATA IS BLED BY THE ILLEGAL COLLEGIUM JUDICIARY, YOU CAN BE COCK SURE LIBERAL JUDGES NARIMAN OR CHANDRACHUD WILL BE IN THE BENCH..
THESE TWO JUDGES WERE IN THE BENCH WHICH LEGALIZED HOMOSEXUALITY AND ADULTERY..
CHILDLESS MODI STOOD IN THE SHADOWS AND MADE THIS JEWISH DEEP STATE WISH HAPPEN TO DECIMATE OUR PRICELESS CULTURE. INDIA IS NO LONGER A MORAL NATION..
BITCOIN IS USED IN THE PEDOPHILE / PORN INDUSTRY. . BITCOIN IS USED TO BUY DRUGS , PAY TERRORISTS/ MERCENARIES TO MURDER PEOPLE..
73% OF BITCOIN TRANSACTIONS ARE USED FOR ILLICIT ACTIVITIES..
I WROTE AN 18 PART POST , DESCRIBING WHAT BITCOIN IS..
http://ajitvadakayil.blogspot.com/2018/04/blockchain-bitcoin-must-be-kicked-out.htmlBOTH PM MODI AND NSA AJIT DOVAL HAVE BEEN BLEEDING BHARATMATA WHILE CLAIMING TO BE GREAT DESH BHAKTS..
WITH KAYASTHAS LAW MINISTER RAVISHANKAR PRASAD AND I&B MINISTER PRAKASH JAVEDEKAR, THIS GRUESOME FOURSOME HAS ENSURED THAT THE WATAN IS GOING TO THE DOGS..EVERYBODY KNEW THAT ROTHSCHILD DARLING PAPA CHANDRACHUD WOULD BE CJI.. YOU SHOULD HAVE SEEN THE TANDAV OF THIS FELLOW IN 1976 TARGETING INDIRA GANDHI..
NOW EVERYBODY KNOWS THAT BETA CHANDRACHUD WILL BE CJI -- THIS IS HOW MUCH THINGS ARE PREDICTABLE IN INDIA..
I WROTE A 33 PART POST DESCRIBING WHAT SHELL COMPANIES ARE AND HOW THE WATAN IS BEING BLED ...
TODAY WE KNOW HOW INDIAN BANKS ARE LENDING HUGE MONEY TO SHELL COMPANIES RUN BY PEOPLE WHO DONATE TO BJP.
http://ajitvadakayil.blogspot.com/2017/03/shell-companies-for-money-laundering_31.html
I WANT ALL MY READERS TO READ THE BELOW POST CAREFULLY.. HOW BITCOIN BRIBED JUDGES ARE ALLOWED TO PLAY GOD BY SLAVES TO THE JEWISH DEEP STATE WHO RULE INDIA..
https://ajitvadakayil.blogspot.com/2020/01/we-people-are-done-with-illegal.html
THERE IS NO WAY TO RETRIEVE BITCOIN THAT HAS BEEN STOLEN OR CONNED, AND NO WAY TO REVERT THE TRANSACTION.
IN TEN YEARS OF BLOCKCHAIN EXISTENCE ALL IT HAS DONE IS TO REGULARISE GRABBED LAND IN ISRAEL AND GEORGIA BY JEWS..
CHANDRABABU NAIDU WANTED TO USE BLOCKCHAIN TO COVER UP GRABBED LAND IN AMARAVATI..
BITCOIN MAKES IT EASY FOR SCAMMERS , TERRORISTS , PEDOPHILES TO GET THEIR ILLEGAL MONEY AND RUN.
ARTIFICIAL INTELLIGENCE IS USED TO KEEP BITCOIN OFF THE WATCH CRIME RADAR..FALSE POSITIVES ARE USED AS DECOY..
I HAVE PENNED A 16 PART BLOG SERIES ( UNFINISHED ) ON AI..
https://ajitvadakayil.blogspot.com/2020/03/what-artificial-intelligence-cannot-do.html
ALMOST ALL BITCOIN IS USED IN THE DARK WEB ANONYMOUSLY AND THIS IS A TOOL FOR MONEY LAUNDERING AND RANSOMWARE. TRACKS ARE COVERED..
THE BIGGEST CRIMINALS OF INDIA ARE BITCOIN TRADING GUJARATIS FROM SURAT WHO DONATE TO BJP..
capt ajit vadakayil
..
ON THE CONTRARY IT CAN SPAWN FOUL AND RUTHLESS GLOBAL FRAUD ( CLIMATE CHANGE DUE TO CO2 ) WITH DELIBERATE BLACK BOX ALGORITHMS, JUST FEW AMONG MORE THAN 40 CRITICAL INHERENT DEFICIENCIES.
HUMANS HAVE THINGS A COMPUTER CAN NEVER HAVE.. A SUBCONSCIOUS BRAIN LOBE, REM SLEEP WHICH BACKS UP BETWEEN RIGHT/ LEFT BRAIN LOBES AND FROM AAKASHA BANK, A GUT WHICH INTUITS, 30 TRILLION BODY CELLS WHICH HOLD MEMORY, A VAGUS NERVE , AN AMYGDALA , 73% WATER IN BRAIN FOR MEMORY, 10 BILLION MILES ORGANIC DNA MOBIUS WIRING ETC.
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AS CHANAKYA
SAID “I MAKE THE ENEMY SEE MY STRENGTHS
AS WEAKNESSES AND MY WEAKNESSES AS STRENGTHS WHILE I CAUSE HIS STRENGTHS TO
BECOME WEAKNESSES AND DISCOVER WHERE HE IS NOT STRONG… I CONCEAL MY TRACKS SO
THAT NONE CAN DISCERN THEM; I KEEP SILENCE SO THAT NONE CAN HEAR ME.”
THIS IS THE
BASIS OF DECEPTION TECHNOLOGY. IT REVERSES THE COMMON SITUATION. THE STEALTHY
CYBER-ATTACKER BECOMES THE STEALTHY CYBER-ATTACKED.
Many organizations
install advanced security systems to protect themselves against APTs and other
emerging threats – with many of these systems themselves utilizing AI and
machine-learning techniques.
By doing that,
organizations often believe that the problem has been taken care of; once an
advanced response has been installed, they can sit back and relax, confident
that they are protected.
However, that is the
kind of attitude that almost guarantees they will be hacked. As advanced a system
as they install, hackers are nearly always one step ahead
Two years ago, Microsoft
Corp’s Azure security team detected suspicious activity in the cloud computing
usage of a large retailer: One of the company’s administrators, who usually
logs on from New York, was trying to gain entry from Romania. And no, the admin
wasn’t on vacation. A hacker had broken in.
Microsoft quickly
alerted its customer, and the attack was foiled before the intruder got too
far.
Machine learning
poisoning is a way for criminals to circumvent the effectiveness of AI. They
study how the machine learning (ML) process works, which is different on a
case-by-case basis, and once a vulnerability is spotted, they will try to
confuse the underlying models.
To poison, the machine
learning engine is not that difficult if you can poison the data pool from
which the algorithm is learning.
Hackers are just as
sophisticated as the communities that develop the capability to defend
themselves against hackers. They are using the same techniques, such as
intelligent phishing, analyzing the behavior of potential targets to determine
what type of attack to use, “smart
malware” that knows when it is being watched so it can hide
Hackers get inside information
( bribes/ honey traps ) how companies
train their systems and use the data to evade or corrupt the algorithms.
Convolutional Neural
Networks (ConvNets or CNNs) are a class of artificial neural networks that have
proven their effectiveness in areas such as image recognition and
classification. Autonomous vehicles also utilize this technology to recognize
and interpret street signs.
To work properly,
however, CNNs require considerable training resources, and they tend to be
trained in the cloud or partially outsources to third parties. That is where
the problems may occur. While there, hackers may find a way to install a
backdoor in the CNN so that it would perform as normal on all inputs except
those small (and hidden) amounts of inputs chosen by hackers.
One-way hackers could do that is by poisoning the training set with “backdoor” images created by the hackers. Another way is through a man-in-the-middle attack, which is a scenario possible when the attacker intercepts the data sent to the Cloud GPU service (i.e., the service that trains the CNN).
One-way hackers could do that is by poisoning the training set with “backdoor” images created by the hackers. Another way is through a man-in-the-middle attack, which is a scenario possible when the attacker intercepts the data sent to the Cloud GPU service (i.e., the service that trains the CNN).
What works to their
advantage is the fact that these types of cyber attacks are difficult to detect
and thus evade the standard validation testing.
We are already living
in a world brimming with chatbots: customer service bots, support bots,
training bots, information bots, porn bots, you name it. It is a trend that
probably exists because the majority of people would prefer to text than talk
over the phone
Chatbots are cash cows
as far as cybercrime is concerned because of their ability to analyze, learn,
and mimic people’s behavior.
Criminals are in some
cases ahead of the game and routinely utilize bots and automated attacks. These
will rapidly get more sophisticated.
Usually, chatbots are
programmed to start and then sustain a conversation with users with the
intention to sway them to reveal personal, account or financial information,
open links or attachments, or subscribe to a service
Mobile bot farms are
the place where bots are refashioned to appear more human-like to be more
difficult to differentiate between humans and bots. For instance, Google’s Allo
message app learns how to imitate the people it is communicating with.
Most people probably do
not realize how much personal information AI-driven conversational bots, such
as the Google Assistant and Amazon’s Alexa, may know about them. Due to their
nature to always be in “Listen Mode,” in combination with the ubiquity of the
Internet of Things (IoT) technology, private conversations within the living
quarters are most likely not as private as they might seem.
In addition, regular
commercial chatbots often come unequipped for secure data transmissions – they
do not support HTTPS protocols or Transport Level Authentication (TLA) – which
makes them easy prey for experienced cybercriminals.
AI-based chatbots could
automate ransomware by communicating with the victims to help them pay up the
ransom easier, and even make an in-detail assessment of the potential value of
the encrypted data so that the ransom amount would fit the bill.
The process of creating
malware is predominantly manual. Cybercriminals need to write the code of
computer viruses/Trojans, use rootkits, password scrapers, as well as any other
tool that can help them disseminate and execute their malware. Imagine if they
have something that will speed up that process.
AI scales down the
enormous proportions of effort and time hackers must put in vulnerability
intelligence, identifying targets, and spear phishing campaigns.
A startup has been
using AI to deep scan the dark web for trails of its customers’ personally
identifiable information posted for sale on the black market. Would not be
possible cybercriminals to do the same but to further their agenda?
‘RAISING THE NOISE
FLOOR’ OCCURS WHEN AN ADVERSARY BOMBARDS A TARGETED ENVIRONMENT WITH DATA THAT
CONSISTS OF NUMEROUS FALSE POSITIVES, WHICH IS PRESUMABLY DETECTED BY ML
DEFENSE MECHANISMS.
EVENTUALLY, THE ADVERSARY FORCES UPON THE DEFENDER RECALIBRATION OF HIS DEFENSE MEASURES SO THAT THEY WILL NOT BECOME AFFECTED BY ALL THESE FALSE POSITIVES. THE DOOR IS SET AJAR, AND THAT IS EXACTLY WHEN THE ATTACKER HAS THE OPPORTUNITY TO SNEAK IN.
EVENTUALLY, THE ADVERSARY FORCES UPON THE DEFENDER RECALIBRATION OF HIS DEFENSE MEASURES SO THAT THEY WILL NOT BECOME AFFECTED BY ALL THESE FALSE POSITIVES. THE DOOR IS SET AJAR, AND THAT IS EXACTLY WHEN THE ATTACKER HAS THE OPPORTUNITY TO SNEAK IN.
We’ve seen more and
more attacks over the years take on morphing characteristics, making them
harder to predict and defend against Now, leveraging more machine learning
concepts, hackers can build malware that can learn about a target’s network and
change up its attack methodology on the fly
Fully autonomous AI
tools will act like undercover agents or lone warriors that operate behind
enemy lines. Once sent on their mission, they have free will and forge they
plan of action on their own. Typically, they blend in with the normal network
activities, learn fast from their environments, and decide what course of
action is the best while remain buried deep inside enemy turf (i.e., digital
networks).
Similarly, to a HIV,
offensive AI mechanisms that have continuous access to a targeted digital
network weaken its immune system from within and create a surreptitious
exploitation infrastructure (e.g., backdoors, importing malware, escalating
privileges, exporting data, etc.).
An Internet-connected
device, for instance, is one convenient entry point. Every object that can be
connected to the Internet is a potential point of compromise, even the most
absurdly looking IoT products. For example, cybercriminals entered a network in
a North American casino through a connected fish tank. Then, they scanned the
surroundings, found more vulnerabilities, and eventually moved laterally
further into the attacked network.
First nuances of a
real-world AI-powered cyberattack occurred in 2017 when the cybersecurity
company Darktrace Inc. detected an intrusion in the network of an India-based
firm. It displayed characteristics of rudimentary machine learning that observe
and learn patterns of normal behavior while lurking inside the network.
After a
while, the malicious software began to mimic behavior to blend into the
background … and voila, suddenly, these chameleon-like tricks make things foggy
for security mechanisms in place. Just as a sophisticated flu virus could
mutate and become resistant to antibiotics, AI could teach malware and
ransomware to evade their respective antidotes.
Something like this
case of a piece of malware that manipulates environment was demonstrated at
DEFCON 2017 where a data scientist from Endgame, a U.S. security vendor, showed
how an automated tool could learn to mask a malicious file from virus
prevention software with various code morphing techniques.
Internet infrastructure
is “particularly susceptible” to a “gradient descent algorithm,” which is a
mathematical process whose goal is to find the most optimal solution to a
complex function
We would witness
intelligent clusters of hijacked devices called “hivenets” that will leverage
self-learning and act without explicit instructions of the botnet herder.
These
features, in combination with the ability of the devices to communicate with
each other to maximize their effectiveness at a local level, will allow these
clusters to grow into swarms, making them an ideal weapon for a simultaneous,
blitzkrieg-style attack against multiple victims.
A subfield of AI, the
definition of swarm technology is “collective behavior of decentralized,
self-organized systems, natural or artificial” and has already been employed in
drones and fledgling robotics devices.
CAPTCHA seems to be no
match for the rising intelligence of machines. A paper “I am Robot” claims a
98% success rate of breaking Google’s reCAPTCHA.
The adversarial use of
artificial intelligence (AI) and machine learning (ML) in malicious ways by
attackers may be embryonic, but the prospect is becoming real. It's
evolutionary: AI and ML gradually have found their way out of the labs and
deployed for security defenses, and now they're increasingly being weaponized
to overcome these defenses by subverting the same logic and underlying
functionality.
Hackers and CISOs alike
have access to the power of these developments, some of which are turning into
off-the-shelf offerings that are plug-and-play capabilities enabling hackers to
get up and running quickly. It was only a matter of time before hackers started
taking advantage of the flexibility of AI to find weaknesses as enterprises
roll it out in their defensive strategies.
The intent of
intelligence-based assaults remains the same as "regular" hacking.
They could be politically motivated incursions, nation-state attacks,
enterprise attacks to exfiltrate intellectual property, or financial services
attacks to steal funds — the list is endless. AI and ML are normally considered
a force for good. But in the hands of bad actors, they can wreak serious
damage.
Automated penetration
testing using ML is a few years old. Now, tools such as Deep Exploit can be
used by adversaries to pen test their targeted organizations and find open
holes in defenses in 20 to 30 seconds — it used to take hours. ML models speed
the process by quickly ingesting data, analyzing it, and producing results that
are optimized for the next stage of attack.
Cloud computing and
access to powerful CPUs/GPUs are increasing the risk of these adversaries
becoming experts at wielding these AI/ML tool sets, which were designed for the
good guys to use.
When combined with AI,
ML provides automation platforms for exploit kits and, essentially, we're fast
approaching the industrialization of automated intelligence to break down cyber
defenses that were constructed with AI and ML.
Many of these
successful exploit kits enable a new level of automation that makes attackers
more intelligent, efficient, and dangerous. DevOps and many IT groups are using
AI and ML for gaining insights into their operations, and attackers are
following suit.
attackers will learn
how the enterprise defends itself with ML, then inject the unique computational
algorithms and statistical models used by the enterprises with corrupt data to
throw off their defensive machine learning models. Ingested data is the key to
the puzzle that enables ML to unlock the AI knowledge.
Many ML models in
cybersecurity solutions, especially deep learning models, are considered to be
black boxes in the industry.
They can use over
100,000+ feature inputs to make their determinations and detect the patterns of
knowledge to solve a problem, such as the detection of anomalous cyber exploit
behaviors in an organization or network.
From the point of view
of the security team, this can require trust in a model or algorithm within the
black box that they don't understand, and coupled with the level of trust
required
One improvement on the
horizon is the ability to enable teams in the security operations center to
understand how ML models reach their conclusions rather than having to flat-out
trust that the algorithms are doing their jobs. So, when the model says there
is anomalous risky behavior, the software can explain the reasoning behind the
math and how it came to that conclusion.
This is extremely
important when it's difficult to detect if adversaries have injected bad data —
or "poisoned" it — into defensive enterprise security tools to
retrain the models away from their attack vectors. Adversaries can create a
baseline behavioral paradigm by poisoning the ML model data, so their
adversarial behaviors artificially attain a low risk score within the
enterprise and are allowed to continue their ingress.
For other intents —
influencing voters, for example — bad actors run ML against Twitter feeds to
spot patterns of influence that politicians are using to influence specific
groups of voters. Once their ML algorithms find these campaigns and identify
their patterns, they can create their own counter-campaigns to manipulate
opinion or poison a positive campaign that is being pushed by a political
group.
Then, there is the
threat of botnets. Mirai was the first to cause widespread havoc, and now there
are variants that use new attack vectors to create the zombie hordes of
Internet of Things devices.
Mirai is a malware that
turns networked devices running Linux into remotely controlled bots that can be
used as part of a botnet in large-scale network attacks. It primarily targets
online consumer devices such as IP cameras and home routers.
The Mirai botnet was first found in August
2016 by MalwareMustDie, a white hat malware research group, and has
been used in some of the largest and most disruptive distributed denial of
service (DDoS) attacks,
Mirai works by
exploiting the weak security on many IoT devices. ... Mirai infects devices
with malware that forces them to report to a central control server, turning
them into a bot that can be used in DDoS attacks..
At its core, Mirai is a
self-propagating worm, that is, it's a malicious program that replicates itself
by finding, attacking and infecting vulnerable IoT devices. It is also
considered a botnet because the infected devices are controlled via a central
set of command and control (C&C) servers..
Botnets are created by infecting multiple systems with malware
(malicious software) and thus rendering them to be slave systems to the botnet
operator. This malware can be introduced to a computer system in various forms,
for example: A trojan within an email attachment..
Cyber criminals use botnets to instigate
botnet attacks, which include malicious activities such as credentials leaks,
unauthorized access, data theft and DDoS attacks
There are even more
complex industrial IoT attacks focused on taking down nuclear facilities or
even whole smart cities. Researchers have studied how potential advanced
botnets can take down water systems and power grids.
The use of AI and ML is
off-the-shelf and available to midlevel engineers who no longer need to be data
scientists in order to master it. The one thing that keeps this from being a
perfect technology for the good actors or the bad actors is how to operationalize
machine learning to greatly reduce false positives and false negatives.
That is what new
"cognitive" technologies are aspiring to become — more than the sum
of their AI and ML parts — by not just detecting patterns of bad behavior in
big data with complete accuracy, but also justifying recommendations about how
to deal with them by providing context for the decision-making.
Machine learning – the
heart of what we call artificial intelligence today – gets “smart” by observing
patterns in data, and making assumptions about what it means, whether on an
individual computer or a large neural network.
So, if a specific
action in computer processors takes place when specific processes are running,
and the action is repeated on the neural network and/or the specific computer,
the system learns that the action means that a cyber-attack has occurred, and that
appropriate action needs to be taken.
But here is where it
gets tricky. AI-savvy malware could inject false data that the security system
would read – the objective being to disrupt the patterns the machine learning
algorithms use to make their decisions. Thus, phony data could be inserted into
a database to make it seem as if a process that is copying personal information
is just part of the regular routine of the IT system, and can safely be
ignored.
The entire IoT—so
rosily celebrated during the past few years—is mostly child’s play for
AI-driven attacks,
Many if not most of the
devices that comprise the IoT are ridiculously unprotected, sporting
easy-to-guess passwords, which are often issued en masse with the same exact
characters by manufacturers and never changed by IoT users.
Most of these devices
are manufactured at low cost and lack elaborate security features
AI cybersecurity tools
are still so new, the tech has acquired a reputation in many instances for
triggering too many false-positive alerts.
Too often, behavior that AI
identifies as suspicious sometimes turns out to be benign. And files AI
sometimes identifies as threatening sometimes turn out to be innocuous.
INSTEAD OF TRYING TO OUTFOX
INTELLIGENT MACHINE-LEARNING SECURITY SYSTEMS, HACKERS SIMPLY “MAKE FRIENDS”
WITH THEM – USING THEIR OWN CAPABILITIES AGAINST THEM, AND HELPING THEMSELVES
TO WHATEVER THEY WANT ON A SERVER.
WINNING WITHOUT A FIGHT USING A
DECOY IS AN ART OF WAR-- CHANAKYA
DIGRESSION : MEANING
OF DECOY
A young man was arrested in front of a ladies college for ogling . The teachers called the police, as they noticed that the girls were giggling, blushing and inattentive in class .
Police arrested him and they found a huge banana inside his undies, under his jeans .
The police asked him" What the fuck is this?"
He says " Well that is mE decoy!"
A tactic involves what
I call “bobbing and weaving,” where hackers insert signals and processes that
have no effect on the IT system at all – except to train the AI system to see
these as normal. Once it does, hackers can use those routines to carry out an
attack that the security system will miss – because it’s been trained to
“believe” that the behavior is irrelevant, or even normal.
Another way hackers
could compromise an AI-based cybersecurity system is by changing or replacing
log files – or even just changing their timestamps or other metadata, to
further confuse the machine-learning algorithms.
One tactic hackers use
to attack is inundating an AI system with low-quality data in order to confuse
it. To protect against this, security systems need to account for the
possibility of encountering low-quality data.
AI IS USED BY
JEWS TO OBFUSCATE , HIDE HUMONGOUS CRIMES
, EMBEZZLE MONEY, RUN DRUGS, BRING DOWN GOVERNMENTS ( BY HYPER
INFLATION EXAMPLE VENEZUELA ) , GRAB
LAND ( BLOCKCHAIN IN ISRAEL) , SUSTAIN TERRORISM -- AND THE WHOLE WORLD HAS ACQUIESCED..
I WAS ONCE
ASKED TO FLY TO EUROPE THE MEET THE REAL OWNER OF THE SHIPPING COMPANY ON WHOSE
SHIP I WAS TO WORK.
AS A CAPTAIN
YOU RARELY GET TO TALK TO THE SHIPOWNER..
THE OWNER, A
YOUNG BLOKE, TOOK ME FOR DINNER AND THEN WHILE WE WERE SIPPING
PREMIUM WINE, ASKED ME IF THERE IS ANY WAY TO STOP EMBEZZELMENT IN HIS
COMPANY.. HIS COMPANY AND SHIP HAD ALL
WHITES.. I WAS A OUTSIDER ,, A BROWN MAN..
HE SAID THAT
HE WAS TOLD ONLY CAPT AJIT VADAKAYIL CAN PUT HIS FINGER AT THE RIGHT PLACE AND
CONNECT DOTS .
SO I TOLD
HIM..
“YOUR COMPANY HAS A SOFTWARE—A BLANKET ONE ENCOMPASSING
ALL SHIP AND SHORE OPERATIONS — WHICH IS
“DELIBERATELY “ KEPT TOO COMPLICATED..
THE BIGGEST DRAW BACK IS THAT YOU DON’T GET A
BIRDs EYE VIEW OF THE SITUATION.. YOU ARE FORCED TO BE IN TUNNEL VISION MODE
WITH NARROW SPECTRUM POP UP WINDOWS. NO NORMAL BRAIN CAN COPE UP WITH THIS LOAD..
YOU ARE ARM TWISTED TO TRUST THE SOFTWARE..
UNLESS YOU ARE EXTREMELY BRIGHT, YOU CANT
FIGURE OUT HOW MONEY IS BEING BLED..
PEOPLE IN YOUR OFFICE ARE STEALING LEFT,
RIGHT AND CENTRE.. THE TECHNICAL
SUPERINTENDENTS ARE IN LEAGUE WITH SHIP STAFF TO MAKE FALSE REQUISITIONS , SIGN
FAKE INVOICES ( FOR SHORT SUPPLY/
SERVICES ) ”
THIS SHIPOWNER
NEARLY CHOKED .. AND HE BEGGED ME TO GIVE SOLUTIONS TO MITIGATE
I DID !
The consequences of
embezzlement can be catastrophic to a small business who though they employed
trustworthy staff
Embezzlement is when an
employee or someone else in a trusted position steals from your business. They
use the money or other assets for their own use.
Embezzlement often
implies a white collar crime where funds are taken from bank accounts, or
perhaps where check forgery or payroll fraud is involved. But it’s not limited to those circumstances.
Here are a few
embezzlement examples and workplace thefts to watch out for:--
Forging Checks
The employee writes
company checks or makes electronic payments to himself. The employee then cooks
the books to hide the theft.
This classic
embezzlement example is made easier when a company uses a signature stamp of an
executive’s signature. A signature stamp
is literally like handing employees a blank check because they can “sign”
checks without your knowledge.
Prevention: Separate responsibilities: one worker to
process checks and another to reconcile transactions and approve documentation.
If you don’t have enough staff for separate functions, then reconcile bank
statements yourself. Require purchase orders or invoices for every payment. And
stop using a signature stamp — or keep it under lock and key.
Cashing Customer Checks
The employee endorses
and cashes customer checks payable to the company, then keeps the funds.
Today, as more payments
become electronic the essential crime is the same. The employee may set up a
bank account with a fictitious name similar to the employer’s to divert
electronic payments into. Small banks and credit unions can be lax in allowing
accounts to be established by the employee using fake “doing business as”
names.
Prevention: Separate the functions so that one person is
responsible for processing payments and another for reconciling accounting
entries. Implement controls to track customer payments at every step to avoid
this kind of embezzlement.
Faking Vendor Payments
Next on our list of
embezzlement examples is when an employee steals company funds, but tries to
hide them as payments to vendors. Faithless employees may create fake vendor
invoices and change accounting system entries to hide their tracks.
Prevention: Regularly review detailed expense reports
(not just summary reports) broken down by vendor, amount and purpose. If you
stay familiar with your numbers, it’s easier to spot when a payment or
accounting entry looks suspicious. If your company is big enough, separate the
functions employees perform.
Overbilling Customers
The employee overbills
customers, keeps the extra money and covers it up with false accounting
entries.
Sometimes this a
large-scale fraud where each customer or transaction is overbilled by a small
“fee” for years. Other times it involves double billing the same amount twice
or tacking on charges for items the customer did not buy.
You might be tempted to
think of this as stealing from customers, but it’s really a type of
embezzlement. Your company bears responsibility for overbillings and will have
to make good to customers.
Prevention: Conduct a periodic audit of customer
billings. Pay close attention to customer complaints about billing errors and
require thorough explanations from staff of how they occurred. Complaints may
be a warning sign of a bigger problem.
Theft of Customer Card
Data
An employee who takes
phone orders may later use the customer’s credit card data to charge personal
purchases online. Or a gas station manager may use a skimmer device to skim
card data from terminals at the pumps.
A more nerdy version is
when an employee downloads credit card data from company IT systems. Then he or
she sells it on the dark web.
Prevention: Limit access to customer data to only those
who need it. Deploy technology that redacts credit card numbers or only prints
out the last digits, to limit trash harvesting or unintentional sharing. Change
permissions when someone with IT access leaves the company. If you use card
terminals, install anti-skimming technology — some municipalities now require
it.
Padding An Expense
Account
Padding examples range
from the occasional attempt to justify an expensive lunch using a “creative”
description, all the way to elaborate embezzlement schemes.
Large enterprises take
padding seriously – shouldn’t you? A Hewlett Packard CEO was ousted back in
2010 in the face of allegations he padded his expense account to hide an
extramarital affair. HP saw the issue as one of trust.
Prevention: Have a written policy detailing what is — and
is not — reimbursable. Go over the policy in staff meetings. If employees do a
lot of business travel, consider using an expense management app to control
approvals and see scanned receipts all in one place.
Double Dipping
Next on our list of
embezzlement examples is when there’s a single legitimate business expense, but
the employee gets two reimbursements. She first pays for an expense with the
company credit card. Later she submits a cash reimbursement request for the
same expense.
Prevention: Insist on seeing underlying receipts for all
expenses (don’t just review the credit card statement). Use expense management
software if your employees incur a lot of reimbursable expenses.
Using a Company Credit
Card For Personal Use
The employee pays for
personal expenses using a company credit card. The good news is, often these
thefts are sporadic and the amounts are small.
However, what if the
same employee also manages the accounting system and realizes no one but her
pays attention? Using a company credit
card for personal use can turn into massive embezzlement examples when combined
with falsified accounting records.
Prevention: Always have
two people involved in the process: one to approve expenses and one to handle
accounting. Require documentation of the expense purpose.
Voiding Transactions At
The Cash Register
An associate at the
cash register voids transactions and pockets the cash. This is a common way of
skimming money from a retail small business.
Prevention: Newer point-of-sale systems have security
protocols to help prevent this kind of theft. For example, they allow clearance
levels so you can require manager approval to void a sale. Employee ID numbers
track how often a particular staff member voids transactions so you can spot
repeat offenders.
Siphoning Off Cash
Deposits
Before dropping off the
cash deposit bag at the bank in the evening, the employee pockets some of the
cash. The amount may be small enough not to be missed — perhaps $100. But week
after week, it adds up to thousands of dollars.
Prevention: Personally count the day’s cash, complete the
deposit slip and enter the amount into the accounting records yourself before
handing off the bag. Or separate the functions so two people are involved.
Other strategies may help, such as security cameras in the area where cash is
counted along with using locked deposit bags. See more tips for cash
processing.
Raiding the Petty Cash
Box or Safe
This theft can be as
simple as the employee taking $200 out of the safe or petty cash box.
Prevention: Lock up large sums and keep the key yourself,
to minimize access and temptation by employees. Or use security cameras. Read:
20 Cash Handling Best Practices.
Pocketing Cash From
Fundraisers
Skimming fundraiser
money is all too common in non-profits. But this type of fraud also occurs in businesses that take on a
charitable cause. If one person has complete control over the money, from start
to finish, the temptation to steal can prove too great.
Prevention: Always have
at least two people involved in the workflow of collecting, recording,
depositing and remitting donations. Don’t give temptation a chance.
Stealing Office
Supplies
It’s shocking how many
employees seem to feel it is okay to take large amounts of office supplies
home. Theft of supplies usually involves consumable items like postage stamps,
Post-it notes or coffee supplies.
The owners of one
business started during the Great Depression had a solution. They were so
frugal they required employees to turn in their pencils at the end of each
day! You don’t need to keep THAT tight a
rein. But reasonable controls are a best practice.
Prevention: Put most of your supplies under lock and key
and replenish an open supply area sparingly, to keep shrinkage small. A
security camera may help. Discuss the use of supplies in a company meeting to
set the tone and convey company values.
In construction and
manufacturing businesses, an employee may hide company property in a dumpster
or storage area and retrieve it after hours.
Equipment theft also
occurs in offices. Think laptops or small document scanners that can be slipped
into a backpack or handbag.
Prevention: Lock up or bolt down valuable items if
feasible. Label important equipment with a number and let employees know you
plan regular audits to ensure items are still on site. Use security cameras and
electronic access systems.
Stealing Products
The employee steals
company products. Examples include jewelry or perfume from a high end retail
shop. Typical victims are small retailers that lack shrinkage controls. It is
stunning how many owners simply stuff inventory into a storeroom with no
tracking system.
Another variation is when
a waiter does not charge friends for food or drinks in a restaurant.
Prevention: Use security cameras. Implement an inventory
management system and regularly check inventory levels. There’s even POS
technology that tracks voided transactions and discounts, and alerts the owner
or manager.
Burglarizing Company
Premises
Think classic inside
job — with or without accomplices. The employee leaves a door unlocked or uses
a key to get in after hours. Your company gets ripped off.
Prevention: Install security cameras. Implement an
electronic security system to secure after-hours access, and record who is
coming and going.
Stealing Returned
Merchandise
This theft can occur in
a retail or ecommerce setting, or in any business that swaps out old equipment.
The employee simply takes returned items home or resells them on Craigslist or
eBay.
A lack of controls
makes this theft easier. In some small businesses, returns may be stacked
haphazardly in a corner. Is it any wonder they disappear?
Prevention: Implement control systems for managing
returns and other property.
Claiming a Company
Laptop Was Lost
The employee gives a
laptop or mobile device to a family member and tells the employer it was lost.
The company then replaces the item.
Prevention: Use device
management software that enables the company to disable lost devices and track
their location.
Setting Up Fake
Employees
The embezzling employee
sets up fake employees, pockets the pay, and cooks the books to hide the
transaction. This happens in businesses with absentee owners or over-trusting
owners who do not pay attention.
Prevention: Implement systems to reconcile headcount with
staffing expenses. Regularly review a detailed headcount report breaking down
expenses by employee. Remember, detailed reports are your friend. Embezzlement
is much harder to spot if all you ever look at are summary reports or a
high-level P&L.
Falsifying Overtime
This may include
schemes where co-workers clock in and out for each other. Or it may involve a
payroll clerk creating false entries for supposed overtime that he pays
himself.
Prevention: Use electronic timesheet systems. Watch
overtime pay closely for unusual increases. Compare detailed reports to
identify exactly which employees are getting overtime and when — you may spot
suspicious patterns.
Failing To Remit
Payroll Tax Money
The employee embezzles
money earmarked for the employer’s payroll tax remittances or other tax money.
Eventually the taxing authority will come down hard on the business owner for
not sending in the tax money, and may file a lien against the business or seize
property. So not only do you face losses from embezzlement, but you have the
IRS on your tail — a double whammy!
This embezzlement
example is perpetrated by dishonest bookkeepers, financial staff, payroll
clerks and even small outside payroll services.
Prevention: Outsource to a large reputable payroll
service such as Paychex or ADP. It goes a long way to prevent an embezzlement
nightmare. Or require a regular audit by an outside accounting firm.
Collecting Kickbacks
From Vendors
In this scheme, the
employee gets vendor kickbacks and you are unaware. Kickbacks can be cash. They
also can take the form of additional products and services used in an
employee’s side business or home. A warning sign is an unusually close
relationship between a vendor and an employee.
Prevention: Get involved in choosing vendors yourself.
This minimizes collusion between vendors and faithless employees.
Selling Trade Secrets;
Corporate Espionage
The employee sells
sensitive information to a competitor. Or the employee takes confidential documents and trade secrets with
him when switching jobs.
You see this in high
tech startups. For example, a former Google executive was indicted on criminal
charges for stealing 14,000 files for self-driving car technology and taking
them to a startup later acquired by Uber.
Prevention: Have strong employee agreements. Shared cloud
storage systems help you manage and track who has access to what.
Business Identity Theft
An employee secures a
line of credit or loan in your company name, using the money for personal
purchases. The embezzler then uses company funds to make the payments. Typical
embezzlers are finance staff or bookkeepers with access to accounting records
and legitimate accounts used to cover their tracks.
A similar theft is when
a partner or family member in a family business takes out unauthorized loans in
the company name.
Prevention: Implement internal controls for checks and
balances. Require detailed reports to see where money is going. Sudden cash
flow issues or a negative change in your company credit score may be warning
signs of embezzlement. Pay particular
attention to services like PayPal and others than allow pre-approved loans or
advances against your account.
Starting A Business
Using Company Resources
In this situation,
employees start their own businesses on company time. In the worst situations
employees use company resources such as software code in their new software
product, or steal raw materials.
Make no mistake about
it: this is theft. Yet, some delusional souls brag on social media about what
they are doing!
Still, the employer may
get the last laugh. Why? Because generally speaking, an employer owns all work
product created on company time.
Prevention: Set expectations properly with employees —
and make your policy clear, whatever is. Some employers encourage side
businesses but others have a no moonlighting policy. Even if you allow side
businesses, make it clear that activities
should not be conducted during work hours, and company resources may not
be used.
Final Thoughts on
Embezzlement
It’s important to be an
engaged business owner. Pay attention, ask questions and review detailed
reports. Deploy technology to control access and approval levels, and provide
early warning of anything unusual. Most of all, implement checks and balances
in your processes to make sure no single employee has complete control. Steps
like these help protect the livelihoods of everyone in the business.
-
https://asiatimes.com/2020/03/china-suppressed-covid-19-with-ai-and-big-data/
SOMEBODY ASKED ME-- CAPTAIN, WHAT IS BIG DATA?
AI HAS THE CAPABILITY OF WORKING ON ENORMOUS AMOUNTS OF DATA. THE DATA COLLECTED IS NOT JUST SIMPLE DATA BUT RATHER DATA THAT INCLUDES EVERY MINUTE DETAIL ABOUT AN EVENT OR TRANSACTION. THIS IS ALSO KNOWN AS “BIG DATA”.
BIG DATA IS A FIELD THAT TREATS WAYS TO ANALYZE, SYSTEMATICALLY EXTRACT INFORMATION FROM, OR OTHERWISE DEAL WITH DATA SETS THAT ARE TOO LARGE OR COMPLEX TO BE DEALT WITH BY TRADITIONAL DATA-PROCESSING APPLICATION SOFTWARE.
DATA WITH MANY CASES (ROWS) OFFER GREATER STATISTICAL POWER, WHILE DATA WITH HIGHER COMPLEXITY (MORE ATTRIBUTES OR COLUMNS) MAY LEAD TO A HIGHER FALSE DISCOVERY RATE.BIG DATA CHALLENGES INCLUDE CAPTURING DATA, DATA STORAGE, DATA ANALYSIS, SEARCH, SHARING, TRANSFER, VISUALIZATION, QUERYING, UPDATING,
INFORMATION PRIVACY AND DATA SOURCE. BIG DATA WAS ORIGINALLY ASSOCIATED WITH THREE KEY CONCEPTS: VOLUME, VARIETY, AND VELOCITY.
WHEN WE HANDLE BIG DATA, WE MAY NOT SAMPLE BUT SIMPLY OBSERVE AND TRACK WHAT HAPPENS. THEREFORE, BIG DATA OFTEN INCLUDES DATA WITH SIZES THAT EXCEED THE CAPACITY OF TRADITIONAL SOFTWARE TO PROCESS WITHIN AN ACCEPTABLE TIME AND VALUE.
KOSHER INSURANCE COMPANIES MANIPULATE BIG DATA TO LAUGH ALL THE WAY TO THE BANK..
ON AUG. 6, 2019, THE U.S. FOOD AND DRUG ADMINISTRATION (FDA) PUBLICLY REBUKED THE SWISS PHARMACEUTICAL COMPANY NOVARTIS FOR MANIPULATING DATA INVOLVING ITS $2.1 MILLION GENE THERAPY.
RESULTS ARE OFTEN DIFFICULT TO REPRODUCE ACCURATELY, BEING OBSCURED BY NOISE, ARTIFACTS, AND OTHER EXTRANEOUS DATA.
THAT MEANS THAT EVEN IF A SCIENTIST DOES FALSIFY DATA, THEY CAN EXPECT TO GET AWAY WITH IT – OR AT LEAST CLAIM INNOCENCE IF THEIR RESULTS CONFLICT WITH OTHERS IN THE SAME FIELD.
THERE ARE NO "SCIENTIFIC POLICE" WHO ARE TRAINED TO FIGHT SCIENTIFIC CRIMES; ALL INVESTIGATIONS ARE MADE BY EXPERTS IN SCIENCE BUT AMATEURS IN DEALING WITH CRIMINALS.
IT IS RELATIVELY EASY TO CHEAT ALTHOUGH DIFFICULT TO KNOW EXACTLY HOW MANY SCIENTISTS FABRICATE DATA
https://www.statnews.com/2019/10/29/data-falsification-still-problematic-china-india-generic-drug-plants/
FABRICATION IS MAKING UP RESULTS AND RECORDING OR REPORTING THEM. THIS IS SOMETIMES REFERRED TO AS "DRYLABBING". A MORE MINOR FORM OF FABRICATION IS WHERE REFERENCES ARE INCLUDED TO GIVE ARGUMENTS THE APPEARANCE OF WIDESPREAD ACCEPTANCE, BUT ARE ACTUALLY FAKE, OR DO NOT SUPPORT THE ARGUMENT.
FALSIFICATION IS MANIPULATING RESEARCH MATERIALS, EQUIPMENT, OR PROCESSES OR CHANGING OR OMITTING DATA OR RESULTS SUCH THAT THE RESEARCH IS NOT ACCURATELY REPRESENTED IN THE RESEARCH RECORD.
FOR CASES OF FABRICATED EVIDENCE, THE CONSEQUENCES CAN BE WIDE-RANGING, WITH OTHERS WORKING TO CONFIRM (OR REFUTE) THE FALSE FINDING, OR WITH RESEARCH AGENDAS BEING DISTORTED TO ADDRESS THE FRAUDULENT EVIDENCE.
THE PILTDOWN MAN / PEKING MAN/ LUCY FRAUDS ARE CASES IN POINT—TO PROVE THAT MAD MAN CHARLES DARWIN IS RIGHT.. EVEN GOOGLE PLAYED KOSHER BALL GIVING A DOODLE TO LUCY THE MISSING LINK..
EVIL PHARMA HIRES HIT MEN MERCENARIES TO KILL WHISTLE BLOWERS..
IN STATISTICS, A MISLEADING GRAPH, ALSO KNOWN AS A DISTORTED GRAPH, IS A GRAPH THAT MISREPRESENTS DATA, CONSTITUTING A MISUSE OF STATISTICS AND WITH THE RESULT THAT AN INCORRECT CONCLUSION MAY BE DERIVED FROM IT.
THERE IS A FOREIGN PAYROLL LOBBY IN INDIA TOSHOWCASE THAT INDIA IS A BEGGAR NATION ( UNDER MODI ) , WHEN INDIA IS THE ONLY ROARING ECONOMY ON THIS PLANET,, THE ONLY ONE WITH POTENTIAL.
CONTINUED TO 2-
CURRENT
USAGE OF THE TERM BIG DATA TENDS TO REFER TO THE USE OF PREDICTIVE ANALYTICS,
USER BEHAVIOR ANALYTICS, OR CERTAIN OTHER ADVANCED DATA ANALYTICS METHODS THAT
EXTRACT VALUE FROM DATA, AND SELDOM TO A PARTICULAR SIZE OF DATA SET.
DATA SETS
GROW RAPIDLY, TO A CERTAIN EXTENT BECAUSE THEY ARE INCREASINGLY GATHERED BY
CHEAP AND NUMEROUS INFORMATION-SENSING INTERNET OF THINGS DEVICES SUCH AS
MOBILE DEVICES, AERIAL (REMOTE SENSING), SOFTWARE LOGS, CAMERAS, MICROPHONES,
RADIO-FREQUENCY IDENTIFICATION (RFID) READERS AND WIRELESS SENSOR NETWORKS
BIG DATA
ANALYTICS EFFICIENTLY HELPS OPERATIONS TO BECOME MORE EFFECTIVE. THIS HELPS IN
IMPROVING THE PROFITS OF THE COMPANY. BIG DATA ANALYTICS TOOLS LIKE HADOOP
HELPS IN REDUCING THE COST OF STORAGE. THIS FURTHER INCREASES THE EFFICIENCY OF
THE BUSINESS.
HADOOP IS AN OPEN-SOURCE SOFTWARE FRAMEWORK USED FOR STORING AND
PROCESSING BIG DATA IN A DISTRIBUTED MANNER ON LARGE CLUSTERS OF COMMODITY
HARDWARE.
CAN
AI EVER BE THE JUDGE OF A HUMOR CONTEST ?
The Dragonfly project
was an Internet search engine prototype created by Google that was designed to
be compatible with China's state censorship provisions. The
public learned of Dragonfly's existence in August 2018, when The Intercept
leaked an internal memo written by a Google employee about the project.
Project Dragonfly, was a
censored search engine that will blacklist search results about human rights,
democracy and religion in China
In July 2019, Google
announced that work on Dragonfly had been terminated
If the AI used is
ultimately deemed to be a product, then it will be subject to the product
liability framework.
AI is not a singular
technology but rather a multitude of techniques deployed for different
commercial and policy objectives which – in order to be called "artificial
intelligence" – fulfill three criteria: --
The system can (i)
perceive its environment and process what is perceived,
(ii) independently
solve problems, make decisions and act, and
(iii) learn from the
results and effects of these decisions and actions.
The term ‘AI’ is often
used to describe a basket of different computing methods, which are used in
combination to produce a result but which aren’t necessarily AI by themselves.
For many AI applications the data used to
train the system also contains personal data within the meaning of applicable
data protection regulation. Companies established in the EU and beyond will
thus have to comply with the requirements of the GDPR when developing or using
AI applications.
Also for data
processing in AI applications, the fundamental principles set out in Article 5
of the GDPR apply. Some of these principles can be quite challenging for both,
the developing phase and the execution phase:
The fairness principle:
AI seems to be objective, but it is no more objective than the data used in the
training phase. So, it might (and often will) be that the data is somehow
biased. If it is not ensured that arbitrary discriminatory treatment of
individual persons can be ruled out, this would be in violation of the fairness
principle.
The transparency
principle: As AI outgrows its original programming, in many cases it is
difficult to understand why the AI application has produces a particular output.
Particularly, this black box makes it hard to explain how information is
correlated and weighed to produce a particular result.
The data minimization
principle: According to this principle, data used shall be adequate, relevant
and limited to what is necessary in relation to the purposes for which they are
processed. This means that in the learning process only such personal data
might be used that proves relevant for the training. But what if the algorithms
are already adequately trained and further input data does not provide any
added value? It might be argued that such further input data might well be
adequate, relevant and necessary for the training of the AI as only with such
data it can be shown that the AI has "reached its learning peak".
The purpose limitation
principle: Also for AI applications, the reason for processing personal data
must be clearly established and indicated when the data is collected. Thus, it
has to be taken into account for the purpose definition that in course of the training
data will most probably be combined by the AI system to create new results in
an unexpected way. Further, when re-using personal data for several purposes
each "new" data processing has to be assessed separately.
None of these
principles is a show-stopper per se, but it can sometimes prove challenging to
develop AI applications meeting the high standards of Article 5 of the GDPR.
.
Principles relating to
processing of personal data (Article 5 GDPR)
Lawfulness, fairness
and transparency. ...
Purpose limitation. ...
Data minimisation. ...
Accuracy. ...
Storage limitation. ...
Integrity and
confidentiality. ...
Accountability.
https://www.complianceforge.com/eu-general-data-protection-regulation-gdpr-compliance/
Lawful processing
In general, personal
data might only be processed if such processing can be justified by one of the grounds
of justification mentioned in Articles 6 and 9 of the GDPR. Whereas one might
think of having consents as the legal basis for their AI application, this is
definitely the wrong choice: In practice, besides not being able to obtain all
consents needed, one has to deal with the situation that a data subject
withdraws their consent. As a better solution, data processing within AI
applications might be based on "overriding legitimate interests" of
the AI developer or the user of an AI application. Noteworthy, this ground of
justification does not apply for "special categories of data" (e.g.
health data), but other grounds of justification might be available depending
on the specific AI application and the purpose(s) pursued.
Automated decisions and
profiling
The GDPR limits how
organizations can use personal data to make individual automated decisions.
Individual automated decisions are decisions relating to individuals that are
based on machine processing. Under the GDPR, individual fully-automated decisions
are permitted only in certain circumstances, e.g. with the explicit consent of
the data subject. As mentioned, obtaining (and keeping) valid consent from all
data subjects is quite unrealistic. Thus, for not having to rely on consent
declarations, AI systems should not automatically decide on something which has
legal effect on the data subject (e.g. whether a loan is granted) or similarly
significantly affects a person (e.g. not being able to select certain payment
methods) without some form of "human intervention" in the
decision-making process.
Information obligation
For AI applications
performing individual automated decisions, in addition to the general
information obligations, the data controller must also provide to the data
subject "meaningful information about the logic involved, as well as the
significance and the envisaged consequences of such processing for the data
subject".
Data subject rights
Sometimes, for an AI
application reaching and maintaining its full potential, some of the data used
in the learning process has to remain stored in the AI system, otherwise the
learning could not be refined any further. If this is the case, it has to be
ensured that e.g. valid data subject requests for erasure or rectification can
be fulfilled nonetheless.
Data protection impact
assessment
The European Data
Protection Board has endorsed guidelines setting out nine criteria for when
processing activities are subject to a data protection impact assessment. If
two or more of these criteria are fulfilled, a data processing activity is
"likely to result in high risk for the data subject" and, thus, a
data protection impact assessment has to be performed for the respective data
processing activities. AI applications often fulfil one or more of the criteria
"automated decision making with legal or similar significant effect",
"data processed on a large scale", "matching or combining
datasets", and/or "innovative use or applying new technological or
organizational solutions". If personal data is involved, an obligation to
perform a data protection impact assessment for an AI application is likely to
exist.
The answer is: AI and
GDPR are neither friends nor foes. They are, however, closely interrelated: If
AI applications process personal data, such AI applications have to be in line
with all applicable GDPR requirements. This is not per se impossible, but –
depending on the AI application – may prove quite challenging. Yet, as data
protection regulations also becomes tougher in other jurisdictions outside
Europe as well, there is a need for privacy-friendly development and use of AI.
.
Replicability or
reproducibility involves the extent to which an AI decision making process can
be repeated with the same outcome. One problem with this approach is the
absence of universal standards governing the data capture, curation and
processing techniques to allow for such replicability. A second problem is that
AI experiments often involve humans repeatedly running AI models until they
find patterns in data and difficulty of distinguishing correlation from
causation. A third problem is the sheer dynamism of this technology –
reproducing results with so much change is difficult.
Summit or OLCF-4 is a
supercomputer developed by IBM for use at Oak Ridge National Laboratory, which is
the fastest supercomputer in the world, capable of 200 petaFLOPS. Its current LINPACK benchmark is clocked at
148.6 petaFLOPS. . Summit is the first supercomputer to reach exaop (a
quintillion operations per second) speed, achieving 1.88 exaops during a
genomic analysis and is expected to reach 3.3 exaops using mixed precision
calculations.
The IBM-built Summit
supercomputer is the world's smartest and most powerful AI machine. Its racks
are connected by over 185 miles of fiber-optic cables
Nvidia is one of the
longest established AI companies and still plays an important role today.
Nvidia's graphics processors are the be-all and end-all for machine learning
and artificial intelligence. The Delaware-based company is active in
healthcare, higher education, retail, and robotics.
Machine learning is a
process where your system learns from the occurrences, experience and keeps in
improving its skills and decision-making ability.
Supervised machine
learning applies to situations where you know the outcome of your input data. Supervised machine learning solves two types
of problems: classification and regression. Classification problems categorize
all the variables that form the output. Problems
that can be classified as regression problems include types where the output
variables are set as a real number.
The format for this problem often follows a
linear format. With supervised learning,
you feed the output of your algorithm into the system. This means that in
supervised learning, the machine already knows the output of the algorithm
before it starts working on it or learning it.
Unsupervised machine
learning algorithms can analyze the data and find the features that are less
relevant and can be dropped to simplify the model without losing valuable
insights. One of the benefits of unsupervised learning is that it doesn’t
require the laborious data labeling process that supervised learning must go
through.
During the process of unsupervised learning, the
system does not have concrete data sets, and the outcomes to most of the
problems are largely unknown. In simple terminology, the AI system and the ML
objective is blinded when it goes into the operation. Unsupervised learning has the ability to
interpret and find solutions to a limitless amount of data, through the input
data and the binary logic mechanism present in all computer systems. The system
has no reference data at all.
Reinforcement Learning
spurs off from the concept of Unsupervised Learning, and gives a high sphere of
control to software agents and machines to determine what the ideal behavior
within a context can be. This link is formed to maximize the performance of the
machine in a way that helps it to grow. Simple feedback that informs the
machine about its progress is required here to help the machine learn its
behavior. The growth in Reinforcement Learning has led to the production of a
wide variety of algorithms that help machines learn the outcome of what they
are doing.
1. Supervised vs
Reinforcement Learning: In Supervised Learning we have an external supervisor
who has sufficient knowledge of the environment and also shares the learning
with a supervisor to form a better understanding and complete the task, but
since we have problems where the agent can perform so many different kind of
subtasks by itself to achieve the overall objective, the presence of a
supervisor is unnecessary and impractical. We can take up the example of a
chess game, where the player can play tens of thousands of moves to achieve the
ultimate objective.
Creating a knowledge base for this purpose can be a really
complicated task. Thus, it is imperative that in such tasks, the computer learn
how to manage affairs by itself. It is hence more feasible and pertinent for
the machine to learn from its own experience. Once the machine has started
learning from its own experience, it can then gain knowledge from these
experiences to implement in the future moves.
This is probably the biggest and
most imperative difference between the concepts of reinforcement and supervised
learning. In both these learning types, there is a certain type of mapping
between the output and input. But in the concept of Reinforcement Learning,
there is an exemplary reward function, unlike Supervised Learning, that lets the
system know about its progress down the right path.
2. Reinforcement vs.
Unsupervised Learning: Reinforcement Learning basically has a mapping structure
that guides the machine from input to output. However, Unsupervised Learning
has no such features present in it. In Unsupervised Learning, the machine
focuses on the underlying task of locating the patterns rather than the mapping
for progressing towards the end goal.
For example, if the task for the machine
is to suggest a good news update to a user, a Reinforcement Learning algorithm
will look to get regular feedback from the user in question, and would then
through the feedback build a reputable knowledge graph of all news related
articles that the person may like. On the contrary, an Unsupervised Learning
algorithm will try looking at many other articles that the person has read,
similar to this one, and suggest something that matches the user’s preferences.
It is an Artificial
Intelligence (AI), application learning skills by the system. It does not
require our instruction to take decisions it keeps on learning itself.
Artificial intelligence
solutions in the system help it to find it some sort of pattern in the data
itself and from there it can perform its own task and make its decision taking
ability eventually better for future purposes. The main objective of machine
learning is to enable the system to take its decision automatically without any
human interference, assistance or guiding the system to take precise or
accurate decisions.
Artificial intelligence
(AI), in the field of computer science AI is the term that actually perceives
its environment. Basically, it helps a system to increase its work efficiency,
thinking ability, decision-making ability and helps a system to work as a human
with the help of machine learning.
.
There are big
differences between the analytical and theoretically unbiased intelligence
you’ll find in AI systems and the way humans think.
While machines are good
at making decisions, humans are better at understanding the wider consequences
of decisions and at putting them through a moral/ethical framework. In
addition, when it comes to interacting with intelligent machines, people will
still prefer to interact with people.
Artificial Intelligence
(AI) is the property of machines, computer programs and systems to perform the
intellectual and creative functions of a
person, independently find ways to solve problems, be able to draw conclusions
and make decisions. Most artificial intelligence systems have the ability to
learn, which allows people to improve their performance over time.
Machine learning is a
process where your system learns from the occurrences, experience and keeps in
improving its skills and decision-making ability.
It is an Artificial
Intelligence (AI), application learning skills by the system. It does not
require our instruction to take decisions it keeps on learning itself.
Artificial intelligence
solutions in the system help it to find it some sort of pattern in the data
itself and from there it can perform its own task and make its decision taking
ability eventually better for future purposes. The main objective of machine learning
is to enable the system to take its decision automatically without any human
interference, assistance or guiding the system to take precise or accurate
decisions.
Artificial intelligence
(AI), in the field of computer science AI is the term that actually perceives
its environment. Basically, it helps a system to increase its work efficiency,
thinking ability, decision-making ability and helps a system to work as a human
with the help of machine learning.
.
There are big
differences between the analytical and theoretically unbiased intelligence
you’ll find in AI systems and the way humans think.
While machines are good
at making decisions, humans are better at understanding the wider consequences
of decisions and at putting them through a moral/ethical framework. In
addition, when it comes to interacting with intelligent machines, people will
still prefer to interact with people.
Artificial Intelligence
(AI) is the property of machines, computer programs and systems to perform the
intellectual and creative functions of a
person, independently find ways to solve problems, be able to draw conclusions
and make decisions. Most artificial intelligence systems have the ability to
learn, which allows people to improve their performance over time.
AI can never replace humans entirely.
Machine-learning
systems can be duped or confounded by situations they haven’t seen before. A
self-driving car gets flummoxed by a scenario that a human driver could handle
easily.
An Artificial
Intelligence system laboriously trained to carry out one task (identifying
cats, say) has to be taught all over again to do something else (identifying
dogs). In the process, it’s liable to lose some of the expertise it had in the
original task. Computer scientists call this problem “catastrophic forgetting.”
Catastrophic
interference, also known as catastrophic forgetting, is the tendency of an
artificial neural network to completely and abruptly forget previously learned
information upon learning new information.
There is a flaw in neural networks, in that they can change their mind
too quickly based on recent evidence. Especially if that previous evidence was
in the middle. In simple terms when you have trained a model on Task A, and
using the same weights for learning a new Task B, then your model forgets
learned information about Task A. This means it catastrophically forgets
previous information.
A computer that
remembers too little won't be able to do anything that requires connecting past
experiences to new ones — like understanding a pronoun in a sentence, even if
the person it refers to was named just one sentence before.
These memory lapses
are known as "catastrophic forgetting."
But one that remembers
too much loses the ability to see the big picture. This is called overfitting:
focusing entirely on the particulars of past experiences at the cost of the
ability to extract general concepts from them.
Effect of catastrophic
forgetting is that a machine learning a new task can lose the ability to do an
old one — like a language learner forgetting their native tongue.
To solve this, some
researchers are adding in memory modules that can set aside learned patterns,
so that they don't get overwritten by new information— like adding a
SUB-CONSCIOUS BRAIN LOBE ( right for right handers ) ..
A trick humans do
during REM sleep may be key to moving AI closer to the way we learn Machines
can mimic this with a process called "experience replay," which
weaves in memories of previously learned tasks alongside new lessons.
This helps them
remember the old and the new — and because the memories are not kept separate,
a computer could use parts of one to help learn the other
Deep neural network AI
systems are designed for learning narrow tasks.
As a result, one of several things can happen when learning new tasks.
Systems can forget old tasks when learning new ones, which is called
catastrophic forgetting.
Systems can forget some
of the things they knew about old tasks, while not learning to do new ones as
well. Or systems can fix old tasks in place while adding new tasks -- which
limits improvement and quickly leads to an AI system that is too large to
operate efficiently. Continual learning, also called lifelong-learning or
learning-to-learn, is trying to address the issue
When asking a deep
neural network to learn a new task, the Learn to Grow framework begins by
conducting something called an explicit neural architecture optimization via
search. What this means is that as the network comes to each layer in its
system, it can decide to do one of four things: skip the layer; use the layer
in the same way that previous tasks used it; attach a lightweight adapter to
the layer, which modifies it slightly; or create an entirely new layer.
This architecture
optimization effectively lays out the best topology, or series of layers,
needed to accomplish the new task. Once this is complete, the network uses the
new topology to train itself on how to accomplish the task -- just like any
other deep learning AI system.
Artificial Intelligence in any form is
artificial and unnatural which requires regular supervision. Businesses are in
still very much need to plan, design and run the marketing campaign.
Technically humans are
the ones feeding the AI system with all the new information required for them
to learn in the first place. Artificial Intelligence, is merely a program and it is can only do
what it is programmed to do.
However, it normally does this extremely well, but
unlike a real person, it is unable to make split-second judgments. Artificial Intelligence does not have any
ability to show emotion.
The AI software
requires regular upgrades in order to adapt to the continually changing
business environment.
AI lacks creativity.
Machines simply lack the ability to be creative. Unlike machines, humans can
think and feel, which often guides their decision making when it comes to being
creative.
Yes, AI can definitely
assist in terms of helping to determine what sort of imagery, for example, a
consumer is likely to click on – from color preferences to style and price. But
when it comes to originality and creative thinking, a machine simply cannot
compete with the human brain. We still need the collaboration of both humans
and machines.
The lines of code that
animate these machines will inevitably lack nuance, forget to spell out
caveats, and end up giving AI systems goals and incentives that don’t align
with our true preferences.
Asking a machine to optimize
a “reward function” — a meticulous description of some combination of goals —
will inevitably lead to misaligned AI, because it’s impossible to include and
correctly weight all goals, subgoals, exceptions and caveats in the reward
function, or even know what the right ones are.
Giving goals to free-roaming,
“autonomous” robots will be increasingly risky as they become more intelligent,
because the robots will be ruthless in pursuit of their reward function and
will try to stop us from switching them off.
The ultimate source of
information about human preferences is human behavior.
Labs are teaching
robots how to learn the preferences of humans who never articulated them and
perhaps aren’t even sure what they want. The robots can learn our desires by
watching imperfect demonstrations and can even invent new behaviors that help
resolve human ambiguity.
Humans aren’t even
remotely rational, because it’s not computationally feasible to be: We can’t
possibly calculate which action at any given moment will lead to the best
outcome trillions of actions later in our long-term future; neither can an AI.
Whereas a reinforcement
learning system figures out the best actions to take to achieve a goal, an
inverse reinforcement learning system deciphers the underlying goal when given
a set of actions.
Inverse reinforcement learning (IRL) is the field of learning
an agent’s objectives, values, or rewards by observing its behavior.
If computers don’t know
what humans prefer — they could do some kind of inverse reinforcement learning
to learn more.
Inverse reinforcement
learning (IRL) is the field of learning an agent's objectives, values, or
rewards by observing its behavior
There is no one
dimension, metric or capability that encapsulates human intelligence. On the
contrary, what we reductively call “intelligence” is in fact a constellation of
capabilities spanning perception, memory, language skills, quantitative skills,
planning, abstract reasoning, decision-making, creativity and emotional depth,
among others.
Given that human
intelligence is vastly multi-dimensional, it stands to reason that artificial
intelligence likewise cannot be reduced to any one specific function or technology.
After all, AI is ultimately nothing more than humanity's effort to replicate
its own cognitive capabilities in machines.
This leads us to perhaps the best
one-sentence definition that we can hope for: AI is best thought of as an
entire field of study oriented around developing computing systems capable of
performing tasks that otherwise would require human intelligence.
Over the years and to
the present, initiatives falling under the broad auspices of artificial
intelligence have included computer vision, speech recognition, natural
language processing, language translation, manipulation of physical objects,
navigation through physical environments, logical reasoning, game-playing,
prediction, long-term planning and continuous learning, among many others.
Computers are certainly
able to make choices based on the data that is available to them, however that
it is very different from what is meant by “judgment.” Judgment is based on
values, and values are learned from life experience.
An artificial intelligence
(in summary, a very advanced computer) still doesn’t experience life as we know
it, and so does not develop what we would call “values,” and therefore we
cannot call the decisions they make judgements.
They have no
significant basis on which to set targets or objectives (they react to
instructions given to them).
The work of the future
will not be exclusively reserved for artificial intelligence, capable of
processing trillions of bits of data in a second, but for a team made up of
both “robots” and human beings, who— expressing the strengths of both— will
manage to act in the best possible way for the common good.
As the pace of advances
in artificial intelligence accelerates, mechanisms must be in place to address
responsibility and transparency. Otherwise, there’s a chance it could run amok.
AI systems learn from
vast amounts of complex, unstructured information and turn it into actionable
insights.
Deep learning is ideal
for finding patterns and using them to recognize, categorize, and predict
things These systems can still make
mistakes, stemming either from limitations in the training set or from an
unknown bias in the algorithms, caused by a lack of understanding of the way
the neural network models are operating.
That is the main
difference between humans and robots—instinctively knowing what is right and
wrong. Moral intelligence is not something that has been mastered with AI, and
in all honesty, it’s debatable whether it ever will be.
Even if an AI system
can take over the bookkeeping for a company, an accountant will still be needed
to check for errors.
“Algorithmic bias” is when seemingly harmless
programming takes on the prejudices of its creators or the data it’s fed, as
highlighted in the examples at the start of this article. However, and putting
it simply, machine bias is human bias.
“Algorithms’ are only
as good as the data that gets packed into them.. And if a lot of discriminatory data gets
packed in, if that’s how the world works, and the algorithm is doing nothing
but sucking out information about how the world works, then the discrimination
is perpetuated.
Algorithms are written
by humans, who are inherently biased — and that can seep into the way they
frame the analysis that underlies their code. Artificial intelligence software
is trained on data that contains all kinds of human biases, which can then
appear in its own inferences. Algorithms
risk reproducing or even amplifying human biases in certain cases.
The key to dealing with the issue
depends on technology companies, engineers, and developers all taking visible
steps to safeguard against accidentally creating an algorithm that
discriminates. By carrying out algorithmic auditing and maintaining
transparency at all times, we can be confident of keeping bias out of our AI
algorithms.
AI can be divided into
two main categories: transparent and opaque.
Transparent systems use
self-learning algorithms that can be audited to show their workings and how
they have arrived at their decision. Opaque systems do not. Instead, it works
things out for itself and cannot show its reasoning.
Simpler forms of
automated decision-making, such as predictive analytics, tend to follow fairly
transparent models. Opaque AI can form deep insights beyond the dreams of its
developers, but in exchange it takes away a degree of control from the
developer.
For example, with Google speech-to-text recognition or Facebook
facial recognition, the algorithms are mostly accurate, but cannot explain
their workings.
The ethical programming
of robots must also be a priority—not just the technological.
Android is a great
system to develop your apps. For machinery or equipment that needs only a
specific program to run on them, an Android panel PC would be ideal to operate
those processes.
Unlike traditional
cyberattacks that are caused by “bugs” or human mistakes in code, AI attacks
are enabled by inherent limitations in the underlying AI algorithms that
currently cannot be fixed. AI attacks
fundamentally expand the set of entities that can be used to execute
cyberattacks.
For the first time, physical objects can be now used for
cyberattacks (e.g., an AI attack can transform a stop sign into a green light
in the eyes of a self-driving car by simply placing a few pieces of tape on the
stop sign itself). Data can also be weaponized in new ways using these attacks,
requiring changes in the way data is collected, stored, and used.
The terrorist of the
21st century will not necessarily need bombs, uranium, or biological weapons.
He will need only electrical tape and a good pair of walking shoes. Placing a
few small pieces of tape inconspicuously on a stop sign at an intersection, he can
magically transform the stop sign into a green light in the eyes of a
self-driving car.
Done at one sleepy intersection, this would cause an
accident. Done at the largest intersections in leading metropolitan areas, it
would bring the transportation system to its knees. It’s hard to argue with
that type of return on a $1.50 investment in tape.
The artificial
intelligence algorithms that are being called upon to deliver this future have
a problem: by virtue of the way they learn, they can be attacked and controlled
by an adversary.
This vulnerability is
due to inherent limitations in the state-of-the-art AI methods that leave them
open to a devastating set of attacks that are as insidious as they are
dangerous. Under one type of attack, adversaries can gain control over a
state-of-the-art AI system with a small but carefully chosen manipulation,
ranging from a piece of tape on a stop sign1 to a sprinkling of digital dust
invisible to the human eye on a digital image.
Under another, adversaries can poison AI systems, installing backdoors
that can be used at a time and place of their choosing to destroy the system.
Whether it’s causing a car to careen through a red light, deceiving a drone
searching for enemy activity on a reconnaissance mission, or subverting content
filters to post terrorist recruiting propaganda on social networks, the danger
is serious, widespread, and already here.
AI attacks are
different from the cybersecurity problems that have dominated recent headlines.
These attacks are not bugs in code that can be fixed—they are inherent in the
heart of the AI algorithms. As a result, exploiting these AI vulnerabilities
requires no “hacking” of the targeted system. In fact, attacking these critical
systems does not even always require a computer.
This is a new set of
cybersecurity problems, and cannot be solved with the existing cybersecurity
and policy toolkits governments and businesses have assembled. Instead,
addressing this problem will require new approaches and solutions.
Machine learning
algorithms powering AI systems “learn” by extracting patterns from data.
Poisoning attack—can
stop an AI system from operating correctly in situations, or even insert a
backdoor that can later be exploited by an adversary.
AI attacks can be
pernicious and difficult to detect. Attacks can be completely invisible to the
human eye.
An artificial
intelligence attack (AI attack) is the purposeful manipulation of an AI system
with the end goal of causing it to malfunction.
They strike at different weaknesses in the underlying algorithms:
Input Attacks:
manipulating what is fed into the AI system in order to alter the output of the
system to serve the attacker’s goal. Because at its core every AI system is a
simple machine—it takes an input, performs some calculations, and returns an
output—manipulating the input allows attackers to affect the output of the
system.
Poisoning Attacks:
corrupting the process during which the AI system is created so that the
resulting system malfunctions in a way desired by the attacker. One direct way
to execute a poisoning attack is to corrupt the data used during this process.
This is because the state-of-the-art machine learning methods powering AI work
by “learning” how to do a task, but they “learn” from one source and one source
only: data. Data is its water, food, air, and true love. Poison the data,
poison the AI system. Poisoning attacks can also compromise the learning
process itself.
As AI systems are
integrated into critical commercial and military applications, these attacks
can have serious, even life-and-death, consequences. AI attacks can be used in
a number of ways to achieve a malicious end goal:
Cause Damage: the
attacker wants to cause damage by having the AI system malfunction. An example
of this is an attack to cause an autonomous vehicle to ignore stop signs. By
attacking the AI system so that it incorrectly recognizes a stop sign as a
different sign or symbol, the attacker can cause the autonomous vehicle to
ignore the stop sign and crash into other vehicles and pedestrians.
Hide Something: the
attacker wants to evade detection by an AI system.
An example of this is an
attack to cause a content filter tasked with blocking terrorist propaganda from
being posted on a social network to malfunction, therefore letting the material
propagate unencumbered.
Degrade Faith in a
System: the attacker wants an operator to lose faith in the AI system, leading
to the system being shut down. An example of this is an attack that causes an
automated security alarm to misclassify regular events as security threats,
triggering a barrage of false alarms that may lead to the system being taken
offline.
For example, attacking a video-based security system to classify a
passing stray cat or blowing tree as a security threat may cause the security
system to be taken offline, therefore allowing a true threat to then evade
detection.
AI attacks exist
because there are fundamental limitations in the underlying AI algorithms that
adversaries can exploit in order to make the system fail. Unlike traditional
cybersecurity attacks, these weaknesses are not due to mistakes made by
programmers or users.
They are just shortcomings of the current
state-of-the-art methods. Put more bluntly, the algorithms that cause AI
systems to work so well are imperfect, and their systematic limitations create
opportunities for adversaries to attack.
Many current AI systems
are powered by machine learning, a set of techniques that extract information
from data in order to “learn” how to do a given task. A machine learning
algorithm “learns” analogously to how humans learn. Humans learn by seeing many
examples of an object or concept in the real world, and store what is learned
in the brain for later use.
Machine learning algorithms “learn” by seeing many
examples of an object or concept in a dataset, and store what is learned in a
model for later use. In many if not most AI applications based on machine
learning, there is no outside knowledge or other magic used in this process: it
is entirely dependent on the dataset and nothing else.
The key to
understanding AI attacks is understanding what the “learning” in machine
learning actually is, and more importantly, what it is not. Recall that machine
learning “learns” by looking at many examples of a concept or object in a
dataset.
More specifically, it uses algorithms that extract and generalize
common patterns in these examples. These patterns are stored within the model.
If the algorithm sees enough examples in all of the different ways the target
naturally appears, it will learn to recognize all the patterns needed to
perform its job well.
A significant
vulnerability is that it is wholly
dependent on the dataset. Because the dataset is the model’s only source of
knowledge, if it is corrupted or “poisoned” by an attacker, the model learned
from this data will be compromised. Attackers can poison the dataset to stop
the model from learning specific patterns, or more insidiously, to install
secret backdoors that can be used to trick the model in the future.
Backdoor attack is,
where the attacker's goal is to create a backdoor into a learning-based
authentication system, so that he can easily circumvent the system by
leveraging the backdoor. The victim’s learning system will be misled to
classify the backdoor instances as a target label specified by the adversary.
Adversaries can
introduce backdoors or “trojans” in machine learning models by poisoning
training sets with malicious samples . The resulting models perform as expected
on normal training and testing data, but behave badly on specific
attacker-chosen inputs. By examining and clustering the neural activations in
the training samples, we can identify which samples are legitimate and which
ones are manipulated by an adversary.
This defence method has shown good results
for known backdoor attacks. To identify a backdoor trigger, you must
essentially find out three unknown variables: which class the trigger was
injected into, where the attacker placed the trigger and what the trigger looks
like
Backdoor trojan
injection is often done in a two-step process to bypass security rules
preventing the upload of files above a certain size. The first phase involves
installation of a dropper—a small file whose sole function is to retrieve a
bigger file from a remote location. It initiates the second phase—the
downloading and installation of the backdoor script on the server.
Once installed,
backdoors are very hard to weed out. Traditionally, detection involves using
software scanners to search for known malware signatures in a server file
system. This process is error prone, however. Backdoor shell files are almost
always masked through the use of alias names and—more significantly—code
obfuscation (sometimes even multiple layers of encryption).
Detection is further
complicated since many applications are built on external frameworks that use
third-party plugins; these are sometimes laden with vulnerabilities or built-in
backdoors. Scanners that rely on heuristic and signature-based rules might not
be able to detect hidden code in such frameworks.
Even if a backdoor is
detected, typical mitigation methods (or even a system reinstallation) are
unlikely to remove it from an application. This is particularly true for
backdoors having a persistent presence in rewritable memory.
Patterns “learned” by
current state-of-the-art machine learning models are relatively brittle. As a
result, the model only works on data that is similar in nature to the data used
during the learning process. If used on data that is even a little different in
nature from the types of variations it saw in the original dataset, the model
may utterly fail.
This is a major limitation attackers can exploit: by
introducing artificial variations the attacker can disrupt the model and control
its behavior based on what artificial pattern is introduced. Because the amount
of data used to build the model is finite but the amount of artificial
variations an attacker can create are infinite, the attacker has an inherent
advantage.
Very small artificial
manipulations chosen in just the right way can break the relatively brittle
patterns the model learned, and have preposterously huge impacts on the model’s
output.
Popular culture has
shaped a widespread but erroneous belief that machine learning actually
“learns” in a human sense of the word. Humans are good at truly learning
concepts and associations. If a stop sign is distorted or defaced with graffiti
or dirt, even a human who has never seen graffiti or a dirty stop sign would
still reliably and consistently identify it as a stop sign, and certainly would
not mistake it for an entirely different object altogether, such as a green
light.
But we now know current AI systems do not work in the same way. Even a
model that can almost perfectly recognize a stop sign still has no knowledge of
the concept of a stop sign, or even a sign for that matter, as a human does. It
only knows that certain learned patterns correspond to a label named “stop
sign.”
AI systems can be made
to fail even if they are extremely successful under “normal” conditions.
the most popular
machine learning algorithms powering AI, like neural networks, are referred to
as “black boxes”: we know what goes in, we know what comes out, but we do not
know exactly what happens in between. We cannot reliably fix what we do not
understand.
And for this same reason, it is difficult if not impossible to even tell if a model is being attacked or just doing a bad job. While other data science methods, such as decision trees and regression models, allow for much more explainability and understanding, these methods do not generally deliver the performance that the widely used neural networks are capable of providing.
And for this same reason, it is difficult if not impossible to even tell if a model is being attacked or just doing a bad job. While other data science methods, such as decision trees and regression models, allow for much more explainability and understanding, these methods do not generally deliver the performance that the widely used neural networks are capable of providing.
Decision trees are interpretable – as long as they are short.
The number of terminal nodes increases quickly with depth. The more terminal
nodes and the deeper the tree, the more difficult it becomes to understand the
decision rules of a tree.
Again, Machine learning
works by “learning” relatively brittle patterns that work well but are easy to
disrupt. Contrary to popular belief, machine learning models are not “intelligent”
or capable of truly mimicking human ability on tasks, even tasks they perform
well. Instead, they work by learning brittle statistical associations that are
relatively easy to disrupt. Attackers can exploit this brittleness to craft
attacks that destroy the performance of an otherwise excellent model.
Dependence solely on
data provides a main channel to corrupt a machine learning model. Machine
learning “learns” solely by extracting patterns from a set of examples known as
a dataset. Unlike humans, machine learning models have no baseline knowledge
that they can leverage—their entire knowledge depends wholly on the data they
see. Poisoning the data poisons the AI system. Attacks in this vein essentially
turn an AI system into a Manchurian candidate that attackers can activate at a
time of their choosing.
The black box nature of
the state-of-the-art algorithms makes auditing them difficult. Relatively
little is understood about how the widely used state-of-the-art machine
learning algorithms, such as deep neural networks, learn and work—even today
they are still in many ways a magical black box.
This makes it difficult, if not currently impossible, to tell if a machine learning model has been compromised, or even if it is being attacked or just not performing well. This characteristic sets AI attacks apart from traditional cybersecurity problems where there are clear definitions of vulnerabilities, even if they are hard to find.
This makes it difficult, if not currently impossible, to tell if a machine learning model has been compromised, or even if it is being attacked or just not performing well. This characteristic sets AI attacks apart from traditional cybersecurity problems where there are clear definitions of vulnerabilities, even if they are hard to find.
Taken together, these
weaknesses explain why there are no perfect technical fixes for AI attacks.
These vulnerabilities are not “bugs” that can be patched or corrected as is
done with traditional cybersecurity vulnerabilities. They are deep-seated
issues at the heart of current state-of-the-art AI itself.
Input attacks trigger an
AI system to malfunction by altering the input that is fed into the system.
This is done by adding an “attack pattern” to the input
Input attacks do not
require the attacker to have corrupted the AI system in order to attack it.
Completely state-of-the-art AI systems that are highly accurate and have never
had their integrity, dataset, or algorithms compromised are still vulnerable to
input attacks. And in stark contrast to other cyberattacks, the attack itself
does not always use a computer!
These attacks are
particularly dangerous because the attack patterns do not have to be
noticeable, and can even be completely undetectable. Adversaries can be
surgical, changing just a small aspect of the input in a precise and exact way
to break the patterns learned previously by the model.
For attacks on physical
objects that must be captured by a sensor or camera before being fed into an AI
system, attackers can craft small changes that are just big enough to be
captured by the sensor.
Input attacks on AI
systems are like snowflakes: no two are exactly alike.
Input attack forms can
be characterized along two axes: perceivability and format. Perceivability
characterizes if the attack is perceivable to humans (e.g., for AI attacks on
physical entities, is the attack visible or invisible to the human eye). Format
characterizes if the attack vector is a physical real-world object (e.g., a
stop sign), or a digital asset (e.g., an image file on a computer).
Perceivable attacks can
be crafted to hide in plain sight.
Rather than crafting an
attack to be as small as possible, it may actually be more effective for it to
be large but blend into its surroundings.
Imperceivable attacks
can take many forms. For digital content like images, these attacks can be
executed by sprinkling “digital dust” on top of the target Technically, this dust is in the form of
small, unperceivable perturbations made to the entire target. Each small
portion of the target is changed so slightly that the human eye cannot perceive
the change, but in aggregate, these changes are enough to alter the behavior of
the algorithm by breaking the brittle patterns learned by the model.
These imperceivable
attacks are particularly pernicious from a security standpoint. Unlike visible
attacks, there is no way for humans to observe if a target has been
manipulated. This poses an additional barrier to detecting these attacks.
Imperceivable attacks
are highly applicable to targets that the adversary has full control over, such
as digital images or manufactured objects. This allows the attacker unfettered
and, for all practical purposes, unaltered distribution of the content without
detection.
An input attack is
relatively easy to craft if the attacker has access to the AI model being
attacked. Armed with this, the attacker can automatically craft attacks using
simple textbook optimization methods. Attackers can also use Generative
Adversarial Networks (GANs), a method specifically created to exploit
weaknesses in AI models, to craft these attacks.
GANs are basically made
up of a system of two competing neural network models which compete with each
other and are able to analyze, capture and copy the variations within a
dataset. In GANs, there is a generator and a discriminator. The generator tries
to fool the discriminator, and the discriminator tries to keep from being
fooled.
The Generator generates fake
samples of data(be it an image, audio, etc.) and tries to fool the
Discriminator. The Discriminator, on the other hand, tries to distinguish
between the real and fake samples. The Generator and the Discriminator are both
Neural Networks and they both run in competition with each other in the
training phase.
The steps are repeated several times and in this, the Generator
and Discriminator get better and better in their respective jobs after each
repetition. GANs can create images that look like photographs of human faces,
even though the faces don't belong to any real person.
The model itself is
just a digital file living on a computer, no different from an image or
document, and therefore can be stolen like any other file on a computer.
Because models are not always seen as highly sensitive assets, the systems
holding these models may not have high levels of cybersecurity protection.
Even in cases where the
attacker does not have the model, it is still possible to mount an input
attack. If attackers have access to the dataset used to train the model, they
can use it to build their own copy of the model, and use this “copy model” to
craft their attack.
Like models themselves, datasets are made widely available
as part of the open source movement, or could similarly be obtained by hacking
the system storing this dataset. In an even more restrictive setting where the
dataset is not available, attackers could compile their own similar dataset,
and use this similar dataset to build a “copy model” instead.
In an increasingly more
restrictive case where attackers do not have access to the model or the
dataset, but have access to the output of the model, they can still craft an
attack. This situation occurs often in practice, with businesses offering
Artificial Intelligence as a Service via a public API.
This service gives users the output of an AI
model trained for a particular task, such as object recognition. While these
models and their associated datasets are kept private, attackers can use the
output information from their APIs to craft an attack. This is because this
output information replaces the need for having the model or the dataset.
In the hardest case
where nothing about the model, its dataset, or its output is available to the
attacker, the attacker can still try to craft attacks by brute force
trial-and-error. For example, an attacker trying to beat an online content
filter can keep generating random attack patterns and uploading the content to
see if it is removed. Once a successful attack pattern is found, it can be used
in future attacks.
Poisoning attacks are
the second class of AI attacks. In poisoning attacks, the attacker seeks to
damage the AI model itself so that once it is deployed, it is inherently flawed
and can be easily controlled by the attacker. Unlike input attacks, model
poisoning attacks take place while the model is being learned, fundamentally
compromising the AI system itself.
Poisoning attacks
involve attackers intentionally injecting false data into the network or
infrastructure. This allows them to
steal sensitive data or perform other malicious activities.
Different types of
poisoning attacks.
Web cache poisoning
Caching is a common
term that refers to storing commonly requested data to save time and minimize
network traffic.
Web cache poisoning
involves adding notorious websites to the cache by making requests from an
attacker-controlled system.
This means that when
the victim’s system makes a request, the notorious websites may be served.
These websites may
contain links to other sites that host malicious software. When the victim
unknowingly accesses the site, the systems are infected with malicious
software.
Web cache poisoning is
an advanced technique whereby an attacker exploits the behavior of a web server
and cache so that a harmful HTTP response is served to other users.
Fundamentally, web
cache poisoning involves two phases. First, the attacker must work out how to
elicit a response from the back-end server that inadvertently contains some
kind of dangerous payload. Once successful, they need to make sure that their
response is cached and subsequently served to the intended victims.
DNS cache poisoning
This attack aims at
exploiting vulnerabilities to direct web traffic to fraudulent servers, instead
of the legitimate ones.
The DNS converts
human-readable websites to IP addresses that can be understood and processed by
computers.
Computers, internet
service providers, and routers have their own DNS caches to refer to.
Attackers may poison
the DNS servers with incorrect entries to perform a DNS cache poisoning attack.
This would mean that a
legitimate website would be associated with a malicious IP address, causing the
computer to redirect the victim to an attacker-controlled site.
This may also spread to
other DNS servers, updating them with the incorrect information.
DNS spoofing, also
referred to as DNS cache poisoning, is a form of computer security hacking in
which corrupt Domain Name System data is introduced into the DNS resolver's
cache, causing the name server to return an incorrect result record, e.g. an IP
address. This results in traffic being diverted to the attacker's computer (or
any other computer).
ARP cache poisoning
When an attacker
modifies the Media Access Control (MAC) address to update the system’s ARP
cache with false ARP request and response packets, the attack is called ARP
poisoning.
The Address Resolution
Protocol (ARP) associates a physical address of a network interface to an IP
address.
Sending false ARP
response causes a device to update its cache with it, to be used for
transaction routing.
This attack can cause
traffic to be routed to attacker-controlled systems allowing the compromise of
sensitive data. Usually, this form of attack remains undetected by the victim.
ARP poisoning is sending
fake MAC addresses to the switch so that it can associate the fake MAC
addresses with the IP address of a genuine computer on a network and hijack the
traffic
Model poisoning
This is a type of
attack launched on artificial intelligence and machine learning systems.
Attackers influence the
training datasets used to manipulate the results according to their needs.
With the rise in AI and
machine learning globally, notorious actors will find more opportunities to
exploit.
Such vulnerabilities
must be considered while designing systems to minimize incidents of model
poisoning.
The fact that you are
using a large database is a two-way street, it is an opportunity for the
adversary to inject poison into the database.
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.
To poison an AI system,
the attacker must compromise the learning process in a way such that the model
fails on certain attacker-chosen inputs, or “learns” a backdoor that the
attacker can use to control the model in the future. One motivation is to
poison a model so that it fails on a particular task or types of input. For
example, if a military is training an AI system to detect enemy aircraft, the
enemy may try to poison the learned model so that it fails to recognize certain
aircraft.
Data is a major avenue
through which to execute a poisoning attack. Because information in the dataset
is distilled into the AI system, any problems in the dataset will be inherited
by the model trained with it. Data can be compromised in multiple ways. One way
is to corrupt an otherwise valid dataset, By switching valid data with poisoned
data, the machine learning model underpinning the AI system itself becomes
poisoned during the learning process.
In normal machine
learning the learning algorithm extracts
patterns from a dataset, and the “learned” knowledge is stored in the machine
learning model—the brain of the system. In a poisoning attack , the attacker
changes the training data to poison the learned model.
A way to compromise data in order to execute a
poisoning attack is to attack the dataset collection process, the process in
which data is acquired. This effectively poisons the data from the start,
rather than changing an otherwise valid dataset
The ability to attack
the dataset collection process represents the beginning of a new era of
attitudes towards data. Today, data is generally viewed as a truthful
representation of the world, and has been successfully used to teach AI systems
to perform tasks within this world.
As a result, data collection practices
today resemble a dragnet: everything that can be collected is collected. The
reason for this is clear: AI is powered almost entirely by data, and having
more data is generally correlated with better AI system performance.
However, now that the
dataset collection process itself may be attacked, AI users can no longer
blindly trust that the data they collect is valid. Data represents the state of
something in the world, and this state can be altered by an adversary.
This
represents a new challenge: even if data is collected with uncompromised
equipment and stored securely, what is represented in the data itself may have
been manipulated by an adversary in order to poison downstream AI systems. This
is the classic misinformation campaign updated for the AI age.
In the face of AI
attacks, today’s dragnet data collection practices may soon be a quaint relic
of a simpler time. If an AI user’s data collection practices are known by an
adversary, the adversary can influence the collection process in order to
attack the resulting AI system through a poisoning attack. As a result, the age
of AI attacks requires new attitudes towards data that are in stark contrast to
current data collection practices.
To implement a
poisoning attack, the attacker targets one of the assets used in the learning
process: either the dataset used to learn the model, the algorithm used to
learn the model, or the model itself. Regardless of the method, the end result
is a model that has a hidden weakness or backdoor that can later be attacked by
exploiting this known weakness.
The most direct way to
poison a model is via the dataset. As previously discussed, the model is wholly
dependent on the dataset for all of its knowledge: poison the dataset, poison
the model. An attacker can do this by introducing incorrect or mislabeled data
into the dataset.
Because the machine learning algorithms learn a model by
recognizing patterns in this dataset, poisoned data will disrupt this learning
process, leading to a poisoned model that may, for example, have learned to
associate patterns with mislabeled outcomes that serve the attacker’s purpose.
Alternatively, the adversary can change its behavior so that the data collected
in the first place will be wrong.
Discovering poisoned
data in order to stop poisoning attacks can be very difficult due to the scale
of the datasets. Datasets routinely contain millions of samples. These samples
many times come from public sources rather than private collection efforts.
Even in the case when the dataset is collected privately and verified, an
attacker may hack into the system where the data is being stored and introduce
poisoned samples, or seek to corrupt otherwise valid samples.
Another avenue to execute
a poisoning attack takes advantage of weaknesses in the algorithms used to
learn the model. This threat is particularly pronounced in Federated Learning,
a new cutting-edge machine learning algorithm that is emerging.
Federated
Learning is a method to train machine learning models while protecting the
privacy of an individual’s data. Rather than centrally collecting potentially
sensitive data from a set of users and then combining their data into one
dataset, federated learning instead trains a set of small models directly on
each user’s device, and then combines these small models together to form the
final model.
Because the users’ data never leaves their devices, their privacy
is protected and their fears that companies may misuse their data once collected
are allayed. Federated learning is being looked to as a potentially
groundbreaking solution to complex public policy problems surrounding user
privacy and data, as it allows companies to still analyze and utilize user data
without ever needing to collect that data.
Federated learning is a machine learning technique that trains
an algorithm across multiple decentralized edge devices or servers holding
local data samples, without exchanging their data samples. This approach stands
in contrast to traditional centralized machine learning techniques where all
data samples are uploaded to one server, as well as to more classical
decentralized approaches which assume that local data samples are identically
distributed.
However, there is a
weakness in the federated learning algorithm that leaves it vulnerable to model
poisoning attacks. As attackers have control over their own data on their
device, they can manipulate both the data and algorithm running on their device
in order to poison the model.
A final avenue to
poison a model is to simply replace a legitimate model with a poisoned one.
This is simple to do with a traditional cyberattack. Once trained, a model is
just a file living within a computer, no different than an image or PDF
document.
Attackers can hack the systems holding these models, and then either
alter the model file or replace it entirely with a poisoned model file. In this
respect, even if a model has been correctly trained with a dataset that has
been thoroughly verified and found not poisoned, this model can still be
replaced with a poisoned model at various points in the distribution pipeline.
AI systems are already
integrated into many facets of society, and increasingly so every day. For
industry and policy makers, the five most pressing vulnerable areas are content
filters, military systems, law enforcement systems, traditionally human-based
tasks being replaced with AI, and civil society.
Content filters are society’s digital immune
systems. By removing foreign assets that are dangerous, illegal, or against the
terms-of-service of a particular application, they keep platforms healthy and
root out infections.
AI-based content
filters allow website and platform operators to efficiently and effectively
scan the millions of images uploaded each minute for illicit content, and
immediately destroy offending images. In addition to custom tools companies
built for their own use, this detection software was eventually provided via
the Software as a Service (SaaS) distribution model, where one large company
like Microsoft offered an API-based content filter that website owners could
use instead of building their own.
In an environment with
AI attacks, content filters cannot be trusted to perform their job. Because
content filters are now being used as the first and, in many respects, only
line of defense against terrorism, extremism, and political attack on the
Internet, important parts of society would be left defenseless in the face of
successful AI attacks. These attacks give adversaries free reign to employ
these platforms with abandon, and leave these societal platforms unprotected
when protection is needed more than ever.
Because content
filtering is applied to digital assets, it is particularly well suited to the
“imperceivable” input attacks. Further, unlike many other cyberattacks in which
a large-scale theft of information or system shutdown makes detection evident,
attacks on content filters will not set off any alarms. The content will simply
fall through the filter unnoticed.
Military applications
of AI will be a critical component of the next major war. The U.S. Department
of Defense has recently made the integration of artificial intelligence and
machine learning into the military a high priority with its creation of the
Joint Artificial Intelligence Center (JAIC). In India our brain dead 75 year old NSA Ajit
Doval roams around like James Bond with
dyed hair and shades.
The JAIC has “the
overarching goal of accelerating the delivery of AI-enabled capabilities,
scaling the Department-wide impact of AI, and synchronizing DoD AI activities
to expand Joint Force advantages.”
The Pentagon’s Project
Maven applies AI to the analysis of full motion video (FMV), highlighting the
military’s desire to use AI to identify ground-based assets. The Defense Advanced Research Projects Agency
(DARPA) and Air Force Research Laboratory’s (AFRL) release of the Moving and
Stationary Target Acquisition and Recognition dataset is aimed at building AI
techniques to classify and recognize targets of interest. Attacking these military AI systems is the
logical successor of General Patton’s FUSAG.
The contested
environments in which the military operates creates a number of unique ways for
adversaries to craft attacks against these military systems, and
correspondingly, a number of unique challenges in defending against them.
First, adversaries may
capture the physical equipment, including drones and weapon systems, on which
AI systems will live.
The loss and capture of this equipment will be routine in
future conflicts, and the threat this poses to AI systems will grow as more and
more AI-enabled systems are deployed in the field or on equipment that can be
captured by an adversary. This trend will further increase with the
proliferation of “edge computing” in military contexts.
Edge computing is
defined by bringing compute capabilities to where the mission is taking place
in the field, and means that data does not have to travel back to a data center
to be processed. “In the military world, it is termed “ tip of the spear “..
JUST FOUR RUSSIAN
FIGHTER JETS WITH BATTLE MANAGEMENT SYSTEMS CAN SPLASH ALL 36 RAFALE FIGHTER
JETS WHICH INDIA IS BUYING AT A HUMONGOUS COST ( FOR MORE PERCENTAGE KICK BACKS
IN FOREIGN ACCOUNTS ).
The US Army is already
seeing results from its experimental tactical cloud when it comes to virtual
training
This virtual training
is part of the Army's Network Cross Functional Team's tactical cloud pilot
that's experimenting with using cloud capabilities on battlefield networks.
An artificial
intelligence arms race is a competition between two or more states to have its
military forces equipped with the best "artificial intelligence" (AI)
The Russian ‘Prometheus’,
the S-500 , the advanced air and missile defense system is coming ..
The S-500 system is
reported to be able to cover a radius of to 600km defending against ballistic
missiles that are launched from a range of up to 3,500 km. It will be able to
engage aircraft, at a range up to 400km.
The S-500 has an operational radius of 600 kilometers, and the
capability to simultaneously engage 10 targets, including aircraft, ballistic
missiles, and even high speed hypersonic aerial target..
With a response time of three seconds, S-500
is three-times faster to respond to new threats, over the S-400. Since it will
be able to intercept targets at altitudes of 200 km above the earth atmosphere,
S-500 is also considered to become an anti-satellite weapon.
Modi is buying
useless and expensive NATO Jewish anti-missile system to wrangle a Nobel prize-these systems have ZERO percent success against incoming hypersonic missiles. Even Iranians screwed America.
In edge computing,
rather than sending data to a centralized cloud infrastructure for processing,
the data and AI algorithms are stored and run directly on the devices deployed
in the field. The US DoD has made the development of “edge computing” a
priority, as the bandwidth needed to support a cloud-based AI paradigm is
unlikely to be available in battlefield environments.
This
reality will require these systems to be treated with care. Just as the
military recognizes the threat created when a plane, drone, or weapon system is
captured by an enemy, these AI systems must be recognized and treated as a
member of this same protected class so that the systems are not compromised if
captured by an enemy.
Second, the military’s
unique domain necessitates the creation of similarly unique datasets and tools,
both of which are likely to be shared within the military at-large. Because
these datasets and systems will be expensive and difficult to create, there
will be significant pressures to share them widely among different applications
and branches. However, when multiple AI systems depend on this small set of
shared assets, a single compromise of a dataset or system would expose all
dependent systems to attack.
Despite this risk,
shared datasets are expected to become widespread within military AI
operations. The DoD has already stated that the foundation for its AI efforts
“includes shared data, reusable tools, frameworks, libraries, and standards…
The initial DoD AI applications, which focus
on extracting information from aerial images and video, illustrate why sharing
datasets is attractive. These datasets are critical to developing a set of
powerful AI systems, but are expensive—both in terms of time and money—to
collect and prepare.40 As a result,
there is a logical desire to share and reuse these datasets across many
different applications rather than creating a separate dataset for each
application.
However, this creates a
single point of vulnerability for system-wide attacks. If this data is hacked
or compromised, every application developed using this data would be
potentially compromised. If a large number of applications depended on this
same shared dataset, this could lead to widespread vulnerabilities throughout
the military.
In the case of input attacks, an adversary would then be easily
able to find attack patterns to engineer an attack on any systems trained using
the dataset. In the case of poisoning attacks, an adversary would only need to
compromise one dataset in order to poison any downstream models that are later
trained using this poisoned dataset.
Further, the process
associated with creating these unique datasets can lead to vulnerabilities that
can be exploited. When building AI-enabled weapons and defense systems, the
individual data samples used to train the models themselves become a secret
that must be protected. However, because this preparation work is exceedingly
time consuming, it may rely on a large number of non-expert labelers or even
outsourced data labeling and preparation services.
This trend has already
manifested itself in the private sector, where firms like Facebook have turned
to outsourced content moderators as well
as in initial military AI efforts. Expected similar trends here could make high
confidence guarantees on data-access restrictions and oversight of proper data
handling, labeling, and preparation difficult to achieve.
While these types of
procedural oversight concerns are not new, best practices have been established
in other fields such as nuclear. However, because of its infancy, these best
practices are lacking in the AI field. Forming these best practices will
require new policies managing data acquisition and preparation.
Beyond the threats
posed by sharing datasets, the military may also seek to re-use and share models
and the tools used to create them. Because the military is a, if not the, prime
target for cyber theft, the models and tools themselves will also become
targets for adversaries to steal through hacking or counterintelligence
operations.
History has shown that computer systems are an eternally vulnerable
channel that can be reliably counted on as an attack avenue by adversaries. By
obtaining the models stored and run on these systems, adversaries can
back-solve for the attack patterns that could fool the systems.
Finally, the military
faces the challenge that AI attacks will be difficult, if not impossible, to
detect in battle conditions. This is because a hack of these systems to obtain
information to formulate an attack would not by itself necessarily trigger a
notification, especially in the case where an attacker is only interested in
reconnaissance aimed at learning the datasets or types of tools being used.
Further, once adversaries develop an attack, they may exercise extreme caution
in their application of it in order to not arouse suspicion and to avoid
letting their opponent know that its systems have been compromised.
Accordingly, attacks may be limited only to situations of extreme importance.
In this respect, there may be no counter-indications to system performance
until after the most serious breach occurs. This is also a problem inherent in
traditional cyberattacks.
Detecting AI attacks in
the face of their rare application would focus on two methods: detecting
intrusions into systems holding assets used to train models, and analysis of
model performance. Traditional intrusion detection methods could be used to
detect if a dataset or resource has been compromised.
If an asset has been
compromised, the AI systems using those assets may have to be shut down or
re-trained. Alternatively, AI attack detection could be based on complex
performance analysis of the system whenever an AI attack is suspected, such as
events surrounding a surprising decrease in AI system performance.
Beyond these defensive
concerns, the military may also choose to invest in offensive AI attack
capabilities.
Without even having an
infrastructure our CJI wants to dive off the deep end into AI.. Our lawyers
turned judges are the bottom dregs of the school cerebral barrel and the
discards of the loser lawyer pool.
Unlike traditional
cybersecurity vulnerabilities, the problems that create AI attacks cannot be
“fixed” or “patched.” Traditional
cybersecurity vulnerabilities are generally a result of programmer or user
error. As a result, these errors can be identified and rectified. In contrast,
the AI attack problem is more intrinsic: the algorithms themselves and their
reliance on data are the problem.
This difference has
significant ramifications for policy and prevention. Mitigating traditional
cybersecurity vulnerabilities deals with fixing “bugs” or educating users in
order to stop adversaries from gaining control or manipulating an otherwise
sound system.
Reflecting this, solutions to cybersecurity problems have focused
on user education, IT department-led policy enforcement, and technical
modifications such as code reviews and bug bounties aimed at finding and
correcting flaws in the code.
However, for AI attacks, a robust IT department
and 90-letter passwords won’t save the day. The algorithms themselves have the
inherent limitations that allow for attack. Even if an AI model is trained to
exacting standards using data and algorithms that have never been compromised,
it can still be attacked. This bears repeating: among the state-of-the-art
methods, there is currently no concept of an “unattackable” AI system.
As such,
protecting against these intrinsic algorithmic vulnerabilities will require a
different set of tools and strategies. This includes both taking steps to make
executing these attacks more difficult, as well as limiting the dependence and
reach of applications built on top of AI systems.
Despite this
fundamental difference, the two are linked in important ways. Many AI attacks
are aided by gaining access to assets such as datasets or model details. In
many scenarios, doing so will utilize traditional cyberattacks that compromise
the confidentiality and integrity of systems, a subject well studied within the
cybersecurity CIA triad.
Traditional confidentiality attacks will enable
adversaries to obtain the assets needed to engineer input attacks. Traditional
integrity attacks will enable adversaries to make the changes to a dataset or
model needed to execute a poisoning attack. As a result, traditional
cybersecurity policies and defense can be applied to protect against some AI
attacks.
While AI attacks can certainly be crafted without accompanying
cyberattacks, strong traditional cyber defenses will increase the difficulty of
crafting certain attacks.
Any cyber vulnerability
can be turned into a cyber weapon. The same holds true for AI attacks,
especially in the military and intelligence contexts. The potential promise of
this is based on the belief that other countries may begin to integrate AI and
machine learning into military decision making pipelines and automated weapons.
Address space layout
randomization (ASLR) is a computer security technique involved in preventing
exploitation of memory corruption vulnerabilities. In order to prevent an
attacker from reliably jumping to, for example, a particular exploited function
in memory,
ASLR randomly arranges the address space positions of key data areas
of a process, including the base of the executable and the positions of the
stack, heap and libraries.
As it’s designed for
battery-powered devices, Android uses fewer system resources than most other
operating systems, so it requires a less powerful processor. This design saves
energy and allows Android to run on smaller devices.
Android panel PCs are a
lower-cost alternative to traditional Windows panel PCs for several reasons.
Android computers typically include Arm processors, which are more
cost-effective than Intel processors. Android industrial computers aren’t
subject to the operating-system license fees required with Windows-based
computers.
Android industrial
computers don’t require additional storage capacity and memory requirements
that are necessary for Windows-based computers. The significant cost savings
associated with these differences is driving the industrial factory automation
industry toward Android-based computing.
Since units operating
on Android are also generally smaller with less-demanding computing
requirements, operations can reduce costs by deploying just the right-sized
device needed. . Machine learning can’t happen without a datum first. It is
what the machine acts upon. It’s like an input to the machine, and without
input, there is no output.
There are a number of data
types in machine learning. The data can be a table with numerical values in it,
videos, text, audio, images, or from many other probable sources. Whatever data
type it is, different methods (such as speech signal frequency, the value of
RBG per pixel in a picture, etc) can be used to encode them. This improves the
data and makes it a high dimension.
To acquire the data,
there are also a number of methods that are also used. A website with a large
database, such as Google AI can be used. Y
Some programmable
functions of AI systems include planning, learning, reasoning, problem solving,
and decision making.
Artificial intelligence
systems are powered by algorithms, using techniques such as machine learning,
deep learning and rules. Machine learning algorithms feed computer data to AI
systems, using statistical techniques to enable AI systems to learn. Through
machine learning, AI systems get progressively better at tasks, without having
to be specifically programmed to do so.
AI can encompass
anything from Google's search algorithms, to IBM's Watson, to autonomous
weapons. AI technologies have transformed the capabilities of businesses
globally, enabling humans to automate previously time-consuming tasks, and gain
untapped insights into their data through rapid pattern recognition.
All artificial
intelligence systems - real and hypothetical - fall into one of three types:--
Artificial narrow
intelligence (ANI), which has a narrow range of abilities;
Artificial general
intelligence (AGI), which is on par with human capabilities; or
Artificial
superintelligence (ASI), which is more capable than a human.
Artificial Narrow
Intelligence (ANI) / Weak AI / Narrow AI
Artificial narrow
intelligence (ANI), also referred to as weak AI or narrow AI, is the only type
of artificial intelligence we have successfully realized to date. Narrow AI is
goal-oriented, designed to perform singular tasks - i.e. facial recognition,
speech recognition/voice assistants, driving a car, or searching the internet -
and is very intelligent at completing the specific task it is programmed to do.
While these machines
may seem intelligent, they operate under a narrow set of constraints and
limitations, which is why this type is commonly referred to as weak AI. Narrow
AI doesn't mimic or replicate human intelligence, it merely simulates human
behaviour based on a narrow range of parameters and contexts.
Consider the speech and
language recognition of the Siri virtual assistant on iPhones, vision
recognition of self-driving cars, and recommendation engines that suggest
products you make like based on your purchase history. These systems can only
learn or be taught to complete specific tasks.
Narrow AI has
experienced numerous breakthroughs in the last decade, powered by achievements
in machine learning and deep learning. For example, AI systems today are used
in medicine to diagnose cancer and other diseases with extreme accuracy through
replication of human-esque cognition and reasoning.
Narrow AI's machine
intelligence comes from the use of natural language processing (NLP) to perform
tasks. NLP is evident in chatbots and similar AI technologies. By understanding
speech and text in natural language, AI is programmed to interact with humans
in a natural, personalised manner.
Narrow AI can either be
reactive, or have a limited memory. Reactive AI is incredibly basic; it has no
memory or data storage capabilities, emulating the human mind's ability to
respond to different kinds of stimuli without prior experience. Limited memory
AI is more advanced, equipped with data storage and learning capabilities that
enable machines to use historical data to inform decisions.
Most AI is limited
memory AI, where machines use large volumes of data for deep learning. Deep
learning enables personalised AI experiences, for example, virtual assistants
or search engines that store your data and personalise your future experiences.
Examples of narrow
AI:--
Rankbrain by Google /
Google Search
Siri by Apple, Alexa by
Amazon, Cortana by Microsoft and other virtual assistants
IBM's Watson
Image / facial
recognition software
Disease mapping and
prediction tools
Manufacturing and drone
robots
Email spam filters /
social media monitoring tools for dangerous content
Entertainment or
marketing content recommendations based on watch/listen/purchase behaviour
Self-driving cars
Artificial General
Intelligence (AGI) / Strong AI / Deep AI
Artificial general
intelligence (AGI), also referred to as strong AI or deep AI, is the concept of
a machine with general intelligence that mimics human intelligence and/or
behaviours, with the ability to learn and apply its intelligence to solve any
problem. AGI can think, understand, and act in a way that is indistinguishable
from that of a human in any given situation.
AI researchers and
scientists have not yet achieved strong AI. To succeed, they would need to find
a way to make machines conscious, programming a full set of cognitive
abilities. Machines would have to take experiential learning to the next level,
not just improving efficiency on singular tasks, but gaining the ability to
apply experiential knowledge to a wider range of different problems.
Strong AI uses a theory
of mind AI framework, which refers to the ability to discern needs, emotions,
beliefs and thought processes of other intelligent entitles. Theory of mind
level AI is not about replication or simulation, it's about training machines
to truly understand humans.
The immense challenge
of achieving strong AI is not surprising when you consider that the human brain
is the model for creating general intelligence. The lack of comprehensive
knowledge on the functionality of the human brain has researchers struggling to
replicate basic functions of sight and movement.
Fujitsu-built K, one of
the fastest supercomputers, is one of the most notable attempts at achieving
strong AI, but considering it took 40 minutes to simulate a single second of
neural activity, it is difficult to determine whether or not strong AI will be
achieved in our foreseeable future. As image and facial recognition technology
advances, it is likely we will see an improvement in the ability of machines to
learn and see.
Artificial super
intelligence (ASI), is the hypothetical AI that doesn't just mimic or
understand human intelligence and behaviour; ASI is where machines become
self-aware and surpass the capacity of human intelligence and ability.
Superintelligence has
long been the muse of dystopian science fiction in which robots overrun,
overthrow, and/or enslave humanity. The concept of artificial superintelligence
sees AI evolve to be so akin to human emotions and experiences, that it doesn't
just understand them, it evokes emotions, needs, beliefs and desires of its
own.
In addition to
replicating the multi-faceted intelligence of human beings, ASI would
theoretically be exceedingly better at everything we do; math, science, sports,
art, medicine, hobbies, emotional relationships, everything. ASI would have a
greater memory and a faster ability to process and analyse data and stimuli.
Consequently, the decision-making and problem solving capabilities of super
intelligent beings would be far superior than those of human beings.
The potential of having
such powerful machines at our disposal may seem appealing, but the concept
itself has a multitude of unknown consequences. If self-aware super intelligent
beings came to be, they would be capable of ideas like self-preservation. The
impact this will have on humanity, our survival, and our way of life, is pure
speculation.
Autonomous weapons are
AI systems programmed to kill. In the hands of the wrong person, autonomous
weapons could inadvertently lead to an AI war, and mass casualties, potentially
even the end of humanity. Such weapons may be designed to be extremely
difficult to “turn off”, and humans could plausibly, rapidly lose control. This
risk is prevalent even with narrow AI, but grows exponentially as autonomy
increases.
AI could be programmed
to do something beneficial, but develop a destructive method for achieving its
goal.
It can be difficult to
program a machine to complete a task, when you don’t carefully and clearly
outline your goals. Consider you ask an intelligent car to take you somewhere
as fast as possible. The instruction “as fast as possible” fails to consider
safety, road rules, etc. The intelligent car may successfully complete its
task, but what havoc may it cause in the process?
The line between
computer programs and AI is opaque. Mimicking narrow elements of human
intelligence and behaviour is relatively easy, but creating a machine version
of human consciousness is a totally different story.
One of the key
factors of AI systems is the ability to forget knowledge that is not useful. K
Online retailers like
Amazon are using A.I. to automate many of the steps that come between placing
an order on your smartphone and receiving a package on your doorstep.
Artificial Idiots: From
a cybersecurity standpoint, AI throws up some serious questions. You’re
essentially leaving systems and entire processes, at times, on autopilot.
Systems are fed with huge amounts of data so that they can learn trends and
formats from that data and act accordingly.
But what if a malicious hacker or
insider fed in malicious and directed data to make the AI infrastructure think
and behave in a particular way? The cybersecurity world has often talked about
how humans are the weakest link. In situations like these, computers too become
the weakest link.
AI in the Wrong Hands:
If machines fall into the wrong hands they could be used to commit new-age
terrorism, automated cyber-attacks and other vicious things limited only by the
extent of a malicious individual’s imagination. Researchers have found ways to
manipulate a speaker’s speech into new speech that’s also lip synced. So you
could actually make someone say things they never actually said and that too in
their own voice tone6. Fake news would hit record levels.
Too Much Data:
There is little doubt
that the quantities of data now available are indeed large, but that's not the
most relevant characteristic of this new data ecosystem. Analysis of data sets can find new
correlations to "spot business trends, prevent diseases, combat crime and
so on."
Scientists, business executives, practitioners of medicine,
advertising and governments alike regularly meet difficulties with large
data-sets in areas including Internet searches, fintech, urban informatics, and
business informatics. Scientists encounter limitations in e-Science work,
including meteorology, genomics, connectomics, complex physics simulations,
biology and environmental research.
The world's technological per-capita capacity
to store information has roughly doubled every 40 months since the 1980s , as of 2012, every day 2.5 exabytes (2.5×260
bytes) of data are generated. Based on
an IDC report prediction, the global data volume will grow exponentially from
4.4 zettabytes to 44 zettabytes between 2013 and 2020. By 2025, IDC predicts
there will be 163 zettabytes of data. One question for large enterprises is
determining who should own big-data initiatives that affect the entire
organization.
Hadoop was developed,
based on the paper written by Google on the MapReduce system and it applies
concepts of functional programming. You need to code to conduct numerical and
statistical analysis with massive data sets. Some of the languages you should
invest time and money in learning are Python, R, Java, and C++ among others.
...
Finally, being able to think like a programmer will help you become a good
big data analyst. Hadoop is not a programming language. ... Hadoop [which
inclueds Distributed File system[HDFS] and a processing engine [Map
reduce/YARN] ] and its ecosystem are set of tools which helps it large data
processing. To work on Hadoop, you required basic Java and some basic Computer
science understanding.
Cloud computing,
machine learning, Internet of Things (IoT), robotics, blockchain and
cybersecurity are being deployed by utilities in their operations. These
technologies are the big current and future investment avenues for utilities
and they all have one thing in common, they generate a huge amount of data.
Data Privacy: A large
number of AI-enabled devices today gather a significant amount of personal
information on individuals. Take the example of IoT gadgets – health monitors,
fitness gadgets, smart televisions, and a whole host of other IoT gadgets gather
a lot of information on individuals and even the environment around them.
This
data can be very revealing in the wrong hands. A lot of the new-fangled devices
do not address key cybersecurity questions and concerns such as – where is the
data being stored, what protection measures are being used to protect the data,
who controls this data, is the data shared, was consent taken, and other such
difficult but critical questions.
Given that these devices are today part of an
individual’s daily life, it affects the organization where the individual works
as well and changes the whole dynamic of the way cybersecurity needs to be
approached in organizations. When you buy a Fitbit had devise, why do you have
to surrender so much of personal information.. Whose idea is all this?
An applicant tracking
system (ATS) is a software application that enables the electronic handling of
recruitment and hiring needs. An ATS can be implemented or accessed online at
enterprise- or small-business levels, depending on the needs of the
organization; free and open-source ATS software is also available.
An ATS is
very similar to customer relationship management (CRM) systems, but are
designed for recruitment tracking purposes. In many cases they filter
applications automatically based on given criteria such as keywords, skills,
former employers, years of experience and schools attended. This has caused
many to adapt resume optimization techniques similar to those used in search
engine optimization when creating and formatting their résumé
Black box AI is a
problem in machine learning where even the designers of an algorithm cannot
explain why and how it arrived at a specific decision. The fundamental problem
here is: if we cannot figure out how AI has come up with its decisions, how can
we trust AI?
When an AI-based system
makes a decision to deny a claim or hike a premium, customers will want an
explanation. But algorithmic reasoning can be hard to fathom; third-party
suppliers of algorithms may claim their inner workings are proprietary.
When an
algorithm manifests as a “black box,” many may feel skeptical about the
results. For example, an AI system could find a powerful correlation between a
given characteristic and a risk of fraud, but unless an insurer can demonstrate
a causal relationship, the resulting decision may be challenged as
discriminatory.
Individuals affected by
insurance claim rejection or higher premiums to have a “right to an
explanation” of how algorithms are used in decisions.
his trust issue led to
the failure of IBM Watson (especially (Watson for Oncology), one of the
best-known AI innovations in recent times. The main problem with a black box
model is its inability to identify possible biases in the machine learning
algorithms.
Biases can come through prejudices of designers and faulty training
data, and these biases lead to unfair and wrong decisions. Bias can also happen
when model developers do not implement the proper business context to come up
with legitimate outputs.
The same problem is
relevant in the banking industry as well. If regulators pose a question: how AI
has reached at a conclusion with regard to a banking problem, banks should be
able to explain the same.
For example, if an AI solution dealing with
anti-money laundering compliance comes up with an anomalous behaviour or
suspicious activity in a transaction, the bank using the solution should be
able to explain the reason why the solution has arrived at that decision. Such
an audit is not possible with a black box AI model.
It is mandatory that banks
/ Insurance companies should take the necessary oversight to prevent their AI
models from being a black box. They need to ensure that their AI solutions are
trustworthy and have the required transparency to satisfy internal and external
audits. In short, the bright future of AI in banking could be assured only
through explainable AI.
Coders have consciously
designed algorithmic neural networks that can learn from data, but once they've
released their creations into the wild, such neural nets have operated without
programmers being able to see what exactly makes them behave the way they do.
Hence, companies don't find out that their AI is biased until it's too late.
More startups and
companies are offering solutions and platforms based around explainable and
interpretable AI. One of the most interesting of these is Fiddler Labs. Based
in San Francisco and founded by ex-Facebook and Samsung engineers, it offers
companies an AI engine that makes all decision-relevant factors visible (their
unverified claim )
Complex AI algorithms
today are black-boxes; while they can work well, their inner workings are
unknown and unexplainable, which is why we have situations like the Apple
Card/Goldman Sachs controversy.
AI systems must omply
with regulations, such as Articles 13 and 22 of the EU's General Data
Protection Regulation (GDPR), which stipulates that individuals must have recourse
to meaningful explanations of automated decisions concerning them.
Data is certainly not a
new revelation for the insurance industry. Insurance companies have collected
massive amounts of data including customer demographic data, property data,
automotive data, historical claims pay out data, historical applicant risk
data, and sales/pricing data with regards to premiums.
For years, it has used
this information to help guide the most critical business decisions. But the
advancement of technology and innovation has also created an explosion of new
data sources, making it harder for insurance companies to understand good data
from bad data or planted data by hackers .
The old adage of
garbage in, garbage out, is fundamentally applied in AI and machine learning
solutions. After all, AI is only as smart as the data it consumes. What the
insurance industry really needs as it develops its AI strategy is to integrate,
clean, link, and supplement their data so they have an accurate foundation on
which to build the ground truth data that drives real AI innovation.
But even
if the datasets are curated and validated, one single error in the data or in
the training sets used to create predictive models could potentially be
catastrophic. Insurance companies cannot afford that risk.
Data annotation (also
commonly called data labelling) is the initial step in ensuring AI and machine
learning projects can scale with accurate information. This cannot be treated as a cheap
“sweatfactory’ employing outsourced third world illiterates..It provides the
setup for training a machine learning model with what it needs to understand
and how to discriminate against various inputs to come up with accurate
outputs.
Data annotation can be applied to any type of data asset. It can range
from images and video to text and audio – essentially any information that can
be used as the basis of AI training data will benefit from going through the
annotation process. But the machines can’t do this alone, at least not at the
beginning.
Humans are needed to identify and annotate specific data so machines
can learn to identify and classy information. Without these labels, the machine
learning algorithm will have a difficult time computing the necessary
attributes.
When it comes to
processing and analyzing insurance applications, insurance claims, reviewing
medical records for identifying risk, or even gauging customer sentiment,
having high-quality annotated data will help drive success across many areas
where AI is being employed. Below is a list of some of the most popular ways
the insurance claims and insurance underwriting industry is beginning to use AI
to change perceptions of a storied industry.
Conversational AI
Natural language
processing (NLP) is a sub-set of artificial intelligence that deals with
programming software to process and analyze large amounts of data that has been
captured to reflect the ways humans write, speak, or document information.
An insurance carrier
could use NLP to develop a conversational interface/chatbot that can answer
questions from customers or allow them to file a claim from the chat window.
Chatbots enabled by NLP AI deal with recognizing the intent within text data,
as well as responding to customers with text. In essence, the NLP software
needs to “learn” the appropriate text responses to text it receives.
Conversational
Interfaces/Chatbots allow customers to file claims, move payment dates, and get
auto insurance quotes.
Customers can use the
chatbot to get a quote for auto insurance, file a new claim or schedule a
payment.
Chatbots can help
business owners by answering initial questions such as “what is a deductible?”
and “how does the insurance claims process work?”
Customer Service NLP
enables large insurance enterprises to offer:---
Improved customer
service and better buying experiences, especially to millennial customers that
are accustomed to seamless, digital channels.
Insurance carriers
might prefer to develop omni-channel conversational interfaces to make it
easier for their customers to access information about their policies or file
claims through chat messages.
Internet of Things
(IoT)
The Internet of Things
or IoT refers to the larger connected device environment that is emerging from
the combination of electronics and internet capabilities. This includes smart
home devices such as Amazon Echo or Google Home and wearable devices such as
smartwatches or fitness trackers.
Insurance firms can use the data being collected
by all these different devices to personalize insurance products. AI-enabled
IoT devices are seeing the most use in auto insurance; drivers can install
devices in their cars or download an app on their smartphones. With IoT
technology, Insurers track their driving behaviors, feeding this data into an
auto insurer’s predictive analytics algorithm.
Some auto insurance
companies offer customers an IoT sensor that can be placed in cars to collect
data about individual driving habits, such as how hard a driver breaks or how
wide their turns are. The company can use this IoT sensor data combined with
customer demographic data to offer customers an auto insurance rate that is
tailored specifically to them.
The auto insurer could then use this information
to decide whether or not to onboard an applicant and how much the applicant’s
policy should cost. Furthermore, the insurer could adjust the price of an
existing customer’s policy for good or bad driving habits, decreasing or
increasing the premium the customer pays respectively.
Similar use cases can
be found in other insurance verticals. Health insurance providers are very
interested in their customers’ everyday health habits. Data from smart watches
can be used to influence premiums and keep track of anomalies that could cost
them money in the long-term.
Computer Vision
Computer vision is a
type of machine learning that allows computers to “see” entities within images
and videos. In doing so, the user can verify the existence of these entities
and run analytics on them that can inform business decisions.
Home insurers can use
computer vision algorithms to run through satellite images of a property to
determine if the property is prone to flooding or if the property has a
trampoline. It can use this data to determine whether or not to underwrite a
property.
The algorithm may stop at pointing out an element of the property, or
it may include a predictive analytics aspect that recommends the insurer to
approve or reject an applicant based on the risk the property poses.
Insurers can better
manage insurance for catastrophe risk management for homes and businesses
applying for reinsurance. AI can help insurers to evaluate properties on
whether or not they had a pool enclosure, if it is located in an area prone to
floods or fires etc.
What’s more, some auto
insurers allow their customers to take pictures of their car’s damage using
their smartphones. These images are uploaded to the insurer’s system and run
through a computer vision algorithm paired with predictive analytics
capabilities.
Based on the damage, make, and model of the car, the algorithms
provide an estimate on how much the auto insurer should compensate the customer
on their claim. This reduces the time it takes for customers to receive their
pay outs and avoids claims leakage, saving insurers money.
As robots become
cheaper and more capable, they’re moving from their traditional roles on
factory floors into new industries. Collaborative robots or “cobots” too are
being integrated into more types of environments and performing more varied
tasks—from stocking shelves and assisting factory workers to performing
services in the home.
In industries like retail, manufacturing, agriculture,
healthcare, and beyond, robots and cobots, automation, and AI (artificial
intelligence) technologies are having a tremendous impact on the enterprise,
often creating opportunities for organizations to achieve cost savings and
helping industries overcome human labor shortages.
Mamut, an autonomous
robot that captures data about crop fields and individual plants, as well as an
actuator that can replicate a human’s grip on a piece of fruit, thereby opening
the door for robots to pick delicate produce without crushing or damaging it.
Instead of programming
the computer every step of the way, machine learning makes use of learning
algorithms that make inferences from data to learn new tasks.
Machine learning (ML)
is the scientific study of algorithms and statistical models that computer
systems use to perform a specific task without using explicit instructions,
relying on patterns and inference instead.
Inference is the stage
in which a trained model is used to infer/predict the testing samples and
comprises of a similar forward pass as training to predict the values. Unlike
training, it doesn't include a backward pass to compute the error and update
weights.
Training refers to the
process of creating an machine learning algorithm. ... Inference: Inference
refers to the process of using a trained machine learning algorithm to make a
prediction
Inference is the stage
in which a trained model is used to infer/predict the testing samples and
comprises of a similar forward pass as training to predict the values. Unlike
training, it doesn't include a backward pass to compute the error and update
weights
https://www.youtube.com/watch?v=_TVKa-noVKE
Machine learning models
often aren’t robust enough to handle changes in the input data type, and can’t
always generalize well. By contrast, causal inference explicitly overcomes this
problem by considering what might have happened when faced with a lack of
information.
When humans rationalize the world, we often think in terms of
cause and effect — if we understand why something happened, we can change our
behavior to improve future outcomes.
Causal inference is a statistical tool
that enables our AI and machine learning algorithms to reason in similar ways
in a stupid manner
Training: Training
refers to the process of creating an machine learning algorithm. Training
involves the use of a deep-learning framework (e.g., TensorFlow) and training
dataset. IoT data provides a source of training data that data scientists and
engineers can use to train machine learning models for a variety of use cases,
from failure detection to consumer intelligence.
Inference: Inference
refers to the process of using a trained machine learning algorithm to make a
prediction. IoT data can be used as the input to a trained machine
learning model, enabling predictions
that can guide decision logic on the device, at the edge gateway or elsewhere
in the IoT system
As machine learning is used more often in
products and services, there are some significant considerations when it comes
to users’ trust in the Internet. Several issues must be considered when
addressing AI, including, socio-economic impacts; issues of transparency, bias,
and accountability; new uses for data, considerations of security and safety,
ethical issues; and, how AI facilitates the creation of new ecosystems.
At the same time, in
this complex field, there are specific challenges facing AI, which include: a
lack of transparency and interpretability in decision-making; issues of data
quality and potential bias; safety and security implications; considerations
regarding accountability; and, its potentially disruptive impacts on social and
economic structures.
Algorithms are a
sequence of instructions used to solve a problem. Algorithms, developed by
programmers to instruct computers in new tasks, are the building blocks of the
advanced digital world we see today.
Computer algorithms organize enormous
amounts of data into information and services, based on certain instructions
and rules. It’s an important concept to understand, because in machine
learning, learning algorithms – not computer programmers – create the rules.
Instead of programming
the computer every step of the way, this approach gives the computer
instructions that allow it to learn from data without new step-by-step
instructions by the programmer. This means computers can be used for new,
complicated tasks that could not be manually programmed. Things like photo
recognition applications for the visually impaired, or translating pictures
into speech.
The basic process of
machine learning is to give training data to a learning algorithm. The learning
algorithm then generates a new set of rules, based on inferences from the data.
This is in essence generating a new algorithm, formally referred to as the machine
learning model.
By using different training data, the same learning algorithm
could be used to generate different models. For example, the same type of
learning algorithm could be used to teach the computer how to translate
languages or predict the stock market.
Inferring new
instructions from data is the core strength of machine learning. It also
highlights the critical role of data: the more data available to train the
algorithm, the more it learns. In fact, many recent advances in AI have not been
due to radical innovations in learning algorithms, but rather by the enormous
amount of data enabled by the Internet.
While the learning
algorithm may be open and transparent, the model it produces may not be. This
has implications for the development of machine learning systems, but more
importantly for its safe deployment and accountability. There is a need to
understand why a self-driving car chooses to take specific actions not only to
make sure the technology works, but also to determine liability in the case of
an accident.
In machine learning,
the model’s algorithm will only be as good as the data it trains on – commonly
described as “garbage in, garbage out”. This means biased data will result in
biased decisions. Reliable data is critical, but greater demand for training
data encourages data collection.
This, combined with AI’s ability to identify
new patterns or re-identify anonymized information, may pose a risk to users’
fundamental rights as it makes it possible for new types of advanced profiling,
possibly discriminating against particular individuals or groups.
The problem of
minimizing bias is also complicated by the difficulty in understanding how a
machine learning model solves a problem, particularly when combined with a vast
number of inputs. As a result, it may be difficult to pinpoint the specific
data causing the issue in order to adjust it. If people feel a system is
biased, it undermines the confidence in the technology.
When a machine learns
on its own, programmers have less control. While non-machine learning
algorithms may reflect biases, the reasoning behind an algorithm’s specific
output can often be explained. It is not so simple with machine learning.
In most countries,
programmers are not liable for the damages that flaws in their algorithms may
produce. This is important, as programmers would likely be unwilling to
innovate if they were. However, with the advancement of IoT technologies, such
issues may become more immediate.
As flaws in algorithms result in greater damages,
there is a need for clarified liability on the part of the manufacturer,
operator, and the programmer. With AI, the training data, rather than the
algorithm itself, may be the problem.
By obscuring the reasoning behind an
algorithm’s actions, AI further complicates the already difficult question of
software liability. And as with many fields, it may well be liability that
drives change.
AI system designers and
builders need to apply a user-centric approach to the technology. They need to
consider their collective responsibility
in building AI systems that will not pose security risks to the Internet
and Internet users.
Decisions made by an AI agent should be
possible to understand, especially if those decisions have implications for
public safety, or result in discriminatory practices.
“Algorithmic Literacy”
must be a basic skill
Algorithmic literacy
involves: Recognizing the inherent biases in computer programming. Critically
evaluating the information we receive online, and not assuming that the highest-ranked
information is the "best" information. Understanding that engaging
with digital platforms involves sacrificing a degree of privacy.
Most algorithms we
encounter in our daily life are ‘prediction machines’: They are models that use
existing data to predict missing data. In many cases, algorithms are both
better (higher accuracy) and cheaper than humans to accomplish prediction
tasks.
This is especially true in environments that offer a lot of structured
data, fairly consistent patterns and only a limited number of possible
outcomes. For example, it’s comparatively easy for Google Maps to predict how
crowded a restaurant will be at a given time, but quite difficult to forecast
rare events such as earthquakes. Algorithms feed on data.
Training data is used
to create an AI model in the first place, input data used to come up with
predictions, and feedback data to refine the model. Some of your data might
have already been used as training data, and it is used almost permanently as
input data as we browse the web..
By overly relying on the decision support
provided by algorithms, we risk getting dependent on algorithms and
organizations that deploy them. GPS navigation works well as long as the US
government grants access to the satellites, you have sufficient battery charge
on your mobile device, access to map data and clear line-of-sight to the sky.
Miss any of these ingredients, and you are back to reading physical maps.
Algorithm literacy must be part of the NCERT syllabus. We are no longer in the
“grease and tackle” age..
Branching statements
give us code which is optionally executable, depending on the outcome of
certain tests or you can say certain cases which we can define. Looping
statements are used to repetition of a section of code a number of times or
until a condition has been fulfilled.
A branch is merely an alternative path in
the uni-directional flow of control. It’s like trousers (possibly with multiple
legs :-). You simply pass through exactly one of them, and only once.
Branching is a transfer
of control from the current statement to another statement or construct in the
program unit. A branch alters the execution sequence. This means that the
statement or construct immediately following the branch is usually not
executed.
Branching statements
include :
if statement
if-else statement
else-if ladder
switch statement
nested if
Looping statement are
the statements execute one or more statement repeatedly several number of
times.
Looping statements are:
do while,while and for loop. A loop is a cycle; when you reach the end, you get
back at the beginning (unless you break from it). There are a few kinds of
loops in C allowing you to break the loop either at the beginning (while and
for loops) or at the end (do-while loop). Additionally, you may break any loop
immediately by the break statement and re-start (or rather skip the current
iteration) it by the continue statement.
In machine code, both
branch and loop is implemented merely as a (conditional) jump to an address in
the code memory segment. While branching means a simple forward jump (or two),
loop always means a backward jump (either). Looping statements are used to
repetition of a section of code a number of times or until a condition has been
fulfilled.
Decision trees are
unstable, meaning that a small change in the data can lead to a large change in
the structure of the optimal decision tree. They are often relatively
inaccurate. Many other predictors perform better with similar data.
Machine Learning is
autonomous but highly susceptible to errors. Suppose you train an algorithm
with data sets small enough to not be inclusive. You end up with biased
predictions coming from a biased training set. This leads to irrelevant
advertisements being displayed to customers.
In the case of ML, such blunders
can set off a chain of errors that can go undetected for long periods of time.
And when they do get noticed, it takes quite some time to recognize the source
of the issue, and even longer to correct it.
A major challenge is
the ability to accurately interpret results generated by the algorithms. You
must also carefully choose the algorithms for your purpose.
Machine Learning
requires massive data sets to train on, and these should be inclusive/unbiased,
and of good quality. There can also be times where they must wait for new data
to be generated.
Should you use neural
networks or traditional machine learning algorithms? It's a tough question to
answer because it depends heavily on the problem you are trying to solve.
Consider the "no free lunch theorem," which roughly states there is
no "perfect" machine learning algorithm that will perform well at any
problem. For every problem, a certain method is suited and achieves good
results, while another method fails heavily.
This is why a lot of
banks don’t use neural networks to predict whether a person is creditworthy —
they need to explain to their customers why they didn't get the loan, otherwise
the person may feel unfairly treated.
Neural networks usually
require much more data than traditional machine learning algorithms, as in at
least thousands if not millions of labeled samples. This isn’t an easy problem
to deal with and many machine learning problems can be solved well with less
data if you use other algorithms.
State of the art deep
learning algorithms, which realize successful training of really deep neural
networks, can take several weeks to train completely from scratch. By contrast,
most traditional machine learning algorithms take much less time to train,
ranging from a few minutes to a few hours or days.
The amount of
computational power needed for a neural network depends heavily on the size of
your data, but also on the depth and complexity of your network. For example, a
neural network with one layer and 50 neurons will be much faster than a random
forest with 1,000 trees. By comparison, a neural network with 50 layers will be
much slower than a random forest with only 10 trees.
Whether it is the
curating of information in social media platforms or self-driving cars, users
need to be aware and have a basic understanding of the role of algorithms and
autonomous decision-making. Such skills will also be important in shaping
societal norms around the use of the technology. For example, identifying
decisions that may not be suitable to delegate to an AI.
Provide the public with
information: While full transparency around a service’s machine learning
techniques and training data is generally not advisable due to the security
risk, the public should be provided with enough information to make it possible
for people to question its outcomes.
Principle: The capacity
of an AI agent to act autonomously, and to adapt its behavior over time without
human direction, calls for significant safety checks before deployment, and
ongoing monitoring.
Humans must be in
control: Any autonomous system must allow for a human to interrupt an activity
or shutdown the system ( VETO “off-switch”).
There must be a need to
incorporate human checks on new decision-making strategies in AI system design,
especially where the risk to human life and safety is great.
Make safety a priority:
Any deployment of an autonomous system should be extensively tested beforehand
to ensure the AI agent’s safe interaction with its environment (digital or
physical) and that it functions as intended. Autonomous systems should be
monitored while in operation, and updated or corrected as needed.
AI systems must be data
responsible. They should use only what they need and delete it when it is no
longer needed (“data minimization”). They should encrypt data in transit and at
rest, and restrict access to authorized persons (“access control”). AI systems
should only collect, use, share and store data in accordance with privacy and
personal data laws and best practices.
AI systems should not
be trained with data that is biased, inaccurate, incomplete or misleading.
Keep in mind that the
computer isn’t seeing the bat, ball, or rainbow stripped bricks. It “sees” a
bunch of numbers.
-
https://en.wikipedia.org/wiki/Krasukha_(electronic_warfare_system)
INDIA IS VERY POOR IN THE DEPT OF ELECTRONIC WARFARE
MODI MUST TAKE HELP FROM PUTIN FOR ELECTRONIC AND CYBER WARFARE , GPS JAMMING AND SPOOFING TACTICS
ELECTRONIC WARFARE SYSTEMS TO JAM AND INTERCEPT COMMUNICATIONS SIGNALS, JAM AND SPOOF GPS RECEIVERS, AND TAP INTO CELLULAR NETWORKS AND HACK CELL PHONES.
https://www.youtube.com/watch?v=BvyieACbfGQ
AN ELECTRONIC COUNTERMEASURE (ECM) IS AN ELECTRICAL OR ELECTRONIC DEVICE DESIGNED TO TRICK OR DECEIVE RADAR, SONAR OR OTHER DETECTION SYSTEMS, LIKE INFRARED (IR) OR LASERS. IT MAY BE USED BOTH OFFENSIVELY AND DEFENSIVELY TO DENY TARGETING INFORMATION TO AN ENEMY.
THE SYSTEM MAY MAKE MANY SEPARATE TARGETS APPEAR TO THE ENEMY, OR MAKE THE REAL TARGET APPEAR TO DISAPPEAR OR MOVE ABOUT RANDOMLY. IT IS USED EFFECTIVELY TO PROTECT AIRCRAFT FROM GUIDED MISSILES.
MOST AIR FORCES USE ECM TO PROTECT THEIR AIRCRAFT FROM ATTACK. IT HAS ALSO BEEN DEPLOYED BY MILITARY SHIPS AND RECENTLY ON SOME ADVANCED TANKS TO FOOL LASER/IR GUIDED MISSILES.
IT IS FREQUENTLY COUPLED WITH STEALTH ADVANCES SO THAT THE ECM SYSTEMS HAVE AN EASIER JOB. OFFENSIVE ECM OFTEN TAKES THE FORM OF JAMMING. SELF-PROTECTING (DEFENSIVE) ECM INCLUDES USING BLIP ENHANCEMENT AND JAMMING OF MISSILE TERMINAL HOMERS.
https://www.youtube.com/watch?v=WaPTnNXIHwo
WE ASK
DOES MODI KNOW THAT THE DRONES THAT HE IS BUYING FROM USA AT HUMONGOUS COST, CAN BE SCREWED BY CHEAP AND SIMPLE RUSSIAN EQUIPMENT ?
capt ajit vadakayil
..
WHAT IS THE GREATEST FRAUD OF THIS
MILLENNIUM?
THE MALICIOUS USE OF AI BY THE JEWISH DEEP STATE TO
ESTABLISH THAT CLIMATE CHANGE IS DUE TO INDIA BURNING COAL TO POWER OUR
ELECTRIC PLANTS, AND THUS GENERATING BAAAAAD CARBON DIOXIDE.
CAPT AJIT VADAKAYIL WAS THE ONLY VOICE ON
INTERNET TO OPPOSE PARIS COP21.. STATING THAT CO2 IS A GOOD AND LIFE SAVING
GAS.. THAT METHANE AND NITROUS OXIDE ARE
THE BIGGEST CULPRITS.
BRAKES HAVE BEEN APPLIED ON THE CAPABILITY OF JEWS TO MAKE MONEY OUT OF THIN AIR BY CARBON CREDIT, CAP AND TRADE , CARBON OFFSET ETC.
BRAKES HAVE BEEN APPLIED ON THE CAPABILITY OF JEWS TO MAKE MONEY OUT OF THIN AIR BY CARBON CREDIT, CAP AND TRADE , CARBON OFFSET ETC.
JEWISH DEEP STATE TRIED TO MAKE MENTAL AND PHYSICAL
MIDGET JEWESS GRETA THUNBERG AS THE MESSIAH OF THIS PLANET.
AT DAVOS GRETA BABY WAS ROYALLY IGNORED BY ALL , EXCEPT ILLITERATE CHAIWAALA MODI AND JEW MACRON
, WHOM JEW ROTHSCHILD HAS GIVEN JOINT “CHAMPIONS
OF THE EARTH “ TITLE..
BOTH ARE CHILDLESS AND HAVE NO PERSONAL STAKE
ON THIS PLANET – KOSHER BIG BROTHER HAS CHOSEN WELL..
WHEN I WRITE THE LEGACY OF MODI I WILL LIST
OUT HOW MANY TIMES THIS FELLOW GUJJU NO 2 MODI HAS BLED BHARATMATA..
ROTHSCHIILDs AGENT GUJJU NO 1 KATHIWARI JEW GANDHI
WILL PALE IN COMPARISON.
Buying carbon credits
in exchange for a clean conscience while you carry on flying, buying diesel
cars and powering your home with fossil fuels is bullshit..
In August 2018 , UN
Climate Change released a video promoting carbon offsets as an easy remedy to
climate change. Entitled “keep calm and
offset”, the advertisement declared smugly that viewers could lead a carbon-heavy
lifestyle as long as they offset their emissions.
For Jews everything
boils down to money.
.
JEW ROTHSCHILD WHO COOLED UP PALI SPEAKING / PIG EATING GAUTAMA BUDDHA AND PALI SCRIPT FOR THIS BURMESE DIALECT , CONVERTED—
SHRADDHA TO SADDA
CHAKRA TO CHAKKA,
DHARMA TO DHAMMA,
KARMA TO KAMMA,
SUTRA TO SUTTA ,
SATVA TO SATTA ,
PUTRA TO PUTTA ,
VASTU TO VATTU ETC..
SO ROMILA BABY , LET US ALL DANCE TO HUMMA ( HARAMI ).
https://www.youtube.com/watch?v=IhdUyiK-TTI
capt ajit vadakayil
..
SEND THIS COMMENT TO ROMILA THAPAR
SHRADDHA TO SADDA
CHAKRA TO CHAKKA,
DHARMA TO DHAMMA,
KARMA TO KAMMA,
SUTRA TO SUTTA ,
SATVA TO SATTA ,
PUTRA TO PUTTA ,
VASTU TO VATTU ETC..
SO ROMILA BABY , LET US ALL DANCE TO HUMMA ( HARAMI ).
https://www.youtube.com/watch?v=IhdUyiK-TTI
capt ajit vadakayil
..
SEND THIS COMMENT TO ROMILA THAPAR
-
Justice Govind Mathur of chief of Allahabad High Court who slams Yogi's Administration and ordered them to remove the banner of Anti-CAA protestors was a card-carrying member of SFI as a student.Became a judge in 2004 with the blessings of Harkishan Singh Surjeet a communist leader.
source twitter.-
A FEW KAYASTHA FAMILIES CONTROL INDIAN JUDICIARY EVEN TODAY.. THIS IS WHAT THE COLLEGIUM SYSTEM IS ALL ABOUT..
http://ajitvadakayil.blogspot.com/2019/07/we-never-heard-words-kayastha-and.html
IF I WERE MODI I WILL TELL THE JUDICIARY
MIND YOUR OWN FUCKIN' BUSINESS!!
FOR THIS YOU NEED BALLS AND PROVEN CAPABILITY TO PRODUCE CHILDREN-- SHOWIMG THAT YOU ARE A MARD NOT A TATTU NAPUNASAK..
capt ajit vadakayil
..
-
THIS POST IS NOW CONTINUED TO PART 17, BELOW--
CAPT AJIT VADAKAYIL
..
https://www.thehindu.com/news/cities/kolkata/allahabad-hc-stays-recovery-order-against-caa-protesters/article31012454.ece
ReplyDeleteMODI, PRASAD , ATTORNEY GENERAL ARE ALLOWING JUDGES IN FOREIGN PAYROLL TO PLAY GOD..
JEWISH DEEP STATE DARLING PM MODI IS THE MOST USELESS PM INDIA EVER HAD OR WILL HAVE IN FUTURE..
UNDER HIS WATCH SUPREME COURT STRUCK DOWN NJAC-- AFTER BEING PASSED IN BOTH HOUSES BY 100% UNANIMITY AND AFTER OBTAINING SIGNATURE OF THE PRESIDENT..
https://ajitvadakayil.blogspot.com/2020/01/we-people-are-done-with-illegal.html
JUST WHO THE HELL IS THE JUDICIARY TO LEGALISE BITCOIN ?
capt ajit vadakayil
..
https://www.thehindu.com/news/cities/kolkata/allahabad-hc-stays-recovery-order-against-caa-protesters/article31012454.ece
ReplyDeleteJUDICIARY HAS NO SUCH POWERS TO STOP ELECTED EXECUTIVE FROM NAMING AND SHAMING VANDALISERS CAUGHT ON VIDEO..
MODI, PRASAD , ATTORNEY GENERAL ARE ALLOWING JUDGES IN FOREIGN PAYROLL TO PLAY GOD..
JEWISH DEEP STATE DARLING PM MODI IS THE MOST USELESS PM INDIA EVER HAD OR WILL HAVE IN FUTURE..
UNDER HIS WATCH SUPREME COURT STRUCK DOWN NJAC-- AFTER BEING PASSED IN BOTH HOUSES BY 100% UNANIMITY AND AFTER OBTAINING SIGNATURE OF THE PRESIDENT..
https://ajitvadakayil.blogspot.com/2020/01/we-people-are-done-with-illegal.html
JUST WHO THE HELL IS THE JUDICIARY TO LEGALISE BITCOIN ?
BITCOIN IS USED IN THE PEDOPHILE / PORN INDUSTRY. . BITCOIN IS USED TO BUY DRUGS , PAY TERRORISTS/ MERCENARIES TO MURDER PEOPLE..
73% OF BITCOIN TRANSACTIONS ARE USED FOR ILLICIT ACTIVITIES..
I WROTE AN 18 PART POST , DESCRIBING WHAT BITCOIN IS..
http://ajitvadakayil.blogspot.com/2018/04/blockchain-bitcoin-must-be-kicked-out.html
I WROTE A 33 PART POST DESCRIBING WHAT SHELL COMPANIES ARE AND HOW THE WATAN IS BEING BLED ...
TODAY WE KNOW HOW INDIAN BANKS ARE LENDING HUGE MONEY TO SHELL COMPANIES RUN BY PEOPLE WHO DONATE TO BJP.
http://ajitvadakayil.blogspot.com/2017/03/shell-companies-for-money-laundering_31.html
THERE IS NO WAY TO RETRIEVE BITCOIN THAT HAS BEEN STOLEN OR CONNED, AND NO WAY TO REVERT THE TRANSACTION.
IN TEN YEARS OF BLOCKCHAIN EXISTENCE ALL IT HAS DONE IS TO REGULARISE GRABBED LAND IN ISRAEL AND GEORGIA BY JEWS..
CHANDRABABU NAIDU WANTED TO USE BLOCKCHAIN TO COVER UP GRABBED LAND IN AMARAVATI..
BITCOIN MAKES IT EASY FOR SCAMMERS , TERRORISTS , PEDOPHILES TO GET THEIR ILLEGAL MONEY AND RUN.
ARTIFICIAL INTELLIGENCE IS USED TO KEEP BITCOIN OFF THE WATCH CRIME RADAR..FALSE POSITIVES ARE USED AS DECOY..
I HAVE PENNED A 16 PART BLOG SERIES ( UNFINISHED ) ON AI..
https://ajitvadakayil.blogspot.com/2020/03/what-artificial-intelligence-cannot-do.html
ALMOST ALL BITCOIN IS USED IN THE DARK WEB ANONYMOUSLY AND THIS IS A TOOL FOR MONEY LAUNDERING AND RANSOMWARE. TRACKS ARE COVERED..
THE BIGGEST CRIMINALS OF INDIA ARE BITCOIN TRADING GUJARATIS FROM SURAT WHO DONATE TO BJP..
capt ajit vadakayil
..
PUT ABOVE COMMENT IN WEBSITES OF--
DeleteYOGI ADITYANATH
DGP OF UP
UP GOVERNOR
PUT ABOVE COMMENT IN WEBSITES OF--
RBI
RBI GOVERNOR
FINANCE MINISTER/ MINISTER
TRUMP
PUTIN
AMBASSADORS TO AND FROM ABOVE NATIONS.
UN CHIEF
CHANDRACHUD
NARIMAN
I&B MINISTER / MINISTRY
NCERT
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PMO
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ED
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HOME MINISTRY
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ALL 3 ARMED FORCE CHIEFS-- PLUS TOP CDS CHIEF
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MOHANLAL
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THE QUINT
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MK VENU
MADHU TREHAN
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RAJDEEP SARDESAI
PAAGALIKA GHOSE
NAVIKA KUMAR
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JOHN BRITTAS
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ROMILA THAPAR
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KANCHA ILAIH
TEESTA SETALVAD
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SHABANA AZMI
KUNHALIKKUTTY
ASADDUDIN OWAISI
FAZAL GHAFOOR ( MES )
FATHER CEDRIC PRAKASH
ANNA VETTICKAD
DEEPIKA PADUKONE
ARURAG KASHYAP
RAHUL GANDHI
SONIA GANDHI
PRIYANKA VADRA
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KAPILSIBAL
ABHI SEX MAANGTHA SINVI
DIG VIJAY SINGH
AK ANTONY
TEHSIN POONAWAALA
SANJAY JHA
AATISH TASEER
MANI SHANGARAN AIYYERAN
STALIN
ZAINAB SIKANDER
RANA AYYUB
BARKHA DUTT
SHEHLA RASHID
TAREK FATAH
UDDHAV THACKREY
RAJ THACKREY
KARAN THAPAR
ASHUTOSH
KAVITA KRISHNAN
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JEAN DREZE
BELA BHATIA
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RAHUL BAJAJ
HARDH MANDER
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UMAR KHALID
MANISH TEWARI
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RAJIV SHUKLA
SANJAY NIRUPAM
PAVAN KHERA
RANDEEP SURJEWALA
DEREK O BRIEN
ADHIR RANJAN CHOWDHURY
MJ AKBAR
ARUN SHOURIE
SHAZIA ILMI
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RATA TATA
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SADGURU JAGGI VASUDEV
MATA AMRITANANDA MAYI
BABA RAMDEV
SPREAD ON SOCIAL MEDIA..
SPREAD MESSAGE VIA WHATS APP
ALL MUST PARTICIPATE
ASK RBI, RBI GOVERNOR, FINANCE MINISTER, PMO, PM MODI, LAW MINISTER ,, I&B MINITER , AJIT DOVAL FOR AN ACK..
Sent mails to:
Deletecmup@nic.in,
yogi.adityanath@sansad.nic.in,
info.nia@gov.in,
information@cbi.gov.in,
contact@amitshah.co.in,
nsitharaman@gmail.com,
narendramodi1234@gmail.com,
amitshah.mp@sansad.nic.in,
mosfinance@nic.in,governor@rbi.org.in,
shaktikanta.das@nic.in
https://twitter.com/Sashwatdharma/status/1236490339768135682 - president, embassies
DeleteSent emails to dgps, igs, cms, governors and law ministers.
Sent
DeleteTo Modi, Putin & Trump, CM UP , Yogi Adityanath, CM's and dgp's of several states
governor@rbi.org.in
shaktikanta.das@nic.in
jsabc-dea@nic.in
jsrev@nic.in
info.nia@gov.in
information@cbi.gov.in
ed-del-rev@nic.in
contact@amitshah.co.in
Nirmala Sitharaman • nsitharaman@gmail.com
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minister.inb@gov.in
minister.hrd@gov.in
amitabh.kant@nic.in
ravis@sansad.nic.in
unrco.in@one.un.org
kkvenu@vsnl.com
secy.president@rb.nic.in
mvnaidu@sansad.nic.in
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Sundar Pichai • sundar@google.com
contact@pgurus.com
PMOPG/E/2020/0113310
MINHA/E/2020/02363. DEPOJ/E/2020/01034
MOIAB/E/2020/00538. DPLNG/E/2020/00271
MODEF/E/2020/00719. DMAFF/E/2020/00037
DLGLA/E/2020/00387. DSEHE/E/2020/01062
MINIT/E/2020/00576. CAGAO/E/2020/01658
DORVU/E/2020/00431 CBODT/E/2020/06769
DOCOM/E/2020/00253. DODIV/E/2020/00084
DOCAF/E/2020/00987. DOEAF/E/2020/00169
MOSJE/E/2020/00566. GOVGJ/E/2020/02082
DOEXP/E/2020/01015 DOSAT/E/2020/00059
MOSPI/E/2020/00027. DOIPP/E/2020/00126
DOLDR/E/2020/00103
Mailed to-
Deleteyogi.adityanath@sansad.nic.in
cmup@nic.in
hgovup@gov.in
https://twitter.com/rakeshsivan/status/1236524524134256640
Deletehttps://twitter.com/rakeshsivan/status/1236524390986088449
Namaste captain,
DeleteMail sent to following contacts :
governor@rbi.org.in
shaktikanta.das@nic.in
jsabc-dea@nic.in
jsrev@nic.in
info.nia@gov.in
information@cbi.gov.in
ed-del-rev@nic.in
contact@amitshah.co.in
nsitharaman@gmail.com
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info@rfgindia.org
info@cssscal.org
sunil@cis-india.org
contact@pgurus.com
Thanks and regards,
Hemanth
Hi captain
DeleteSent the emails to:
C
cmup@nic.in
Y
yogi.adityanath@sansad.nic.in
I
info.nia@gov.in
I
information@cbi.gov.in
C
contact@amitshah.co.in
N
nsitharaman@gmail.com
narendramodi1234@gmail.com
A
amitshah.mp@sansad.nic.in
M
mosfinance@nic.in
G
governor@rbi.org.in
S
shaktikanta.das@nic.in
A
amitabh.kant@nic.in
A
ambuj.sharma38@nic.in
B
bshastri@gmail.com
B
bsshuklabhu@gmail.com
C
cmubhu@gmail.com
G
g.shekhawat@sansad.nic.in
G
gsshastri@gmail.com
G
governor@rajbhavangoa.org
I
Info@janamtv.com
I
Info@vhp.org
I
infinity.foundation.india@gmail.com
J
jp.nadda@sansad.nic.in
M
mphamirpur@gmail.com
M
malyadri.sriram@sansad.nic.in
N
news.dd@doordarshan.gov.in
P
pmo@govmu.org
P
prahladp@sansad.nic.in
R
ratanlal.kataria@sansad.nic.in
S
supremecourt@nic.in
V
vedicinstitute@gmail.com
W
webmaster.indianarmy@nic.in
tarek.fatah@gmail.com
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Srinandan@aol.com
send to pmo in both category: public grievances & suggestion/feedback:-
Deleteregistration number PMOPG/E/2020/0115360
registration number PMOPG/E/2020/0115366 .
rest of them via email, email id provided by others readers.
WASIM JAFFER HAS RETIRRED TODAY..
ReplyDeleteHIS FIRST CLASS AVERAGE IS 51.
VIRAT KOHLI IS 54
VINOD KAMBLI IS 60
AJAY SHARMAs CAREER WAS SABOTAGES BY BCCI . HIS FC AVERAGE IS 67.5
TENDULKARs FIRST CLASS AVERAGE IS 57.8
SHANTANU SUGWEKARS FC AVERAGE IS 63.1
IF YOU WANT TO PLAY FOR INDIA YOU HAVE TO SUCK THE BALLS OF BCCI
JUST WHO THE HELL IS MSK PRASAD WHO SELECTS PLAYERS TODAY? HIS TEST AVERAGE IS 11 RUNS.. HIS ODI AVERAGE IS 14 RUNS MIND YOU HE WAS A BATSMAN..
THIS BASTARD MSK PRASAD SABOTAGED THE CAREER OF FELLOW TELUGU AMBATI RAYUDU WHOSE ODI AVERAGE IS 47.1.. WE HAVE SEEN AMBATI RAYUDU WINNING MATCHES ON HIS OWN STEAM IN IPL..
TENDULKARs ODI AVERAGE IS 44.8
https://timesofindia.indiatimes.com/city/allahabad/name-shame-hoardings-allahabad-high-court-takes-suo-motu-cognizance/articleshow/74533590.cms
ReplyDeleteWHY ARE JUDGES PLAYING GOD..
CONSTITUTION DOES NOT PROVIDE PRIVACY TO FOREIGN FUNDED VANDALISERS..
http://ajitvadakayil.blogspot.com/2017/08/right-to-privacy-in-india-is-not.html
JUDICIARY IS THE BIGGEST DESH DROHI ORG OF INDIA...THEY CREATED THE NAXAL RED CORRIDOR AND FACILITATED ETHNIC CLEANSING OF KASHMIRI PANDITS,
https://ajitvadakayil.blogspot.com/2020/01/we-people-are-done-with-illegal.html
capt ajit vadakayil
..
PUT ABOVE COMMENT IN WEBSITES OF--
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SADGURU JAGGI VASUDEV
MATA AMRITANANDA MAYI
BABA RAMDEV
SPREAD ON SOCIAL MEDIA..
SPREAD MESSAGE VIA WHATS APP
ALL MUST PARTICIPATE
ASK RBI, RBI GOVERNOR, FINANCE MINISTER, PMO, PM MODI, LAW MINISTER ,, I&B MINITER , AJIT DOVAL FOR AN ACK..
https://twitter.com/Sashwatdharma/status/1236500932432560128 - upcm, presidents and embassies
Deletesent emails to gov, cms, dgps, igs and law mins.
Shubha prabhatam evm pranaam Guruji,
DeleteRegistration Number is : PMOPG/E/2020/0113374
PMOPG/E/2020/0113398 & Trump
DeleteMINHA/E/2020/02366 DEPOJ/E/2020/01035
MOIAB/E/2020/00539. DPLNG/E/2020/00272
DLGLA/E/2020/00388. MODEF/E/2020/00720
DMAFF/E/2020/00039 MOSJE/E/2020/00567
DSEHE/E/2020/01063 MOPRJ/E/2020/00287
MINPA/E/2020/00153. LGVED/E/2020/00075
DORVU/E/2020/00432.
Judiciary had become much more worse.It seems like judges who are in deep state payroll are spread over all High courts.Judiciary is bleeding india day after day,no minister condemns its foolish judgments.There seems to be no single lawyer or judge who is loyal to the WATAN.
Deletehttps://twitter.com/rakeshsivan/status/1236522368006148096
Deletehttps://twitter.com/rakeshsivan/status/1236522329082957827
Ask to ravi shankar parsad on twitter & also via email.
Deletehttps://twitter.com/AKJANGRA17/status/1236965705242689539?s=20
email id of rsparsad:- mcitoffice@gov.in
https://twitter.com/prashantjani777/status/1236994310886895616
Deletehttps://twitter.com/prashantjani777/status/1236998182799802369
https://twitter.com/prashantjani777/status/1236998562820521984
many more and sent on whats app too
pranaam captain,
DeleteI found this comment on facebook - https://www.facebook.com/MehtaRahulC2/posts/captain-ajit-vadakayil-supports-right-to-recall-supreme-court-judges-and-other-r/1072188472844630/
Wikipedia has deleted article related to TCP - https://en.wikipedia.org/wiki/Transparent_Complaint_Procedure
mentioned by you - http://ajitvadakayil.blogspot.com/2016/03/sanatana-dharma-hinduism-exhumed-and.html?showComment=1458046366539#c4698577537057076018
thanks for providing details about the jury system and nanavati case
http://ajitvadakayil.blogspot.com/2019/01/justice-be-damned-enforce-law-not-any.html
hope this will help current and future readers to bring back jury system and get rid of collegium judiciary
https://nationalinterest.org/blog/skeptics/brutal-tragedy-idlib-why-us-should-stay-out-syria-and-dump-nato-130247
ReplyDeleteWE ASK PUTIN
SHOOT DOW ANY TURKISH FIGHTER JET WHICH ENTERS SYRIAN TERRITORY..
TEACH JEW ERDOGAN A LESSON WHICH HE WILL TAKE TO HIS GRAVE..
Ajit Sir,
DeleteI have sent this as mail to amb.moscow@mea.gov.in with slight modifications and ofcourse your name at the end.
Hope Russia doesnot sell S-300 & S-400 Missiles to TURKEY.
https://twitter.com/prashantjani777/status/1237011644913463301
DeleteSent earlier comment on bitcoin to(These includes secretaries in FM,Law ministry,defense min,I&B ministry too)
ReplyDeleteshaktikanta.das@nic.in
governor@rbi.org.in
dsimhelpdesk@rbi.org.in
mosfinance@nic.in
appointment.fm@gov.in
rsecy@nic.in
ravis@sansad.nic.in
secy-jus@gov.in
secylaw-dla@nic.in
contact@amitshah.co.in
amitshah.mp@sansad.nic.in
ambuj.sharma38@nic.in
prakash.j@sansad.nic.in
minister.inb@gov.in
secy.inb@nic.in
supremecourt@nic.in
nalsa-dla@nic.in
INDERJEET.ARORA@nic.in
REVAS-MOF@gov.in
jsrev@nic.in
jsinv-dea@nic.in
yogi.adityanath@sansad.nic.in
jscpg-mha@nic.in
mos-mhrd@gov.in
minister.hrd@gov.in
ed-del-rev@nic.in
alokmittal.nia@gov.in
info.nia@gov.in
swamy39@gmail.com
rajeev@rajeev.in
rammadhav@gmail.com
jpnadda@gmail.com
rmo@mod.nic.in
38ashokroad@gmail.com
secy-dea@nic.in
defsecy@nic.in
mos-defence@gov.in
mot_fb@nic.in
s.mohan@nic.in
minister-mowr@nic.in
mohan.pai@manipalglobal.com
amitabh.kant@nic.in
vch-niti@gov.in
eam@mea.gov.in
rajeevkumar@nic.in
parmar.anand@nic.in
https://timesofindia.indiatimes.com/india/as-pm-expresses-concern-ib-ministry-revokes-ban-on-channels-orders-probe/articleshow/74533039.cms
ReplyDeleteIN MALAYALAM WE CALL IT "OTHU KALI"..
YOU SEE PRAKASH JAVEDEKAR SAYS PM MODI WAS SO CONCERNED THAT HE CRIED BITTERLY -- LIKE HOW THE ITALIAN WAITRESS CRIED BITTERLY ON SEEING SLAIN TERRORISTS OF BATLA HOUSE ..
By far the most useless minister in bjp. He even bragged that we have not made any changes to the school textbooks as if that is something to be proud of. He is given stiff cccmpetition by ravi shankar prasad whom i have tweeted to feverishly since the sc overruled rbi on bitcoin and then say that ngos can receive money from abroad for protests.
DeleteThere is video going around showing Mcap ratios of various private banks saying above 4 is a danger signal.
ReplyDeleteNothing on goI run banks
Dear Ajit Sir,
ReplyDeleteThis is really ridiculous.
Unacceptable.
https://www.timesnownews.com/amp/business-economy/companies/article/ed-arrests-yes-bank-founder-rana-kapoor-in-money-laundering-case/562381?__twitter_impression=true
They mention DHFL which was already ailing to be one of the reasons among innumerable reasons for YESBANK going bankrupt.
From the past two days we are putting information on Shell Companies & they are yet to mention it.
You did mention very clearly with a resounding warning that after #PMC_BANK_SCAM, people have sided their money to Private Banks.
What the Nonsense did Modi do?
Today, they are using technical jargons like NPA being the reason for Bank Collapse.
I think Mr.Shaktikanta Das must be asked to pack up.
Enough of Odia Pride.
No need.
NPA का ढिंढोरा पिट रहे हैं।
Too much...!!!
All damn pathetic हेरा फेरी
The recurring theme in your blog has been about
ReplyDeleteCrony capitalism
Shell companies
CO2 hoax in global warming
Illegal Collegium judiciary running amok
Anti Hindu politicians
Foreign sponsored
Rothschild and his grand plans
Even a dunce will become a scholar
Except Modi government.
Oh My God,
ReplyDeleteNever expected this one
One by one, the lid is blown
Ajit Sir,
You are an amazing person.
See this you have mentioned, it Took years but now it's for people's scrutiny.
Now they will understand - The Stupid people how money is vacuumed out.
Below:
#YesBank founder Rana Kapoor had bought @priyankagandhi's painting for Rs 2 crore pay off is now under the lens of the I-T Dept
Priyanka Gandhi is a Painter like Momoto Bonarjee & her Doss poisa painting bought from Sarada Chit fund.
This we will rock today on Twitter.
Stupid abstract scary scribblings on Drawing Board is sold for Crores.
This is damn the most ridiculous thing that Page3 elites attend to see.
Is this an entertainment?
These people are Psychotics.
your registration number is : pmopg/e/2020/0113727
ReplyDeletejudiciary has no such powers to stop elected executive from naming and shaming vandalisers caught on video ..
Sometimes it can get depressing talking to people as can be seen in this thread when I referred to an article by captain. No wonder this country got fooled so much. https://m.facebook.com/story.php?story_fbid=2473012549694882&id=100009584832476
ReplyDeleteYour Registration Number is : PMOPG/E/2020/0113769
ReplyDeleteWhy are judges playing God ...
Also spread on WhatsApp
Sir,
ReplyDeleteBoth judiciary and Bitcoin related comments have been sent through twitter or mail.
Regards
ReplyDeleteNEHRUs YOUNGEST SISTER KRISHNA WAS MARRIED TO GUNOTTAM HUTHEESING OF ROTHSCHILDs OPIUM DRUG RUNNING SHIPOWNING FAMILY HUTHEESING ( JEW MARWARIS WHO PRETEND TO BE JAINS ).
THE RICHEST INDIANS IN 1947 WERE ALL ROTHSCHILDs DRUG RUNNERS.
http://ajitvadakayil.blogspot.com/2010/11/drug-runners-of-india-capt-ajit.html
HUTHEESING FAMILY GAVE AWAY MANY OF INDIAs PRICESS ARTIFACTS AND DIAMONDS TO JEW ROTHSCHILD.. I HAVE SEEN SOME OF THEM IN THE BRITSH MUSEUM.
http://ajitvadakayil.blogspot.com/2010/12/british-museum-house-of-stolen.html
THE WIFE ON SHANTIDAS JHAVERI HARKUVAR SHETHANI WAS AWARDED BY QUEEN VICTORIA WITH A TITLE OF NEK-NAMI SHEKHAWATI BAHADUR— FOR GIVING AWAY PRICELESS ANTIQUE INDIAN DIAMONDS TO HER AND ALSO SENDING TO HER PREMIUM LAUDANUM OPIUM TONIC REGULARLY ...
MIND NUMBING LAUDANUM IS A TINCTURE OF OPIUM CONTAINING APPROXIMATELY 10% POWDERED OPIUM BY WEIGHT --. REDDISH-BROWN AND EXTREMELY BITTER, LAUDANUM CONTAINS ALMOST ALL OF THE OPIUM ALKALOIDS, INCLUDING MORPHINE AND CODEINE.
HARKUVAR SHETHANI TOOK OVER HER HUSBAND’S ENTIRE OPIUM SHIPPING BUSINESS AFTER HER HUSBAND DIED.
MUMBAI’S VT TERMINUS STATION WAS BUILT WITH MONEY AND ARTIANS DONATED BY MAGAN-BHAI HUTHEESING, SON OF HUTHEESING KESRISING.
JOHN LOCKWOOD KIPLING ( FATHER OF RUDYARD KIPLING ) AND MAGANBHAI HUTHEESING WERE PARTNERS.
KASTURBHAI LALBHAI ( FOUNDER OF ARVIND MILLS ) WAS DESCENDANT OF SHANTIDAS JHAVERI, A ROYAL JEWELER OF AKBAR AND AN OSWAL JAIN FROM THE MARWAR REGION..
EVEN TODAY THE RICHEST ANTWERP DIAMOND TRADING JAINS ARE ALL JEWS. IF YOU VISIT THE AKSHARDHAM TEMPLE IN ANTWERP ON A SUNDAY YOU CAN SEE ALL OF THEM.. TODAY THEY ALSO HAVE A BASE IN DUBAI..
MODI INAUGURATED THE AKSHARDHAM TEMPLE AT DUBAI AND LIED THAT IT IS A HINDU TEMPLE..
SORRY GHANDHYAM PANDE IS NOT A HINDU GOD. AKSHARDHAM TEMPLES ( WHICH ALWAYS HAVE THE FAKE RADHA STATUE ) ARE NOT HINDU TEMPLES..
https://www.youtube.com/watch?v=EwJTs4bXl8k
TODAY THE BIGGEST BITCOIN TARDERS ARE CRYPTO JEW GUJARATIS FROM SURAT AND AHMEDABAD. THEY ARE POLITICAL DONORS OF BJP.
VIKRAM SARABHAI ( SPACE SCIENTIST ) IS THE SON OF AMBALAL SARABHAI..
LEELAVATI HUTHEESING, THE ELDER SISTER OF KASTURBHAI LALBHAI WHO WAS CLOSELY ASSOCIATED WITH AMBALAL SARABHAI WAS A KEEN SUPPORTER OF KATHIWARI JEW AND ROTHSCHILD’S AGENT GANDHI
WHEN MY REVELATIONS REACH THE 97% SEGMET ( IT IS AT 60% NOW ) THE BALLS AD TWATS OF THE WHOLE PLANET WILL GO TRRR PRRR BRRRR ( APOLOGIES TO SWARA BHASKAR )..
http://ajitvadakayil.blogspot.com/2019/07/how-gandhi-converted-opium-to-indigo-in.html
TAGORE’S GRANDFATHER WAS AN OPIUM DRUG RUNNING SHIPOWNER PARTNER F ROTHSCHILD/ SASSOON AND ALSO THE OWNER OF THE BIGGEST WHOREHOUSE OF THE PLANET SONAGACHI WHICH SPECIALIZED IN PRETEEN BOYS AND GIRLS FOR WHITE BRITISH OFFICERS.
http://ajitvadakayil.blogspot.com/2011/08/opium-drug-running-tagore-family-capt.html
capt ajit vadakayil
..
PUT ABOVE COMMENT IN WEBSITES OF--
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ASHOK PANDIT
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MEENAKSHI LEKHI
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SONAL MANSINGH
MADHU KISHWAR
SUDHIR CHAUDHARY
GEN GD BAKSHI
RSN SINGH
E SREEDHARAN
MOHANLAL
SURESH GOPI
CHANDAN MITRA
THE QUINT
THE SCROLL
THE WIRE
THE PRINT
MK VENU
MADHU TREHAN
ARNAB GOSWAMI
RAJDEEP SARDESAI
PAAGALIKA GHOSE
NAVIKA KUMAR
ANAND NARASIMHAN
SRINIVASAN JAIN
SONAL MEHROTRA KAPOOR
VIKRAM CHANDRA
NIDHI RAZDAN
FAYE DSOUZA
ZAKKA JACOB
RAVISH KUMAR
PRANNOY JAMES ROY
AROON PURIE
VINEET JAIN
RAGHAV BAHL
SEEMA CHISTI
DILEEP PADGOANKAR
VIR SANGHVI
KARAN THAPAR
SHEKHAR GUPTA
ARUDHATI ROY
SHOBHAA DE
JULIO RIBEIRO
ADVANI
MURLI MNOHAR JOSHI
KAMALAHASSAN
PRAKASH KARAT
BRINDA KARAT
SITARAM YECHURY
D RAJA
ANNIE RAJA
SUMEET CHOPRA
DINESH VARSHNEY
PINARAYI VIYAYAN
KODIYERI BALAKRISHNAN
JOHN BRITTAS
THOMAS ISAAC ( KERALA FINANCE MINISTER)
SIDHARTH VARADARAJAN
NANDINI SUNDAR
SHEHLA RASHID
ROMILA THAPAR
IRFAN HABIB
NIVEDITA MENON
AYESHA KIDWAI
SWARA BHASKAR
ADMIRAL RAMDAS
KAVITA RAMDAS
LALITA RAMDAS
JOHN DAYAL
KANCHA ILAIH
TEESTA SETALVAD
JEAN DREZE
JAVED AKTHAR
SHABANA AZMI
KUNHALIKKUTTY
ASADDUDIN OWAISI
FAZAL GHAFOOR ( MES )
FATHER CEDRIC PRAKASH
ANNA VETTICKAD
DEEPIKA PADUKONE
ARURAG KASHYAP
RAHUL GANDHI
SONIA GANDHI
PRIYANKA VADRA
SANJAY HEDGE
KAPILSIBAL
ABHI SEX MAANGTHA SINVI
DIG VIJAY SINGH
AK ANTONY
TEHSIN POONAWAALA
SANJAY JHA
AATISH TASEER
MANI SHANGARAN AIYYERAN
STALIN
ZAINAB SIKANDER
RANA AYYUB
BARKHA DUTT
SHEHLA RASHID
TAREK FATAH
UDDHAV THACKREY
RAJ THACKREY
KARAN THAPAR
ASHUTOSH
KAVITA KRISHNAN
JAIRAM RAMESH
SHASHI THAROOR
JEAN DREZE
BELA BHATIA
FARAH NAQVI
KIRAN MAJUMDAR SHAW
RAHUL BAJAJ
HARDH MANDER
ARUNA ROY
UMAR KHALID
MANISH TEWARI
PRIYANKA CHATURVEDI
RAJIV SHUKLA
SANJAY NIRUPAM
PAVAN KHERA
RANDEEP SURJEWALA
DEREK O BRIEN
ADHIR RANJAN CHOWDHURY
MJ AKBAR
ARUN SHOURIE
SHAZIA ILMI
CHANDA MITRA
MANISH SISODIA
ASHISH KHETAN
SHATRUGHAN SINHA
RAGHAV CHADDHA
ATISHI MARLENA
YOGENDRA YADAV
MUKESH AMBANI
RATA TATA
ANAND MAHINDRA
KUMARAMANGALAMBIRLA
LAXMI MNARAYAN MITTAL
AZIM PREMJI
KAANIYA MURTHY
RAHUL BAJAJ
RAJAN RAHEJA
NAVEEN JINDAL
GOPICHAND HINDUJA
DILIP SHANGHVI
GAUTAM ADANI
SRI SRI RAVISHANKAR
SADGURU JAGGI VASUDEV
MATA AMRITANANDA MAYI
BABA RAMDEV
SPREAD ON SOCIAL MEDIA..
PMOPG/E/2020/0113955 MINHA/E/2020/02373
DeleteDEPOJ/E/2020/01047 MOIAB/E/2020/00545
DPLNG/E/2020/00274 DMAFF/E/2020/00042
MODEF/E/2020/00723 DORVU/E/2020/00434
CBODT/E/2020/06781 MINPA/E/2020/00156
LGVED/E/2020/00076 DOEAF/E/2020/00172
DOPAT/E/2020/01073. DSEHE/E/2020/01071
DCLTR/E/2020/00152. DLGLA/E/2020/00394
Done captain. On Facebook, WhatsApp.
Deletehttps://twitter.com/prashantjani777/status/1236986701106601991
Deletehttps://twitter.com/prashantjani777/status/1236987004107264000
https://twitter.com/prashantjani777/status/1236987130800414720
https://twitter.com/prashantjani777/status/1236987343841697792
https://twitter.com/prashantjani777/status/1236987469012361216
https://twitter.com/prashantjani777/status/1236987714215579650
https://twitter.com/prashantjani777/status/1236987830985003008
https://twitter.com/prashantjani777/status/1236987981908578304
https://twitter.com/prashantjani777/status/1236988108429840384
https://twitter.com/prashantjani777/status/1236988465302159363
https://twitter.com/prashantjani777/status/1236988639181144065
https://twitter.com/prashantjani777/status/1236988888075440133
https://timesofindia.indiatimes.com/business/india-business/dozen-shell-firms-44-costly-paintings-and-rs-2000-cr-investments-of-rana-kapoor-under-ed-scanner/articleshow/74536293.cms
ReplyDeleteI HAVE WRITTEN A 33 PART POST ON SHELL COMPANIES
http://ajitvadakayil.blogspot.com/2017/03/shell-companies-for-money-laundering_31.html
WRITING THIS 33 POSTS FOR MODI HAS CHANGED MY PERSONALITY FOR THE WORSE..
I CAN GIVE AJIT DOVAL AND MODI A MONTH TO PERUSE THESE BLOGS--
AFTER THAT I WILL PUT THEM ON A 100 ITEM EXAM.. ONLY SIMPLE BASICS
THIS BRAIN DEAD 75 YEAR OLD FELLOW WILL SCORE ZERO.. MODI MAY SCORE 3%
capt ajit vadakayil
..
PUT ABOVE COMMENT IN WEBSITES OF--
DeleteAJIT DOVAL
MODI
PMO
RAW
RBI
RBI GOVERNOR
FINANCE MINISTER NIRMALA SITARAMAN
FINANCE MINISTRY
NITI AYOG
AMITABH KANT
CBI
NIA
ED
RAW
Sent to
Deleteshaktikanta.das@nic.in
governor@rbi.org.in
Nirmala Sitharaman • nsitharaman@gmail.com
jsrev@nic.in
connect@mygov.nic.in
info.nia@gov.in
alokmittal.nia@gov.in
amitabh.kant@nic.in
ed-del-rev@nic.in
information@cbi.gov.in
PMOPG/E/2020/0113979 MINHA/E/2020/02374
DORVU/E/2020/00435 DOEAF/E/2020/00173
DOCOM/E/2020/00255 DPLNG/E/2020/00275
Sent mails to governor@rbi.org.in,
Deleteshaktikanta.das@nic.in
Cc: info.nia@gov.in,
information@cbi.gov.in,
contact@amitshah.co.in,
nsitharaman@gmail.com,
narendramodi1234@gmail.com,
amitshah.mp@sansad.nic.in
https://twitter.com/prashantjani777/status/1236992206910742529
Deletehttps://swarajyamag.com/insta/yes-bank-scam-rana-kapoor-purchased-priyanka-gandhis-painting-for-rs-2-crore
ReplyDeleteARREST ITALIAN HALF BREED MAINO JEWESS PRIYANKA VADRA
INITIALLY ALMOST ALL BOLLYWOOD ACTORS , PRODUCERS AND DIRECTORS WERE JEWS.. PRETENDING TO BE MUSLIMS AND HINDUS..
ReplyDeleteALMOST ALL KAPOORS WHOSE ANCESTRAL HOME ( GREAT GRANDFATHER ) WERE AT PESHWAR ARE JEWS.. PRETENDING TO ME HINDUS.. THEY HAVE FAIR SKIN AND PALE EYES..
EVER HEARD OF DEWAN BASHESWARNATH KAPOOR?
SAME WITH THE HINDU OBEROIS WHOSE ANCESTORS ARE FROM PESHAWAR..
SAME WITH MUSLIM PATHAN KHANS WHOSE ANCESTORS ARE FROM PESHAWAR..
MANY PALE EYED FAIR SKINNED KHATRIS FROM PESHAWAR ARE JEWS.. ALL TEN SIKH GURUS AND THEIR WIVES ARE KHATRIS..
PASHTUN AFRIDIS CONTROLLED THE KHYBER PASS. EX-PRESIDENT ZAKIR HUSSAIN IS AN AFRIDI. HE OPENLY WENT TO THE JEWISH SYNAGOGUE IN KHAN MARKET DELHI TO PRAY ( ALONE).
HIS GRANDSON IS SALMAN KHURSHID..
DNA STUDIES ARE NO CONTEST.. NOBODY CAN GIVE BULL.
IMRAN KHAN THE PAKISTANI CRICKETER IS A JEW..
YESTER YEAR ACTOR DILIP KUMAR ( YUSUF KHAN ) IS A JEW..
JEWISH DEEP STATE HAS ENSURED THAT JEWISH PESHWAR AREA IS THE LAWLESS "WILD WEST".. OPIUM IS GROWN HERE..
https://www.youtube.com/watch?v=s25JLGE9vvI
MALALA YOUSAFZAI IS A JEW
KHAN ABDUL GHAFFAR KHAN IS A JEW
MAULANA ABDUL KALAM AZAD IS A QURESHI JEW
MOST ANSARIs ARE JEWS..
PATHAN JEWS DONT CARE FOR QURAN..
https://www.youtube.com/watch?v=s25JLGE9vvI
ONE OF MY PAKISTANI KHAN CHIEF OFFICERS WAS A PATHAN JEW. WHEN HIS GRANDFATHER GOT HOPPING MAD WITH HIM HE WORLD SCREAM " YOU BANI ISRAEL JEW, WAIT TILL I CATCH YOU "..
ROTHSCHILD CROSS BRED THE PATHANS OF NW FRONTIER WITH CRIMINAL TRIBES .. LIKE HOW HE DID WITH HEAD HUNTERS IN ARUNACHAL PRADESH.. THIS IS WHY THESE PESHWAR JEW PATHANS HAVE A SHORT FUSE..
https://ajitvadakayil.blogspot.com/2019/12/ilp-cab-nrc-why-china-wants-arunachal.html
capt ajit vadakayil
..
Respected Sir and Ma'm,
ReplyDeleteToday is celebrated as Women's day.
Of course am not a feminist but the YouTube is flooded with videos on this theme.at that moment I could not but help thinking of your wife especially after watching a video on Swami Vivekananda's thoughts on women.of whatever you have told of her coupled with my understanding she is the best woman that I have known,the fountain head of happiness in your home.i believe both of you bring out the best in each other making it a beautiful cherishable relationship.i don't think I can match upto her but she is an inspiration for me always!!
Both of you are like parents for me...guiding me in my life.
Cheers and best wishes...
Thank you and good day.
MY WIFE IS AN EMPOWERED WOMAN..
DeleteSHE IS A HOME MAKER.. SHE CAN AFFORD TO EMPLOY COOKS, BUT SHE DOES ALL THE COOKING ..
HER BLISS IS DERIVED FROM PROVIDING HAPPINESS TO HER HUSBAND AND CHILDREN , BY RUNNING A HAPPY HOME.. THERE IS NOTHING MY SONS WONT DO FOR HER..
SHE HAS MAINTAINED OUR ANCIENT HINDU TRADITIONS ..
I WAS AN ATHEIST FOR 5 YEARS BEFORE I MARRIED HER..I GLEANED THE GLORY OF SANATANA DHARMA FROM HER DAILY ROUTINES..
THE DEEP STATE WANTS TO WRECK HARMONY IN HINDU HOMES..
THIS IS WHY THEY ENCOURAGE MOVIE LIKE THAPPAD..
IF YOU SLAP YOUR 13 YEAR DAUGHTER BECAUSE YOU FOUND CONDOMS IN HER SCHOOL BAG AND VIDEOS OF HER GETTING ANALLY FUCKED BY BOYS STANDING IN A QUEUE IN HER MOBILE PHONE-- SHE WILL BREAK OFF ALL TIES ..
I salute guru maata ji with grateful heart million times.
DeleteHariom..
Ajit Sir,
DeleteMy mother is same as Mrs.Vadakayil, deeply rooted to Hindu tradition and I started washing my clothes from 6th standard bcoz my Mother would take so much pain washing clothes of all We siblings including my Father.
I decided to do my own to reduce my Mother's pain.
Same thing, my Mother would feel bad if my Father even prepares a simple tea just bcoz my Mother is watching a daily soap on TV and my Father don't want to interrupt her.
My Mother would say, she would prepare, coz repeat telecast is there to see if she misses.
My parents are such lovely Human beings.
I think about them when I am alone.
https://timesofindia.indiatimes.com/india/up-name-shame-posters-recovery-hoardings-unjust-encroach-on-personal-liberty-says-allahabad-hc/articleshow/74535452.cms
ReplyDeletePRIVACY IS NOT FOR PAKISTANI ISI FUNDED DESH DROHIS..
http://ajitvadakayil.blogspot.com/2017/08/right-to-privacy-in-india-is-not.html
SOMEBODY CALLED ME UP AND SAID
ReplyDeleteCAPTAIN
WHEN I HEARD THAT ROMAN EMPEROR JULIUS CAESAR MARRIED EGYPTION QUEEN CLEOPATRA , I THOUGHT IT IS FAKE HISTORY
NOW THAT YOU HAVE REVEALED THE TRUTH-- IT IS EASY TO BELIEVE -- BOTH ARE KERALA HINDUS THIYYAS..
CAESARION WAS THE ELDEST SON OF CLEOPATRA AND THE ONLY BIOLOGICAL SON OF JULIUS CAESAR, AFTER WHOM HE WAS NAMED ..
CAESARION WAS BORN IN EGYPT ON 23 JUNE 47 BC.
CAESARION SPENT TWO OF HIS INFANT YEARS, FROM 46 TO 44 BC, IN ROME
AFTER CAESAR'S ASSASSINATION ON 15 MARCH 44 BC, CLEOPATRA AND CAESARION RETURNED TO EGYPT.
CAESAR'S ADOPTED SON, OCTAVIAN, BECAME JEALOUS AND CAESARIONs LIFE WAS IN DANGER
IN 30 BC, WHEN OCTAVIAN INVADED EGYPT AND SEARCHED FOR CAESARION . CLEOPATRA SECRETLY SENT HIM TO BERNICE ( EGYPTIAN RED SEA PORT ) WHERE THE CALICUT KINGS SHIP WAS WAITING TO BRING HIM BACK TO CALICUT.
16 YEAR OLD CAESARION IDENTIFIED HIMSELF WITH HIS FATHERs SWORD ( METORITE STEEL IN METALLURY ) WHICH WAS PRESENTED TO JULIUS CAESAR BY THE CALICUT KING PERSONALLY..
THIS SWORD HAD A WAVY BLADE . IT WAS CALLED "SWORD OF KRISHNA"
THIS SUPER SWORD COULD PENETRATE STEEL ARMOUR LIKE BUTTER .. PLUTARCH HAS WRITTEN ABOUT THIS..
OCTAVIAN ( LATER AUGUSTUS CAESAR ) CAPTURED THE CITY OF ALEXANDRIA ON 1 AUGUST 30 BC.. BOTH MARK ANTONY AND CLEOPATRA COMMITTED SUICIDE..
KERALA HINDU THIYYA PRINCE CAESARIONs INFANT IMAGE APPEARS ON SOME GOLD COINS OF CLEOPATRA
JULIUS CAESARS ONLY SON CAESARION WAS CHRISTENED AS PTOLEMY XV PHILOPATOR PHILOMETOR CAESAR ON BIRTH. HE WAS EIGIBLE TO RULE BOTH ROMAN EMPIRE AND EGYPT . SUCH WAS HIS ROYAL LINEAGE.
OCTAVIAN WAS NAMED IN CAESAR'S WILL AS HIS ADOPTED SON AND HEIR.
MARK ANTONY HAD CHARGED THAT OCTAVIAN HAD EARNED HIS ADOPTION BY CAESAR THROUGH SEXUAL FAVOURS
OCTAVIAN , MARK ANTONY, AND MARCUS LEPIDUS FORMED THE SECOND TRIUMVIRATE TO DEFEAT THE ASSASSINS OF CAESAR.
FOLLOWING THEIR VICTORY AT THE BATTLE OF PHILIPPI, THE TRIUMVIRATE DIVIDED THE ROMAN REPUBLIC AMONG THEMSELVES AND RULED AS MILITARY DICTATORS. THE TRIUMVIRATE WAS EVENTUALLY TORN APART BY THE COMPETING AMBITIONS OF ITS MEMBERS.
LEPIDUS WAS FORCED INTO RETIREMENT BY OCTAVIAN IN 36 BC. OCTAVIAN AND MARK ANTONY WERE THEN LEFT IN CONTROL OF THE WESTERN AND EASTERN PROVINCES RESPECTIVELY.
MARK ANTONY COMMITTED SUICIDE ( FALLING ON HIS OWN SWORD ) FOLLOWING HIS DEFEAT AT THE BATTLE OF ACTIUM BY OCTAVIAN IN 31 BC.
AFTER JULIUS CAESAR’S DEATH, CLEOPATRA NEEDED TO MAINTAIN A CLOSE RELATIONSHIP WITH MARCUS ANTONIUS ( MARK ANTONY ) BECAUSE HE CONTROLLED THE EASTERN ROMAN EMPIRE, HER GEOGRAPHICAL NEIGHBOR.
CLEOPATRA HAD TWINS, ALEXANDER HELIOS AND CLEOPATRA SELENE, AS WELL AS ANOTHER SON, PTOLEMY PHILADELPHOS, ALL BY MARCUS ANTONIUS.
BECAUSE OF PLUTARCH ( 46 AD TO 120 AD ) WE KNOW OF APOLLONIUS OF TYANA ON WHOM JESUS CHRIST ( WHO NEVER EXISTED ) WAS MODELLED AFTER
WE HAVE THIS IN OUR RECORDS “ PRIME MINISTER CHANAKYA WAS CONCERNED THAT CHANDRAGUPTA MIGHT BE POISONED BY HIS ENEMIES, SO STARTED INTRODUCING SMALL AMOUNTS OF POISON INTO THE EMPEROR’S FOOD IN ORDER TO BUILD UP A TOLERANCE.
CHANDRAGUPTA WAS UNAWARE OF THIS PLAN, AND SHARED SOME OF HIS FOOD WITH HIS WIFE DURDHARA WHEN SHE WAS VERY PREGNANT WITH THEIR FIRST SON. DURDHARA DIED, BUT CHANAKYA THE PROFESSOR OF SURGERY AT TAXILA UNIVERSITY RUSHED IN AND PERFORMED AN EMERGENCY OPERATION TO REMOVE THE FULL-TERM BABY.
THE INFANT BINDUSARA SURVIVED, BUT A BIT OF HIS MOTHER’S POISONED BLOOD TOUCHED HIS FOREHEAD, LEAVING A BLUE BINDU SPOT THAT INSPIRED HIS NAME.
BIMBISARA , THE BUDDHIST HERO NEVER EXISTED..
http://ajitvadakayil.blogspot.com/2019/06/deliberately-buried-truths-about-buddha.html
I WONDER WHY WE ATTRIBUTE CAESARIAN BIRTHS TO A ROMAN KING —FOR HE CAME MORE THAN 2 CENTURIES LATER .
http://ajitvadakayil.blogspot.in/2014/08/chanakya-taxila-university-professor.html
capt ajit vadakayil
..
LOST ARK OF THE COVENANT
Delete####################################
https://en.wikipedia.org/wiki/Ark_of_the_Covenant
SOLOMON AND QUEEN OF SHEBA ( MAKEDA ) HAD A SON MENELIK , WHOM MAKEDA DELIVERED IN CALICUT AS WAS THE HINDU CUSTOM..
WHEN MENELIK GREW UP AND BECAME A 19 YEAR OLD MAN HE WENT TO VISIT SOLOMON AT JERUSALEM..
JEWS AND CHRISTIANS HAVE SPENT MILLIONS OF DOLLARS SEARCHING FOR THE LOST ARK OF THE COVENANT..
IT WAS SENT BACK TO CALICUT ( SHEBA ) BY HINDU KING SOLOMON .. SOLOMON GAVE HIS SON , THE ARK OF THE COVENANT TO KEEP IN THE SAFETY OF THE KINGDOM OF SHEBA ( MALABAR )..
SHEBA IS NORTH KERALA, INDIA - NOT ETHIOPIA..
http://ajitvadakayil.blogspot.com/2017/05/land-of-punt-ophir-and-sheba-is-north.html
THE ORIGINAL ARK OF THE COVENANT HAD SHIVA AND SHAKTI ON THE TOP COVER.. THE BOX CONTAINED A HEAVY SHIVA LINGAM STONE AND A BRASS POT WITH A PINHOLE HOLE AT THE BOTTOM.
IT IS A LIE THAT ARK OF THE COVENANT CONTAINED TWO ROCK TABLETS OF MOSES ( WHO NEVER EXISTED ) , THE ROD OF AARON ( WHO NEVER EXISTED ) AND BULLSHIT POT OF MANNA ..
THIS LIE WAS COOKED UP BY JEWESS HELENA , THE MOTHER OF ROMAN EMPEROR CONSTANTINE THE GREAT..
THE REAL EXODUS WAS FROM SHEBA ( CALICUT, INDIA ) TO PALESTINE .. THE ARK OF THE COVENANT CONTAINED A HEAVY SHIVA LINGAM ( BLACK METEORITE STONE ) WITH GOLDEN IDOLS OF SHIVA AND WIFE PARVATI ON THE TOP COVER..
IT WAS THE CALICUT KING WHO FIRST SEGREGATED LEFT HANDED ANAL SEX RECEIVING / POUNDING HOMOSEXUALS IN THE ISLAND OF MINICOY.. MOSTLY KERALA NAMBOODIRI YOUNGER BROTHERS..
THE KING MADE IT MANDATORY THAT GAYS MUST BE CIRCUMCISED TO PREVENT SHIT AND PIN WORMS FROM GETTING UNDERNEATH THE FORESKIN..
THESE ARE THE ORIGINAL SEMITES WHO ESCAPED TO PALESTINE BY SHIP-- PARTING THE RED SEA LONGITUDINALLY, NOT TRANSVERSELY..
KERALA NAMBOODIRI ELDER BROTHERS ( SYMBOL HINDU SWASTIKA ) ARE THE ORIGINAL ARYANS AND THEIR YOUNGER BROTHERS , THE ORIGINAL SEMITES ( SYMBOL HINDU SIX POINTED STAR) ..
WHY THIS HOMOSEXUALITY AMONG SEMITES?
ANSWER:--DUE TO SEVERE INBREEDING.. ( LIKE PARSIS WHO HAVE MAXIMUM HOMOSEXUALS IN INDIA BY PERCENTAGE )
http://ajitvadakayil.blogspot.com/2011/10/worst-racists-on-planet-earth-capt-ajit.html
MOSES WAS IN REALITY HINDU THIYYA EGYPTIAN KING AMENHOTEP IV ( AKHENATHEN ) ..
http://ajitvadakayil.blogspot.com/2019/09/istanbul-deep-seat-of-jewish-deep-state.html
LIES WONT WORK !
Capt ajit vadaakayil
..
https://timesofindia.indiatimes.com/entertainment/hindi/bollywood/news/kalki-koechlins-empowering-message-on-international-womens-day-2020-is-all-things-feminist/articleshow/74536756.cms
ReplyDeleteHERE IS SOME FACE LOTION FOR THIS AROON PURIE ( AND HIS DAUGHTER KOEL WHO MARRIED A FRENCH RINCHET MAN ) SPONSORED UGLY FRENCH WOMAN.. AAAARGGGHH PPTTHHEEOOOYY..'
Pranaam Guruji,
ReplyDeleteJust one simple question about Lord Ganesh ji. We haven't heard from you about his pet animal rat (mushak). Kindly please tell about it. I didn't find about it in blogsite.
Thank you.
Regards
Nishant Goyal
Lord Ganesha's vimana (vahana) resembles the shape of a rat. Captain has spoken about it in his previous blog.
DeleteBrahma has lotus symbols creation with energy power Saraswati, Vishnu has garuna symbols controlling with prosperity power laxmi, Shiva has ox symbols universe with destruction power parwati. What an amazing theory. They are cosmic allegories. Human avatars have not any attached animals expect yaziman narsimha.
DeleteOh I remembered something now and found some Guruji's quote as follows-
"Ganapathi is in charge of the Muladhara bandhana and NO expansion of consciousness is possible without his express permission. Ganesha’s KARMA ADHYAKSHA sets the wheel of Karma into motion- he knows every subtle thing of your mind and past deeds. Ganesha decides if the animal soul should enter a conscious body."
https://www.indiatvnews.com/entertainment/news/neena-gupta-instagram-video-married-man-594547
ReplyDeleteONE OF MY OFFIECRs WIVES WHO WAS FLOOR MANAGER OF A 7 STAR HOTEL TOLD ME THAT VIVIAN RICHARDS USED TO LEAVE STRICT INSTRUCTIONS THAT NEENA GUPTA MUST NOT BE ALLOWED INTO HIS ROOM..
YET THIS SHAMELESS SLUT USED TO BARGE IN ..
Namaskar captain please throw a light on dynamic universe model of snp Gupta .this guy is rejecting big bang theory,black hole and a lot of existing theory by supporting Newtonian equations .
ReplyDeleteIt's time to revoke RBI's Autonomy, operating without any kind of accountability. Bring them directly under finance ministry.
ReplyDeleteTHE INDIAN JUDICIARY DEMANDS TO KNOW BY "WHICH LAW" UP CM YOGI ADITYANATH "NAMED AND SHAMED" PAKISTANI ISI FUNDED VANDALISERS WHO WERE CAUGHT ON VIDEO TAPE..
ReplyDeleteTHIS IS WORSE THAN ASKING BY WHICH LAW YOU WENT FOR A SHIT IN THE MORNING.. EVERYTHING IN LIFE IS NOT GOVERNED BY THE CONSTITUTION..
THIS IS WHY THE WHOLE WORLD KNOWS THAT INDIAN JUDICIARY IS THE MOST STUPID ON THE PLANET..
ABOVE THE CONSTITUTION LIES "WE THE PEOPLE"...
ABOVE THIS LIES "THE WATAN"..
ABOVE ALL LIES "THE RULE OF DHARMA".. THE WEST CALLS THIS NATURAL LAW..
OUR PM MODI IS THE MOST USELESS PM INDIA HAS EVER PRODUCED OR WILL PRODUCE IN FUTURE..
OUR USELESS LAW MINISTER RAVI SHANKAR PRASAD HAS NO IDEA IF HE IS COMING OR GOING...
PM, LAW MINISTER AND ATTORNEY GENERAL ARE NAPUNSAKS OF THE FIRST ORDER.. THEY ALLOW PEA BRAINED JUDGES TO PLAY GOD..
IN THEIR WATCH SUPREME COURT STRUCK DOWN NJAC, WHICH WAS PASSED WITH 100% UNANIMITY IN BOTH LOK / RAJYA SABHA-- AND SIGNED BY THE PRESIDENT..
OUR TRAITOR JUDICIARY CREATED THE NAXAL RED CORRIDOR AND CAUSES ETHNIC CLEANSING OF KASHMIRI PANDITS..
JUDICIARY HAS NO POWERS TO STOP ELECTED EXECUTIVE FROM FOLLOWING THE RULE OF DHARMA..
SO SO SO -- UNDER WHICH LAW HAVE YOU PREVENTED THE FOREIGN BACK MAMBA FROM BITING YOUR BABY?..
MODI IS NAIVE TO BELIEVE THAT JEWS WHO SPONSORED HIM WITH A SIKH TURBAN IN 1976, IS ON HIS SIDE.. WE ASK MODI TO WORK FOR BHARATMATA NOT HIS JEWISH DEEP STATE MASTERS ..
WE KNOW HOW MODI STOOD IN THE SHADOWS AND ALLOWED DEEP STATE PAYROLL JUDICIARY TO KICK BHARATMATA INTO THE KOSHER ADULTERY/ HOMOSEXUALITY MANDI.. INDIA IS NO LONGER A MORAL NATION..
WE ASK AJIT DOVAL.. AS NSA WHAT IS YOUR JOB?.. IS IT TO GO AROUND LIKE DESI JAMES BOND, CONSOLING MUSLIMS AFTER CAA DELHI RIOTS ?.. WHAT IS ALL THIS?..
MANY INDIAN JOURNALISTS , COLLEGIUM JUDGES , PROFESSORS OF SOCIAL SCIENCES IN ELITE INDIAN COLLEGES ARE IN DEEP STATE PAYROLL.
WHY IS HARSH MANDAR NOT IN JAIL ?..
WHY HAS JUDICIARY LEGALIZED BITCOIN WHICH IS USED TO FUND ISLAMIC MERCENARIES IN KASHMIR AND DESH DROHIS IN INDIA?...
HARSH MANDER WHO IS SPONSORED BY LIBERAL INDIAN JUDGES IS THE CHAIRMAN OF GEORGE SOROS’S OPEN SOCIETY FOUNDATION’S HUMAN RIGHTS INITIATIVE ADVISORY BOARD..
WE WANT THIS FOREIGN FUNDED DESH DROHI ORG KARWAN E MOHABBAT TO BE PROFILED..
THE WHITE JEW KNOWS THAN IN 13 YEARS INDIA WILL BE THIS PLANETs NO 1 SUPERPOWER AND IT PLANS TO MAKE INDIA IMPLODE FROM WITHIN..
HARSH MANDER WHO TRIGGERED THE DELHI ANTI-CAA MUSLIM RIGHTS RIOTS IS AN AGENT OF JEW SOROS WHO HAS DONATED ONE BILLION USD TO FIGHT HINDUS AND CREATE DISCORD IN INDIA....
WE ASK MODI , SUMMON CJI BOBDE -- ASK HIM WHY HE HAS NOT DECLARED THAT UNHCHR HAS NO POWERS TO FILE A PETITION AGAINST CAA..
JUDICIARY HAS NO POWERS TO INTERFERE IN BHARATMATAs INTERNAL AND EXTERNAL SECURITY..
THERE IS NO PROVISION FOR PIL AND JUDICIAL REVIEW IN OUR CONSTITUTION..
PAKISTANI ISI FUNDED NGOs SENT GUJARAT HOME MINISTER AMIT SHAH INTO JAIL FOR A PAKISTANI ISI FUNDED ISLAMIC TERRORIST NAME SOHRABUDDIN..
ALL THE JUDICIARY CAN DO IS TO INTERPRET THE CONSTITUTION..
JEW ROTHSCHILD LEFT INDIA IN 1947, BUT HE STILL RULES INDIA BY PROXY--- WHY? HOW?..
IN THE KANHAIYA KUMAR CASE, THE TERM "STATE " FOR SEDITION CASES INVOLVES ONLY THE ELECTED EXECUTIVE , OF CENTRAL GOVT - WHICH IS REPRESENTED BY PM..IT DOES NOT APPLY TO CM OF DELHI KEJRIWAL..
SEDITION IS TO THE WATAN NEVER A STATE ..THE DELHI CM IS NOT EVEN IN CHARGE OF POLICE..
DELHI CMs POST IS JUST THAT OF A GLORIFIED MAYOR. LT GOVERNOR RULES DELHI..
WILL USA OR EUROPE ALLOW AN ARTERY ROAD OF ITS CAPITAL TO BE BLOCKED BY MUSLIM WOMEN WITH SMALL BABIES?..
BHARATMATA WILL NOT SURVIVE THIS DECADE IF WE DO NOT CLEANSE THE ILLEGAL COLLEGIUM JUDICIARY OF TRAITORS IN FOREIGN PAYROLL…
WE THE PEOPLE WATCH.. RETRIBUTION AWAITS..
https://ajitvadakayil.blogspot.com/2020/01/we-people-are-done-with-illegal.html
READ ALL 8 PARTS OF THE POST BELOW--
https://ajitvadakayil.blogspot.com/2019/01/justice-be-damned-enforce-law-not-any.html
capt ajit vadakayil
..
PUT ABOVE COMMENT IN WEBSITES OF---
DeleteCM YOGI ADITYANATH
PMO
PM MODI
AMIT SHAH
HOME MINISTRY
CJI BOBDE
SUPREME COURT JUDGES/ LAWYERS
ATTORNEY GENERAL
LAW MINISTER PRASAD / MINISTRY CENTRE AND STATES
CHIEF JUSTICES OF ALL STATE HIGH COURTS
I&B MINISTER / MINISTRY
NSA
AJIT DOVAL
RAW
IB
CBI
NIA
ED
DEFENCE MINISTER/ MINISTRY
ALL 3 ARMED FORCE CHIEFS -- PLUS TOP CDS CHIEF
ALL DGPs OF INDIA
ALL IGs OF INDIA
COLLECTORS OF MAJOR CITIES OF INDIA
ALL CMs OF INDIA
ALL STATE GOVERNORS
EVERY MP OF INDIA
EVERY MLA OF INDIA
NCERT
EDUCATION MINISTRY/ MINISTER
NITI AYOG
AMITABH KANT
PRESIDENT OF INDIA
VP OF INDIA
SPEAKER LOK SABHA
SPEAKER RAJYA SABHA
RSS
AVBP
VHP
MOHAN BHAGWAT
RAM MADHAV
SOLI BABY
FALI BABY
KATJU BABY
SALVE BABY
MOHANDAS PAI
RAJEEV CHANDRASHEKHAR
PGURUS
SWAMY
RAJIV MALHOTRA
DAVID FRAWLEY
STEPHEN KNAPP
WILLIAM DALRYMPLE
KONRAED ELST
FRANCOIS GAUTIER
NALIN KOHLI
GVL NARASIMHA RAO
SAMBIT PATRA
ASHOK PANDIT
ANUPAM KHER
KANGANA RANAUT
VIVEK AGNIHOTRI
MEENAKSHI LEKHI
SMRITI IRANI
PRASOON JOSHI
SWAPAN DASGUPTA
MADHU KISHWAR
SUDHIR CHAUDHARY
GEN GD BAKSHI
RSN SINGH
ARNAB GOSWAMI
NAVIKA KUMAR
ANAND NARASIMHAN
UDDHAV THACKREY
RAJ THACKREY
SHAZIA ILMI
CHANDA MITRA
SRI SRI RAVISHANKAR
SADGURU JAGGI VASUDEV
BABA RAMDEV
SPREAD ON SOCIAL MEDIA..
SPREAD BY WHATS APP
ASK MODI AND PRASAD FOR AN ACK
https://twitter.com/rakeshsivan/status/1236831766456881153
Deletehttps://twitter.com/rakeshsivan/status/1236831818633981952
Mailed To
Deletecmup@nic.in
Yogi Adityanath • yogiadityanath72@gmail.com
contact@amitshah.co.in
supremecourt@nic.in
webmaster.indianarmy@nic.in
PRO IAF • proiaf.dprmod@nic.in
pronavy.dprmod@nic.in
info.nia@gov.in
information@cbi.gov.in
ed-del-rev@nic.in
ravis@sansad.nic.in
17akbarroad@gmail.com
eam@mea.gov.in
kkvenu@vsnl.com
minister.hrd@gov.in
minister.inb@gov.in
amitabh.kant@nic.in
secy.president@rb.nic.in
secy-jus@gov.in
rajeev.c@sansad.nic.in
governor-wb@nic.in
governor.ap@nic.in
governor.goa@gov.in
governor@hry.nic.in
governorbihar@nic.in
governor-mh@nic.in
governor.rbblr-ka@gov.in
psec.law-up@gov.in
law.dept@rajasthan.gov.in
lawmin.od@nic.in
lawsec@tn.gov.in
min.law@kerala.gov.in
Law-jk@nic.in
lawsecy-hp@nic.in
lawsecypb@gmail.com
lawdept.cg@nic.in
law.deptt-meg@gov.in
editor@thequint.com
The Wire • editorial@thewire.in
contact@pgurus.com
divyayoga@divyayoga.com
infinity.foundation.india@gmail.com
Office of Sri Sri Ravi Shankar • secretariat@artofliving.org
rajthackeray@gmail.com
contact@republicworld.com
info@vhp.org
contactus@rss.org
Abvp Kendra • abvpkendra@gmail.com
chairperson.cbfc@nic.in
Also mailed CM's Dgp's
PMOPG/E/2020/0114660
MINHA/E/2020/02381 DEPOJ/E/2020/01057
MOIAB/E/2020/00550 DPLNG/E/2020/00278
DLGLA/E/2020/00398 MODEF/E/2020/00727
DMAFF/E/2020/00045 DSEHE/E/2020/01078
CAGAO/E/2020/01661 MINPA/E/2020/00159
LGVED/E/2020/00078 MOSJE/E/2020/00573
DORVU/E/2020/00437 DOPAT/E/2020/01080
MOMAF/E/2020/00167 MOPRJ/E/2020/00290
Tweeted.
Deletesent emails to governors, collectors, cms, dgps, igs, mps and mlas.
Registration Number : DLGLA/E/2020/00400
DeleteRegistration Number : MINHA/E/2020/02398
Registration Number : PMOPG/E/2020/0115386
Registration Number : PMOPG/E/2020/0115391
https://twitter.com/prashantjani777/status/1236966275693379584
Deletehttps://twitter.com/prashantjani777/status/1236966775750828033
Mailed to:
Deletecmup@nic.in
yogiadityanath72@gmail.com,
contact@amitshah.co.in,
info.nia@gov.in,
information@cbi.gov.in
PMOPG/E/2020/0115431
Deleteemails sent to ids shared by Debdoot (thanks Debdoot)
Dear Sir,
DeleteSent to narendramodi1234@gmail.com
regards
Divij
Happy Holi in advance
AK-203 CAN BE CONSIDERED DEADLY ONLY IF IT HAS AN UNDER BARREL GRENADE LAUNCHER..
ReplyDeleteAND DONT GIVE THIS BULLSHIT THAT WITH INSAS RIFLE YOU CANT CLIMB MOUNTAINS.. HEY, HOW ABOUT SWIMMING UNDERWATER YOU CUNTS ..
http://ajitvadakayil.blogspot.com/2011/09/ak-47-rifle-which-stood-test-of-time.html
https://en.wikipedia.org/wiki/Krasukha_(electronic_warfare_system)
ReplyDeleteINDIA IS VERY POOR IN THE DEPT OF ELECTRONIC WARFARE
MODI MUST TAKE HELP FROM PUTIN FOR ELECTRONIC AND CYBER WARFARE , GPS JAMMING AND SPOOFING TACTICS
ELECTRONIC WARFARE SYSTEMS TO JAM AND INTERCEPT COMMUNICATIONS SIGNALS, JAM AND SPOOF GPS RECEIVERS, AND TAP INTO CELLULAR NETWORKS AND HACK CELL PHONES.
https://www.youtube.com/watch?v=BvyieACbfGQ
AN ELECTRONIC COUNTERMEASURE (ECM) IS AN ELECTRICAL OR ELECTRONIC DEVICE DESIGNED TO TRICK OR DECEIVE RADAR, SONAR OR OTHER DETECTION SYSTEMS, LIKE INFRARED (IR) OR LASERS. IT MAY BE USED BOTH OFFENSIVELY AND DEFENSIVELY TO DENY TARGETING INFORMATION TO AN ENEMY.
THE SYSTEM MAY MAKE MANY SEPARATE TARGETS APPEAR TO THE ENEMY, OR MAKE THE REAL TARGET APPEAR TO DISAPPEAR OR MOVE ABOUT RANDOMLY. IT IS USED EFFECTIVELY TO PROTECT AIRCRAFT FROM GUIDED MISSILES.
MOST AIR FORCES USE ECM TO PROTECT THEIR AIRCRAFT FROM ATTACK. IT HAS ALSO BEEN DEPLOYED BY MILITARY SHIPS AND RECENTLY ON SOME ADVANCED TANKS TO FOOL LASER/IR GUIDED MISSILES.
IT IS FREQUENTLY COUPLED WITH STEALTH ADVANCES SO THAT THE ECM SYSTEMS HAVE AN EASIER JOB. OFFENSIVE ECM OFTEN TAKES THE FORM OF JAMMING. SELF-PROTECTING (DEFENSIVE) ECM INCLUDES USING BLIP ENHANCEMENT AND JAMMING OF MISSILE TERMINAL HOMERS.
https://www.youtube.com/watch?v=WaPTnNXIHwo
WE ASK
DOES MODI KNOW THAT THE DRONES THAT HE IS BUYING FROM USA AT HUMONGOUS COST, CAN BE SCREWED BY CHEAP AND SIMPLE RUSSIAN EQUIPMENT ?
capt ajit vadakayil
..
Captain,
DeleteI have sent your blog links on EMP attacks to ministry of electronics and information technology. They forwarded them to drdo,but the useless drdo closed it citing "suggestion "
Remarks
Received on 05.11.19. It is a suggestion Hence CLOSED.
Officer Concerns To
Officer Name
Shri GS Gupta
Officer Designation
Sc G and Director Personnel
Contact Address
Email Address
dte_pers@hqr.drdo.in
Contact Number
01123007218
Happy Holi dear captain
ReplyDeleteGod bless you and your family
Respected Elder Figure Ajit, Happy Holi to you and your family. :-)
ReplyDeleteNamaste Captain Ajitji,
ReplyDeleteCrude oil prices decline by 30% today.. Right down to 30 USD/bbl. As per reports rift between Russia and OPEC has increased.
-----------------------------------------------------------
I have been little inactive in spreading blog messages due to travel. Will resume this in full force soon.
Dear Captain,
ReplyDeleteWhat a terrific post on Corona Virus!
NanonGold, Gaumutra, Ganges Water,Conch combination! It makes so much sense to your readers. Thank you.
Sir, is it true that conch sound can also destroy virus?
I made a mental note of all people promoting anything which is anti Sanatan Dharma and have started boycotting their movies, even if they feature in a single frame. Also, boycotting their endorsed products. Only watched Tanhaji to show my daughter what bravery means.
Recently, i came across a stunning revelation that BIKAJI products are Halal certified. I have stopped consuming anything that endorses halal, including McDonalds, Pizza Hut etc. Also, stopped purchasing any products from Muslim owners coz this money (in the form of Zakat) is used to foment anti India activities. While purchasing furniture recently, I preferred to pay more money to buy furniture from a Hindu owner in a furniture lane dominated entirely by the Muslims.
Only a total economic boycott will bring them to their knees.
Regards.
Gautam
Easy said than done, People couldn't even boycott chinese firecrackers during diwali despite numerous appeals, but youre boasting about muslim product ban. Same with swadeshi and made in India plans all failed in this age of globalization and liberalization.
Delete@words are magic
DeleteNope
We showed our unity by boycotting Chhapak, Subh Mangal Zyada Savdhaan & now Thappaad.
Thank God Delhi people showed the World that Indians are not Morons.
People know very well that #Shaheen_Bagh protest was an all political party Gamble to make people judge & vote their choice of political party.
Delhi voted very wisely inspite of Muslim vandalism.
This shows it all & has alarmed those who are acting against India.
All of a sudden, now Shaheen_Bagh has stopped bcoz there is no election right now, game over in Delhi.
Also, Any group of Muslims can come & cry that their money evaporated in Yes Bank scam.
No noise.
All damn hands in gloves.
Shaheen_Bagh became a bad investment by BJP.
Next stop protest in Jadavpur University Campus.
Mamata Banerjee will play her cards & innumerable lives will be butchered right under the nose of pathetic Amit Shah.
Namaste Captain Ajitji,
ReplyDeleteHappy Holi to you and family. We will love to see your pictures celebrating with full gusto.
https://twitter.com/pbhushan1/status/1236840947456954368?s=20
ReplyDeletethis sentence " Laughing all the way with the banks! " is copy paste from your blog.
First case of
ReplyDeleteHIV - CONGO
NIPAH - MALAYSIA
EBOLA - SOUTH SUDAN
BIRD FLU.- HONGKONG
DENGUE - MANILA
CORONA - CHINA
MERS - MIDDLE EASTERN COUNTRIES
CRORES attend Kumbh Mela in India,
CRORES attend Pushkaraalu in India,
CRORES attend Medaram and other Jaataras in India
One river and at the sametime crores take bath.
Not a single virus was born or spread. Viruses are created in laboratory anyways! No Cholera outbreak or Typhoid or even E.Coli epidemic.
That's India.
The cruelest eating habits of some of the countries should be stopped.
If you don't respect nature
NATURE WILL DESTROY YOU
Only harmony is spread not a single virus
Thanks to GOD
We are born in The Great Nation: BHARAT
Dear Captain and readers,
ReplyDeleteWish you all a happy Holi 🙏
Dear Capt Ajit sir,
ReplyDeleteLooks Corono Virus was the indicator and the stock market crash is the real impactful thing happening to India and Indians...can somebody stop thus bloodbath, which is bleeding India....only you can save India Captain.
https://www.deccanherald.com/amp/business/economy-business/markets-live-bloodbath-on-d-street-sensex-crashes-over-1600-points-811053.html
Captain,
ReplyDeletehttps://www.facebook.com/505781099622414/posts/1330789887121527/
Trueindology account is spreading the fake Radha on his account. I thought he is awakened hindu account but unfortunately he is following poison injected R theory ... Sad.
Sir and readers,
ReplyDeleteSee this Muslim guy tweeting anti-Hindu tweets and spreading venom. To be added in desh-drohi watch list.
https://twitter.com/OpusOfAli/status/1236298218796756994?s=20
Putin shows Erdogan who is the Boss:-
ReplyDeletehttps://twitter.com/rose_k01/status/1236869135402852352?s=20
https://twitter.com/BhaveshNair/status/1236911388666114049?s=20
Dear Capt Ajit sir,
ReplyDeleteWishing you and your family....a very happy Holi festivities at your community...which is always a great celebration.
https://www.wionews.com/india-news/saudi-arabia-planning-to-stop-funding-mosques-in-foreign-countries-report-276940
ReplyDeleteYour Registration Number is : PMOPG/E/2020/0115290
ReplyDeleteThe indian judiciary demands to know by which law UP CM yogi adityanath named shamed pakistani isi funded vandalisers who were caught on video tape ...
Dear Captainji
ReplyDeleteWishing you and your family a blessed Holi with love, respect & gratitude
ALways in my thoughts Gurubaba
CR
Dear Capt Ajit Sir,
ReplyDeleteAs per wiki... "Holika Dahan also Kamudu pyre is celebrated by burning Holika, an asura. For many traditions in Hinduism, Holi celebrates the killing of Holika by Vishnu in order to save Prahlad, a devotee of God Vishnu in the city of Multan Pakistan, and thus Holi gets its name."
Was Prahlad not from Kerala since Holika is revered upon in Kathakali culture for long ? Which way Holi is traditionally celebrated in Kerala, as we all know that it's a north Indian festival based on Movies culture thrusted on us.
Dear Capt Ajit sir,
DeleteI forgot it's not just only Diwali but Holi also which is not celebrated in Kerala... " KATHAKALI OF KERALA IS MILLENNIUMS OLD, THE OLDEST DANCE AND SONG FROM ON THIS PLANET..
IT WAS BASICALLY A REPRESENTATION OF THE STORY OF DANAVA KING HIRANYAKASHIPU AND PRINCESS HOLIKA ( AUNT OF PRAHALAD ) OF KERALA.
IN KATHAKALI RECITALS YOU ALWAYS SEE THE FEMALE CHARACTER WEARING HOLIKAs MAGIC CAPE ( FIRE PROOF CLOAK ) .
http://ajitvadakayil.blogspot.com/2017/04/kathakali-dance-form-of-kerala-capt.html
SINCE WE WERE AT THE RECEIVING END OF VISHNU AVATARS VAMANA AND NARASIMHA , WE DONT CELEBRATE DIWALI AND HOLI IN KERALA., THE LAND OF DANAVAS."
http://ajitvadakayil.blogspot.com/2018/09/sanatana-dharma-hinduism-exhumed-and_28.html
AAA- WHY WAS MF HUSSAIN FAMOUS?
ReplyDeleteBECAUSE HE WAS A JEW..
BBB- WHY DID THE KING OF QATAR GIVE HIM RERFUGE ?
BECAUSE AL THANISs ARE JEWS..
CCC- WHY WAS FRANCIS NEWTON SOUZA SO FAMOUS?
BECAUSE HE MARRIED A WHITE JEWESS.
DDD-- WHY DID SRI SRI RAVISHANKAR ADOPT PANDHARPUR AND ITS GODS VITTALA/ VIHOBA/ PANDURANGA?
BECAUSE PANDHARPUR IS ROTHSCHILDs JEWISH TOWN MADE FOR CHITPAVAN JEWS TO DESTROY SANATANA DHARMA FROM WITHIN. EVERY BHAKTI MOVEMENT HERO ASSOCIATED WITH PANDHARUR ARE ROTHSCHLDs CREATIONS.
http://ajitvadakayil.blogspot.com/2015/09/francis-newton-souza-birth-painting.html
JEW MF HUSSAIN WAS BORN IN PANDHARPUR IN A JEWISH BOHRA FAMILY..
AJIT DOVAL
DeletePMO
PM MODI
RSS
AVBP
VHP
MOHAN BHAGWAT
RAM MADHAV
MOHANDAS PAI
RAJEEV CHANDRASHEKHAR
PGURUS
SWAMY
RAJIV MALHOTRA
DAVID FRAWLEY
STEPHEN KNAPP
WILLIAM DALRYMPLE
KONRAED ELST
FRANCOIS GAUTIER
NALIN KOHLI
GVL NARASIMHA RAO
SAMBIT PATRA
ASHOK PANDIT
ANUPAM KHER
KANGANA RANAUT
VIVEK AGNIHOTRI
MEENAKSHI LEKHI
SMRITI IRANI
PRASOON JOSHI
SWAPAN DASGUPTA
MADHU KISHWAR
SUDHIR CHAUDHARY
GEN GD BAKSHI
RSN SINGH
ARNAB GOSWAMI
NAVIKA KUMAR
ANAND NARASIMHAN
UDDHAV THACKREY
RAJ THACKREY
SHAZIA ILMI
CHANDA MITRA
SRI SRI RAVISHANKAR
SADGURU JAGGI VASUDEV
BABA RAMDEV
SPREAD ON SOCIAL MEDIA..
SPREAD BY WHATS APP
https://twitter.com/prashantjani777/status/1237017137987883015
Deletehttps://twitter.com/prashantjani777/status/1237017352342036483
https://twitter.com/prashantjani777/status/1237017487822262273
the following post immediatemy came to mind captain: http://ajitvadakayil.blogspot.com/2011/02/modern-abstract-art-and-picasso-capt.html
quote: "So many talented artists and singers have died penniless, yet the pretenders laugh all the way to the bank. It is all about marketing"
https://twitter.com/shree1082002/status/1237020352032595969
DeleteYour Registration Number is : PMOPG/E/2020/0116462
DeleteDear Capt Ajit sir,
ReplyDeleteThere should be karmic retribution for this heinous crime...Cows in China are being skinned alive and their limbs cut off so they can't escape...
https://www.thepetitionsite.com/968/425/456/cows-in-china-are-being-skinned-alive-and-their-limbs-cut-off-so-they-cant-escape/?taf_id=64198368&cid=fb_na
horrible. also, cow looks indian breed with hump and long throat skin. all this because sanatan dharma is decimated currently.
Deletehttps://twitter.com/GretaThunberg/status/1236615590560890880
ReplyDeleteMENTAL MIDGET JEWESS GRETA
MUST MEN AND WOMEN FART THE SAME AMOUNT?
http://ajitvadakayil.blogspot.com/2019/10/greta-thunberg-puppet-of-jewish-deep.html
https://timesofindia.indiatimes.com/videos/international/did-russian-president-putin-humiliate-turkish-president-erdogan/videoshow/74551132.cms
ReplyDeleteJEW ERDOGANs TIME IS OVER..
THIS BASTARD MUST GO FOR THE GOOD OF THIS PLANET..
Pranaam Guruji,
ReplyDeleteWishing you, your family and faithful readers an Happy Holi.
High glory and big salute to sister Holika who sacrificed for greater good.
Since 2 days I am avoiding "Holika" word for dahan/burning related any type of communication because it hurts my heart which respect you and danava civilization. I am seeing any burning place as just a simple burning ritual which purify environment.
Happy Holi again.
Your well-wisher
Nishant Goyal and family
Your Registration Number is : PMOPG/E/2020/0115630
ReplyDeleteAlso, spread on Whatsapp
- Why was mf hussain famous?
Shraddheya Shri Gurudev
ReplyDeleteWho is this person from TN , don't know if he's connected to TN politics or not --
https://twitter.com/Indumakalktchi/status/1236954573119447041?s=20
See this tweet of him , it boils my blood seeing all these dull headed duffer Hindus don't have shit for brain m dare take ur name in a derogatory manner , there are many twitter accts with decent follower count have very severe hatred for truth.
Pranam
Name Of Complainant
ReplyDeleteDebdoot Sarkar
Date of Receipt
03/03/2020
Received By Ministry/Department
Electronics & Information Technology
Grievance Description
Namaskar
Kindly acknowledge the below comment taken from Captain Ajit Vadakayil's blog
GOOGLE, TWITTER, FACEBOOK, YOUTUBE BANS ANY ANTI-JEWISH TRUTHS..
INDIA MUST START OUR OWN SOCIAL MEDIA EQUIVALENT PLATFORMS ALONG WITH RUSSIA..
arstechnica.com/tech-policy/2017/08/gab-the-right-wing-twitter-rival-just-got-its-app-banned-by-google/
en.wikipedia.org/wiki/Gab_(social_network)
IF YOU PUT A DAY OR WEEK TIME FILTER ON GOOGLE SEARCH -- MY BLOGPOSTS ARE ALL SUNK.. AND THIS DESPITE MY BLOGSITEs PROFILE VIEW BEING 1022 MILLION, THIS PLANETs NO 1 ..
JEWS DECIDE WHAT IS TRUTH AND WHAT IS FALSEHOOD..
JEW COMMIES ( EXAMPLE BERNIE SANDERS ) ARE ANTI-HINDU AND ANTI-INDIA..
THIS IS WHY WE GET "SIX MILLION JEWS DEAD" .. WHEN IN REALITY LESS THAN HALF A MILLION JEWS WERE KILLED IN WW2, AND THAT TOO BY JEW HITLER..
JEW HITLER KILLED ONLY ANAL SEX RECEIVING JEWS WHO WERE UNFIT TO ENTER THE PROMISED LAND..
ajitvadakayil.blogspot.com/2015/10/if-zionist-jews-created-isis-who.html
MILLIONS OF ROMANI GYPSIES WERE EXTERMINATED .. NOBODY TALKS ABOUT THEM..
ajitvadakayil.blogspot.com/2019/11/history-of-romani-gypsies-capt-ajit.html
NOBODY WRITES THAT ROTHSCHILDs COMMUNISM KILLED 200 MILLION INNOCENT PEOPLE..GERMAN JEW MARX WAS ROTHSCHILDs BLOOD RELATIVE..
READ ALL 9 PARTS OF MY POST BELOW--
ajitvadakayil.blogspot.com/2020/01/communism-failed-because-jew-karl-marx.html
capt ajit vadakayil
..
Thanks & Regards
Debdoot Sarkar.....
Current Status
Case closed
Date of Action
09/03/2020
Remarks
With reference to your grievance, it is hereby requested to provide your concrete suggestions. Your suggestions and inputs will be given due considerations.
Officer Concerns To
Officer Name
Dr Gaurav Gupta
Officer Designation
Scientist E
Contact Address
ELECTRONICS NIKETAN 6 CGO Complex New Delhi
Email Address
gupta.gaurav@deity.gov.in
Contact Number
24301203
Namaskar Guruji, below the is the link i had replied to @Indumakalktchi. Sorry to have replied in a bad manner but could not tolerate such an allegation towards you. You have transformed the whole world in a silent way and these uneducated politicians just throw tantrums on you.
ReplyDeletehttps://twitter.com/shree1082002/status/1237017403176804353
Name Of Complainant
ReplyDeleteDebdoot Sarkar
Date of Receipt
03/03/2020
Received By Ministry/Department
Electronics & Information Technology
Grievance Description
Namaskar Modiji
Kindly acknowledge the below comment taken from Captain Ajit Vadakayil's blog
WE THE PEOPLE OF INDIA WARN TWITTER AND FACEBOOK..
WE KNOW THAT PRIVATE DATA FROM INDIA IS STOLEN AND FED TO FOREIGN AGENCIES ..
INDIA IS NOW READY TO SEVERELY FINE AND INCARCERATE FOREIGN COMPANIES WHO MALICIOUSLY DAMAGE INDIAs SECURITY..
WE THE PEOPLE OF INDIA WARN TWITTER/ FACEBOOK-- DO NOT BE ANTI-HINDU AND ANTI-INDIA.. TOLERANCE HAS LIMITS.. DO NOT BITE THE HAND THAT FEEDS..
IF TRUTHS ARE TOLD BY CAPT AJIT VADAKAYILs FOLLOWERS TO THEIR OWN PM AND PRESIDENT , TWITTER / FACEBOOK BANS THEIR ACCOUNTS.. AND HUMILIATES THEM.. ARE WE SLAVES ?
ENOUGH IS ENOUGH..
IF INDIA BANS TWITTER / FACEBOOK / GOOGLE, AND STARTS OUR OWN PLATFORM, THE WHOLE WORLD KNOW THAT THESE COMPANIES WILL COLLAPSE UNDER THEIR OWN WEIGHTS.. THE EFFECT WILL CASCADE WORLDWIDE..
capt ajit vadakayil
..
Thanks And Regards
Debdoot Sarkar....
Current Status
Case closed
Date of Action
09/03/2020
Remarks
With reference to your grievance, it is hereby requested to provide your concrete suggestions. Your suggestions and inputs will be given due considerations.
Officer Concerns To
Officer Name
Dr Gaurav Gupta
Officer Designation
Scientist E
Contact Address
ELECTRONICS NIKETAN 6 CGO Complex New Delhi
Email Address
gupta.gaurav@deity.gov.in
Contact Number
24301203
Capt,
ReplyDeleteU were right about corona virus strategy to tackle . It's Architecture gave away the solution..They in Germany have found the cure on this basis Hoffmann et al
[09/03, 8:31 PM] Prashant Uchil: camostat mesylate is cure for corona virus
[09/03, 8:35 PM] Prashant Uchil: Corona virus uses lung cell serine protease TMPRSS2 for its S protein priming. Camostat mesylate is an TMPRSS2 inhibitor .when used S protein priming is not possible for corona virus resulting in non entry to human lung cells
ONLY VADAKAYIL GETS IT RIGHT !
DeleteTHIS IS MY PERSONAL RECORD.. AT SEA ALL KNEW THIS..
TO INFECT A CELL, CORONAVIRUSES USE A ‘SPIKE’ PROTEIN THAT BINDS TO THE CELL MEMBRANE, A PROCESS THAT'S ACTIVATED BY SPECIFIC CELL ENZYMES.
CORONAVIRUSES ENTER HUMAN CELLS THROUGH AN INTERACTION WITH ANGIOTENSIN-CONVERTING ENZYME 2 (ACE2).
COVID-19 HAS BEEN SHOWN TO BIND TO ACE2 VIA THE S PROTEIN ON ITS SURFACE.
DURING INFECTION, THE S PROTEIN IS CLEAVED INTO SUBUNITS, S1 AND S2. S1 CONTAINS THE RECEPTOR BINDING DOMAIN (RBD) WHICH ALLOWS CORONAVIRUSES TO DIRECTLY BIND TO THE PEPTIDASE DOMAIN (PD) OF ACE2. S2 THEN LIKELY PLAYS A ROLE IN MEMBRANE FUSION.
THE SPIKE PROTEIN BINDS TO A RECEPTOR ON HUMAN CELLS — KNOWN AS ANGIOTENSIN-CONVERTING ENZYME 2 (ACE2) — AT LEAST TEN TIMES MORE TIGHTLY THAN DOES THE SPIKE PROTEIN IN THE SARS VIRUS.
GOLD COLLOIDS ACT AS DECOY LIGANDS OR ANTIBODIES TO SPECIFICALLY TARGET ACE2 OR CORONAVIRUS SPIKE PROTEINS TO PREVENT VIRAL INFECTION.
https://ajitvadakayil.blogspot.com/2020/02/coronavirus-deaths-nano-gold-colloids.html
MIND YOU THIS GOLD COLLOID TREATMENT WAS WRITTEN IN CHARAKA SAMHITA 6200 YEARS AGO..
capt ajit vadakayil
..
Ajit Sir,
DeleteJapan is using Cow's Urine to make its soil fertile.
Only Indians will ridicule indigenous remedies & heritage.
But the Cow shown in the NEWS link is a Jersey Cow/Cattle.
Everything of India has to pass the litmus test of West before getting accepted by Indians.
This is the worst practice/stereotype that Indians have and must drop it like a hot potato.
Our Cow's are simply God incarnate.
We must accept this & should be intolerant to any joke on our Vedic Humped Cows & Cattles.
OM
ReplyDeleteHappy holi captain to you, your family and all this blogsite readers.
OM
Dear Ajit Sir,
ReplyDeleteSo Afghanistan will never accept anything called Peace.
They will forever be in quarrels.
If there's no enemy attacking, they will fight among themselves.
This intrigues me about them.
What kind of people are they!!!
For years I have been trying to study them but only it's you who can disclose.
Why these Short fuse?
What exactly went wrong with these people that they became the quintessential Savage?
request to everyone tweet with hashtag #Allahabadhighcourt & keep it in trending.. and expose collegium judiciary system.
ReplyDeleteThis is fifth generation warfare system & we can defeat evil forces by single laptop & two finger.
happy holi to you captain, your family and all readers
ReplyDeleteJustice Govind Mathur of chief of Allahabad High Court who slams Yogi's Administration and ordered them to remove the banner of Anti-CAA protestors was a card-carrying member of SFI as a student.Became a judge in 2004 with the blessings of Harkishan Singh Surjeet a communist leader.
ReplyDeletesource twitter.
A FEW KAYASTHA FAMILIES CONTROL INDIAN JUDICIARY EVEN TODAY.. THIS IS WHAT THE COLLEGIUM SYSTEM IS ALL ABOUT..
Deletehttp://ajitvadakayil.blogspot.com/2019/07/we-never-heard-words-kayastha-and.html
IF I WERE MODI I WILL TELL THE JUDICIARY
MIND YOUR OWN FUCKIN' BUSINESS!!
FOR THIS YOU NEED BALLS AND PROVEN CAPABILITY TO PRODUCE CHILDREN-- SHOWIMG THAT YOU ARE A MARD NOT A TATTU NAPUNASAK..
capt ajit vadakayil
..
Ajit Sir,
DeleteYogiadityanath ji got the balls of Steel.
He declined to pull down the posters inspite of Court's intimidations.
Yogi ji is showing the judges, the mirror.
Hmmm...
Ajit Sir's information worked out very well.
Kudos to Captain!!!
https://twitter.com/KapilMishra_IND/status/1236984371636125697/photo/1
ReplyDeleteWE ASK AJIT DOVAL
PROFILE NIDHI RAZDAN-- A KAHSMIRI PANDIT
INVESTIGATE HER LINKS WITH ISLAMIC KASHMIRI TERRORISTS.. AND OF COURSE OMAR ABDULLAH ..
https://twitter.com/KapilMishra_IND/status/1236984371636125697/photo/2
capt ajit vadakayil
..
PUT ABOVE COMMENT IN WEBSITES OF--
NIDHI RAZDAN
PRANNOY JAMES RY
I&B MINISTER/ MINISTRY
PMO
PM MODI
AJIT DOCAL
RAW
AMIT SHAH
HOME MINISTRY
ED
NIA
EB
CBI
done captain
Deletesend to pmo:-
Your Registration Number is : PMOPG/E/2020/0115799
rest of them via twitter:-
https://twitter.com/AKJANGRA17/status/1237060100256223232?s=20
tweeted to delhi police,uttar pradesh police
Deletehttps://twitter.com/Sashwatdharma/status/1237218768209637376
DeleteYour Registration Number is : PMOPG/E/2020/0116456
DeleteCaptain Sir,
ReplyDeleteTwitter is trending with #behavejustitutes with one of the tweet:
Thanos hindustani
@navitej
#BehaveJustitutes It took 134 years for courts to deliver verdict on RamLalla while they celebrated Holiday on RamNavami every year. But to help rioters, they open court even on Sunday. #BehaveJustitutes
I received following replies from pgportal:
1. MOIAB/E/2020/00531
Received By Ministry/Department
Information and Broadcasting
Grievance Description
HONORABLE INFORMATION AND BROADCASTING MINISTER JI,
JEW GEORGE SOROS IS A WEE AGENT OF JEW ROTHSCHILD WHO RULED INDIA...
JEW GEORGE SOROS IS BEING USED BY THE JEWISH DEEP STATE TO MAKE INDIA IMPLODE FROM WITHIN..
THE WHITE JEW KNOWS THAN IN 13 YEARS INDIA WILL BE THIS PLANETs NO 1 SUPERPOWER AND IT PLANS TO MAKE INDIA IMPLODE FROM WITHIN..
HARSH MANDER WHO TRIGGERED THE DELHI ANTI-CAA MUSLIM RIGHTS RIOTS IS AN AGENT OF JEW SOROS WHO HAS DONATED ONE BILLION USD TO FIGHT HINDUS AND CREATE DISCORD IN INDIA....
www.opensocietyfoundations.org/who-we-are/boards/human-rights-initiative-advisory-board
HARDH MANDAR IS A BOARD MEMBER OF OPEN SOCIETY FOUNDATIONS -- A JEWISH DEEP STATE ORGANISATION LED BY GEORGE SOROS ..
www.opindia.com/2020/01/george-soros-1-billion-dollar-fight-nationalists-pm-modi-usa-china-russia/
ONE BILLION USD DONATION BY GEORGE SOROS TO CREATE ANTI-HINDU SENTIMENTS IN INDIA IS THE TIP OF THE ICEBERG.. AS GEORGE SOROS HAS ALREADY DONATED 32 BILLION TO OPEN SOCIETY FOUNDATIONS..
www.opensocietyfoundations.org/george-soros
WE ASK AJIT DOVAL.. AS NSA WHAT IS YOUR JOB?.. IS IT TO GO AROUND CONSOLING MUSLIMS AFTER CAA DELHI RIOTS ?..
MANY INDIAN JOURNALISTS , COLLEGIUM JUDGES , PROFESSORS OF SOCIAL SCIENCES IN ELITE INDIAN COLLEGES ARE IN DEEP STATE PAYROLL.
WHY IS HARDH MANDAR NOT IN JAIL ?..
WHY HAS JUDICIARY LEGALIZED BITCOIN WHICH IS USED TO FUND ISLAMIC MERCENARIES IN KASHMIR AND DESH DROHIS IN INDIA?...
HARSH MANDER WHO IS SPONSORED BY LIBERAL INDIAN JUDGES IS THE CHAIRMAN OF GEORGE SOROS'S OPEN SOCIETY FOUNDATION'S HUMAN RIGHTS INITIATIVE ADVISORY BOARD..
WE WANT THIS FOREIGN FUNDED DESH DROHI ORG KARWAN E MOHABBAT TO BE PROFILED..
MODI IS NAIVE TO BELIEVE THAT JEWS WHO SPONSORED HIM WITH A SIKH TURBAN IN 1976, IS ON HIS SIDE.. WE ASK MODI TO WORK FOR BHARATMATA NOT HIS JEWISH MASTERS ..
WE WATCH..
Regards,
C Prabhu
Current Status
Case closed
Date of Action
09/03/2020
Remarks
The case does not come under the purview of this Ministry. The same may be taken up with Ministry of Electronic Information Technology.
Officer Concerns To
Officer Name
P. K. Abdul Kareem
Officer Designation
Economic Advisor
Contact Address
Email Address
pka.kareem@nic.in
Contact Number
01123383374
2. DEPOJ/E/2020/00227
Date of Receipt
15/01/2020
Received By Ministry/Department
Justice
https://ajitvadakayil.blogspot.com/2020/01/we-people-are-done-with-illegal.html
we the people who are above the constitution want illegal collegium judiciary to be disbanded..
njac still stands.. the judicial review striking down njac is unconstitutional and hence null and void..
we want the entire constitution to be revised, bearing in mind that we are in the modern age..
there are loopholes which the foreign payroll judges have been taking advantage of..
section 14 is for wee internal rules withing judiciary.. not majestic laws for the entire nation..
we the people will not allow artificial intelligence black box algorithms ( as cji bobde wants ) to hijack our constitution .. dharma is subjective.. not one single judge knows how to do root cause analysis..
Regards,
C Prabhu
Current Status
Case closed
Date of Action
21/01/2020
Remarks
The grievance is closed/disposed with the remarks that the subject matter of the grievance does not pertain to Department of Justice.
Officer Concerns To
Officer Name
Smt Sushma Taishete
Officer Designation
Joint Secretary
Contact Address
Jaisalmer House, 26 Mansingh Road, New Delhi
Email Address
jsst-doj@gov.in
Contact Number
01123385020
Captain Sir,
ReplyDeleteAlso received following from pgportal:
3. DCOYA/E/2020/00526
Date of Receipt
04/03/2020
Received By Ministry/Department
Corporate Affairs
Grievance Description
HONORABLE MINISTRY OF CORPORATE AFFAIRS MINISTER JI,
###### SUBJECT--- SUPEREME COURT ALLOWS CRYPTO CURRENCY TRADING #########
WHY IS SUPREME COURT PLAYING GOD?...
WE THE PEOPLE ASK CJI BOBDE...
....
OUR JUDICIARY IS THE WORST ON THE PLANET# TRAITORS WHO CREATED THE NAXAL RED CORRIDOR AND CAUSED ETHNIC CLEANSING OF KASHMIRI PANDITS ..
SINCE 1947, RBI HAS BEEN CONTROLLED BY JEW ROTHSCHILD.. ARE WE A BANANA REPUBLIC?...
ajitvadakayil.blogspot.com/2020/01/we-people-are-done-with-illegal.html
READ ALL 18 PARTS OF THE POST BELOW-
ajitvadakayil.blogspot.com/2018/04/blockchain-bitcoin-must-be-kicked-out.html
WE WATCH..
Regards,
C Prabhu
Current Status
Case closed
Date of Action
05/03/2020
Reason
Others
Remarks
Matter does not pertain to the M/o Corporate Affairs. Please forward this grievance to appropriate authority.
Officer Concerns To
Officer Name
Shri V. K. Khubchandani
Officer Designation
Director
Contact Address
Email Address
vijay.k.khubchandani@mca.gov.in
Contact Number
01123389602
4. CBODT/E/2020/06420
eceived By Ministry/Department
Central Board of Direct Taxes (Income Tax)
Grievance Description
HONORABLE CBDT CHAIRPERSON AND MEMBERS JI,
###### SUBJECT--- SUPEREME COURT ALLOWS CRYPTO CURRENCY TRADING #########
WHY IS SUPREME COURT PLAYING GOD?...
...
SINCE 1947, RBI HAS BEEN CONTROLLED BY JEW ROTHSCHILD.. ARE WE A BANANA REPUBLIC?...
ajitvadakayil.blogspot.com/2020/01/we-people-are-done-with-illegal.html
READ ALL 18 PARTS OF THE POST BELOW-
ajitvadakayil.blogspot.com/2018/04/blockchain-bitcoin-must-be-kicked-out.html
WE WATCH..
Regards,
C Prabhu
Current Status
Case closed
Date of Action
05/03/2020
Remarks
शिकायत की विषय-वस्तु इस कार्यालय के क्षेत्राधिकार में नहीं आती है । The subject matter of the grievance does not come under the purview of this office.
Officer Concerns To
Officer Name
P.Dam Kanunjna
Officer Designation
Addl. Director E-Services
Contact Address
Email Address
delhi.dittps2@incometax.gov.in
Contact Number
01123416133
5. DEPOJ/E/2020/00981
Date of Receipt
04/03/2020
Received By Ministry/Department
Justice
Grievance Description
HONORABLE LAW MINISTERs JI,
###### SUBJECT--- SUPEREME COURT ALLOWS CRYPTO CURRENCY TRADING #########
WHY IS SUPREME COURT PLAYING GOD?...
....
READ ALL 18 PARTS OF THE POST BELOW-
ajitvadakayil.blogspot.com/2018/04/blockchain-bitcoin-must-be-kicked-out.html
WE WATCH..
Regards,
C Prabhu
Current Status
Case closed
Date of Action
05/03/2020
Remarks
Adjudication and administration of matters from which petitioner is aggrieved does not falls under the prerogative of Department of Justice.
Officer Concerns To
Officer Name
Smt Sushma Taishete
Officer Designation
Joint Secretary
Contact Address
Jaisalmer House, 26 Mansingh Road, New Delhi
Email Address
jsst-doj@gov.in
Contact Number
01123385020
5. DLGLA/E/2020/00364
Date of Receipt
05/03/2020
Received By Ministry/Department
Legal Affairs
Grievance Description
HONORABLE DEPARTMENT OF LEGAL AFFAIRS MINISTER JI,
###### SUBJECT--- SUPEREME COURT ALLOWS CRYPTO CURRENCY TRADING #########
WHY IS SUPREME COURT PLAYING GOD?...
.....
SINCE 1947, RBI HAS BEEN CONTROLLED BY JEW ROTHSCHILD.. ARE WE A BANANA REPUBLIC?...
ajitvadakayil.blogspot.com/2020/01/we-people-are-done-with-illegal.html
READ ALL 18 PARTS OF THE POST BELOW-
ajitvadakayil.blogspot.com/2018/04/blockchain-bitcoin-must-be-kicked-out.html
WE WATCH..
Regards,
C Prabhu
Current Status
Case closed
Date of Action
09/03/2020
Reason
Complaint details inadequate or not legible
Remarks
Officer Concerns To
Officer Name
Shri Rama Nand
Officer Designation
Under Secretary
Contact Address
Email Address
ramanand.60@gov.in
Contact Number
01123387168
Pranaam Gurudev. Wish you a very happy holi Gurudev. Always at your feet.
ReplyDeletePurbesh
Captain Some of the replies on the cryptocurrency comment:
ReplyDeleteGrievance Status for registration number : MINHA/E/2020/02360
Date of Receipt
07/03/2020
Received By Ministry/Department
Home Affairs
Grievance Description
critical comment on cryto currecy by capt ajit vadakayil. Please read and acknowledge.
Grievance Document
Current Status
Case closed
Date of Action
09/03/2020
Reason
Others
Remarks
Quoting from someone s blog does not constitute a grievance. This is personal view of blog writer.
Officer Concerns To
Officer Name
Saheli Ghosh Roy
Officer Designation
Joint Secretary C&IC
Contact Address
Room No. 188, North Block, New Delhi
Email Address
jscpg-mha@nic.in
Contact Number
23092392
=======================
Date of Receipt
07/03/2020
Received By Ministry/Department
Information and Broadcasting
Grievance Description
A critical comment by Capt Ajit Vadakayil on crypto currency. Please acknowledge
Grievance Document
Current Status
Case closed
Date of Action
09/03/2020
Remarks
The case does not come under the purview of this Ministry. The same may be taken up with Ministry of Electronic Information Technology.
Officer Concerns To
Officer Name
P. K. Abdul Kareem
Officer Designation
Economic Advisor
Contact Address
Email Address
pka.kareem@nic.in
Contact Number
01123383374
============================
looks like we may need to format the comments just so they take cognizance of it. they just dismissed it as the personal opinions of a blogger without addressing the subject matter.
DeleteSir..
ReplyDeleteTo you..your family and all your readers.. best wishes on Holi..
Thanking you
Sameer
Twitter - Sindhuahimsa
TOI - Agnijal
Wish you a very Happy Holi Captain
ReplyDeleteHoli Hain
Om Namah Shivaya
Pranaam Guruji _/\_
ReplyDeleteWish You, Your Family And Everyone A Very Happy Holi
Thanks And Regards
Debdoot Sarkar
Dear Ajit Sir and Readers,
ReplyDeleteLife should be full of Colors.
I wish Ajit Sir and family a Happy Holi, wish all the Readers a Happy Holi.
Organic Herbal Holi, Safe Holi.
Jai Shri Jagannatha!
Jai Shri Krishna
Jai Shri Ram
That Lucknow judge ma"Thoo"r may be wants his poster around Lucknow. The Yogi govt if push comes to a shove can oblige splash this thoo fellow all over Lucknow.
ReplyDeleteMay be he will understand power of a democracy.
These nemesis of Justice and mockeries of the judge's seat types need newer methods to learn.
Rothschild just"ice" versus Sanatan Dharma viewpoint ..
Naming and shaming of offenders and deshdrohis is an accepted practice in Rajdharma.
The kings would send around heralds to remote villages with his messages .
Ma thoo r has forgotten his roots.
The government has a duty to warn the lay people so that they don't become victims and collaborators.
PS happy Holi . By the way it's Holi for us readers everyday as this blog has colours a plenty! All different shades too.
ReplyDelete########## SUBJECT -- JUDICIARY WANTS TO KNOW BY WHICH LAW YOGI ADITYANATH NAMED AND SHAMED DESH DROHIS WHO VANDALIZED DURING CAA RIOTS #############
ReplyDeleteHEY MELORD, BY WHICH LAW DID YOU GO FOR A SHIT IN THE MORNING ? WHICH PART OF THE CONSTITUTION ALLOWS YOU TO CLEAR YOUR BOWELS ?...
EVERYTHING IN LIFE IS NOT COVERED BY THE CONSTITUTION... COMMONSENSE CANNOT BE ABANDONED..
WHAT IS MORE IMPORTANT-- THE SECURITY OF BHARATMATA OR THE PRIVACY OF PAKISTANI ISIS FUNDED VANDALISERS?,,,
BY WHICH LAW DID YO SEND GUJARAT HOME MINISTER INTO JAIL, WHEN SOME PAKISTANI ISI FUNDED NGO COMPLAINED ON BEHALF OF DREADED TERRORIST SOHRABUDDIN? .. CAN THIS HAPPEN ANYWHERE ELSE ON THE PLANET?..
THIS IS WHY THE WHOLE WORLD KNOWS THAT INDIAN JUDICIARY IS THE MOST STUPID ON THE PLANET.. BOTTOM DREGS OF THE SCHOOL CEREBRAL BARREL AND DISCARDS OF THE LOSER LAWYER POOL..
ABOVE THE CONSTITUTION LIES "WE THE PEOPLE"...
ABOVE THIS LIES "THE WATAN"..
ABOVE ALL LIES "THE RULE OF DHARMA".. THE WEST CALLS THIS NATURAL LAW..
PM AND LAW MINISTER ARE NAPUNSAKS OF THE FIRST ORDER.. THEY ALLOW STUPID JUDGES TO PLAY GOD..
IN THEIR WATCH SUPREME COURT STRUCK DOWN NJAC, WHICH WAS PASSED WITH 100% UNANIMITY IN BOTH LOK / RAJYA SABHA-- AND SIGNED BY THE PRESIDENT.. THERE IS NO SUCH PROVISION FOR JUDICIAL REVIEW IN OUR CONSTITUTION..
WE KNOW HOW MODI/ PRASAD STOOD IN THE SHADOWS AND ALLOWED DEEP STATE PAYROLL JUDICIARY TO KICK BHARATMATA INTO THE KOSHER ADULTERY/ HOM0SEXUAL1TY MANDI.. INDIA IS NO LONGER A MORAL NATION..
OUR TRAITOR JUDICIARY CREATED THE NAXAL RED CORRIDOR AND CAUSES ETHNIC CLEANSING OF KASHMIRI PANDITS..
JUDICIARY HAS NO POWERS TO STOP ELECTED EXECUTIVE FROM FOLLOWING THE RULE OF DHARMA OR PROVIDING NATURAL JUSTICE..
CJI WHO CANNOT GO BEYOND THE OBJECTIVE HAS NO POWERS TO STOP PRESIDENT AND STATE GOVERNORS WHO HAVE BEEN EMPOWERED WITH ENORMOUS SUBJECTIVE POWERS INCLUDING VETO POWERS ..
SO SO SO -- UNDER WHICH LAW HAVE YOU PREVENTED THE FOREIGN BLACK MAMBA FROM BITING YOUR BABY?..BATAAOH NAH.. PLEAJJE…
MANY INDIAN JOURNALISTS , COLLEGIUM JUDGES , PROFESSORS OF SOCIAL SCIENCES IN ELITE INDIAN COLLEGES ARE IN DEEP STATE PAYROLL…
WHY HAS JUDICIARY LEGALIZED BITCOIN WHICH IS USED TO FUND ISLAMIC MERCENARIES IN KASHMIR AND DESH DROHIS IN INDIA?..
THE WHITE JEW KNOWS THAN IN 13 YEARS INDIA WILL BE THIS PLANETs NO 1 SUPERPOWER AND IT PLANS TO MAKE INDIA IMPLODE FROM WITHIN..
HARSH MANDER WHO TRIGGERED THE DELHI ANTI-CAA MUSLIM RIGHTS RIOTS IS AN AGENT OF JEW SOR0S WHO HAS DONATED ONE BILLION USD TO FIGHT HINDUS AND CREATE DISCORD IN INDIA....
WE ASK MODI , SUMMON CJI BOBDE -- ASK HIM WHY HE HAS NOT DECLARED THAT UNHCHR HAS NO POWERS TO FILE A PETITION AGAINST CAA..
BHARATMATA WILL NOT SURVIVE THIS DECADE IF WE DO NOT CLEANSE THE ILLEGAL COLLEGIUM JUDICIARY OF TRAITORS IN FOREIGN PAYROLL...
ALL THE JUDICIARY CAN DO IS TO INTERPRET THE CONSTITUTION.. THEY CANNOT RULE THE NATION...
STARE DECISIS IN NOT ALLOWED BY THE INDIAN CONSTITUTION WHERE JUDGES TREAT THEIR PAST STUPID JUDGEMENTS SANS CONTEXT AND SUBJECTIVITY AS LAWS..
WE THE PEOPLE WILL NOT ALLOW TYRANNY OF THE UNELECTED, THESE COLLEGIUM JUDGES CHOSEN BY FOREIGN FORCES..
https://ajitvadakayil.blogspot.com/2020/01/we-people-are-done-with-illegal.html
WE THE PEOPLE WILL NOT STAND IDLE IF THE JUDICIARY BLEEDS THE WATAN...
capt ajit vadakayil
..
PUT ABOVE COMMENT IN WEBSITES OF---
DeleteCM YOGI ADITYANATH
GOVERNOR OF UP
DGP OF UP
IG OF UP
ALL COLLECTORS OF UP
PMO
PM MODI
AMIT SHAH
HOME MINISTRY
CJI BOBDE
SUPREME COURT JUDGES/ LAWYERS
ATTORNEY GENERAL
LAW MINISTER PRASAD / MINISTRY CENTRE AND STATES
CHIEF JUSTICES OF ALL STATE HIGH COURTS
I&B MINISTER / MINISTRY
NSA
AJIT DOVAL
RAW
IB
CBI
NIA
ED
DEFENCE MINISTER/ MINISTRY
ALL 3 ARMED FORCE CHIEFS -- PLUS TOP CDS CHIEF
ALL DGPs OF INDIA
ALL IGs OF INDIA
COLLECTORS OF MAJOR CITIES OF INDIA
ALL CMs OF INDIA
ALL STATE GOVERNORS
EVERY MP OF INDIA
EVERY MLA OF INDIA
NCERT
EDUCATION MINISTRY/ MINISTER
NITI AYOG
AMITABH KANT
PRESIDENT OF INDIA
VP OF INDIA
SPEAKER LOK SABHA
SPEAKER RAJYA SABHA
RSS
AVBP
VHP
MOHAN BHAGWAT
RAM MADHAV
SOLI BABY
FALI BABY
KATJU BABY
SALVE BABY
MOHANDAS PAI
RAJEEV CHANDRASHEKHAR
PGURUS
SWAMY
RAJIV MALHOTRA
DAVID FRAWLEY
STEPHEN KNAPP
WILLIAM DALRYMPLE
KONRAED ELST
FRANCOIS GAUTIER
NALIN KOHLI
GVL NARASIMHA RAO
SAMBIT PATRA
ASHOK PANDIT
ANUPAM KHER
KANGANA RANAUT
VIVEK AGNIHOTRI
MEENAKSHI LEKHI
SMRITI IRANI
PRASOON JOSHI
SWAPAN DASGUPTA
MADHU KISHWAR
SUDHIR CHAUDHARY
GEN GD BAKSHI
RSN SINGH
ARNAB GOSWAMI
NAVIKA KUMAR
ANAND NARASIMHAN
UDDHAV THACKREY
RAJ THACKREY
SHAZIA ILMI
CHANDA MITRA
SRI SRI RAVISHANKAR
SADGURU JAGGI VASUDEV
BABA RAMDEV
SPREAD ON SOCIAL MEDIA..
SPREAD BY WHATS APP
ASK MODI AND PRASAD AND ATTORNEY GENERAL FOR AN ACK
Have asked for ack from modi, prasad and attorney general.
DeleteTweeted and sent emails to governors and collectors. while sending for mlas I reached limit.
Rules and decisions are made by elected executive for governing the activities of the state.Every rule and decision will not be part of the laws.Even a school going child will laugh at the stupid judgements and arguments made by these lawyers and judges.
DeleteMailed and asked for ack-
yogi.adityanath@sansad.nic.in
cmup@nic.in
hgovup@gov.in
ravis@sansad.nic.in
secylaw-dla@nic.in
secy-jus@gov.in
kkvenu@vsnl.com
amitshah.mp@sansad.nic.in
contact@amitshah.co.in
supremecourt@nic.in
nalsa-dla@nic.in
gn.raju@nic.in
sk.mishra74@nic.in
minister.inb@gov.in
rmo@mod.nic.in
narendramodi1234@gmail.com
38ashokroad@gmail.com
ombirlakota@gmail.com
alokmittal.nia@gov.in
info@nibindia.in
k.biswal@nic.in
mljoffice@gov.in
WARNING
DeleteREADERS WHO DO NOT PARTICIPATE IN THIS SILENT REVOLUTION MUST NOT MAKE COMMENTS.. I HAVE NO TIME FOR YOU OR YOUR COMMENTS ..
TODAY I SAY THIS..
READERS WHO DISREGARD THE ABOVE WARNING, WILL SUFFER SEVERE CONSEQUENCES..
pmo: PMOPG/E/2020/0116120
Deleteemails sent to:
yogi.adityanath@sansad.nic.in
cmup@nic.in
hgovup@gov.in
ravis@sansad.nic.in
secylaw-dla@nic.in
secy-jus@gov.in
kkvenu@vsnl.com
amitshah.mp@sansad.nic.in
contact@amitshah.co.in
supremecourt@nic.in
nalsa-dla@nic.in
gn.raju@nic.in
sk.mishra74@nic.in
minister.inb@gov.in
rmo@mod.nic.in
narendramodi1234@gmail.com
38ashokroad@gmail.com
ombirlakota@gmail.com
alokmittal.nia@gov.in
info@nibindia.in
k.biswal@nic.in
mljoffice@gov.in
Sent and asked for acknowledgement
Deletecmup@nic.in
Yogi Adityanath • yogiadityanath72@gmail.com
contact@amitshah.co.in
supremecourt@nic.in
webmaster.indianarmy@nic.in
PRO IAF • proiaf.dprmod@nic.in
pronavy.dprmod@nic.in
info.nia@gov.in
information@cbi.gov.in
ed-del-rev@nic.in
hgovup@gov.in
ravis@sansad.nic.in
minister.inb@gov.in
Nirmala Sitharaman • nsitharaman@gmail.com
minister.hrd@gov.in
amitabh.kant@nic.in
secy.president@rb.nic.in
mvnaidu@sansad.nic.in
kkvenu@vsnl.com
contactus@rss.org
info@vhp.org
Abvp Kendra • abvpkendra@gmail.com
dmagr@nic.in
dmali@nic.in
dmall@nic.in
dmamethi-up@nic.in
dmaza@nic.in
dmbar@nic.in
dmbul@nic.in
dmetw@nic.in
dmgha@nic.in
dmluc@nic.in
dmmee@nic.in
dmvar@nic.in
dmsah@nic.in
dmskn@nic.in
dmmuz@nic.in
dmmor@nic.in
dmkap@nic.in
dmgbn@nic.in
divyayoga@divyayoga.com
Office of Sri Sri Ravi Shankar • secretariat@artofliving.org
koenraadelst@hotmail.com
info@ishafoundation.org
PMOPG/E/2020/0116096 MINHA/E/2020/02406
DEPOJ/E/2020/01072 MOIAB/E/2020/00563
DPLNG/E/2020/00283 MODEF/E/2020/00734
DMAFF/E/2020/00046. DSEHE/E/2020/01092
MOSJE/E/2020/00585 DLGLA/E/2020/00406
CAGAO/E/2020/01751. MINPA/E/2020/00162
LGVED/E/2020/00080. DOEAF/E/2020/00180
MOPRJ/E/2020/00293
PMOPG/E/2020/0116186
Deleteand email send
yogi.adityanath@sansad.nic.in
cmup@nic.in
hgovup@gov.in
ravis@sansad.nic.in
secylaw-dla@nic.in
secy-jus@gov.in
kkvenu@vsnl.com
amitshah.mp@sansad.nic.in
contact@amitshah.co.in
supremecourt@nic.in
nalsa-dla@nic.in
gn.raju@nic.in
sk.mishra74@nic.in
minister.inb@gov.in
rmo@mod.nic.in
narendramodi1234@gmail.com
38ashokroad@gmail.com
ombirlakota@gmail.com
alokmittal.nia@gov.in
info@nibindia.in
k.biswal@nic.in
mljoffice@gov.in
Master Ji,
DeleteSent mail to following email'ids
dcp.hq@delhipolice.gov.in, dcp-vigilance-dl@nic.in, Yogi.Adityanath@sansad.nic.in, Nirmala Sitharaman , chairperson-ncw@nic.in, wim-dfs@nic.in, jsrev@nic.in, jsabc-dea@nic.in, pjavadekar@gmail.com, pseam@mea.gov.in, jpnadda@gmail.com, pstohrm@gov.in, vijaympingale@gmail.com, ravis@sansad.nic.in, nitin.gadkari@nic.in, vaibhav.dange@nic.in, jscpg-mha@nic.in, request-hrd@gov.in, secy.inb@nic.in, pjamwal@gmail.com, spprabhu1@gmail.com, sambitswaraj@gmail.com, vasundhararajeofficial@gmail.com, governor@rbi.org.in, urijitpatel@rbi.org.in, office@arunjaitley.com, csoffice@nic.in, cs@punjab.gov.in, cmup@nic.in, feedback-mha@nic.in, minister.yas@nic.in, ms-ncw@nic.in, mib.inb@nic.in, mvnaidu@sansad.nic.in, m.subbarayan@nic.in, manoharparrikar@yahoo.co.in, sharma.rekha@gov.in, supremecourt@nic.in, sushma.sahu@gov.in, jsncw-wcd@nic.in, pp.chaudhary@sansad.nic.in, rawat.alok@gov.in, rsdalal@hry.nic.in, drhrshvardhan@gmail.com, lk-admin@nic.in, amitabh.kant@nic.in, 17akbarroad@gmail.com, ajaitley@sansad.nic.in, admin@nic.in, kashish.mittal@ias.nic.in, smritizirzni@gmail.com, smritizirani@sansad.nic.in, abvpasom@gmail.com, abvpbihar@rediffmail.com, abvpcentralup@gmail.com, abvpdelhi@gmail.com, abvpharyana@gmail.com, abvphp@gmail.com, abvpkarnataka@yahoo.com, abvpnestates@gmail.com, abvptn@gmail.com, abvputtaranchal@gmail.com, abvpwesternup@gmail.com, advanilk@sansad.nic.in, alokkumar.up@nic.in, arpolice@rediffmail.com, bjpandaman1990@rediffmail.com, bjphqo@gmail.com, bk.gupta@nic.in, chief.advisor@telangana.gov.in, chiefminister@karnataka.gov.in, chiefminister@kerala.gov.in, cm@maharashtra.gov.in, cm@mp.nic.in, cm_nagaland@yahoo.com, cmcell@tn.gov.in, cmo@nic.in, cmsect-jk@nic.in, contact@amitshah.co.in, contact@hindujagruti.org, contactus@rss.org, cs-manipur@nic.in, cs-mizoram@nic.in, cs@hry.nic.in, dg.prisons@kerala.gov.in, dgp-bih@nic.in, dgp-gs@gujarat.gov.in, dgp-mnp@nic.in, dgp-rj@nic.in, dgp.punjab.police@punjab.gov.in, dgp@and.nic.in, dgp@appolice.gov.in, dgp@keralapolice.gov.in, dgpmp@mppolice.gov.in, dgpms.mumbai@mahapolice.gov.in, dgptripura@yahoo.co.in, dirhq-cbdt@nic.in, eam@mea.gov.in, gandhim@nic.in, gandhim@sansad.nic.in, gaur_piyush@rediffmail.com, indiaportal@gov.in, info@vhp.org, jharkhandabvp@gmail.com, jp.nadda@sansad.nic.in, jse@nic.in, keralaprisons@gov.in, kk.rao@gov.in, lk.advani@sansad.nic.in, manoharpaaricar@yahoo.co.in, meghpol@hotmail.com, mpofficebhopal@gmail.com, nahmad@jharkhandpolice.gov.in, neera.bali@nic.in, nirmal_chouhan@hotmail.com, nsab.nscs@nic.in, office@wgs-cet.in, officelka@gmail.com, p.chhabra@nic.in, padma.ravi@nic.in, piyush@bjp.org, pnath@nic.in, police-chd@nic.in, pscm@hry.nic.in, ramvilas.paswan@sansad.nic.in, s.kalyanaraman@nic.in, s_mahajan@nic.in, sachin.rane@schneider-electric.com, secy.president@rb.nic.in
hgovup@gov.in,secylaw-dla@nic.in,secy-jus@gov.in,kkvenu@vsnl.com,amitshah.mp@sansad.nic.in
,nalsa-dla@nic.in,gn.raju@nic.in,sk.mishra74@nic.in,minister.inb@gov.in,rmo@mod.nic.in,narendramodi1234@gmail.com
38ashokroad@gmail.com,ombirlakota@gmail.com,alokmittal.nia@gov.in,info@nibindia.in,k.biswal@nic.in,mljoffice@gov.in
Posted in twitter
https://mobile.twitter.com/kannanlp/status/1237251855094177792
Thanks
Kannan
sent above comment to all the email ids posted by charishma. it appears as though yogi yday said he refuses to take down those posters, if that is true and he makes good on that then we have one bachelor with balls in the centre.
Deletehttps://www.ndtv.com/india-news/uttar-pradesh-no-shame-poster-removed-yogi-adtyanath-s-adviser-talks-of-options-2192497
also tweeted to a few lawyers and twitter ids regarding the same but not with your content.
https://twitter.com/dastardicious/status/1237062257332211713?s=20
https://twitter.com/dastardicious/status/1237059482598834177?s=20
https://twitter.com/dastardicious/status/1236637457212473344?s=20
rs prasad needs to be replaced. we need someone as adept is what jaishankar is who is not even a career politician to keep a muzzle on the courts.
I have again mailed to:
Deleteyogi.adityanath@sansad.nic.in
cmup@nic.in
hgovup@gov.in
Cc: amitshah.mp@sansad.nic.in,
contact@amitshah.co.in,
sk.mishra74@nic.in,
narendramodi1234@gmail.com,
ombirlakota@gmail.com,
alokmittal.nia@gov.in
Dear Captain,
DeleteTweeted!
Also mail Niti ayog, chief policy making organization of Central Government. webadm-niti@gov.in
DeleteSir,
DeleteSent to narendramodi1234@gmail.com
regards
Divij
Dear Capt Ajit sir,
DeleteSent....
https://twitter.com/IwerePm/status/1237282252700053504?s=19
Dear Captain,
DeleteEmailed to UP CM Office
Posted in twitter, however, coming under more replies.. So far unable to find a reason.. will check using other handles
https://twitter.com/NIA_India/status/1235238897346637826?s=20
E mailing to all addresses provided by Charisma.
DeleteYour Registration Number is : PMOPG/E/2020/0116418
DeleteRegistration Number : MINHA/E/2020/02414
Registration Number : DDPRO/E/2020/00113
Registration Number : MOIAB/E/2020/00567
capt ji send to pmo in both category public grievances & suggestion/feedback:-
Deleteregistration number PMOPG/E/2020/0116406
registration number PMOPG/E/2020/0116409 .
also emailed to all email address provided by others readers.
Tweeted to Amit Shah https://twitter.com/WordsAreMagic2/status/1237313508221517824
DeleteNamasthe Captain,
DeleteMail sent to all the ids mentioned in Kannan's message. around 140+ high profiles. I am sure all or many of them are already aware. Otherwise the posters of name and shame wont come up the moment you mentioned in the blog.
PS: Please take care of your fingers and use audio auto type with CAPS *ON.
Regards,
Krishna
Emails sent out, I know they are listening to you otherwise Yogi wouldn't have put up banners as suggested by you.
DeleteTweeted Sir
DeletePMOPG/E/2020/0116777. Posted on twitter.
Deletehttps://mobile.twitter.com/KshitijGundale1/status/1237392593199095811
PMOPG/E/2020/0116813
Deletehttps://twitter.com/prashantjani777/status/1237400460543303680
https://twitter.com/prashantjani777/status/1237401707228942336
https://twitter.com/prashantjani777/status/1237402104769191936
https://twitter.com/prashantjani777/status/1237402512501719041
+ many more
DEPOJ/E/2020/01084
DLGLA/E/2020/00409
Emailed to first email list to politician
DeleteTook screenshot and posted in many new people twitter
Mandar
Namaste Captain,
DeleteThe above message is sent to following email contacts :
dcp.hq@delhipolice.gov.in
dcp-vigilance-dl@nic.in
Yogi.Adityanath@sansad.nic.in
chairperson-ncw@nic.in
wim-dfs@nic.in
jsrev@nic.in
jsabc-dea@nic.in
pjavadekar@gmail.com
pseam@mea.gov.in
jpnadda@gmail.com
pstohrm@gov.in
vijaympingale@gmail.com
ravis@sansad.nic.in
nitin.gadkari@nic.in
vaibhav.dange@nic.in
jscpg-mha@nic.in
request-hrd@gov.in
secy.inb@nic.in
pjamwal@gmail.com
spprabhu1@gmail.com
sambitswaraj@gmail.com
vasundhararajeofficial@gmail.com
governor@rbi.org.in
urijitpatel@rbi.org.in
office@arunjaitley.com
csoffice@nic.in
cs@punjab.gov.in
cmup@nic.in
feedback-mha@nic.in
minister.yas@nic.in
ms-ncw@nic.in
mib.inb@nic.in
mvnaidu@sansad.nic.in
m.subbarayan@nic.in
manoharparrikar@yahoo.co.in
sharma.rekha@gov.in
supremecourt@nic.in
sushma.sahu@gov.in
jsncw-wcd@nic.in
pp.chaudhary@sansad.nic.in
rawat.alok@gov.in
rsdalal@hry.nic.in
drhrshvardhan@gmail.com
lk-admin@nic.in
amitabh.kant@nic.in
17akbarroad@gmail.com
ajaitley@sansad.nic.in
admin@nic.in
kashish.mittal@ias.nic.in
smritizirzni@gmail.com
smritizirani@sansad.nic.in
abvpasom@gmail.com
abvpbihar@rediffmail.com
abvpcentralup@gmail.com
abvpdelhi@gmail.com
abvpharyana@gmail.com
abvphp@gmail.com
abvpkarnataka@yahoo.com
abvpnestates@gmail.com
abvptn@gmail.com
abvputtaranchal@gmail.com
abvpwesternup@gmail.com
advanilk@sansad.nic.in
alokkumar.up@nic.in
arpolice@rediffmail.com
bjpandaman1990@rediffmail.com
bjphqo@gmail.com
bk.gupta@nic.in
chief.advisor@telangana.gov.in
chiefminister@karnataka.gov.in
chiefminister@kerala.gov.in
cm@maharashtra.gov.in
cm@mp.nic.in
cm_nagaland@yahoo.com
cmcell@tn.gov.in
cmo@nic.in
cmsect-jk@nic.in
contact@amitshah.co.in
contact@hindujagruti.org
contactus@rss.org
cs-manipur@nic.in
cs-mizoram@nic.in
cs@hry.nic.in
dg.prisons@kerala.gov.in
dgp-bih@nic.in
dgp-gs@gujarat.gov.in
dgp-mnp@nic.in
dgp-rj@nic.in
dgp.punjab.police@punjab.gov.in
dgp@and.nic.in
dgp@appolice.gov.in
dgp@keralapolice.gov.in
dgpmp@mppolice.gov.in
dgpms.mumbai@mahapolice.gov.in
dgptripura@yahoo.co.in
dirhq-cbdt@nic.in
eam@mea.gov.in
gandhim@nic.in
gandhim@sansad.nic.in
gaur_piyush@rediffmail.com
indiaportal@gov.in
info@vhp.org
jharkhandabvp@gmail.com
jp.nadda@sansad.nic.in
jse@nic.in
keralaprisons@gov.in
kk.rao@gov.in
lk.advani@sansad.nic.in
manoharpaaricar@yahoo.co.in
meghpol@hotmail.com
mpofficebhopal@gmail.com
nahmad@jharkhandpolice.gov.in
neera.bali@nic.in
nirmal_chouhan@hotmail.com
nsab.nscs@nic.in
office@wgs-cet.in
officelka@gmail.com
p.chhabra@nic.in
padma.ravi@nic.in
piyush@bjp.org
pnath@nic.in
police-chd@nic.in
pscm@hry.nic.in
ramvilas.paswan@sansad.nic.in
s.kalyanaraman@nic.in
s_mahajan@nic.in
sachin.rane@schneider-electric.com
secylaw-dla@nic.in
secy-jus@gov.in
mljoffice@gov.in
secy.president@rb.nic.in
Thanks and regards,
Hemanth
Guruji pranam
ReplyDeleteWish you and your family happy holi......
IN KERALA WE DO NOT CELEBRATE HOLI..
ReplyDeleteHOLIKA IS A GREAT HEROINE FOR THE DANAVA CIVILIZATION..
I AM A DANAVA..
IN KATHAKALI , THE PLANETs OLDEST SONG AND DANCE FORM, THE FEMALE CHARACTER ON THE STAGE WITH CAPE IS HOLIKA..
THE ORIGINAL FACADE OF A KATHAKALI DANCER IS HOLIKAs BROTHER KING HIRANYAKASHIPU.
HIRANYAKASHIPUs DESCENDANTS ARE VIROCHANA AND HIS SON MAHABALI..
VIROCHANA RULES THE WHOLE PLANET.. IN PERU HE IS GOD..
https://ajitvadakayil.blogspot.com/2019/07/secrets-of-12000-year-old-machu-picchu.html
CHECK OUT HOLIKAs FACE IN THE POST BELOW--
http://ajitvadakayil.blogspot.com/2018/10/sanatana-dharma-hinduism-exhumed-and.html
ALL VISHNU AVATARS ARE ROOTED IN KERALA.. SANATANA DHARMA ORIGINATED IN KERALA..
http://ajitvadakayil.blogspot.com/2019/09/onam-our-only-link-to-planets-oldest.html
THE RICHEST KING ON THE PLANET FROM ANTIQUITY TILL 1799 AD WAS THE CALICUT KING-- HINDUS THIYYAS..
http://ajitvadakayil.blogspot.com/2019/10/perumal-title-of-calicut-thiyya-kings.html
ONAM IS CELEBRATED FOR KERALA KING MAHABALI..
LIES WONT WORK..
KERALA IS THE ONLY PLACE WHERE WE CELEBRATE THE VICTOR ( VISHNU AS VAMANA / NARASIMHA ) AND THE VANQUISHED MAHABALI / HIRANYAKASHIPU / HOLIKA.. SUCH IS THE MATURITY
WHY THERE IS SO MUCH JOY BELOW-- IT IS IN THE DNA..
https://www.youtube.com/watch?v=sxZsOXsePHo
capt ajit vadakayil
..
PUT ABOVE COMMENT IN WEBSITES OF--
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ALL KERALA MOs AND MLAs
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SHAZIA ILMI
CHANDA MITRA
SRI SRI RAVISHANKAR
SADGURU JAGGI VASUDEV
BABA RAMDEV
RAHUL EASHWAR
FAT WOMAN PADMA PILLAI
MATA AMRITANANDAMAYI
SPREAD ON SOCIAL MEDIA..
SPREAD BY WHATS APP
Sent To
DeleteCM's , Dgp's BjP Speakers
PMOPG/E/2020/0116191 MINHA/E/2020/02409. DEPOJ/E/2020/01073
MOIAB/E/2020/00564 DPLNG/E/2020/00284
DCLTR/E/2020/00156. DSEHE/E/2020/01093
Your Registration Number is : PMOPG/E/2020/0116407
Deletehttps://twitter.com/prashantjani777/status/1237422234483986433
Deletehttps://twitter.com/prashantjani777/status/1237424569671090176
Master Ji,
ReplyDeletePosted yesterday night
https://mobile.twitter.com/kannanlp/status/1237092435009757184
Thanks
Happy Holi and best wishes to you Captain Sir, Guruma and family, regards-Anukul
ReplyDeleteCaptain Jee,
ReplyDeleteHappy Holi to you and your family and all your readers and well wishers.
SOMEBODY ASKED ME
ReplyDeleteCAPTAIN—
WHY DOES KERALA NOT CELEBRATE DIWALI, WHICH THE WHOLE OF INDIA CELEBRATES AS THE NO 1 FESTIVAL OF HINDUS ? AFTER ALL FESTIVALS CARRY ON IN TIME .. THESE ARE BASED ON PERMANENT SENTIMENTS..
WELL –
DIWALI IS A FESTIVAL TO CELEBRATE THE VICTORY OF DEVAS OVER DANAVAS IN THE SAMUDRA MANTHAN EPISODE ..
BEFORE I START OFF , I MUST REMIND ALL THAT ALL HINDU GODS ARE COSMIC ELLEGORIES EXCEPT VISHNU AVATARS MORTALS RAMA/ KRISHNA/ AYYAPPA.. NO OTHER GODS HAVE AVATARS..
WHEN THE COSMOS WAS IN DANGER OF DEVASTATION, DEVAS INVITED DANAVAS TO PERFOR SAMUDRA MANTHAN ( CHURNING OF COSMIC WATERS )..
VISHNU TOOK HIS SECOND AVATAR OF KURMA ( TORTOISE )..
ACCORDINGLY MOUNT MERU WAS PLACED ON THE BACK OF KURMA AS A DASHER.. FIVE HEADED SNAKE VASUKI WAS USED AS A ROPE.. 54 DANAVAS WHO WERE MORE ROBUST WAS TO HOLD THE HEAD END OF FIRE BREATHING VASUKI.. 54 DEVAS WOULD HOLD THE TAIL END AND DO THEMANTHAN TO DERIVE AMRIT.
THE JOB WAS SUCCESSFULLY DONE.. AND GID DHANWANTARI ROSE FROM THE COSMIC WATERS HOLDING FOUR GOODIES IN HIS FOUR HANDS.. ONE WAS A POT OF AMRIT ( DIVINE NECTAR ) THE FRUIT OF THE MANTHAN..
http://ajitvadakayil.blogspot.com/2013/03/secrets-of-shankh-or-conch-product-of.html
IN BETWEEN THERE WAS A CRISIS OF COSMIC OCEAN PLOYMERISATION AND SHIVA DRANK UP THE POISON AND SAVED THE COSMOS FROM IMMINENT DANGER .
http://ajitvadakayil.blogspot.com/2014/09/navratri-festival-durga-puja-capt-ajit.html
http://ajitvadakayil.blogspot.com/2011/10/ayudha-puja-9th-day-of-navratri.html
BUT AFTER THE AMRIT WAS OBTAINED, DEVAS PLAYED FOUL AND DEPRIVED THE DANAVAS OFF THEIR 50% SHARE OF AMRIT.. THIS DESPITE ALL 54 DANAVAS BEING BURNT ALL OVER BY THE FIRE OF VASUKI..
HOWEVER THE LEADER OF DANAVAS SWARBANU , SLIPPED INTO THE GROUP OF DEVAS AND SAT DOWN FOR HIS SHARE OF AMRIT..
AS SOON AS SWARBANU TOOK A GULP OF AMRIT, VISHNU CUT OFF HIS HEAD WITH HIS SUDRASHANA CHAKRA .. THE HEAD BECAME RAHU AND THE REST OF THE DECAPITATED BODY BECAME KETU..
IN HINDU ASTROLOGY WE USE RAHU, KETU AND MOON INSTEAD OF URANUS / NEPTUNE AND PLUTO..
RAHU ( MATERIAL ) AND KETU ( SPIRITUAL ) ARE ASCENDING AND DESCENDING NODES OF THE MOON, POINTS OF INTERSECTION OF ORBITS OF THE MOON AND THE EARTH AND TREATED AS SHADOWY PLANETS IN ASTROLOGY .. .
YOUR KETU IS WHO YOU WERE IN YOUR PAST LIFE WHILE YOUR RAHU IS WHO YOU WERE MEANT TO BECOME IN THIS LIFE ( PSYCHO-SOMATIC )..
THE STRENGTH OF RAHU AND KETU WHEN COMPARED TO OTHER PLANETS IS VERY HIGH BY THOUSANDS OF TIMES OR MORE.
KETU DEALS WITH THE PAST KARMA AND RAHU WITH THE FUTURE. RAHU AND KETU ARE THE POINTS WHERE THE GREAT PLANES OF SPIRIT AND MATTER INTERSECT, OR WHERE SPIRIT PASSES THROUGH MATTER.. THIS IS QUANTUM PHYSICS
THE DESCENANT OF SWARBANU IS THE DANAVA HIRANYAKSHA KILLED BY VISHNU AVATAR VARAHA ( BOAR )..
KERALA KING HIRANYAKASHIPU WHO WAS KILLED BY VISHNU AVATAR NARASIMHA IS THE ELDER BROTHER OF HIRANYAKSHA..
AFTER MAHABALI WAS BANISHED TO PATALA ( PERU ) BY VISHNU AVATAR VAMANA, THE DESCENDANT OF SWARBANU/ HIRANYAKSHA , “SWARBANU JUNIOR “ BECAME KERALA KING..
WE PEOPLE OF KERALA DON’T EVEN REMEMBER TIS GREAT KING SWARBANU , WHO IN MY OPINION IS GREATER THAN KING MAHABALI AND HIS FATHER VIROCHANA..
KERALA KING SWARBANU BUILT THE SUDAN PYRAMIDS 12,000 YEARS AGO.. THESE ARE NATURAL WATER EXTRACTER FROM THE GREAT NUBIAN AQUIFER.
http://ajitvadakayil.blogspot.com/2019/08/secrets-of-bent-pyramid-at-dahsur-egypt_1.html
THE BAALABEK TEMPLE IS A SHIVA TEMPLE, BASED ON ADI SHIVA OF DANAVA CIVILIZATION ( POWERFUL DIETY MUTHAPPAN )..
http://ajitvadakayil.blogspot.com/2019/08/secrets-of-12000-year-old-baalbek.html
IN KERALA MUSLIMS AND CHRISTIANS WORSHIP AT MUTHAPPAN TEMPLE.. JUST LIKE SABARIMALA.
http://ajitvadakayil.blogspot.com/2012/10/muthappan-deity-of-north-kerala-capt.html
http://ajitvadakayil.blogspot.com/2013/06/kottiyoor-temple-oldest-temple-and.html
WE HAVE A PRICELESS CULTURE. LET IS NOT SURRENDER THIS TO BULLSHIT “TRICK OR TREAT” WESTERN HALLOWEEN OR GUY FAWKES DAY OR THANKSGIVING OR WHATEVER..
Capt ajit vadakayil
..
PUT ABOVE COMMENT IN WEBSITES OF--
DeleteCM PINARAYI VIJAYAN
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ALL KERALA COLLECTORS
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E SREEDHARAN
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CJI BOBDE
SUPREME COURT JUDGES/ LAWYERS
ATTORNEY GENERAL
LAW MINISTER PRASAD / MINISTRY CENTRE AND STATES
CHIEF JUSTICES OF ALL STATE HIGH COURTS
I&B MINISTER / MINISTRY
NSA
AJIT DOVAL
RAW
IB
CBI
NIA
ED
DEFENCE MINISTER/ MINISTRY
ALL 3 ARMED FORCE CHIEFS -- PLUS TOP CDS CHIEF
ALL DGPs OF INDIA
ALL IGs OF INDIA
COLLECTORS OF MAJOR CITIES OF INDIA
ALL CMs OF INDIA
ALL STATE GOVERNORS
EVERY MP OF INDIA
EVERY MLA OF INDIA
NCERT
EDUCATION MINISTRY/ MINISTER
NITI AYOG
AMITABH KANT
PRESIDENT OF INDIA
VP OF INDIA
SPEAKER LOK SABHA
SPEAKER RAJYA SABHA
RSS
AVBP
VHP
MOHAN BHAGWAT
RAM MADHAV
SOLI BABY
FALI BABY
KATJU BABY
SALVE BABY
MOHANDAS PAI
RAJEEV CHANDRASHEKHAR
PGURUS
SWAMY
RAJIV MALHOTRA
DAVID FRAWLEY
STEPHEN KNAPP
WILLIAM DALRYMPLE
KONRAED ELST
FRANCOIS GAUTIER
NALIN KOHLI
GVL NARASIMHA RAO
SAMBIT PATRA
ASHOK PANDIT
ANUPAM KHER
KANGANA RANAUT
VIVEK AGNIHOTRI
MEENAKSHI LEKHI
SMRITI IRANI
PRASOON JOSHI
SWAPAN DASGUPTA
MADHU KISHWAR
SUDHIR CHAUDHARY
GEN GD BAKSHI
RSN SINGH
ARNAB GOSWAMI
NAVIKA KUMAR
ANAND NARASIMHAN
UDDHAV THACKREY
RAJ THACKREY
SHAZIA ILMI
CHANDA MITRA
SRI SRI RAVISHANKAR
SADGURU JAGGI VASUDEV
BABA RAMDEV
RAHUL EASHWAR
FAT WOMAN PADMA PILLAI
MATA AMRITANANDAMAYI
WENDY DONIGER
SHELDON POLLOCK
AUDREY TRUSCHKE
THAMBI SUNDAR PICHAI
SATYA NADELLA
CEO OF WIKIPEDIA
QUORA CEO ANGELO D ADAMS
QUORA MODERATION TEAM
KURT OF QUORA
GAUTAM SHEWAKRAMANI
SPREAD ON SOCIAL MEDIA..
SPREAD BY WHATS APP
Dear captain...
Deletei send all your messages in whatsapp and sometimes bloglinks as my status....
Just to inform you....will refrain from asking wuires in future...
Thanks..
Jaihind jaibharat.
Dear Capt Ajit sir,
DeleteSent... https://twitter.com/IwerePm/status/1237284879399661570?s=19
Your Registration Number is : PMOPG/E/2020/0116396
Deletehttps://www.quora.com/Who-are-all-the-famous-people-that-studied-from-Kendriya-Vidyalaya
ReplyDeleteAjit Vadakayil is the only name worth being in this list or any list worth appreciating!
DeleteSir you are the best and I guess all other names pale in contrast to your name!!love you from the core of my heart!!
DeleteProud that I share the same school with you Captain.
DeleteDear Guruji...Happy Holi to you and all the readers.
ReplyDeletePranaam
https://timesofindia.indiatimes.com/city/hyderabad/t-grows-golden-rice-thats-fit-for-diabetic-diet-soft-on-heart/articleshow/74558815.cms
ReplyDeletehttp://ajitvadakayil.blogspot.com/2015/09/gm-ir8-rice-high-amylose-content.html
Bobde is an ambitious man. Patriotism is a poor vocation. At the most you get your mug on five rupee stamp and a public holiday declared in your name.
ReplyDeleteMake hay while sun shines .eh?
Happy Dulhandi captain ji
ReplyDeleteWish You and your family a colorful, joyful Holi and Rangpanchami Ajit Sir!! Sorry Been not active recently..
ReplyDeleteHappy Holi Captain ji and all readers ❤
ReplyDeleteDear Capt Ajit sir,
ReplyDeleteJio is supposed to pay 1.25 lakh crore by 31 March, likewise many big ticket Co.s in NPAs list should be reigned in under bankruptcy act....so all this Yes bank and Scindia joining BJP is a huge diversion from the rot in the system, which is shut from public, not exposed...why ?
Modiji is a big fraud, he can give hajaar good jhankari about illegal Collegium judiciary, abolishing Rajyasabha, income tx etc in mann ki baat or as caller tune like corona virus info ....why not...unless people demand, nothing gets done...to make India #1.
Dear Ajit Sir,
ReplyDeleteIt looks so strange, opportunist in BJP like Tajinder Bagga & ilk are celebrating, tweeting "Welcome to family" for Jyotiraditya Scindia.
What family?
BJP got this Scindia just bcoz he got MLAs in MP.
That's all.
Scindia made a demand to Mainos to get a Member of Parliament seat, Rajya Sabha.
Irked by dilly delaying tactics of Congress, finally Scindia called off to quit.
Opportunist are being celebrated & we are onlookers.
Ajit Sir, what do you think about Scindia?
You are very tough on traitor clans
WHAT DOES THE INDIAN LAW SAY ABOUT TREASON AND SEDITION?
DeleteTHE MAIN DIFFERENCE BETWEEN INDIAN CONSTITUTION AND IPC IS THAT CONSTITUTION DESCRIBES ON THE RESTRICTIONS OF A STATE WHEREAS IPC DESCRIBES ON RESTRICTIONS OF PEOPLE.
THE CONSTITUTION CONSTITUTES RIGHTS AND POWERS AND ALSO THE IPC.
IPC DEALS WITH PUNISHMENTS FOR THOSE WHO MISUSE THE POWER AND VIOLATE THE RIGHTS
https://devgan.in/ipc/chapter_06.php
WHEN A VANADLISER IS FUNDED BY PAKISTANI ISI , HE BECOMES A TRAITOR ENGAGED IN TREASON AGAINST THE WATAN.
THERE ARE MANY JUDGES IN FOREIGN PAYROLL – THEY COME UNDER THE UMBRELLA OF “TREASON”..
THE NAXAL RED CORRIDOR WAS CREATED BY TRAITR JUDGES IN DEEP STATE PAYRLL. THIS IS TREASON..
SECTION 121 OF IPC-- WHOEVER WAGES WAR AGAINST THE GOVERNMENT OF INDIA, OR ATTEMPTS TO WAGE SUCH WAR, OR ABETS THE WAGING OF SUCH WAR, SHALL BE PUNISHED WITH DEATH, OR IMPRISONMENT FOR LIFE AND SHALL ALSO BE LIABLE TO FINE.
SEDITION IS THE ILLEGAL ACT OF INCITING PEOPLE TO RESIST OR REBEL AGAINST THE GOVERNMENT IN POWER
APT AJIT VADAKAYIL SAYS--- WHEN SOMEONE ABUSES THE WATAN AND CALLS FOR DISMEMBERMENT OF BHARATMATA , IT IS TREASON. THE PUNISHMENT IS DEATH .....
ABUSING A RULER ON A THRONE OR ANY MORTAL IS NOT SEDITION..IT IS FREE SPEECH..
capt ajit vadakayil
..
Thank you Dear Ajit Sir!!!
DeleteWithout an iota of doubt,I got it.
Its crystal clear and you are very close to our heart.
Happy Holi to you & yours Captain!!
ReplyDeleteYour Registration Number is : PMOPG/E/2020/0116845
ReplyDeleteSomebody asked me Captain - Why does kerala not celebrate diwali ...
https://www.timesnownews.com/india/article/yogi-govt-not-to-take-down-name-and-shame-hoardings-to-challenge-high-court-order/563088
ReplyDeleteRespect ajit sir,
ReplyDeleteHappy holi to you and your family sir,
Regards
Gunjan arya
Shraddheya Shri Gurudev
ReplyDeleteAnother stupid fellow , the person which runs this twitter handle True Indology must have severe heartburn for truth , insulting you and ur reader for calling out Radha , tulsidas , etc as poison injection by the whites --
https://twitter.com/TIinExile/status/1237429987898060801?s=20
https://twitter.com/TIinExile/status/1237424665682796544?s=20
Hope these people learns to accept truth someday gracefully.
Pranam
Happy Holi to you Capt. And your family.
ReplyDeleteThank you for taking the precious time out of your life and doing this service to India.
-Saurin
####### SUBJECT- THE INDIAN JUDICIARY DEMANDS TO KNOW BY "WHICH LAW" UP CM YOGI ADITYANATH "NAMED AND SHAMED" PAKISTANI ISI FUNDED VANDALISERS WHO WERE CAUGHT ON VIDEO TAPE..########
ReplyDeleteTHIS IS WHY THE WHOLE WORLD SAYS THAT INDIAN JUDGES ARE THE MOST STUPID ON THE PLANET AND INDIAN JUDICIARY IS THE MOST USELESS ...
WE ALL KNOW THAT INDIAN "LAWYERS TURNED JUDGES " ARE THE BOTTOM DREGS OF THE SCHOOL CEREBRAL BARREL AND DISCARDS OF THE LOSER LAWYER POOL..
UNLIKE INDIA WHICH HAS A JUDGE SYSTEM USA HAS A JURY SYSTEM..
JURY CONSISTS OF ORDINARY PEOPLE WHO NOW SHIT ABOUT THE LAW OR THE CONSTITUTION..
THE IDEA IS TO DELIVER SUBJECTIVE NATURAL JUSTICE ( DHARMA ) TO THE PEOPLE , NOT ENFORCE BLIND OBJECTIVE LAW..
THE JUDGE ONLY CONDUCTS AND MONITORS THE PROCEEDINGS IN COURT LISPING WORDS LIKE "OBJECTION SUSTAINED", "OBJECTION OVER RULED " ETC..
INDIA HAD A JURY SYSTEM LIKE USA AND OTHER MAJOR FIRST WORLD NATIONS..
INDIAN JURY SYSTEm WAS ABOLISHED AFTER THE NAVAL COMMANDER NANAVATI CASE.
NANAVATI WAS A TATTU PARSI WHO WAS MARRIED TO A NYMPHOMANIAC BRITISH WHITE WOMAN NAMED SYLVIA WHO NEEDED A HARD POUNDING IN ALL HER THREE ORIFICES EVERY DAY..
CUCKOLD NANAVATI ALLOWED A RICH AND VIRILE SINDHI NAMED AHUJA TO KEEP HIS WIFE SEXUALLY SATISFIED.
ALL WAS HUNKY DORY TILL NANAVATI READ A LATTER WHERE HIS WIFE BEGS HER LOVER TO MARRY HER .. A FURIOUS NANAVATI KILLED AHUJA..
RAM JETHNALANI KICKED HIS OWN SINDHI COMMUNITY IN THE TEETH AND BECAME FAMOUS..
READ ALL 8 PARTS OF THE POST BELOW--
https://ajitvadakayil.blogspot.com/2019/01/justice-be-damned-enforce-law-not-any.html
https://ajitvadakayil.blogspot.com/2020/01/we-people-are-done-with-illegal.html
ABOVE THE CONSTITUTION LIES "WE THE PEOPLE"...
ABOVE THIS LIES "THE WATAN"..
ABOVE ALL LIES "THE RULE OF DHARMA".. THE WEST CALLS THIS NATURAL LAW..
PM AND LAW MINISTER ARE NAPUNSAKS OF THE FIRST ORDER.. THEY ALLOW PEA BRAINED JUDGES TO PLAY GOD..
IN THEIR WATCH SUPREME COURT STRUCK DOWN NJAC, WHICH WAS PASSED WITH 100% UNANIMITY IN BOTH LOK / RAJYA SABHA-- AND SIGNED BY THE PRESIDENT..
OUR TRAITOR JUDICIARY CREATED THE NAXAL RED CORRIDOR AND CAUSES ETHNIC CLEANSING OF KASHMIRI PANDITS..
JUDICIARY HAS NO POWERS TO STOP ELECTED EXECUTIVE FROM FOLLOWING THE RULE OF DHARMA..
MANY INDIAN JOURNALISTS , COLLEGIUM JUDGES , PROFESSORS OF SOCIAL SCIENCES IN ELITE INDIAN COLLEGES ARE IN DEEP STATE PAYROLL…
THE WHITE JEW KNOWS THAN IN 13 YEARS INDIA WILL BE THIS PLANETs NO 1 SUPERPOWER AND IT PLANS TO MAKE INDIA IMPLODE FROM WITHIN..
JUDICIARY HAS NO POWERS TO INTERFERE IN BHARATMATAs INTERNAL AND EXTERNAL SECURITY..
CONSTITUTION DOES NOT PROVIDE PRIVACY TO PAKISTANI ISI PAYROLL DESH DROHIS WHO ENGAGE IN TREASON AND SEDITION..
http://ajitvadakayil.blogspot.com/2017/08/right-to-privacy-in-india-is-not.html
WE KNOW HOW MODI/ PRASAD STOOD IN THE SHADOWS AND ALLOWED DEEP STATE PAYROLL JUDICIARY TO KICK BHARATMATA INTO THE KOSHER ADULTERY/ HOMOSEXUALITY MANDI.. INDIA IS NO LONGER A MORAL NATION..
WHY HAS JUDICIARY LEGALIZED BITCOIN WHICH IS USED TO FUND ISLAMIC MERCENARIES IN KASHMIR AND DESH DROHIS IN INDIA?...
WE ASK MODI , SUMMON CJI BOBDE -- ASK HIM WHY HE HAS NOT DECLARED THAT UNHCHR HAS NO POWERS TO FILE A PETITION AGAINST CAA..
HARSH MANDER WHO TRIGGERED THE DELHI ANTI-CAA MUSLIM RIGHTS RIOTS IS AN AGENT OF JEW SOROS WHO HAS DONATED ONE BILLION USD TO FIGHT HINDUS AND CREATE DISCORD IN INDIA..
JUDICIARY HAS NO POWERS TO INTERFERE IN BHARATMATAs INTERNAL AND EXTERNAL SECURITY..
PAKISTANI ISI FUNDED NGOs SENT GUJARAT HOME MINISTER AMIT SHAH INTO JAIL FOR A PAKISTANI ISI FUNDED ISLAMIC TERRORIST NAME SOHRABUDDIN.. CAN THIS HAPPEN ANYWHERE ELSE ON THE PLANET?..
ALL THE JUDICIARY CAN DO IS TO INTERPRET THE CONSTITUTION..BUT HEY THEY RULE INDIA.. CJI GOGOI TREATED CBI DIRECTOR LIKE A CLASS DUNCE...
BHARATMATA WILL NOT SURVIVE THIS DECADE IF WE DO NOT CLEANSE THE ILLEGAL COLLEGIUM JUDICIARY OF TRAITORS IN FOREIGN PAYROLL…
WE THE PEOPLE WATCH.. RETRIBUTION AWAITS..
capt ajit vadakayil
..
PUT ABOVE COMMENT IN WEBSITES OF---
DeleteCM YOGI ADITYANATH
GOVERNOR OF UP
DGP OF UP
IG OF UP
ALL COLLECTORS OF UP
PMO
PM MODI
AMIT SHAH
HOME MINISTRY
CJI BOBDE
SUPREME COURT JUDGES/ LAWYERS
ATTORNEY GENERAL
LAW MINISTER PRASAD / MINISTRY CENTRE AND STATES
CHIEF JUSTICES OF ALL STATE HIGH COURTS
I&B MINISTER / MINISTRY
NSA
AJIT DOVAL
RAW
IB
CBI
NIA
ED
DEFENCE MINISTER/ MINISTRY
ALL 3 ARMED FORCE CHIEFS -- PLUS TOP CDS CHIEF
ALL DGPs OF INDIA
ALL IGs OF INDIA
COLLECTORS OF MAJOR CITIES OF INDIA
ALL CMs OF INDIA
ALL STATE GOVERNORS
EVERY MP OF INDIA
EVERY MLA OF INDIA
NCERT
EDUCATION MINISTRY/ MINISTER
NITI AYOG
AMITABH KANT
PRESIDENT OF INDIA
VP OF INDIA
SPEAKER LOK SABHA
SPEAKER RAJYA SABHA
RSS
AVBP
VHP
MOHAN BHAGWAT
RAM MADHAV
SOLI BABY
FALI BABY
KATJU BABY
SALVE BABY
MOHANDAS PAI
RAJEEV CHANDRASHEKHAR
PGURUS
SWAMY
RAJIV MALHOTRA
DAVID FRAWLEY
STEPHEN KNAPP
WILLIAM DALRYMPLE
KONRAED ELST
FRANCOIS GAUTIER
NALIN KOHLI
GVL NARASIMHA RAO
SAMBIT PATRA
ASHOK PANDIT
ANUPAM KHER
KANGANA RANAUT
VIVEK AGNIHOTRI
MEENAKSHI LEKHI
SMRITI IRANI
PRASOON JOSHI
SWAPAN DASGUPTA
MADHU KISHWAR
SUDHIR CHAUDHARY
GEN GD BAKSHI
RSN SINGH
ARNAB GOSWAMI
NAVIKA KUMAR
ANAND NARASIMHAN
UDDHAV THACKREY
RAJ THACKREY
SHAZIA ILMI
CHANDA MITRA
SRI SRI RAVISHANKAR
SADGURU JAGGI VASUDEV
BABA RAMDEV
THAMBI SUNDAR PICHAI
SATYA NADELLA
CEO OF WIKIPEDIA
QUORA CEO ANGELO D ADAMS
QUORA MODERATION TEAM
KURT OF QUORA
GAUTAM SHEWAKRAMANI
SPREAD ON SOCIAL MEDIA..
SPREAD BY WHATS APP
ASK MODI AND PRASAD AND ATTORNEY GENERAL FOR AN ACK
COMMENTS IN THIS POST IS NOW CLOSED..
ReplyDelete