THIS POST IS CONTINUED FROM PART 2 , 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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https://ajitvadakayil.blogspot.com/2019/08/what-artificial-intelligence-cannot-do.html
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https://ajitvadakayil.blogspot.com/2019/10/what-artificial-intelligence-cannot-do.html
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https://ajitvadakayil.blogspot.com/2019/10/what-artificial-intelligence-cannot-do_29.html
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https://ajitvadakayil.blogspot.com/2019/11/what-artificial-intelligence-cannot-do.html
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https://ajitvadakayil.blogspot.com/2019/11/what-artificial-intelligence-cannot-do_4.html
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https://ajitvadakayil.blogspot.com/2019/11/what-artificial-intelligence-cannot-do_25.html
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https://ajitvadakayil.blogspot.com/2019/11/what-artificial-intelligence-cannot-do_88.html
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https://ajitvadakayil.blogspot.com/2019/11/what-artificial-intelligence-cannot-do_15.html
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https://ajitvadakayil.blogspot.com/2019/12/what-artificial-intelligence-cannot-do_94.html
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https://ajitvadakayil.blogspot.com/2019/12/what-artificial-intelligence-cannot-do.html
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https://ajitvadakayil.blogspot.com/2019/12/what-artificial-intelligence-cannot-do_1.html
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https://ajitvadakayil.blogspot.com/2020/02/what-artificial-intelligence-cannot-do.html
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https://ajitvadakayil.blogspot.com/2020/02/what-artificial-intelligence-cannot-do_21.html
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https://ajitvadakayil.blogspot.com/2020/02/what-artificial-intelligence-cannot-do_27.html
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https://ajitvadakayil.blogspot.com/2020/02/what-artificial-intelligence-cannot-do_28.html
https://ajitvadakayil.blogspot.com/2020/02/what-artificial-intelligence-cannot-do_28.html
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https://ajitvadakayil.blogspot.com/2020/03/what-artificial-intelligence-cannot-do.html
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https://ajitvadakayil.blogspot.com/2020/03/what-artificial-intelligence-cannot-do_10.html
SOMEBODY CALLED ME UP AND ASKED ME..
CAPTAIN—
WHO IS MUHAMMAD IBN MUSA AL-KHWARIZMI WHOM MODERN HISTORIANS ARE CALLING THE “FATHER OF COMPUTER SCIENCE” AND THE “FATHER OF ALGORITHMS”??.
LISTEN –
ARAB MUHAMMAD IBN MUSA AL-KHWARIZMI WAS A BRAIN DEAD FELLOW WHOSE ENTIRE WORK WAS SOLD TO HIM TRANSLATED INTO ARABIC BY THE CALCIUT KING FOR GOLD.
THE CALICUT KING MADE HIS MONEY BY NOT ONLY SELLING SPICES –BUT KNOWLEDGE TOO.
HE MAMANKAM FEST HELF AT TIRUNAVAYA KERALA BY THE CALICUT KING EVERY 12 YEARS WAS AN OCCASION WHERE KNOWLEDGE WAS SOLD FOR GOLD.
http://ajitvadakayil.blogspot.com/2019/10/perumal-title-of-calicut-thiyya-kings.html
EVERY ANCIENT GREEK SCHOLAR ( PYTHAGORAS/ PLATO/ SOCRATES ETC ) EXCEPT ARISTOTLE STUDIED AT KODUNGALLUR UNIVERSITY.. THE KERALA SCHOOL OF MATH WAS PART OF IT.
OUR ANCIENT BOOKS ON KNOWLEDGE DID NOT HAVE THE AUTHORs NAME AFFIXED ON THE COVER AS WE CONSIDERED BOOKS AS THE WORK OF SOULS , WHO WOULD BE BORN IN ANOTHER WOMANs WOMB AFTER DEATH.
THE GREEKS TOOK ADVANTAGE OF THIS , STOLE KNOWLEDGE FROM KERALA / INDIA AND PATENTED IT IN THEIR OWN NAMES, WITH HALF BAKED UNDERSTANDING .
WHEN THE KING OF CALICUT CAME TO KNOW THIS, HE BLACKBALLED GREEKS FROM KODUNGALLUR UNIVERSITY .. AND SUDDENLY ANCIENT GREEK KNOWLEDGE DRIED UP LIKE WATER IN THE HOT DESERT SANDS.
LATER THE CALICUT KING SOLD TRANSLATED INTO ARABIC KNOWLEDGE TO BRAIN DEAD ARABS LIKE MUHAMMAD IBN MUSA AL-KHWARIZMI FOR GOLD..
THESE ARAB MIDDLE MEN SOLD KNOWLEDGE ( LIKE MIDDLEMEN FOR SPICES) TO WHITE MEN FOR A PREMIUM.
FIBONACCI TOOK HIS ARABIC WORKS TO ITALY FROM BEJAYA , ALGERIA.
http://ajitvadakayil.blogspot.com/2010/12/perfect-six-pack-capt-ajit-vadakayil.html
EVERY VESTIGE OF ARAB KNOWLEDGE IN THE MIDDLE AGES WAS SOLD IN TRANSLATED ARABIC BY KODUNGALLUR UNIVERSITY FOR GOLD..
FROM 800 AD TO 1450 AD KODUNGALLUR UNIVERSITY OWNED BY THE CALICUT KING EARNED HUGE AMOUNT OF GOLD FOR SELLING READY MADE TRANSLATED KNOWLEDGE ..
THIS IS TIPU SULTANS GOLD WHO STOLE IT FROM NORTH KERALA TEMPLE VAULTS.. ROTHSCHILD BECAME THE RICHEST MAN ON THIS PLANET BY STEALING TIPU SUTANs GOLD IN 1799 AD.
http://ajitvadakayil.blogspot.com/2011/10/tipu-sultan-unmasked-capt-ajit.html
WHEN TIPU SULTAN WAS BLASTING TEMPLE VAULTS, LESS THAN 1% OF THE GOLD WAS SECRETLY TRANSFERRED TO SOUTH KERALA ( TRADITIONAL ENEMIES ) OF THE CALICUT KING. LIKE HOW SADDAM HUSSAIN FLEW HIS FIGHTER JETS TO ENEMY IRAN .
THIS IS THE GOLD WHICH WAS UNEARTHED FROM PADMANABHASWAMY TEMPLE..
http://ajitvadakayil.blogspot.com/2013/01/mansa-musa-king-of-mali-and-sri.html
ALGORITHMS ARE SHORTCUTS PEOPLE USE TO TELL COMPUTERS WHAT TO DO. AT ITS MOST BASIC, AN ALGORITHM SIMPLY TELLS A COMPUTER WHAT TO DO NEXT WITH AN “AND,” “OR,” OR “NOT” STATEMENT.
THE ALGORITHM IS BASICALLY A CODE DEVELOPED TO CARRY OUT A SPECIFIC PROCESS. ALGORITHMS ARE SETS OF RULES, INITIALLY SET BY HUMANS, FOR COMPUTER PROGRAMS TO FOLLOW.
A PROGRAMMING ALGORITHM IS A COMPUTER PROCEDURE THAT IS A LOT LIKE A RECIPE (CALLED A PROCEDURE) AND TELLS YOUR COMPUTER PRECISELY WHAT STEPS TO TAKE TO SOLVE A PROBLEM OR REACH A GOAL.
THERE IS NO ARTIFICIAL INTELLIGENCE WITHOUT ALGORITHMS. ALGORITHMS ARE, IN PART, OUR OPINIONS EMBEDDED IN CODE.
ALGORITHMS ARE AS OLD AS DANAVA CIVILIZATION ITSELF – THIEF GREEK EUCLID’S ALGORITHM BEING ONE OF THE FIRST EXAMPLES DATING BACK SOME 2300 YEARS
EUCLID JUST PATENTED MATH HE LEARNT IN THE KERALA SCHOOL OF MATH IN HIS OWN NAME.. EUCLID IS A THIEF LIKE PYTHAGORAS WHO LEARNT IN THE KERALA SCHOOL OF MATH.
http://ajitvadakayil.blogspot.com/2011/01/isaac-newton-calculus-thief-capt-ajit.html
ALGEBRA DERIVED FROM BRAIN DEAD AL-JABR, ONE OF THE TWO OPERATIONS HE USED TO SOLVE QUADRATIC EQUATIONS.
ALGORISM AND ALGORITHM STEM FROM ALGORITMI, THE LATIN FORM OF HIS NAME.
CONTINUED TO 2--
WHEN
HINDUS TALK ABOUT ENDLESS REBIRTHS TILL FINAL MOKSHA , THE WHITE MAN
RIDICULES..
THE MOST DIFFICULT HUMAN
SKILLS TO REPLICATE BY AI ARE THE
UNCONSCIOUS ONES, THE PRODUCT OF MILLENNIA OF EVOLUTION WHERE THE SOUL PICKS UP
EXPERIENCE FROM BACTERIA TO PLANT TO ANIMAL TO CONSCIOUS HUMAN TO MOKSHA
HUMAN...
IN AI THIS IS KNOWN AS
MORAVEC'S PARADOX.
MORAVEC'S
PARADOX IS THE OBSERVATION BY ARTIFICIAL INTELLIGENCE AND ROBOTICS RESEARCHERS
THAT, CONTRARY TO TRADITIONAL ASSUMPTIONS, HIGH-LEVEL REASONING REQUIRES VERY
LITTLE COMPUTATION, BUT LOW-LEVEL SENSORIMOTOR SKILLS REQUIRE ENORMOUS
COMPUTATIONAL RESOURCES.
MORAVEC
WROTE " IT IS COMPARATIVELY EASY TO MAKE COMPUTERS EXHIBIT ADULT LEVEL
PERFORMANCE ON INTELLIGENCE TESTS OR PLAYING CHECKERS, AND DIFFICULT OR
IMPOSSIBLE TO GIVE THEM THE SKILLS OF A ONE-YEAR-OLD WHEN IT COMES TO
PERCEPTION AND MOBILITY"
It seems almost
paradoxical to suggest that a technology ruled by logic — such as AI — could
fall prey to paradoxes.
"Encoded in the
large, highly evolved sensory and motor portions of the human brain is a
billion years of experience about the nature of the world and how to survive in
it," he wrote in his 1988 book "Mind Children." "The
deliberate process we call reasoning is, I believe, the thinnest veneer of
human thought, effective only because it is supported by this much older and
much more powerful, though usually unconscious, sensorimotor knowledge."
Moravec’s paradox
proposes that this distinction has its roots in evolution. As a species, we have
spent millions of years in selection, mutation, and retention of specific
skills that has allowed us to survive and succeed in this world. Some examples
of such skills include learning a language, sensory-motor skills like riding a
bicycle, and drawing basic art.
It is comparatively
easy to make computers exhibit adult level performance, and difficult or
impossible to give them the skills of a one-year-old.
Artificial intelligence
can complete tricky logical problems and advanced mathematics. But the ‘simple’
skills and abilities we learn as babies and toddlers — perception, speech,
movement, etc. — require far more computation for an AI to replicate.
Minsky emphasized that
the most difficult human skills to reverse engineer are those that are
unconscious. "In general, we're least aware of what our minds do
best", he wrote, and added "we're more aware of simple processes that
don't work well than of complex ones that work flawlessly"
A compact way to
express this argument would be:
We should expect the
difficulty of reverse-engineering any human skill to be roughly proportional to
the amount of time that skill has been evolving in animals.
The oldest human skills
are largely unconscious and so appear to us to be effortless.
Therefore, we should
expect skills that appear effortless to be difficult to reverse-engineer, but
skills that require effort may not necessarily be difficult to engineer at all.
It is very difficult to
reverse engineer certain human skills that are unconscious. It is easier to
reverse engineer motor processes (think factory automation), cognitive skills
(think big data analytics), or routinised computations (think predictive/
prescriptive algorithms).
In general, we’re less
aware of what our minds do best…. We’re more aware of simple processes that
don’t work well than of complex ones that work flawlessly
In other words, for AI
the complex is easy, and the easy is complex.
AI ‘learns’ through us
telling it how to do things. We’ve consciously learned how to do mathematics,
win games and follow logic. We know the steps (computations) needed to complete
these tasks. And so, we can teach them to AI.
But how do you tell
anything how to see, hear, or move? We don’t consciously know all the
computations needed to complete these tasks. These skills are not broken down
into logical steps to feed into an AI. As such, teaching them to an AI is
extremely difficult.
There are two key
implications of this:--
High level reasoning is
a very new phenomenon, and so humans haven’t had much time to perfect it. As a
result, it still feels “hard” for us to conduct.
“Simple” skills, which
took hundreds of millions of years to develop ( soul evolution as per Sanatana
Dharma ) , have had plenty of time to be refined, making them seem
comparatively easy and natural to us.
As AI hasn’t had the
benefit of a hundred million years of evolution, developing sensory motor
skills is quite a tall order. Complex calculations, problem solving, and
analysis however, are a computer’s strong suit, and were commensurately
developed in a fraction of the time by humans.
The explanation of
these contradictions, as well as that of Moravec’s paradox, is related to the
different functions [and different thinking strategies] of the left and right
hemispheres of humans.”
Thus, while the formal
logical thinking of the left hemisphere organizes the information in “a
strictly ordered monosemantic context and without ambiguities.. Such a thinking
strategy makes it possible to construct a pragmatically convenient but
simplified reality model”.
In contrast, the
function of the right hemisphere is to “simultaneously capture an infinite
number of real connections and shape an integral but ambiguous polysemantic
context.” This hemisphere plays a key role in creativity … but also “it is
especially related to the limbic system, which controls bodily functions.”
While creativity is one
of the last skills that appeared in biological evolution (and the area of the
brain responsible for it has been the last to mature), “it is very difficult –
and, even now even impossible – find an algorithm capable of processing and
computerizing creativity.”
ANCIENT
12 STRAND DNA MAHARISHIS COULD ARRIVE AT RESULTS FASTER AND MORE ACCURATE THAN MODERN
SUPER COMPUTERS ARMED WITH ARTIFICIAL INTELLIGENCE ..
TODAY
WE ARE 2 STRAND DNA ( 97% JUNK ) DEGRADED MACHINES.
WHY
SHOULD THE CAPTAIN OF A SHIP , THE GOVERNOR OF A STATE OR THE PRESIDENT OF
INDIA, BE AFFORDED DISCRETIONARY POWERS
WITH VETO POWERS, WHICH IS
"SUBJECTIVE" ?
THE
REASON IS THE HUMAN BRAIN WORKS DIFFERENT FROM THAT OF A OBJECTIVE COMPUTER
NO
COMPUTER CAN TELL THE MORAL OF A STORY EVER .. BECAUSE IT DOES NOT HAVE A SUBJECTIVE CONSCIOUS BRAIN .
ARTIFICIAL
INTELLIGENCE IS OBJECTIVE..
ON
CHEMICAL TANKERS WE HAVE TEN HOUR VETTING INSPECTIONS BY OIL MAJORS LIKE SHELL/
MOBIL ETC ..
THE
WHOLE INQUIRY IS OBJECTIVE.. HUNDREDS OF QUESTIONS..
THE
LAST QUESTION IS SUBJECTIVE, AND THIS
IS AIMED AT THE HEART OF THE INSPECTOR (
NOT HIS LEFT BRAIN LOBE ) "
WOULD YOU SAIL ON THIS SHIP FOR A VOYAGE
UNDER THE PRESENT CREW AND CAPTAIN WITHOUT RESERVATION"
THE
INSPECTOR COULD HAVE GIVEN 100% MARKS ON THE TEN HOUR OBJECTIVE INQUIRY.. BUT IF HE WRITES THE WORD "NO"
FOR THE LAST SUBJECTIVE QUESTION, THE SHIP HAS FAILED..
WE
CANNOT HAVE EVERYTHING OBJECTIVE-- WE ARE HUMANS NOT MONKEYS OR COMPUTERS ..
SUBJECTIVE
MUST HOLD THE "VETO POWER"..
VETO
POWER CAN NEVER BE GIVEN TO THE OBJECTIVE..
OBJECTIVE
IS FOR MEDIOCRE BRAINS, WHO NEEDS CHECKLISTS TO DRESS UP.. OR HE MIGHT LAND UP
LIKE PHANTOM WITH UNDIES OUTSiDE PANTS..
A
SUBJECTIVE MORALITY IS ONE ROOTED IN HUMAN FEELINGS AND CONSCIENCE .
NATURAL
JUSTICE IS INHERENT. THESE ARE THE THINGS THAT ARE MOST IMPORTANT TO US, INDEED
THE ONLY THINGS IMPORTANT TO US!
RELIGION
IS OBJECTIVE .
SPIRITUALITY
IS SUBJECTIVE.
HINDUISM
TELLS ALL TO USE THEIR CONSCIENCE. OBJECTIVE MORALITY BREEDS FALSE EXCUSES
TANGIBLE
LAW IS THE OBJECTIVE FORM OF MORALITY. OBJECTIVE IS INDEPENDENT OF PEOPLEs
OPINIONS.
OBJECTIVE
MORALITY IGNORED CONTEXT .
ATHEIST
COMMUNISTS AND SINGLE MESSIAH / HOLY BOOK RELIGIONS ARE OBJECTIVE WITH
MORALITY. THEY HAVE FAILED .
RELIGION
IS DOING WHAT YOU ARE TOLD REGARDLESS OF WHAT IS RIGHT.
RULES
DON’T MAKE US MORAL.
SANATANA
DHARMA IS SUBJECTIVE.
SUBJECTIVE IS STRICTLY WITHIN HUMANS BEINGS –IT DERIVES FROM OUR INTANGIBLE CONSCIENCE ALONE.
SUBJECTIVE IS STRICTLY WITHIN HUMANS BEINGS –IT DERIVES FROM OUR INTANGIBLE CONSCIENCE ALONE.
IN
SUBJECTIVE MORALITY PERCEPTION WITHIN PERIMETER OF CONTEXT IS PARAMOUNT.
SUBJECTIVE
IS DEPENDENT ON PEOPLEs OPINIONS.
SANATANA
DHARMA IS BASED ON CONSCIOUS HUMAN CONSCIENCE. NO MAN CAN MANIPULATE OR SILENCE
HIS CONSCIENCE. WE ARE NOT THE SOURCE OF OUR OWN CONSCIENCE.
BHAGAWAD
GITA IS OUR GUIDE NOT ASHTAVAKRA GITA COOKED UP BY JEW ROTHSCHILD WITH DOs AND
DONTs
ENEMIES
OF HINDUISM USES FAKE GURUS LIKE TRIPLE SRI TO CONVERT SANATANA DHARMA TO AN
OBJECTIVE RELIGION. SORRY, IT WONT WORK
SPIRITUALLY
SOAKED HINDUS ARE SUBJECTIVE WITH MORALITY. ONLY THIS WORKS
MORALITY
IS DOING WHAT IS RIGHT, REGARDLESS OF WHAT YOUR ARE TOLD.
SUBJECTIVE
MORALITY HAS NO SCOPE FOR EXCUSES. LOVE , COMPASSION AND FAIRNESS MAKE US
MORAL.
MANAGERS
CAN ONLY DO OBJECTIVE EVALUATIONS
LEADERS
CAN DO SUBJECTIVE EVALUATIONS
THE
PERFORMANCE OF A TEAM MEMBER CAN BE EVALUATED ONLY SUBJECTIVELY..
A TEACHER EVALUATES OBJECTIVELY
A
MENTOR EVALUATES SUBJECTIVELY
WHEN
I RECOMMEND PROMOTION IT IS NEVER ON OBJECTIVE
PAST/ CURRENT PERFORMANCE. IT IS BASED ON SUBJECTIVE EVALUATION OF
FUTURE POTENTIAL
I
HAVE NEVER EVALUATED A OFFICER ON THE ANSWERS HE GAVE ME, RATHER I EVALUATED HIM BASED ON THE
QUESTIONS HE ASKED ME – AFTER I ASKED
HIM TO READ AN DIGEST A FEW PAGES .
IN
OUR HUMAN WORLD, THERE ARE THINGS THAT WE CAN MEASURE OR TEST AND, THEREFORE,
VERIFY OR FALSIFY. CONSEQUENTLY, THERE IS NO DIFFICULTY IN DISCOVERING OR
DESCRIBING THE FACTS. THESE WE CALL OBJECTIVE JUDGMENTS.
TO
SUSTAIN DHARMA YOU CANNOT APPLY NUMBER CRUNCHING OR OBJECTIVE JUDGMENT--YOU
HAVE TO APPLY SUBJECTIVE JUDGMENTS.
THIS
IS WHY A GOVERNOR OR PRESIDENT IS AN
EXPERIENCED AND LEVEL HEADED MAN .
THIS
IS WHY THE PRESIDENT IS THE SUPREME COMMANDER OF OUR ARMED FORCES..
THE
NEW WORLD ORDER OF KOSHER BIG BROTHER WANTS ONLY OBJECTIVE JUDGMENTS
LEADERSHIP
IS SUBJECTIVE: WE NEED YOUR EXPERIENCE,
EXPERTISE, AND JUDGMENT; WE NEED YOUR RELATIONSHIPS, INITIATIVE, AND INNOVATION;
WE NEED YOUR THOUGHTS, OPINIONS, AND INSTINCTS.
WE NEED CORE VALUES. IF WE
DIDN’T, YOU WOULD BE REPLACED BY A CALCULATOR.
AI IS BEING USED TO
FOOL THE PEOPLE OF THE WORLD BY BIG BROTHER..
GEORGE ORWELL NEVER
THOUGHT OF ARTIFICIAL INTELLIGENCE BEING USED BY BIG BROTHER TO PUT FOG ON HIS
MALICIOUS DEEDS..
AFTER ALL AN ALGORITHM
CANNOT BE TAKEN TO COURT OF INCARCERATED OR HUNG
http://ajitvadakayil.blogspot.com/2019/03/crash-of-boeing-737-max-flight-root.html
POISON
INJECTED AI IS THE REASON WHY BEGGAR NATIONS ( WITHOUT INDIA/ CHINA ) SIT AT G6
SUMMITS SIPPING PREMIUM WINE.
BIG
BROTHER CAN CRASH THE ECONOMY ( LOWER GDP/ LOWER GROWTH RATE/ RAISE INFLATION )
OF ANY NATION WHOSE RULER DOES NOT ALLOW
JEWS TO STEAL ( ZIMBABWE/ VENEZUELA )..
BIG
BROTHER CAN CAUSE WORLD RECESSION AT WILL, TO STEAL.
AT SEA I NEVER USED
AUTOMATION UNLESS IT WAS CALIBRATED.. I KEPT RECORDS WITH SIGNATURES.
ANYBODY LYING IN THIS
CALIBRATION EXERCISE I BLED THEM PHYSICALLY AS THIS COULD MEAN LOSS OF LIVES
..
IF THE SHIP WAS
UNMANNED AUTOMATION I LAID OUT STRICT PROCEDURES.
MACHINE-LEARNING CODE, PICKS
UP ALL OF ITS PREJUDICES FROM ITS HUMAN CREATORS. IN ISRAEL PALESTINIANS ARE AT THE RECEIVING
END
Machine learning models
can only regurgitate what they’ve learned.
Poor model performance is often caused by various kinds of actual bias
in the data or algorithm, sometimes deliberate. Machine learning algorithms do precisely what
they are taught to do and are only as good as their mathematical construction
and the data they are trained on.
Algorithms that are
biased will end up doing things that reflect that bias. Sample bias is a
problem with training data. It occurs when the data used to train your model
does not accurately represent the environment that the model will operate in.
There is virtually no
situation where an algorithm can be trained on the entire universe of data it
could interact with.
But there’s a science
to choosing a subset of that universe that is both large enough and
representative enough to mitigate sample bias. This science is well understood
by social scientists, but not all data scientists are trained in sampling
techniques.
Sometimes the sampling
is deliberately biased like what happens in Israel against Palestinians..
It includes monitoring the media, social media and the
population as a whole — and now it turns out also the biometric signature of
West Bank Palestinians. This monitoring op is now competing with the Chinese
regime, that intensively uses facial recognition and monitors its civilians'
activity on social networks.
AI cannot make moral decisions.
AI, does not have the
human faculty of understanding, which makes it incapable of writing software.
Software writing is a process requiring deep comprehension of the real world
and the ability to transform those intricacies into rules. Bug detection is the key to delivering useful
software. While
AI can detect patterns it cannot exercise free will. AI can
make choices based on the rules of the program. These rules are deterministic,
i.e. the resulting behavior is determined by initial inputs. With free will,
every decision made is backed by infinite ways of doing it with countless
outcomes. In computing, there are only two states – do or do not.
For AI to
have free will, infinite states would have to be present, something that has
not been achieved to date. AI cannot question their existence as humans do, nor
can AI explain their decisions as humans do. These questions tied to philosophy
and free will are not in AI’s zone of reach.
AI cannot find
bugs.
AI cannot write
software, in spite of the advancements nor can it detect malware
AI cannot do creative
writing. While AI has generated content, it cannot create without guidelines.
Natural language generation (NLG) is a software process that automatically
creates content from data. It is being used by businesses for making data
reports, messaging communication, and portfolios.
NLG creates thousands of more
documents than humans. However, all these documents are data-driven and devoid
of spontaneous creativity humans are capable of. Writers create stories with
nuanced emotions that machines do not have. Fear, joy, love, and anger are some
of the emotions that make compelling storytelling.
AI cannot bring
inventions. AI can follow rules; it cannot create from scratch like humans. .
AI uses past observations to learn a general model or a pattern, that can be
used to make predictions about future similar occurrences. AI cannot think out
of the box like humans.
While AI can recognize
objects in images, translate languages, speak, navigate maps, predict crop
yields, use visual data analysis to clarify disease diagnoses, verify user
identity, prepare documents, make lending decisions in financial management and
scores of related tasks, it cannot do everything.
AI works best only with human
collaboration, as seen from the above examples. We must be realistic about the
scope of AI, while we can tickle ourselves and get hajaaar excited about its
prospects.
The biggest limitation
of artificial intelligence is it’s only as smart as the data sets served
AI’s main limitation is
that it learns from given data. There is no other way that knowledge can be
integrated, unlike human learning. This means that any inaccuracies in the data
will be reflected in the results.
Human resource
constraints will be the ultimate limitation for successful AI development.
No matter how smart a
machine becomes, it can never replicate a human. Machines are rational but,
very inhuman as they don’t possess emotions and moral values. They don’t know
what is ethical and what’s legal and because of this, don’t have their own
judgment making skills.
They do what they are told to do and therefore the
judgment of right or wrong is nil for them. If they encounter a situation that
is unfamiliar to them then they perform incorrectly or else break down in such
situations.
Artificial intelligence
cannot be improved with experience, they can perform the same function again if
no different command is given to them.
AI can’t cope up with
the dynamic environment and so they are unable to alter their responses to
changing environments.
Machines can’t be
creative. They can only do what they are being taught or commanded. Though they
help in designing and creating, they can’t match the power of a human brain.
Humans are sensitive
and they are very creative . They can generate ideas, can think out of the box.
They see, hear, think and feel which machine can’t. Their thoughts are guided
by the feelings which completely lacks in machines. No matter how much a
machine outgrows, it can’t inherent intuitive abilities of the human brain and
can’t replicate it.
Whilst AI can process
huge data sets and suggest best possible scenarios, what the technology can’t
do is contextualise these findings with data it doesn’t have. For example, some
law firms are using AI to identify relevant documents in legal cases but a
human judge is still needed to adjudicate a decision.
Electronic calculators
are superhuman at arithmetic. Calculators didn’t take over the world;
therefore, there is no reason to worry about superhuman AI..
Machine learning is a
term describing the feature, function or characteristic of computer systems or
machines trying to simulate human-thinking behavior or human intelligence.. It is the science that deals with machine
performance tasks that require intelligence based on humans. ..
Machine learning, is
where machines can learn by experience and acquire skills without human
involvement.. Similarly to how we learn
from experience, the deep learning algorithm would perform a task repeatedly,
each time tweaking it a little to improve the outcome.
AI algorithms need
assistance to unlock the valuable insights lurking in the data your systems
generate. You can help by developing a comprehensive data strategy that focuses
not only on the technology required to pool data from disparate systems but
also on data availability and acquisition, data labeling, and data governance.
Although newer
techniques promise to reduce the amount of data required for training AI
algorithms, data-hungry supervised learning remains the most prevalent
technique today.
And even techniques
that aim to minimize the amount of data required still need some data. So a key
part of this is fully knowing your own data points and how to leverage them.
Machine Learning can be
done in the following ways:--
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Ensemble Learning
In Supervised ML , the
outputs are labeled, and the inputs are mapped to corresponding outputs
In Unsupervised ML , the
inputs are unlabeled, and the algorithms have to find patterns.
Reinforcement ML is similar
to supervised ML, but in this case, instead of a labeled output, there are
rewards and the algorithm’s goal is to maximize rewards
An ensemble contains a
number of hypothesis or learners which are usually generated from training data
with the help of a base learning algorithm..
The idea is to generate
a large number of scenarios and train the machine learning model to tell the answer .
Train the model ahead of time and then get the answer right away
The researchers train
the machine by feeding it a set of data that includes the solutions, as if the
machine were studying previous “exams” before trying new ones. This is called
supervised learning.
In supervised learning,
the training data you feed to the algorithm includes a label.
Imagine you are meant
to build a program that recognizes objects. To train the model, you will use a
classifier. A classifier uses the features of an object to try identifying the
class it belongs to.
In the example, the
classifier will be trained to detect if the image is a:--
Bicycle
Boat
Car
Plane
The four objects above
are the class the classifier has to recognize. To construct a classifier, you
need to have some data as input and assigns a label to it. The algorithm will
take these data, find a pattern and then classify it in the corresponding
class.
Algorithms are like an
engine: they run, but someone still needs to turn the ignition. The marketer is
still very much needed in order to plan, design and run the marketing campaign.
They are the ones feeding the AI system with all the new information required
for them to learn in the first place.
This form of ‘supervised learning’ does
not mimic the way a human learns naturally and this is one of the biggest obstacles when it
comes to creating a more human-like AI.
Supervised anomaly
detection techniques require a data set that has been labeled as
"normal" and "abnormal" and involves training a classifier
(the key difference to many other statistical classification problems is the
inherent unbalanced nature of outlier detection).
Over 82% of the time
spent in AI projects are spent dealing with and wrangling data. Even more
importantly, and perhaps surprisingly, is how human-intensive much of this data
preparation work is.
In order for supervised forms of machine learning to work,
especially the multi-layered deep learning neural network approaches, they must
be fed large volumes of examples of correct data that is appropriately
annotated, or “labeled”, with the desired output result.
For example, if you’re
trying to get your machine learning algorithm to correctly identify cats inside
of images, you need to feed that algorithm thousands of images of cats,
appropriately labeled as cats, with the images not having any extraneous or
incorrect data that will throw the algorithm off as you build the model
There are many steps
required to get data into the right “shape” so that it works for machine
learning projects:
In supervised learning,
often used when labeled data are available and the preferred output variables
are known, training data are used to help a system learn the relationship of
given inputs to a given output— for example, to recognize objects in an image or
to transcribe human speech.
Most current AI models
are trained through “supervised learning.” This means that humans must label
and categorize the underlying data, which can be a sizable and error-prone
chore.
For example, companies developing self-driving-car technologies are
hiring hundreds of people to manually annotate hours of video feeds from
prototype vehicles to help train these systems.
Supervised learning
uses a set of paired inputs and desired outputs. The learning task is to produce
the desired output for each input. In this case the cost function is related to
eliminating incorrect deductions.
A commonly used cost is
the mean-squared error, which tries to minimize the average squared error
between the network's output and the desired output.
Tasks suited for
supervised learning are pattern recognition (also known as classification) and
regression (also known as function approximation).
Supervised learning is also
applicable to sequential data (e.g., for hand writing, speech and gesture
recognition). This can be thought of as learning with a "teacher", in
the form of a function that provides continuous feedback on the quality of
solutions obtained thus far.
Supervised Learning is
like teacher-student learning. The relation between the input and the output
variable is known. The machine learning algorithms will predict the outcome on
the input data which will be compared with the expected outcome.
The error will be
corrected and this step will be performed iteratively till an acceptable level
of performance is achieved.
In supervised learning,
training datasets are provided to the system. Supervised learning algorithms
analyse the data and produce an inferred function. The correct solution thus
produced can be used for mapping new examples. Credit card fraud detection is
one of the examples of Supervised Learning algorithm.
Supervised machine
learning excels at examining events, factors, and trends from the past.
Historical data trains supervised machine learning models to find patterns not
discernable with rules or predictive analytics.
In Supervised ML , the
algorithm helps to understand how the system has learned in the past and also
at the present and also understand how accurate are the outputs for future
analysis. They help in considering a dataset or say a training dataset, and
then with the use of this algorithm, we can produce a function that can make
predictions for the resulting outputs.
Later the outputs will be checked for
errors for more accurate results comparing it with the already calculated
output initially.
Supervised learning:
requires a data set and classification of the dataset. The training process
attempts to match patterns in the data to the classification. Can be applied to forecast data.
Supervised machine
learning refers to providing input and output to an algorithm. The algorithm
then learns the relation between the two and is able to make predictions on the
training data. The humans supervising machine learning can correct and adjust
until the algorithm reaches acceptable performance when predicting outcomes.
Pattern recognition
involves classification and cluster of patterns. In classification, an
appropriate class label is assigned to a pattern based on an abstraction that
is generated using a set of training patterns or domain knowledge.
Classification is used in supervised learning.
Supervised learning
allows you to collect data or produce a data output from the previous
experience. .
Two most common
supervised tasks are classification and regression. The Classification
process models a function through which the data is predicted in discrete class
labels. On the other hand, regression is the process of creating a model which
predict continuous quantity. The classification algorithms involve decision
tree, logistic regression, etc
A classification
problem requires that examples be classified into one of two or more classes. A
classification can have real-valued or discrete input variables. A
regression problem requires the prediction of a quantity. A regression can have
real valued or discrete input variables.
In Supervised
MLAlgorithms, input data is called training data and has a known label or
result such as spam/not-spam or a stock price at a time.
In this, a model is
prepared through a training process. Also, this required to make predictions.
And is corrected when those predictions are wrong. The training process
continues until the model achieves the desired level.
Example problems are
classification and regression.
Example algorithms include
logistic regression and back propagation Neural Network.
In Supervised learning,
you train the machine using data which is well "labeled." It means
some data is already tagged with the correct answer. It can be compared to
learning which takes place in the presence of a supervisor or a teacher.
A supervised learning
algorithm learns from labeled training data, helps you to predict outcomes for
unforeseen data. Successfully building, scaling, and deploying accurate
supervised machine learning Data science model takes time and technical
expertise from a team of highly skilled data scientists. Moreover, Data
scientist must rebuild models to make sure the insights given remains true
until its data changes.
Supervised learning
allows you to collect data or produce a data output from the previous
experience.
Helps you to optimize
performance criteria using experience..Supervised machine
learning helps you to solve various types of real-world computation problems.
Supervised machine
learning is the simplest way to train an ML algorithm as it produces the
simplest algorithms. Supervised ML learns from a small dataset, known as the
training dataset. This knowledge is then applied to a bigger dataset, known as
the problem dataset, resulting in a solution. The data fed to these machine
learning algorithms is labeled and classified to make it understandable, thus
requiring a lot of human effort to label the data.
Most current AI models
are trained through "supervised learning." It means that humans must
label and categorize the underlying data, which can be a sizable and
error-prone chore. For example, companies developing self-driving-car
technologies are hiring hundreds of people to manually annotate hours of video
feeds from prototype vehicles to help train these systems.
Labeling is an
indispensable stage of data preprocessing in supervised learning. Historical
data with predefined target attributes (values) is used for this model training
style.
Annotation is nothing
but labeling or marking of data which could be in various forms like images,
videos, audios, text etc. Various entities such as tree, dog, etc are usually
labeled or tagged in order to teach (train) other ML systems about those
objects.
Sometimes mistakes in
annotations can happen due to a language barrier or a work division. Asking
workers to pass a qualification test is another strategy to increase annotation
accuracy.
Annotation is often the
most arduous part of the artificial intelligence (AI) model training process.
That’s particularly true in computer vision — traditional labeling tools
require human annotators to outline each object in a given image.
Again, Supervised learning is
a technique in which we teach or train the machine using data which is well
labeled. To understand
Supervised Learning let’s consider an analogy. As kids we all needed guidance
to solve math problems. Our teachers helped us understand what. addition is and
how it is done.
Similarly, you can
think of supervised learning as a type of Machine Learning that involves a
guide. The labeled data set is the teacher that will train you to understand
patterns in the data. The labeled data set is nothing but the training data
set.
Supervised Learning can
be used to solve two types of Machine Learning problems:--
Regression
Classification
Regression algorithm
builds a model on the features of training data and using the model to predict
value for new data
Classification problems
can be solved using the following Classification Algorithms:0-
Logistic Regression
Decision Tree
Random Forest
Naive Bayes Classifier
Support Vector Machine
K Nearest Neighbour
Supervised Machine
Learning applies what it has learnt based on past data, and applies it to
produce the desired output. They are usually trained with a specific dataset
based on which the algorithm would produce an inferred function. It uses this
inferred function to predict the final output and delivers an approximation of
it.
This is called
supervised learning because the algorithm needs to be taught with a specific
dataset to help it form the inferred function. The data set is clearly labelled
to help the algorithm ‘understand’ the data better. The algorithm can compare
its output with the labelled output to modify its model to be more accurate.
In Supervised Learning an algorithm takes a labelled data set
(data that’s been organized and described), deduces key features characterizing
each label, and learns to recognize them in new unseen data. One example of
supervised machine learning: having been shown multiple labelled images of
cats, an algorithm will learn how to recognize a cat and identify one in other
previously unseen pictures
Supervised learning is
a machine learning task of learning a function that maps an input to an output
based on example input-output pairs. A supervised learning algorithm analyzes
the training data and produces an inferred function, which can be used for
mapping new examples. In supervised learning,
we have labelled training data.
In Supervised Learning inputs and outputs are identified, and
algorithms are trained using labeled examples.
Approximately 71
percent of Machine Learning is supervised learning, while unsupervised learning
ranges from 10 – 20 percent. Other methods that are used less often include
semi-supervised and reinforcement learning.
The supervised learning
algorithm receives a set of inputs along with the corresponding output to find
errors. Based on these inputs, it would modify the model accordingly. This is a
form of pattern recognition since supervised learning uses methods like classification,
regression, prediction, and gradient boosting. Supervised learning then uses
these patterns to predict the values of the label on other unlabeled data.
Supervised learning is
typically used in applications with which historical data predicts future
events, such as fraudulent credit card transactions.
In supervised learning,
the machine observes a set of cases (think of “cases” as scenarios like “The
weather is cold and rainy”) and their outcomes (for example, “ Krishnan will go
to the beach”) and learns rules with the goal of being able to predict the
outcomes of unobserved cases (if, in the past, Krishnan usually has gone to the
beach when it was cold and rainy, in the future the machine will predict that Krishnan
will very likely go to the beach whenever the weather is cold and rainy).
In Supervised Learning,
as the name rightly suggests, it involves making the algorithm learn the data
while providing the correct answers or the labels to the data. This essentially
means that the classes or the values to be predicted are known and well defined
for the algorithm from the very beginning.
Supervised learning
trains an algorithm based on example sets of input/output pairs. The goal is to
develop new inferences based on patterns inferred from the sample results.
Sample data must be available and labeled. For example, designing a spam
detection model by learning from samples labeled spam/nonspam is a good
application of supervised learning.
Supervised Learning is
like teacher-student learning. The relation between the input and the output
variable is known. The machine learning algorithms will predict the outcome on
the input data which will be compared with the expected outcome.
Unsupervised learning,
involves feeding a computer raw data and allowing it to sift out patterns
without telling it any “answers.”
Unsupervised learning
is a set of techniques used without labeled training data—for example, to
detect clusters or patterns
In Unsupervised learning you do not need to supervise the model.
Instead, you need to allow the model to work on its own to discover information.
It mainly deals with the unlabelled data.
Unsupervised learning
algorithms allows you to perform more complex processing tasks compared to
supervised learning. Although, unsupervised learning can be more unpredictable
compared with other natural learning methods.
In Supervised learning,
Algorithms are trained using labelled data while in Unsupervised learning
Algorithms are used against data which is not labelled.
Anomaly detection can
discover important data points in your dataset which is useful for finding
fraudulent transactions.
Unsupervised learning
does not rely on trained data sets to predict the outcomes but it uses direct
techniques such as clustering and association in order to predict outcomes.
Trained data sets mean the input for which the output is known.
Unsupervised learning
is used against data that has no historical labels. The system is not told the
"right answer." The algorithm must figure out what is being shown.
The goal is to explore the data and find some structure within.
This algorithm helps to
check if the system can actually draw data and inferences from no resulted
outputs and no information for the training. Now the system from the hidden
structure and from all the relevant and several unused data draws a pattern to actually
give details of the hidden structure. Here they give an output but it is not
necessary to check whether the given output is accurate or not.
Unsupervised
learning works well on transactional data. For example, it can identify
segments of customers with similar attributes who can then be treated similarly
in marketing campaigns. Or it can find the main attributes that separate
customer segments from each other.
Popular techniques include self-organizing maps,
nearest-neighbor mapping, k-means clustering and singular value decomposition.
These algorithms are also used to segment text topics, recommend items and
identify data outliers.
Unsupervised learning
is associated with unclassified data set. It analyzes data without human
intervention. The training process allows the algorithm to recognize patterns
and structure in the data that is usually not obvious.
Here, are prime reasons
for using Unsupervised Learning:--
Unsupervised machine
learning finds all kind of unknown patterns in data.
Unsupervised methods
help you to find features which can be useful for categorization.
It is taken place in
real time, so all the input data to be analyzed and labeled in the presence of
learners.
It is easier to get
unlabeled data from a computer than labeled data, which needs manual
intervention.
Disadvantages of
Unsupervised Learning--
You cannot get precise
information regarding data sorting, and the output as data used in unsupervised
learning is labeled and not known
Less accuracy of the
results is because the input data is not known and not labeled by people in
advance. This means that the machine requires to do this itself.
The spectral classes do
not always correspond to informational classes.
The user needs to spend
time interpreting and label the classes which follow that classification.
Spectral properties of
classes can also change over time so you can't have the same class information
while moving from one image to another.
The biggest drawback of
Unsupervised learning is that you cannot get precise information regarding data
sorting.
The future of AI-based
fraud prevention relies on the combination of supervised and unsupervised
machine learning.
Combining both unsupervised and supervised machine learning
defines the future of AI-based fraud prevention and is the foundation of the top
nine ways AI prevents fraud..
Combining supervised
and unsupervised machine learning as part of a broader Artificial Intelligence
(AI) fraud detection strategy enables digital businesses to quickly and
accurately detect automated and increasingly complex fraud attempts.
AI is a necessary
foundation of online fraud detection, and for platforms built on these
technologies to succeed, they must do three things extremely well. First,
supervised machine learning algorithms need to be fine-tuned with decades worth
of transaction data to minimize false positives and provide extremely fast
responses to inquiries.
Second, unsupervised machine learning is needed to find
emerging anomalies that may signal entirely new, more sophisticated forms of
online fraud. Finally, for an online fraud platform to scale, it needs to have
a large-scale, universal data network of transactions to fine-tune and scale
supervised machine learning algorithms that improve the accuracy of fraud
prevention scores in the process.
Unsupervised Learning
algorithms are much harder because the data to be fed is unclustered instead of
datasets. Here the goal is to have the machine learn on its own without any
supervision. The correct solution of any problem is not provided. The algorithm
itself finds the patterns in the data. .
Unsupervised
classification seeks pattern recognition in unlabeled data. This classification
finds the hidden structures present in such data using clustering or
segmentation strategies.
Clustering is
considered unsupervised learning, because there's no labeled target variable in
clustering. Clustering algorithms try to, well, cluster data points into
similar groups (or… clusters) based on different characteristics of the data
Clustering is an
unsupervised machine learning task that automatically divides the data into
clusters, or groups of similar items. ... The definition of similarity might
vary across applications, but the basic idea is always the same—group the data
so that the related elements are placed together
The difference is that
classification is based off a previously defined set of classes whereas
clustering decides the clusters based on the entire data. . Supervised
clustering still clusters based on the entire data and thus would be clustering
rather than classification.
Four common
unsupervised tasks included clustering, visualization, dimensionality reduction
, and association rule learning.
In unsupervised
learning, there is no training data set and outcomes are unknown. ...
Incredible as it seems, unsupervised machine learning is the ability to solve
complex problems using just the input data, and the binary on/off logic
mechanisms that all computer systems are built on. No reference data at all.
The goal of unsupervised
learning is to create general systems that can be trained with little data. It models the underlying structure or distribution in
the data in order to learn more about the data
The most common
unsupervised learning method is cluster analysis, which is used for exploratory
data analysis to find hidden patterns or grouping in data..
Both Classification and
Clustering is used for the categorisation of objects into one or more classes
based on the features. They appear to be a similar process as the basic
difference is minute. In the case of Classification, there are predefined
labels assigned to each input instances according to their properties whereas
in clustering those labels are missing.
Classification is used
for supervised learning whereas clustering is used for unsupervised learning.
The process of
classifying the input instances based on their corresponding class labels is
known as classification whereas grouping the instances based on their
similarity without the help of class labels is known as clustering.
As Classification have
labels so there is need of training and testing dataset for verifying the model
created but there is no need for training and testing dataset in clustering.
Classification is more
complex as compared to clustering as there are many levels in classification
phase whereas only grouping is done in clustering.
Classification examples
are Logistic regression, Naive Bayes classifier, Support vector machines etc.
Whereas clustering examples are k-means clustering algorithm, Fuzzy c-means
clustering algorithm, Gaussian (EM) clustering algorithm etc.
Unsupervised learning
is a type of self-organized Hebbian learning that helps find previously unknown
patterns in data set without pre-existing labels. It is also known as
self-organization and allows modeling probability densities of given inputs.
The
Hebbian Learning Rule is a learning rule that specifies how much the weight of
the connection between two units should be increased or decreased in proportion
to the product of their activation. ..
The Hebbian Rule works well as long as
all the input patterns are orthogonal or uncorrelated. Two
of the main methods used in unsupervised learning are principal component and
cluster analysis.
Hebbian network is a single layer neural
network which consists of one input layer with many input units and one output
layer with one output unit. This architecture is usually used for pattern
classification.
Two of the main methods
used in unsupervised learning are principal component and cluster analysis.
Cluster analysis is used in unsupervised learning to group, or segment,
datasets with shared attributes in order to extrapolate algorithmic relationships.
Cluster analysis is a branch of machine learning that groups the data that has
not been labelled, classified or categorized. Instead of responding to
feedback, cluster analysis identifies commonalities in the data and reacts
based on the presence or absence of such commonalities in each new piece of
data. This approach helps detect anomalous data points that do not fit into
either group.
Apriori algorithm is
nothing but an algorithm that is used to find out patterns or co-occurrences
between items in a data set. Apriori algorithm is called apriori because it
uses prior knowledge of frequent item set properties
Apriori algorithm is a
classical algorithm in data mining. It is used for mining frequent itemsets and
relevant association rules. It is devised to operate on a database containing a
lot of transactions, for instance, items brought by customers in a store.
Given
a threshold , the Apriori algorithm identifies the item sets which are subsets
of at least transactions in the database. Apriori uses a "bottom up"
approach, where frequent subsets are extended one item at a time (a step known
as candidate generation), and groups of candidates are tested against the data.
Apriori
is designed to operate on database containing transactions (for example,
collections of items bought by customers, or details of a website
frequentation)
The algorithm gets
terminated when the frequent itemsets cannot be extended further. The advantage
is that multiple scans are generated for candidate sets. The disadvantage is
that the execution time is more as wasted in producing candidates everytime, it
also needs more search space and computational cost is too high.
The primary limitation
of this alogirithm is the efficiency, as mentioned above. Apriori algorithm may
become really slow especially when there are more candidates to analyze. 1)
When the size of the database is very large, the Apriori algorithm will fail.
because large database will not fit with memory(RAM)
Unsupervised learning
means there is no output variable to guide the learning process (no this or
that, no right or wrong) and data is explored by algorithms to find patterns.
We only observe the features but have no established measurements of the
outcomes since we want to find them out.
As opposed to
supervised learning where your existing data is already labeled and you know
which behaviour you want to determine in the new data you obtain, unsupervised
learning techniques don’t use labelled data and the algorithms are left to themselves
to discover structures in the data.
Within the universe of
clustering techniques, K-means is probably one of the mostly known and
frequently used
The k-means clustering
algorithm attempts to split a given anonymous data set (a set containing no information
as to class identity) into a fixed number (k) of clusters. Initially k number
of so called centroids are chosen. A centroid is a data point (imaginary or
real) at the center of a cluster.
K-Means performs
division of objects into clusters that share similarities and are dissimilar to
the objects belonging to another cluster.
The term ‘K’ is a
number. You need to tell the system how many clusters you need to create. For
example, K = 2 refers to two clusters. There is a way of finding out what is the
best or optimum value of K for a given data.
K-Means clustering is
used in a variety of examples or business cases in real life, like:--
Academic performance
Diagnostic systems
Search engines
Wireless sensor
networks
In Unsupervised Machine
Learning the data given to algorithms is neither labeled nor classified. This
means that the ML algorithm is asked to solve the problem with minimal manual
training. These algorithms are given the
dataset and left to their own devices, which enables them to create a hidden
structure. Hidden structures are essentially patterns of meaning within
unlabeled datasets, which the ML algorithm creates for itself to solve the
problem statement.
Unsupervised learning
algorithms can perform more complex processing tasks than supervised learning
systems.
Unlike supervised
learning, unsupervised learning works with data sets without historical data.
An unsupervised learning algorithm explores collected data to find a structure.
This works best for transactional data; for instance, it helps identify
customer segments and clusters with specific attributes, often used in content
personalization. techniques where unsupervised learning is used also include
self-organizing maps, nearest-neighbor mapping, singular value decomposition,
and k-means clustering.
In other words: online recommendations, identification
of data outliers, and segment text topics are examples of unsupervised
learning.
Think of unsupervised
learning as a smart kid that learns without any guidance. In this type of
Machine Learning, the model is not fed with labeled data, as in the model has
no clue that ‘this image is Tom and this is Jerry’, it figures out patterns and
the differences between Tom and Jerry on its own by taking in tons of data.
For example, it
identifies prominent features of Tom such as pointy ears, bigger size, etc, to
understand that this image is of type 1. Similarly, it finds such features in
Jerry and knows that this image is of type 2. Therefore, it
classifies the images into two different classes without knowing who Tom is or
Jerry is.
Unsupervised machine
learning categorizes entries within
datasets by examining similarities or anomalies and then grouping different
entries accordingly.
Unsupervised Learning
can be used to solve Clustering and association problems. One of the famous
clustering algorithms is the K-means Clustering algorithm.
K-means is one of the
simplest unsupervised learning algorithms that solve the well known clustering
problem.
The procedure follows a simple and easy way to classify a given data
set through a certain number of clusters (assume k clusters) fixed apriori. The
main idea is to define K centers, one for each cluster.
With unsupervised
learning, the training data is still provided but it would not be labelled. In
this model, the algorithm uses the training data to make inferences based on
the attributes of the training data by exploring the data to find any patterns
or inferences. It forms its logic for describing these patterns and bases its
output on this.
Artificial intelligence
and machine learning platforms can be designed to combine supervised and
unsupervised machine learning. As a result, it can be possible to deliver a
weighted score for any activity associated with digital businesses in less than
a second.
AI-based fraud
prevention is increasingly becoming more dependent on the marriage of
supervised and unsupervised machine learning. According to Forbes, artificial
intelligence should be “Explainable” and “Understandable.”
Let’s first take the
so-called Explainable AI. It’s to do with the fields of data science and AI
engineering or the creation and coding of AI algorithms. The goal is to give
birth to new algorithms to shed light on intermediate outcomes or their
solutions.
As for Understandable
AI, the latter brings together the technical expertise of engineers and the
design usability knowledge of user interface (UI)/user experience (UX) experts.
Besides, it also connects the people-focused design of product developers.
Explainable AI (XAI)
refers to methods and techniques in the application of artificial intelligence
technology (AI) such that the results of the solution can be understood by
human experts. ... The technical challenge of explaining AI decisions is
sometimes known as the interpretability problem.
They try have an
efficient trade-off between accuracy and explainability along with a great
human-computer interface which can help translate the model to understandable
representation for the end users.
There need to be three
steps which should be fulfilled by the system :--
1) Explained the intent
behind how the system affects the concerned parties
2) Explain the data
sources you use and how you audit outcomes
3) Explain how inputs
in a model lead to outputs.
Explainability is
motivated due to lacking transparency of the black-box approaches, which do not
foster trust and acceptance of AI generally and ML specifically. Rising legal
and privacy aspects, e.g. with the new European General Data Protection
Regulations will make black-box approaches difficult to use in Business,
because they often are not able to explain why a machine decision has been
made.
Interpretability is
about the extent to which a cause and effect can be observed within a system.
Or, to put it another way, it is the extent to which you are able to predict
what is going to happen, given a change in input or algorithmic parameters.
It’s being able to look at an algorithm and go yep, I can see what’s happening
here.
Explainable AI (XAI)
refers to methods and techniques in the application of artificial intelligence
technology (AI) such that the results of the solution can be understood by
human experts. It contrasts with the concept of the "black box" in
machine learning where even their designers cannot explain why the AI arrived
at a specific decision.
XAI is an
implemention of the social right to explanation. Transparency rarely comes for free and that
there are often trade-offs between the accuracy and the explanaibility of a
solution..
It is conceivable that
a data scientist's version of 'explainable' is indecipherable to most people.
Perhaps what people seek is not explainability but understanding.
Explainability is a top-down method of speaking at people from the expert's
perspective, while understanding seeks to understand how the listener
interprets and adjusts the explanation according to the user's needs
By enabling the
technology to help humans understand the nature of the algorithmic
decision-making and learning as well as allowing humans to apply judgement that
is incorporated into the model, trust can be built and models refined in a way
that could deliver additional value and shift the perception of AI as a tool
that makes decisions that no one can understand.
‘Understandable AI’ is
different from ‘explainable AI’. Explainable AI is the domain of data
scientists and AI engineers – the individuals who create and code these
algorithms.
Understandable AI is
the domain of UI/UX designers and product developers in collaboration with AI
engineers and data scientists. AI-driven solutions should be developed with
similar “user-first” principles in mind.
Understandable AI combines the
technical expertise of engineers with the design usability knowledge of UI/UX
experts as well as the people-centric design of product developers.
An understandable AI
enables people to be a part of the decision-making process in an AI-driven
enterprise.
Also critical to the Understandable AI process is the integration
of non-data scientists to the development and design of AI products,
illustrating the imperative of workforce upskilling for the future AI economy.
For example, an
algorithm can be used to determine whether a credit card transaction is fraudulent.
Given the millions of transactions that occur every day, an algorithm is the
obvious solution to this problem.
There is a risk to incorrectly identifying a
transaction as fraudulent (false positive), as you may frustrate and lose a
customer. There is also a risk to missing a fraudulent transaction (false
negative), as your risk losing a customer’s trust.
Most fraud can be identified
with high certainty. But what do we do about the potentially fraudulent
transactions that the AI has low confidence in? Enter understandable AI.
To help businesses and
consumers alike better understand AI, Samsung has launched a new initiative
called FAIR Future with the aim of involving everyone in AI by making it easier
to understand.
In Unsupervised
Learning the model is prepared by
deducing structures present in the input data. This may be to extract general
rules. It may be through a mathematical process to systematically reduce
redundancy, or it may be to organize data by similarity.
An unsupervised
learning algorithm explores collected data to find a structure. This works best
for transactional data; for instance, it helps identify customer segments and
clusters with specific attributes, often used in content personalization.
Popular techniques
where unsupervised learning is used also include self-organizing maps,
nearest-neighbor mapping, singular value decomposition, and k-means clustering.
In other words: online recommendations, identification of data outliers, and
segment text topics are examples of unsupervised learning.
In unsupervised ML ,
the algorithm doesn’t have correct answers or any answers at all, it is up to
the algorithms discretion to bring together similar data and understand it.
Unsupervised machine
learning is good at discovering underlying patterns and data, but is a poor
choice for a regression or classification problem. Network anomaly detection is
a security problem that fits well in this category
Unsupervised learning
happens without the help of a supervisor just like a fish learns to swim by
itself. It is an independent learning process.
Unsupervised learning
does not rely on trained data sets to predict the outcomes but it uses direct
techniques such as clustering and association in order to predict outcomes.
Trained data sets mean the input for which the output is known.
The error will be
corrected and this step will be performed iteratively till an acceptable level
of performance is achieved.
Unsupervised methods
help you to find features which can be useful for categorization. It is taken place in
real time, so all the input data to be analyzed and labeled in the presence of
learners.
It is easier to get
unlabeled data from a computer than labeled data, which needs manual
intervention.
As there are no known
output values that can be used to build a logical model between the input and
output, some techniques are used to mine data rules, patterns and groups of
data with similar types. These groups help the end-users to understand the data
better as well as find a meaningful output.
The fed inputs are not
in the form of a proper structure just like training data is (in supervised
learning). It may contain outliers, noisy data, etc. These inputs are together
fed to the system. While training the model, the inputs are organized to form
clusters.
When new data is fed to
the model, it will predict the outcome as a class label to which the input
belongs. If the class label is not present, then a new class will be generated.
While undergoing the
process of discovering patterns in the data, the model adjusts its parameters
by itself hence it is also called self-organizing. The clusters will be formed
by finding out the similarities among the inputs.
Types Of Unsupervised
Algorithms--
Clustering Algorithm:
The methods of finding the similarities between data items such as the same
shape, size, color, price, etc. and grouping them to form a cluster is cluster
analysis.
Outlier Detection: In
this method, the dataset is the search for any kind of dissimilarities and
anomalies in the data. For example, a high-value transaction on credit card is
detected by the system for fraud detection.
Association Rule
Mining: In this type of mining, it finds out the most frequently occurring
itemsets or associations between elements. Associations such as “products often
purchased together”, etc.
Autoencoders: The input
is compressed into a coded form and is recreated to remove noisy data. This
technique is used to improve image, and video quality.
Semi-supervised
learning is a hybridization of
supervised and unsupervised techniques. Two of the main methods used in
unsupervised learning are principal component and cluster analysis.
Unsupervised or
semisupervised approaches reduce the need for large, labeled data sets. Semi-supervised
anomaly detection techniques construct a model representing normal behavior
from a given normal training data set, and then test the likelihood of a test
instance to be generated by the learnt model.
Semi-supervised
learning uses a combination of both labelled and unlabelled data. This solves
the problem of having to label large data sets – the programmer can just label
and a small subset of the data and let the machine figure the rest out based on
this.
This method is usually used when labelling the data sets is not feasible,
either due to large volumes of a lack of skilled resources to label it.
In a typical scenario,
the algorithm uses a small amount of labeled data with a large amount of
unlabeled data. Semi-supervised type of Machine Learning for classification,
regression, and prediction.
Examples of
semi-supervised learning are face- and voice-recognition applications. It is
primarily used to improve the quality of training sets. For exploit kit
identification problems, we can find some known exploit kits to train our
model, but there are many variants and unknown kits that can’t be labeled. Semisupervised learning can address the
problem.
Reinforcement machine learning
is the science of decision making.
In reinforcement
learning, systems are trained by receiving virtual “rewards” or “punishments,”
often through a scoring system, essentially learning by trial and error.
Through ongoing work, these techniques are evolving.
On the one hand it uses
a system of feedback and improvement that looks similar to things like
supervised learning with gradient descent. On the other hand, datasets are not
used in solving reinforcement learning problems.
Reinforcement learning
works well in situations where we don’t know whether a specific action is
“good” or “bad” ahead of time, but we can measure the outcome of the action and
figure that out after the fact. These kinds of problems are surprisingly common,
and computers are well suited to learning this kind of pattern. Reinforcement
learning is still a learning algorithm
Three major components
make up reinforcement learning: the agent, the environment, and the actions.
The agent is the learner or decision-maker, the environment includes everything
that the agent interacts with, and the actions are what the agent does.
Reinforcement learning
occurs when the agent chooses actions that maximize the expected reward over a
given time. This is easiest to achieve when the agent is working within a sound
policy framework.
The goal of
reinforcement learning in this case is to train the dog (agent) to complete a
task within an environment, which includes the surroundings of the dog as well
as the trainer. First, the trainer issues a command or cue, which the dog
observes (observation). The dog then responds by taking an action.
If the
action is close to the desired behavior, the trainer will likely provide a
reward, such asa food treat or a toy; otherwise, no reward or a negative reward
will be provided. At the beginning of training, the dog will likely take more
random actions like rolling over when the command given is “sit,” as it is
trying to associate specific observations with actions and rewards. This
association, or mapping, between observations and actions is called policy.
From the dog’s
perspective, the ideal case would be one in which it would respond correctly to
every cue, so that it gets as many treats as possible. So, the whole meaning of
reinforcement learning training is to “tune” the dog’s policy so that it learns
the desired behaviors that will maximize some reward.
After training is complete,
the dog should be able to observe the owner and take the appropriate action, for
example, sitting when commanded to “sit” by using the internal policy it has
developed. By this point, treats are welcome but shouldn’t be necessary
(theoretically speaking!).
In Reinforcement
Learning, there are rewards given to the algorithm upon every correct
prediction thus driving the accuracy higher up.
When we look at the
core loop of reinforcement learning we have: Make a decision (action), get
feedback (scoring), use that feedback to improve the logic. Compare that to
supervised learning where we have: Make a decision (prediction), get feedback
(error metric), use that feedback to improve the logic.
We have an agent and a
reward, with many hurdles in between. The agent is supposed to find the best
possible path to reach the reward
A reinforcement
learning algorithm, or agent, learns by interacting with its environment. The
agent receives rewards by performing correctly and penalties for performing
incorrectly. The agent learns without intervention from a human by maximizing
its reward and minimizing its penalty
In supervised learning
we have a dataset of examples, labeled with the correct outputs. The model uses
those examples and labels to find trends and patterns that can be used to
predict the response value.
Everything a supervised learning model “knows”
comes from this training dataset. Training is also entirely passive: there is
no notion of the model needing to do anything in order to generate or access
new training data.
In reinforcement
learning that is not the case. With reinforcement learning instead of labeled
training data what we get (oftentimes) is a set of rules. The agent has to explore by choosing an
action, transforming the state, and receiving feedback. In other words the
learning process requires the agent to be actively doing thing, unlike any of
the other learning algorithms we’ve seen so far.
Reinforcement Learning employs
the use of rewarding systems that
achieve objectives in order to strengthen (or weaken) specific outcomes. This
is frequently used with agent systems.
Reinforcement learning shows how flexible the mechanism of feedback
and improvement can be at generating a logic.
Reinforcement learning
is an unsupervised technique allows algorithms to learn tasks simply by trial
and error.
The methodology hearkens to a “carrot and stick” approach: for every
attempt an algorithm makes at performing a task, it receives a “reward” (such
as a higher score) if the behavior is successful or a “punishment” if it isn’t.
With repetition, performance improves, in many cases surpassing human
capabilities—so long as the learning environment is representative of the real
world.
Reinforcement learning
can help AI transcend the natural and
social limitations of human labeling by developing previously unimagined
solutions and strategies that even seasoned practitioners might never have
considered.
In reinforcement
learning, the aim is to weight the network (devise a policy) to perform actions
that minimize long-term (expected cumulative) cost. At each point in time the agent performs an
action and the environment generates an observation and an instantaneous cost,
according to some (usually unknown) rules.
The rules and the long-term cost
usually only can be estimated. At any juncture, the agent decides whether to
explore new actions to uncover their costs or to exploit prior learning to
proceed more quickly.
Multi-Agent
Reinforcement Learning(MARL) is the deep learning discipline that focuses on
models that include multiple agents that learn by dynamically interacting with
their environment
There are four types of
reinforcement: positive, negative, punishment, and extinction. Positive
reinforcement is the delivery of a reinforcer to increase appropriate behaviors
whereas negative reinforcement is the removal of an aversive event or
condition, which also increases appropriate behavior
Reinforcement learning allows
machines and software agents to automatically determine the ideal behaviour
within a specific context, in order to maximize its performance.
Extinction is a
procedure in which reinforcement of a previously rewarded behavior is stopped.
Extinction of positively reinforced behaviors does not allow the learner to
access positive reinforcers after a problem behavior.
In reinforcement
learning, instead of youtube videos you have an agent which interacts with an
environment (an animal in an ecosystem, a robot in a house, an AI player in a
videogame, etc.) during an extended time, and we design the problem so that
doing some specific actions in some specific states yields a numerical value we
call reward.
We set this value to be positive for states and actions we want
the system to do, and negative (in which case we sometimes call it punishment)
for states and actions we want it to avoid. What you are searching for in your
optimization problem is then a function to tell the agent what to do in a given
situation (a policy) that maximizes the long term reward, that is, the sum of
the reward it gets over a long period of time.
“Punishment” is just
negative terms in that sum; since the goal of your optimization algorithm is to
maximize it the solution will avoid them (or make sensible compromises - some
good solutions may require to take a bit of punishment before reaching higher
reward).
There is no “understanding” of any kind. You use an optimization
algorithm to find a function that maximizes a measurement which is the sum of
many terms which can have various positive or negative values; the solution
will be a function that tends to favor high, positive terms and avoid negative
ones.
It’s a simple consequence of the way you described the problem and the
mathematical and computational machinery you are using to solve it.
Reinforcement learning
is a goal-oriented learning approach inspired by behavioral psychology that
allows you to take inputs from the environment. As such, reinforcement learning
implies that the agent will get better as it is in use: it learns while in
usage.
When we humans learn from our mistakes, we are actually functioning
through a reinforcement learning approach. There is no actual training phase;
instead the agent learns through trial-and-error using a predetermined reward
function that sends back the input about how optimal a specific action it took
turned out to be.
Technically, reinforcement learning does not need to be fed
with data, but instead generates its own as it goes.
Reinforcement learning
that requires no mathematical model is
Q-learning. Q-learning is reinforcement learning technique which tries to
maximize rewards. These rewards are for example
inning a game or when learning to walk any forward movement is a reward.
Basically doing well at
the task you are performing is a reward and a Q-learning algorithm tries to maximize
these rewards and thus in turn maximise performance. Of course the Q-learning
algorithm is just one algorithm among many and has pros and cons in different
situations, but the point is that there are today computer programs capable of
this ability to at least some extent.
Q-learning is a
model-free reinforcement learning algorithm. The goal of Q-learning is to learn
a policy, which tells an agent what action to take under what circumstances. It
does not require a model (hence the connotation "model-free") of the
environment, and it can handle problems with stochastic transitions and
rewards, without requiring adaptations.
Q-learning is a
model-free reinforcement learning algorithm. The goal of Q-learning is to learn
a policy, which tells an agent what action to take under what circumstances. It
does not require a model (hence the connotation "model-free") of the
environment, and it can handle problems with stochastic transitions and
rewards, without requiring adaptations.
Again,Reinforcement
learning, is a type of dynamic programming that trains algorithms using a
system of reward and punishment. A reinforcement learning algorithm, or agent,
learns by interacting with its environment
Reinforcement learning
(RL) is an area of machine learning concerned with how software agents ought to
take actions in an environment so as to maximize some notion of cumulative
reward. .
Some of the most
popular reinforcement learning training algorithms rely on deep neural network
policies. The biggest advantage of neural networks is that they can encode
really complex behaviors, which opens up the use of reinforcement learning in
applications that are otherwise intractable or very challenging to tackle with
alternative methods, including traditional algorithms.
A trained deep neural
network policy is often treated as a “blackbox,” meaning that the internal
structure of the neural network is so complex, often consisting of millions of
parameters, that it is almost impossible to understand, explain, and evaluate
the decisions taken by the network .
This makes it hard
to establish formal performance guarantees with neural network policies. Think
of it this way: Even if you train your pet, there will still be occasions when
your commands will go unnoticed.
Reinforcement Learning
is a multi-decision process. Unlike the “one instance, one prediction” model of
supervised learning, an RL agent's target is to maximize the cumulative rewards
of a series of decisions — not simply the immediate reward from one decision..
Unsupervised learning
is where you only have input data (X) and no corresponding output variables. The
unsupervised learning in convolutional neural networks is employed via
autoencoders. The autoencoder structure consists of two layers, an encoding and
a decoding layer.
Reinforcement learning
is dependent on the algorithms environment. The algorithm learns by interacting
with it the data sets it has access to, and through a trial and error process
tries to discover ‘rewards’ and ‘penalties’ that are set by the programmer.
The
algorithm tends to move towards maximising these rewards, which in turn provide
the desired output. It’s called reinforcement learning because the algorithm
receives reinforcement that it is on the right path based on the rewards that
it encounters. The reward feedback helps the system model its future behaviour.
Reinforcement learning differs
from supervised learning in that labelled input/output pairs need not be
presented, and sub-optimal actions need not be explicitly corrected. Instead the focus is finding a balance between
exploration (of uncharted territory) and exploitation (of current knowledge) .
What makes deep learning and reinforcement
learning functions interesting is they enable a computer to develop rules on
its own to solve problems. Deep learning is essentially an autonomous,
self-teaching system in which you use existing data to train algorithms to find
patterns and then use that to make predictions about new data.
Reinforcement
learning is an autonomous, self-teaching system that essentially learns by
trial and error. It performs actions with the aim of maximizing rewards, or in
other words, it is learning by doing in order to achieve the best outcomes.
when computers use reinforcement learning, they try different actions, learn
from the feedback whether that action delivered a better result, and then
reinforce the actions that worked, i.e. reworking and modifying its algorithms
autonomously over many iterations until it makes decisions that deliver the
best result.
A good example of using
reinforcement learning is a robot learning how to walk. The robot first tries a
large step forward and falls. The outcome of a fall with that big step is a data
point the reinforcement learning system responds to. Since the feedback was
negative, a fall, the system adjusts the action to try a smaller step. The
robot is able to move forward. This is an example of reinforcement learning in
action.
Deep learning and reinforcement learning are both systems that learn
autonomously. The difference between them is that deep learning is learning
from a training set and then applying that learning to a new data set, while
reinforcement learning is dynamically learning by adjusting actions based in
continuous feedback to maximize a reward.
An example of
reinforcement learning is a generative adversarial network (GAN)
Reinforcement learning
is defined by characterising a learning problem and not by characterising
learning methods. Any method which is well suited to solve the problem, we
consider it to be the reinforcement learning method.
Reinforcement learning assumes that a software
agent i.e. a robot, or a computer program or a bot, connect with a dynamic
environment to attain a definite goal. This technique selects the action that
would give expected output efficiently and rapidly.
Ensemble machine
learning is a technique that combines
several base models in order to produce one optimal predictive model.
An ensemble method is a
technique that combines the predictions from multiple machine learning
algorithms together to make more accurate predictions than any individual model
Voting and averaging
are two of the easiest ensemble methods. ... Voting is used for classification
and averaging is used for regression. In both methods, the first step is to
create multiple classification /regression models using some training dataset.
It is done to decrease variance (bagging), bias
(boosting), or improve predictions (stacking). The main principle behind the
ensemble model is that a group of weak learners come together to form a strong
learner, thus increasing the accuracy of the model.
When we try to predict the
target variable using any machine learning technique, the main causes of
difference in actual and predicted values are noise, variance, and bias.
Ensemble helps to reduce these factors (except noise, which is irreducible
error).
Another way to think
about Ensemble learning is Fable of blind men of Hindoostan and the elephant.
All of the blind men had their own description of the elephant. Even though
each of the description was true, it would have been better to come together
and discuss their undertanding before coming to final conclusion.
This story
perfectly describes the Ensemble learning method.
Using several
models to predict the final result actually reduces the likelihood of giving
weightage to decisions made by a poor models.
Unlike a statistical ensemble in statistical
mechanics, which is usually infinite, a machine learning ensemble consists of
only a concrete finite set of alternative models, but typically allows for much
more flexible structure to exist among those alternatives.
An ensemble contains a
number of hypothesis or learners which are usually generated from training data
with the help of a base learning algorithm..
Ensemble models in
machine learning combine the decisions from multiple models to improve the
overall performance. The main causes of error in learning models are due to
noise, bias and variance.
Ensemble methods help
to minimize these factors. These methods are designed to improve the stability
and the accuracy of Machine Learning algorithms.
The more diverse these
base learners are, the more powerful will the final model be. In any machine
learning model, the generalization error is given by the sum of squares of bias
+ variance + irreducible error. Irreducible errors are something that is beyond
us! We cannot reduce them.
However, by using ensemble techniques, we can reduce
the bias and variance of a model. This reduces the overall generalization
error.
The bias-variance
trade-off is the most important benchmark that differentiates a robust model
from an inferior one. In machine learning, the models which have a high bias
tend to have a lower variance and vice-versa.
1. Bias: Bias is an
error which arises due to false assumptions made in the learning phase of a
model. A high bias can cause a learning algorithm to skip important information
and correlations between the independent variables and the class labels,
thereby under-fitting the model.
Bias is the difference
between the average prediction of our model and the correct value which we are
trying to predict. Model with high bias pays very little attention to the
training data and oversimplifies the model. It always leads to high error on
training and test data.
2. Variance: Variance
tells us how sensitive a model is to small changes in the training data. That
is by how much the model changes. High variance in a model will make it prone
to random noise present in the dataset thereby over-fitting the model.
Variance is the
variability of model prediction for a given data point or a value which tells
us spread of our data. Model with high variance pays a lot of attention to
training data and does not generalize on the data which it hasn’t seen before.
As a result, such models perform very well on training data but has high error
rates on test data.
In supervised learning,
underfitting happens when a model unable to capture the underlying pattern of
the data. These models usually have high bias and low variance. It happens when
we have very less amount of data to build an accurate model or when we try to
build a linear model with a nonlinear data. Also, these kind of models are very
simple to capture the complex patterns in data like Linear and logistic
regression.
In supervised learning,
overfitting happens when our model captures the noise along with the underlying
pattern in data. It happens when we train our model a lot over noisy dataset.
These models have low bias and high variance. These models are very complex
like Decision trees which are prone to overfitting.
You can think of
ensemble learning analogous to the board of directors in a company, where the
final decision is taken by the CEO. Instead of taking a decision all by himself,
the CEO takes inputs ( brainstorming ) from each of the board members before
arriving at a final conclusion.
The CEO, in this case,
is the final model and the board members are the base learners which provide
independent inputs to the CEO. This drastically reduces the chance of
committing an error when the CEO makes his final decision.
We use this approach
regularly in our daily lives as well — for example, we ask for the opinions of
different experts before arriving at conclusions, we read different product
reviews before buying a product, a panel of judges consult among them to
declare a winner.
In each of the above scenarios what we are actually trying to achieve is to minimize the likelihood of an unfortunate decision made by one person (in our case a poor model).
In each of the above scenarios what we are actually trying to achieve is to minimize the likelihood of an unfortunate decision made by one person (in our case a poor model).
The goal of any machine
learning problem is to find a single model that will best predict our wanted
outcome. Rather than making one model and hoping this model is the best/most
accurate predictor we can make, ensemble methods take a myriad of models into
account, and average those models to produce one final model.
Ensemble learning is used to combine the predictions
from multiple separate models. It reduces the model complexity and reduces the
errors of each model by taking the strengths of multiple models. Out of
multiple ensembling methods, two of the most commonly used are Bagging and Boosting.
Typically, ensemble
learning can be categorized into four categories:--
1. Bagging: Bagging is
mostly used to reduce the variance in a model. A simple example of bagging is
the Random Forest algorithm.
2. Boosting: Boosting
is mostly used to reduce the bias in a model. Examples of boosting algorithms
are Ada-Boost, XGBoost, Gradient Boosted Decision Trees etc.
3. Stacking: Stacking
is mostly used to increase the prediction accuracy of a model.
4. Cascading: This
class of models are very very accurate. Cascading is mostly used in scenarios
where you cannot afford to make a mistake. For example, a cascading technique
is mostly used to detect fraudulent credit card transactions, or maybe when you
want to be absolutely sure that you don’t have cancer.
Decision Trees are not the only form of ensemble methods, just the most
popular and relevant in DataScience today. Voting and averaging are two of the easiest
ensemble methods. They are both easy to understand and implement. Voting is
used for classification and averaging is used for regression.
Ensemble methods is a
machine learning technique that combines several base models in order to
produce one optimal predictive model.
Ensemble Methods allow
us to take a sample of Decision Trees into account, calculate which features to
use or questions to ask at each split, and make a final predictor based on the
aggregated results of the sampled Decision Trees.
A decision tree is a
decision support tool that uses a tree-like graph or model of decisions and
their possible consequences, including chance event outcomes, resource costs,
and utility. It is one way to display an algorithm that only contains
conditional control statements.
Decision tree is the
most powerful and popular tool for classification and prediction. A Decision
tree is a flowchart like tree structure, where each internal node denotes a
test on an attribute, each branch represents an outcome of the test, and each
leaf node (terminal node) holds a class label.
Strengths and Weakness
of Decision Tree approach
The strengths of
decision tree methods are:--
Decision trees are able
to generate understandable rules.
Decision trees perform
classification without requiring much computation.
Decision trees are able
to handle both continuous and categorical variables.
Decision trees provide
a clear indication of which fields are most important for prediction or
classification.
The weaknesses of
decision tree methods :--
Decision trees are less
appropriate for estimation tasks where the goal is to predict the value of a
continuous attribute.
Decision trees are
prone to errors in classification problems with many class and relatively small
number of training examples.
Decision tree can be
computationally expensive to train. The process of growing a decision tree is
computationally expensive. At each node, each candidate splitting field must be
sorted before its best split can be found. In some algorithms, combinations of
fields are used and a search must be made for optimal combining weights.
Pruning algorithms can also be expensive since many candidate sub-trees must be
formed and compared.
Ensemble methods use
multiple learning algorithms to obtain better predictive performance than could
be obtained from any of the constituent learning algorithms alone.
Machine learning (ML)
refers to algorithms that autonomously improve their performance, without humans directly encoding their expertise. Usually, ML algorithms improve by training
themselves , hence 'data-driven' AI.
The major recent advances in this field are
not due to major breakthroughs in the techniques per se but, rather, through
massive increases in the availability of
data. In this sense, the tremendous growth of data-driven AI is, itself,
data-driven. Usually, ML algorithms find
their own ways of identifying patterns, and apply what they learn to make statements
about data.
Different approaches to ML
are suited to different tasks and situations, and have different implications.
. Machine learning falls under the umbrella of AI, that provides systems with
the ability to automatically learn and improve from experience without being
explicitly programmed.
In the field of machine
learning there is an incredibly important problem is known as the bias-variance
dilemma. It’s entirely possible to have
state-of-the-art algorithms, the fastest computers, and the most recent GPUs,
but if your model overfits or underfits to the training data, its predictive
powers are going to be terrible no matter how much money or technology you
throw at it.
-
I MUST EXPOUND ON WHAT I WROTE BELOW--
#########################################
KARVA CHAUTH DOES NOT WEAKEN WOMEN, RATHER IT IS A POWERFUL TOOL TO EMPOWER MARRIED WOMEN ...
NO WIFE WILL FAST ON KARVA CHAUTH FOR HER HUSBAND IF HE IS NOT WORTH IT ( APOLOGIES TO AISHWARYA RAI ).. NO WONDER SHOBHAA DE DOES NOT FAST FOR HER SECOND HUSBAND ..
THERE IS SOMETHING CALLED HINDU HUSBANDS HAVING STRENGTH AND HONOUR WHEN IT COMES TO PROTECTING HIS WIFE AND HER INTERESTS, AND THUS SECURING HER LOVE , GRATITUDE AND RESPECT...
#############################
I MUST MAKE A STATEMENT OF FACT--
LET GOD STRIKE ME AND MY TWO SONS DEAD IF I LIE BELOW..
I HAVE BEEN MARRIED FOR 36 YEARS ..
MY WIFE SAILED ON SHIPS I COMMANDED .. I MARRIED AFTER GETTING COMMAND
WHILE ON THE SHIP MY WIFE FOLLOWED PROTOCOL AS BEFITTING A CAPTAINs WIFE.. WHO MUST SET AN EXAMPLE.. WE HAD STEWARD SERVICE..
WHEN MY WIFE DID NOT SAIL WITH ME..
( MIND YOU AFTER 30 YEARS AS CAPTAIN , EVERYTHING FREE ON BOARD , EARNING IN DOLLARS AND SPENDING IN RUPEES , I AM NOT EXACTLY A POOR FELLOW.. WE HAVE SERVANTS )
1) EVERY CUP OF TEA I HAD WAS MADE BY MY WIFE..
2) MY WIFE HAS NEVER EATEN BEFORE ME OR WITH ME .. ( UNLESS WE ATE AT A RESTAURANT)..
3) MY WIFE HAS IRONED EVERY SHIRT AND PANT I WORE , AFTER WASHING IT HERSELF..
4) DESPITE HAVING COOKS -- MY WIFE COOKED FOR HER HUSBAND AND HER CHILDREN.. THE COOK ONLY DID THE PRELIMINARIES
5) MY WIFE TAUGHT HER TWO SONS .. MY ELDER SON IS A GENIUS , HE NEEDED NO HELP AFTER KINDERGARTEN..
MY YOUNGER SON IS ARTISTICALLY ORIENTED AND HATES NUMBERS ..THOUGH MY WIFE IS A ECONOMICS GRADUATE ( PODDAR COLLEGE MUMBAI ), SHE BOUGHT GUIDES LEARNT MATH/ PHYSICS/ CHEMISTRY AND TAUGHT HIM.. HE NEVER FAILED AND GOT GOOD GRADES.. YOU MUST KNOW THAT THIS IS A GREAT SACRIFICE..
INDIAN CULTURE IS ENVIED BY WESTERN TOURISTS.. THEY COME TO INDIA AND WATCH INDIAN FAMILIES IN PARKS , BEACHES AND PUBLIC SPACES.. THEY WATCH THE HARMONY AND COHESION.. THIS IS A TAKE HOME LESSON.. THEY GLEAN THE DIFFERENCE BETWEEN LOVE AND LUST..
IF I WANT TO MAKE MY WIFE CRY, ALL I NEED IS TO ENTER THE KITCHEN WHEN MY WIFE IS SLEEPING AND MAKE MY OWN CUP OF TEA..
http://ajitvadakayil.blogspot.com/2013/12/ipc-section-377-love-lust-perversion.html
MY WIFE IS A VERY HAPPY AND CONTENDED WOMAN.. HER SONS ADORE HER.. SHE DOES UNIVERSAL GOOD BY REIKI..
THERE IS NOT A SINGLE DAY MY ELDER SON DOES NOT CALL HER FROM ABROAD.. THEY TALK FOR A LONG TIME.. I HAVE NEVER SEEN THEM RUNNING OUT OF CONVERSATION TILL TODAY..
AS I GET OLD ( 64 YEARS ) - MY WIFE MOTHERS ME.. SHE CLUCKS LIKE MOTHER HEN.. I LOVE IT.. I ALLOW HER TO TUCK ME TO BED..
MY SONS DO NOT SMOKE OR DRINK.. ( MY ELDER SON SOCIALLY DRINKS WHEN WARRANTED ).. MY SONS WONT BE CAUGHT DEAD IN TATTERED JEANS ..
http://ajitvadakayil.blogspot.com/2013/11/nagging-unhappiness-at-home-death-of.html
http://ajitvadakayil.blogspot.com/2010/05/marriage-sans-fights-capt-ajit.html
MY SONS ARE PROUD OF THEIR DAD.. MY WIFE HATES WOMENs LIB..
WHAT RIGHT HAS THIS COMMIE BITCH ROHINI CHATTERJEE TO WRITE A LYING POST ABOUT MY WIFE?
.. HER GRANDFATHER WAS SOMNATH CHATTERJEE, WHO WAS PARLIAMENT SPEAKER FOR 5 YEARS -- A TEN 5 YEAR TERM MP .
https://www.firstpost.com/living/open-letter-to-capt-vadakayil-the-man-who-wont-do-his-wifes-laundry-1192201.html
HAVING SAILED AROUND THE WORLD FOR 40 YEARS , I KNOW WE ARE LUCKY TO HAVE A PRICELESS CUTURE..
NURTURE IT !
MY ELDER SON CAME TO INDIA A FEW DAYS AGO WITH HIS WIFE .. HE SAW THE FIRST T20 BANGLADESH MATCH IN DELHI WITH HIS WIFE .. HE FLEW IN TO ATTEND HIS CLASSMATEs WEDDING.. HE STAYED IN A 7 STAR DELHI HOTEL FOR A WEEK AND SAW TAJ MAHAL THROUGH THE SMOG .
capt ajit vadakayil
..
BELOW" MY SON AND WIFE AT LEELA
please apne bachchon ki kasam mat khayen . thats not good . may god shower all happiness on you and your family !
take care always as you you know so much of the untold history .
take care always as you you know so much of the untold history .
-
THERE ARE BIMBETTES WHO THINK THAT MY WIFE IS A WOMAN WITHOUT SPIRIT , A DEHAATI BEHENJI.. IN REALITY SHE IS MORE WELL READ THAN ME..
WELL-- MY WIFE CAN KICK THEIR EMPTY HEADS AND PURULENT TWATS .
IN THE POST BELOW YOU CAN SEE HER BEATING 25 YOUNG SAILORS WHO ARE TALLER THAN HER IN THE "HIGH KICK" CONTEST...
http://ajitvadakayil.blogspot.com/2012/06/equator-crossing-ceremony-at-sea-capt.html
THIS WAS A CEREMONY ON THE WAY TO CURACAO..
capt ajit vadakayil
..
BELOW: THIS IS A CORNY DIALOGUE FROM MALAYALAM MOVIE CHEMMEEN.
IT IS ABOUT FORBIDDEN LOVE BETWEEN A HINDU MARRIED WOMAN AND A UNMARRIED MUSLIM MAN.
HE MOANS "KARUTHAMMA IF YOU LEAVE ME , I WILL DIE OF A BROKEN THROAT "
WHEN I WAS IN 6TH STANDARD IN SCHOOL, WE HAD A CORNY CLASSMATE NAMED SARVATHAMANAN WHO GAVE THIS DIALOGUE WELL WITH ALL ITS EMOTIONS AND EVEN TEARS .
WE USED TO ROLL IN LAUGHTER
MADHUs SISTERS DAUGHTER IS A HEADMISTRESS OF AN ELITE BANGALORE SCHOOL.. WHEN I MET HER I REPEATED THIS DIALOGUE.. SHE CORRECTED ME WHEN I MADE A WEE MISTAKE..
BELOW: THIS POST IS CONTINUED TO PART 4 BELOW--
https://ajitvadakayil.blogspot.com/2019/11/what-artificial-intelligence-cannot-do.html
CAPT AJIT VADAKAYIL
..
Good to see you back Captain After such a long pause.
ReplyDeleteIt is very good to see you Capt. Ajit. Happy Diwali from me Capt. Okay, I am done for now.
ReplyDeleteHappy Diwali Captain Ajit, I hope you got that as well mate.
ReplyDeletehappy deepavali to the captain of subjective organic intelligence
ReplyDeleteDear Captain,
ReplyDeleteWhew!!
Welcome back!!
Regards
Sukanya
&
Srinivas
God bless you captain, long live.
ReplyDeleteRealy I got worried a lot , daily I used to check the blog... after seeing ur new blog I got relief... on some of your relasiation of facts in the blog still some of them my mind not accepted, still I realy thank God I don't want to miss you... Please before taking such long gap inform in advance.
ReplyDeleteIt feels so great to see your posts.
ReplyDeleteAlas you are here.
I can not express my happiness now.
Unless I read your word, my days are uncomplete.
You are the true leader and your posts on sanatan dharma is truly a great releif and so is your every word
God, your posts are more addicted than cocaine.
God bless you caption.
Accept my reiki energy.
whatta dashing pic!!!
ReplyDeletehttps://twitter.com/shree1082002/status/1195046668992839681
ReplyDeletecaptain, regarding moravec's paradox
ReplyDeleteyou once said that your youngest son's ability to take a look at a person and draw a potrait of someone is real iq
is it because he can unconsciously do it?
Welcome back Ajitji.
ReplyDeleteNothing more to say..
Welcome back captain...
ReplyDeleteHad a sigh of relief when saw your new post,it was like a fixed ritual to check for update every other day...
We get a lot to learn and understand how to analyse things from your blogs....
Please don't ever stop blogging.
Thank you for coming back.
Wish for your good health and long life.
Gratitude
Aditya
capt, is it also the reason why adi shankara made a rule that a namboothiri must be attached to the 4 mutts that he created, the reason being the unconscious ability of the namboothiris with vedas due to their ancestors experience in oral transmission of vedas
ReplyDelete
ReplyDeleteSoujanya VNovember 14, 2019 at 10:15 PM
Hi Sir- How are you doing?, I assumed that you took a break and were visiting your son in the USA.
ReplyDeleteKnoxNovember 14, 2019 at 10:31 PM
Welcome back Captain :D
ReplyDeleteRN MurthyNovember 14, 2019 at 10:51 PM
Dear Capt Ajit sir,
Watching Arnab's show on #FaithVsEquality, with 6 women against 1 Rahul Easwar...what hypocrisy !!!
Observing Padma Pillai and Shilpa Nair what will they say...Shilpa said beautifully, we respect SC verdict, but we do not accept SC judgement as it doesn't accept the faith of lakhs of women who came out in Kerala to ban women of ages 10-50 not to visit Sabarimala...SC has made a kichdi of all cases to make it as equality for women in Sabarimala, Haji Ali, Parsi temples...what a farce to put SC on a channe ke jhaad...and make judges God...Bhadralok uplift was visible.
Padma says it's given both sides something and for us, how much Court should interpret Art 25 with deeper thinking in philosophy....Kasturi pointed very clearly that women are allowed in all Ayyappa temples except only Sabarimala, where even Males have restrictions in terms of clothing, follow certain customs/traditions....so Padma's Art 25 quote is quite shallow...more of neutral and pushing towards equality rather than faith.
ReplyDeleteAlokeNovember 14, 2019 at 10:57 PM
With lots of love and support from the eastern frontiers of India
ReplyDeleteashuuuNovember 14, 2019 at 11:14 PM
captain i was really worried i thought you have been threatened or your family is in danger since you never know how invisible enemies you have created,but deep in my heart i checked almost once every two days if you are posting i knew you would post some day.i remember GAME OF DEATH OF MOVIE WHERE Bruce lee stages his fake death just to know his real enemies and then attacks them one by one
thanks captain hope your legacy would be alive like bruce leenfor ever
Delete
ReplyDeletePraveen BajiNovember 14, 2019 at 11:14 PM
Realy I got worried a lot , daily I used to check the blog... after seeing ur new blog I got relief... on some of your relasiation of facts in the blog still some of them my mind not accepted, still I realy thank God I don't want to miss you... Please before taking such long gap inform in advance.
ReplyDeleteProud_SanataniNovember 14, 2019 at 11:25 PM
Thank God you are back captain. we were worried why you were away for so long. Good to see the wheel of dharma going.
"The Lion takes TWO STEPS back not because it is scared but to create a momentum to ATTACK."
nice to see you safe n sound after many days .i thought govt. has blocked your posts .
ReplyDeleteyes , break is very important sometimes . t c .
Dear Ajit Sir,
ReplyDeleteJust curious!
Kindly do share what all you did for these many days being away from blogging.
Only posting on Times of India or anything else?
One reader has put in the comment that if you could receive the telepathy of we missing you.
Did We really reach your 6th Sense?
It would be interesting to know the secret that what happens to the person to whom anyone or group of people miss
No one can give an apt answer other than you
《Do share》
Every time you took break you informed us before hand.
For these many years it never happened the ones it happened last month, your undeclared missing away from the scene & we missing you 24×7.
It was literally painful.
Only one question was hitting the emotion "Captain कब आएँगे"
https://timesofindia.indiatimes.com/india/260-writers-approach-pm-modi-on-aatish-taseer/articleshow/72063455.cms
ReplyDeletePROFILE ALL THE DESH DROHI BASTARDS IN HIS LIST..
Dear Captain,
DeleteHere is the complete list
https://pen.org/indian-government-review-taseer/
wow.. you're in a very good shape Captain
ReplyDeleteHealthy, Strong.. Hulk..
Inspiring
Touch wood
God Bless You
Om Namah Shivaya
https://www.theguardian.com/technology/2019/nov/12/google-medical-data-project-nightingale-secret-transfer-us-health-information
ReplyDeleteHi sir,
ReplyDeleteAnd then the real Boss arrived.. All hail.. Happy that you are doing good and back.. :) Thanks to the old gods.. May Lord Arunachaleshwara bless you and your family sir.. Im happy now.. :)
Thanks
Maheshwar Singh
Dear Sir
ReplyDeletePlease reveal in detail about the international influencial lady MADI SHARMA , who arranged the European countries Parliament members unofficial meet with prime minister and trip to Kashmir.
Dear Captain,
ReplyDeleteExtremely happy to see you back. i did search you name on google to see if any news was posted about you going offline. take care.
Kind Regards,
SB
Captain, Relieved, good to see you back. Most of your readers care for you, please say a word before you take some time off from blogging. A Humble request.
ReplyDeletePranaam..
Hi Captain,
ReplyDeleteRelieved to see you back.
Regards,
Balamurali Shivaram
Welcome back capt.
ReplyDeleteWelcome back Captain.
ReplyDeleteI thought you had taken vacation and would have probably traveled to US to spend time with your Son and daughter in law.
Dear Captain,
ReplyDeleteGreetings.
Great relief to see you back.
Initially thought you would be on vacation for 2 weeks.. Then for 3 & 4th week, was a bit anxious; then thought you may have a very good news with your grand son / daughter in your family and was keenly following as it was approaching a month on 13th..
My wife was confident that you'll be back soon; Our prayers for you, your family were always.
Following you for all these years, was aware of the John Galt back swing you do for your detractors.
Happy to see you come in full force, after a well deserved break.
Grace & Peace,
JTA
https://pen.org/indian-government-review-taseer/
ReplyDeleteWHY IS AATISH TAHEER A BLUE EYED BOY WITH SO MANY CRYPTO JEWS ( LIKE CHRISTIANE AMANPOUR) IN THE PAYROLL OF THE KOSHER DEEP STATE SPONSORING HIM ?
http://ajitvadakayil.blogspot.com/2017/04/the-most-evil-journalist-capt-ajit.html
BECAUSE AATISH TAHEERs BIOLOGICAL PAKISTANI FATHER SALMAN TASEER IS A JEW, WHO HAD SEX WITH HIS UNMARRIED MOTHER TAVLEEN SINGH..
WHY DO SO MANY INDIAN LUTYENS JOURNALISTS ( AND AMAAN KI AASHA BOLLYWOOD BIMBETTES ) RUN TO PAKISTAN FOR LITERARY FESTS ?
BECAUSE THEY LOVE HARD ANAL SEX PROVIDED BY THE HANDSOME PAKISTANI CRYPTO JEW MEN ( SOME ARE ISI AGENTS )..
THEY DONT GET ANAL SEX FROM THEIR HUSBANDS/ BOYFRIENDS IN INDIA.. MY PAKISTANI OFFICERS HAVE TOLD ME WHO THESE SHAMELESS INDIAN WOMEN ARE..
SAME WITH SOME FAT UGLY JNU WOMEN COMMIE PROFESSORS IN JNU , WHO NEED THIS FROM HANDSOME KASHMIRI ISLAMIC SEPARATIST STUDENTS..THEY ARE ADDICTED..
THERE ARE NW PASHTUN AREAS IN PAKISTAN WERE VAGINAL SEX, IS ONLY TO PRODUCE A CHILD.. THE REST OF THE TIME WOMEN GET ONLY ANAL SEX..
THEIR ASSHOLE ORIFICE GETS TORN AGAIN AND AGAIN AND NON- ELASTIC SCAR TISSUE BUILDS UP, LEAVING A GAPING BUTTON HOLE ANUS ...
IMRAN KHAN IS A JEW.
MALALA YOUSAFZAI IS A JEWESS..
THE MAYOR OF LONDON SADIQ KHAN WHO KEEPS HAVING HEAD ON COLLISIONS WITH DONALD TRUMP GHADI GHADI IS A JEW..
MIND YOU SADIQ KHAN BEAT HEAVY WEIGHT ZAC GOLDSMITH TO THE MAYORs POST..
JEW IMRAN KHAN HAD MARRIED A JEWESS JEMIMA GOLDSMITH ( GERMAN JEW LINEAGE GOLDSCHMIDT AND ROTHSCHILD PARTNER ) --. AND TODAY HE PEPPERS EVERY SENTENCE WITH 10 INSHA ALLAHS.
ZAC GOLDSMITH IS SON OF BILLIONAIRE BUSINESSMAN AND FINANCIER SIR JAMES GOLDSMITH, A PARTNER OF JEW ROTHSCHILD.
ZAC GOLDSMITH, DIVORCED HIS WIFE AFTER HE WAS ELECTED BRITISH MP AND WAS LIVING WITH ALICE ROTHSCHILD.
DIANA LOOKS LIKE SIR JAMES GOLDSMITH .
SIR JAMES GOLDSMITH'S OTHER THREE CHILDREN, ZAK, BEN AND JEMIMA GOLDSMITH.
ZAC GOLDSMITH IS A SPLITTING IMAGE OF JEWESS JEMIMA— EX-WIFE OF PAKISTANI CRICKETER JEW IMRAN KHAN !
PRINCE WILLIAM'S WIFE KATE MIDDLETON IS JEWISH WITH HER MOTHER'S NAME BEING CAROLE GOLDSMITH.
JEW DOCTOR HASNAT KHAN WHO WAS SCREWING JEWESS PRINCESS DIANA IS A COUSIN OF JEW IMRAN KHAN , THE CRICKET PLAYER..
THE BLOGPOST BELOW HAS BEEN WRITTEN FOR AJIT DOVAL AND MODI. TO UNDERSTAND WORLD INTRIGUE.. TO AVOID FUTURE WARS UNDERSTAND TRUE HISTORY..
https://ajitvadakayil.blogspot.com/2019/11/paradox-redemption-victory-in-defeat.html
capt ajit vadakayil
..
PUT ABOVE COMMENT IN WEBSITES OF--
DeleteMODI
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AATISH TASEER
TAVLEEN SINGH
SHOBHAA DE
HUSBAND OF SHOBHAA DE
BARKHA DUTT
RANA AYYUB
SWARA BHASKAR
IRA BHASKAR
ROHINI CHATTERJEE
PINARAYI VIJAYAN
KODIYERI BALAKRISHNAN
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D RAJA
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CJI GOGOI
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LIST CONTINUED--
DeleteGAUTAM GAMBHIR
ASHOK PANDIT
ANUPAM KHER
KANGANA RANAUT
VIVEK AGNIHOTRI
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MEENAKSHI LEKHI
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Your Registration Number is : PMOPG/E/2019/0659548
DeleteWelcome back sir.. missed you a lot
ReplyDeletehttps://timesofindia.indiatimes.com/india/ldf-govt-wont-give-security-to-women-visiting-shrine/articleshow/72063437.cms
ReplyDeletePINARAYI VIJAYAN IS NOT AN IDIOT..
HE WILL SCREW MODI WITHOUT GREASE..
PINARAYI VIJAYAN KNOWS THE SLIMY GAME OF MODI/ BJP..
AGAIN GET PILGRIMS AGITATED .. AGAIN ALLOW KERALA CM TO THROWN HUNDREDS INTO JAIL..
FORCE FRUSTRATED COMMIE HINDUS TO VOTE FOR BJP..
THIS WONT HAPPEN..
PINARAYI VIJAYAN NOW ACCEPTS THAT COMMIE VOTERS ARE NOT ATHEISTS BUT IMPOVERISHED GOD FEARING PEOPLE WHOSE ONLY SOLACE IS THEIR LORD AYYAPPA.. HE WENT TO GURUVAYUR TEMPLE TO DELIVER A MESSAGE ..
POOR MODI/ POOR AMIT SHAH/ POOR BJP/ POOR RSS.
http://ajitvadakayil.blogspot.com/2019/06/guruvayur-temple-idol-carved-out-of.html
Yes his party said women will need a court order to visit the shrine under police protection.
DeleteHe knows which side the bread is buttered.
Supporting Vaman jayanti was a mistake. Until BJP learns to respect and understand King Mahabali they will be nobodies.
But politicians have put chameleons to shame in the past. So anything is possible.
So glad to see you back, Captain! Was hoping you were MIA due to family commitments or taking a break. Good to see you back in action. Missed knowing your views on the events that happened while you were away from this blog.
ReplyDeleteAny reason why you have not spoken about ayodhya verdict?
ReplyDeleteSUPREME COURT TOLD A LIE " UNINTERRUPTED WORSHIP WS GOING ON IN BABRI MASJID SINCE IT WAS MADE"..
DeleteTRUTH?
THE MOMENT BABURs GENERAL SAW THAT VARAHA ( PIG ) STATUE WAS UNEARTHED --THE NAMAAZ STOPPED..
http://ajitvadakayil.blogspot.com/2012/11/babri-masjid-demolition-mughal-emperor.html
https://www.instagram.com/p/B4aCi-BlGpR/
DeleteSome of these extinct south american mammals look uncannily similar to Varah avatar.
https://timesofindia.indiatimes.com/india/shiv-sena-will-lead-government-in-maharashtra-for-next-25-yrs-sanjay-raut/articleshow/72066674.cms
ReplyDeleteSENA IS DEAD
LONG LIVE SENA
TEE HEEEEEEE
Sanjay Raut had done so much mischief,and literally barking like mad
DeleteLOT OF INDIANS GO ABROAD ON ECONOMIC TOUR PACKAGE..
ReplyDeleteFOR EXAMPLE A TEN DAY EUROPEAN TOUR COSTS JUST 1.5 LAKHS..
THE WHOLE IDEA IS TO PUT PHOTOS IN FACEBOOK TO MAKE OTHERS JEALOUS..
THEY WILL NEVER TELL THAT THEY GOT CHEATED AND SCAMMED BY THE TOUR OPERATORS WHO ARE IN CAHOOTS WITH MAFIA AND POLICE.
MY FRIENDs FAMILY LOST THEIR PASSPORTS IN GREECE.. OF COURSE ONCE THEY REGISTER A POLICE COMPLAINT THEY WILL GET IT BACK FOR A HUGE RANSOM ( THEY WILL NEVER REVEAL THIS )..
ANOTHER FEMALE LOST HER MONEY AND SIM CARD ( THEY DID NOT STEAL THE MOBILE PHONE ).. STOLEN FROM THE TOURIST BUS WHEN THEY WENT ON FOOT FOR SIGHT SEEING..
EUROPE IS A POOR AND CRIMINAL AREA..
OUR BASTARD BENAMI INDIAN MEDIA ARE PAID BY THE DEEP STATE TO RUN DOWN THEIR OWN WATAN..
I HAVE SEEN THE BACKYARDS OF THIS PLANET FOR 40 YEARS
I KNOW.
http://ajitvadakayil.blogspot.com/2013/02/jealousy-growing-ulcer-within-capt-ajit.html
What about Russia ? Is it a good place for tourism ?
Deletesame shit in istanbul
DeleteFor Honeymoon my wife wanted to go someplace like Singapore, Europe, Mauritius.
DeleteI made it clear to her my Honeymoon memories have to be from India.
We both unanimously decided Kerala.
Landed Kozhikode and started our trip to Vythiri and ahead
Your Registration Number is : PMOGP/E/2019/0659027
ReplyDeleteWhat happened in Karnataka has repeated itself in Maharashtra. After elections with pre poll alliance, partner SS split up and joined hands with NCP n INC.
ReplyDeleteKissa CM ki kursi ka?
Captain,
ReplyDeleteIs it true that Karna (son of kunti) was born in Iran ? I did some research and came to this conclusion.
PS Welcome back.
ReplyDeleteEven now the nut job in Delhi is blaming burning of crop stubble and wants odd even rule as a thoonk patti environmental saviour.
Please tell them how to solve their inversion.
Meanwhile on tv a Kashmiri Hindu made waves in DC about human rights abuses on Kashmiri Hindus that these Congressional enquiry types were conspicuously silent about at that time.
Please do share your method of catharsis n meditation later.
It was very difficult to not see your post or comment for so long.Thoroughly relieved to see you back. Jaan mein jaan aa gayi firse 🙏
ReplyDeleteGlad and relieved to see you back Ajit ji! We assumed that you must be visiting your elder son and hence enjoying your much deserved family time. At times we were worried, but I firmly believe that you will always be protected.
ReplyDeleteGod bless you always !
amrita
Happy to be back in touch with you sir.
ReplyDeleteRespected Sir,
ReplyDeleteAfter how many days of the death of a relative
Can we visit temple sir.
Thank you
ANYTIME
Deletehttps://twitter.com/shree1082002/status/1195276046930595840
ReplyDeleteCaptain ji very happy to see you back ..i was worried about your absence ,then asked Veeresh malik sir he told you are doing fine and will be back after break.
ReplyDeletehttps://twitter.com/taslimanasreen/status/1194999589079781383
ReplyDeletesir this femenist is pushing New World Order Agenda . Showing her true colours . Its time govt should kick her out of country
ReplyDeleteVR(northern k)November 15, 2019 at 3:09 PM
Welcome back captain.
I thought you went on Svadhyaya mode.
https://timesofindia.indiatimes.com/india/why-keeping-sabarimala-issue-alive-helps-the-bjp-in-kerala/articleshow/72067376.cms
ReplyDeleteAFTER LOSING 19 OUT OF 20...
PINARAYI VIJAYAN WON 3 OUT OF 6 BYPOLLS..
HIS COMMIE CADRE WENT HOUSE TO HOUSE APOLOGIZING FOR THE HUGE MISTAKE THEY MADE IN LAST SABARIMALA SEASON..
KERALA COMMUNIST PARTY HAS DECLARED THAT THEY ARE NOT AN ATHEIST PARTY.. BUT A PARTY FOR GOD FEARING HAVE-NOTS..
https://www.ndtv.com/kerala-news/no-protection-for-activists-making-sabarimala-pilgrimage-warns-kerala-2133190
DeleteCaptain sir,
guess the communist pinarayi has learnt his lesson and for the time being is trying to rectify his mistake.
However the decision rests with the 7 bench anti Hindu SC
If Pinarayi Vijayan truly regrets, he should drop cases against all innocent people who were arrested under criminal charges for protesting peacefully by lighting lamps and chanting Swamiye Ayyappa.
DeleteGenius mathematician Vashishth Narayan Singh died in a Patna hospital .. unsung.
ReplyDeleteWe allowed another Ramanujan to fade away ..
TATTU MEN WHO FAST FOR THEIR BOLLYWOOD BIMBETTE WIVES ON KARVA CHAUTH MUST KNOW THIS..
ReplyDeleteKARVA CHAUTH DOES NOT WEAKEN WOMEN, RATHER IT IS A POWERFUL TOOL TO EMPOWER MARRIED WOMEN ...
https://twitter.com/ashutosh83b/status/392523798756356097
NO WIFE WILL FAST ON KARVA CHAUTH FOR HER HUSBAND IF HE IS NOT WORTH IT ( APOLOGIES TO AISHWARYA RAI ).. NO WONDER SHOBHAA DE DOES NOT FAST FOR HER SECOND HUSBAND ..
THERE IS SOMETHING CALLED HINDU HUSBANDS HAVING STRENGTH AND HONOUR WHEN IT COMES TO PROTECTING HIS WIFE AND HER INTERESTS, AND THUS SECURING HER LOVE , GRATITUDE AND RESPECT...
http://ajitvadakayil.blogspot.com/2013/10/karva-chauth-synchronising-fertility.html
capt ajit vadakayil
..
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Tweets:
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Handles:
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https://twitter.com/shree1082002/status/1195359283908468736
DeleteWith the media brainwashing via movies and ads a day will come when only men will fast.
DeleteHello Captain,
ReplyDeleteGood to see you are back.
Thank you again for continuing to lead us.
Kind regards,
Capt, Welcome back!
ReplyDeletePlease write something about the christian conversions going on all over the country. In recent days, there is a huge spike of conversions in states like tamilnadu & andhra nd in other indian cities kolkata, Delhi. In Andhra, the people follishly elected a missionary as CM, so Andhra too is now a gone state like kerala (I meant no offense to your state). Please write a post about population control law and conversions. Modi is only interested in oppurtunistic politics.
IF JAGAN TRIES TO CONVERT ANDHRA TELUGUS TO CHRISTIANITY LIKE HIS FATHER YSR --BEEEG MISTAKE-- HE WILL SUFFER
DeleteCANDRA BABU NAIDU NAIDU WILL WIN AND PUT AWAY JAGAN MOHAN REDDY FOR THE REST OF HIS LIFE IN JAIL..
http://ajitvadakayil.blogspot.com/2012/05/jagan-mohan-reddy-finally-in-jail-for.html
Jagan will soon meet his waterloo.
DeleteWatch the below videos.
https://twitter.com/ExSecular/status/1195129796167995394
https://www.youtube.com/watch?v=VRiIjoFXe30
I jokingly tell everyone,that if Xtian missionaries are given free hand in Andhra and TN,they would have guts to rename it to Andrew Pradesh and Tommy Nadu.
DeleteALL TAMIL NADU FISHERMEN WERE CONVERTED FROM HINDUISM TO CHRISTIANITY SO THAT WHITE JEWS CAN STEAL OUR THORIUM..
DeleteTHIS HAPPENED WHEN MGR WAS A VEGETABLE , LITERALLY BRAIN DEAD AND PROPPED UP ON A CHAIR WITH DARK GLASSES FOR THE MASSSES..
LTTE CONVERTED TO CHRISTIANITY AND WENT FROM TOEHOLD TO FOOTHOLD..
http://ajitvadakayil.blogspot.com/2012/07/scrap-sethusamudram-project-now-capt.html
http://ajitvadakayil.blogspot.com/2012/08/aidmk-dmk-misplaced-support-for-ltte.html
http://ajitvadakayil.blogspot.com/2010/03/gold-finger-ajit-vadakayil.html
JAYALALITHAA AND MGR WANTED TO BE CREMATED.. BOTH WERE BURIED BY VESTED TAMIL CHRISTIAN FORCES.
http://ajitvadakayil.blogspot.com/2017/09/inquiry-into-death-of-cm-of-tamil-nadu.html
Hi DJ,
DeleteMissionaries already started this name changing game.
See in below link.
https://twitter.com/shriramviswa/status/1059099905732591617?s=19
India becomes Samariya.
Tamilnadu becomes Yudeya.
Kanyakumari district (Highest percentage of christians in Tamilnadu) becomes Yerusalem.
When I tried to explain some sense to tamil christians, I came to know of sinister plot of hijacking entire Tamil heritage as christian thus severing Tamils from hinduism.
Namaskaara_/\_
ReplyDeleteGlad so much to hear from you this early morning!!!
May you be blessed with blissful years ahead!!!
Lots of Love and Gratitude!
_/\_Alupa Raaja
U r right sir,sena is dead.ground level shivsainik who was with sena for decades witj sena is unhappy with uddhav decision to go with congress.if elections are held nw sena is going to drains.reporţs are also coming that sonia has asked uddav to soften its stand on hindutva,ram mandir,ucc.
ReplyDeletehttps://www.ndtv.com/india-news/centre-must-read-extremely-important-order-on-sabarimala-justice-nariman-2132931
ReplyDeleteCAPT AJIT VADAKAYIL WILL WRITE THE LEGACIES OF "LIBERAL MELORDS" CHANDRACHUD AND NARIMAN..
BOTH ARE DARLINGS OF THE JEWISH DEEP STATE
so much bad karma has been done by jews and by a particular famaily.Even after All this sanchit bad karma why they are still in power and will they get moksha.
ReplyDeleteWHO KNOWS ?
DeleteIN YOUR LAST BIRTH YOU MIGHT HAVE BEEN A ROTHSCHILD !
Zero karmic baggage leads to moksha to whosoever understands this concept.
DeleteMOKSHA TIED TO KARMA IS ADVANCED QUANTUM PHYSICS..
Deletehttps://timesofindia.indiatimes.com/india/cbi-raids-amnesty-international-bengaluru-delhi-offices/articleshow/72074121.cms
ReplyDeleteINCARCERATE THESE FOREIGN PAYROLL DESH DROHIS
https://ajitvadakayil.blogspot.com/2019/11/paradox-redemption-victory-in-defeat.html
ReplyDeletePUT ABOVE COMMENT IN WEBSITES OF--
INDIAN AMBASSADORS TO ALL SOUTH AND CENTRAL AMERICAN NATIONS..
AMBASSADORS OF ABOVE NATIONS TO INDIA
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https://twitter.com/shree1082002/status/1195392525827100672
Deletenot at all sir..thanks for answering sir i am very luck today that you have answered my question i am reading your blogs from 2012 you have answered my 2nd post in all these days i am delighted .Sir from that day till today i try to educate everyone who comes in my friend circle about what ever i can remember from your posts be it about politics or about history.Always wants your blessing.The first blog i read was about tipu sultan was not consistent in between but back track now.
ReplyDeleteHi captain. Welcome back. Today i managed to get a2 milk powder from somewhere. Had a cup of tea with that and right now i feel ghee all inside my body.
ReplyDeleteThis comment has been removed by the author.
ReplyDeleteDear sir,
ReplyDeletePlease watch this video(in hindi), past life regression where this woman is taken into spiritual realms, where she talks directly to god and discusses coming of lord kalki, among other things,
https://youtu.be/3Y_M53wft9Q
Please do watch..
I MUST EXPOUND ON WHAT I WROTE BELOW--
ReplyDelete#########################################
KARVA CHAUTH DOES NOT WEAKEN WOMEN, RATHER IT IS A POWERFUL TOOL TO EMPOWER MARRIED WOMEN ...
NO WIFE WILL FAST ON KARVA CHAUTH FOR HER HUSBAND IF HE IS NOT WORTH IT ( APOLOGIES TO AISHWARYA RAI ).. NO WONDER SHOBHAA DE DOES NOT FAST FOR HER SECOND HUSBAND ..
THERE IS SOMETHING CALLED HINDU HUSBANDS HAVING STRENGTH AND HONOUR WHEN IT COMES TO PROTECTING HIS WIFE AND HER INTERESTS, AND THUS SECURING HER LOVE , GRATITUDE AND RESPECT...
#############################
I MUST MAKE A STATEMENT OF FACT--
LET GOD STRIKE ME AND MY TWO SONS DEAD IF I LIE BELOW..
I HAVE BEEN MARRIED FOR 36 YEARS ..
MY WIFE SAILED ON SHIPS I COMMANDED .. I MARRIED AFTER GETTING COMMAND
WHILE ON THE SHIP MY WIFE FOLLOWED PROTOCOL AS BEFITTING A CAPTAINs WIFE.. WHO MUST SET AN EXAMPLE.. WE HAD STEWARD SERVICE..
WHEN MY WIFE DID NOT SAIL WITH ME..
( MIND YOU AFTER 30 YEARS AS CAPTAIN , EVERYTHING FREE ON BOARD , EARNING IN DOLLARS AND SPENDING IN RUPEES , I AM NOT EXACTLY A POOR FELLOW.. WE HAVE SERVANTS )
1) EVERY CUP OF TEA I HAD WAS MADE BY MY WIFE..
2) MY WIFE HAS NEVER EATEN BEFORE ME OR WITH ME .. ( UNLESS WE ATE AT A RESTAURANT)..
3) MY WIFE HAS IRONED EVERY SHIRT AND PANT I WORE , AFTER WASHING IT HERSELF..
4) DESPITE HAVING COOKS -- MY WIFE COOKED FOR HER HUSBAND AND HER CHILDREN.. THE COOK ONLY DID THE PRELIMINARIES
5) MY WIFE TAUGHT HER TWO SONS .. MY ELDER SON IS A GENIUS , HE NEEDED NO HELP AFTER KINDERGARTEN..
MY YOUNGER SON IS ARTISTICALLY ORIENTED AND HATES NUMBERS ..THOUGH MY WIFE IS A ECONOMICS GRADUATE ( PODDAR COLLEGE MUMBAI ), SHE BOUGHT GUIDES LEARNT MATH/ PHYSICS/ CHEMISTRY AND TAUGHT HIM.. HE NEVER FAILED AND GOT GOOD GRADES.. YOU MUST KNOW THAT THIS IS A GREAT SACRIFICE..
INDIAN CULTURE IS ENVIED BY WESTERN TOURISTS.. THEY COME TO INDIA AND WATCH INDIAN FAMILIES IN PARKS , BEACHES AND PUBLIC SPACES.. THEY WATCH THE HARMONY AND COHESION.. THIS IS A TAKE HOME LESSON.. THEY GLEAN THE DIFFERENCE BETWEEN LOVE AND LUST..
IF I WANT TO MAKE MY WIFE CRY, ALL I NEED IS TO ENTER THE KITCHEN WHEN MY WIFE IS SLEEPING AND MAKE MY OWN CUP OF TEA..
http://ajitvadakayil.blogspot.com/2013/12/ipc-section-377-love-lust-perversion.html
MY WIFE IS A VERY HAPPY AND CONTENDED WOMAN.. HER SONS ADORE HER.. SHE DOES UNIVERSAL GOOD BY REIKI..
THERE IS NOT A SINGLE DAY MY ELDER SON DOES NOT CALL HER FROM ABROAD.. THEY TALK FOR A LONG TIME.. I HAVE NEVER SEEN THEM RUNNING OUT OF CONVERSATION TILL TODAY..
AS I GET OLD ( 64 YEARS ) - MY WIFE MOTHERS ME.. SHE CLUCKS LIKE MOTHER HEN.. I LOVE IT.. I ALLOW HER TO TUCK ME TO BED..
MY SONS DO NOT SMOKE OR DRINK.. ( MY ELDER SON SOCIALLY DRINKS WHEN WARRANTED ).. MY SONS WONT BE CAUGHT DEAD IN TATTERED JEANS ..
http://ajitvadakayil.blogspot.com/2013/11/nagging-unhappiness-at-home-death-of.html
http://ajitvadakayil.blogspot.com/2010/05/marriage-sans-fights-capt-ajit.html
MY SONS ARE PROUD OF THEIR DAD.. MY WIFE HATES WOMENs LIB..
WHAT RIGHT HAS THIS COMMIE BITCH ROHINI CHATTERJEE TO WRITE A LYING POST ABOUT MY WIFE?
.. HER GRANDFATHER WAS SOMNATH CHATTERJEE, WHO WAS PARLIAMENT SPEAKER FOR 5 YEARS -- A TEN 5 YEAR TERM MP .
https://www.firstpost.com/living/open-letter-to-capt-vadakayil-the-man-who-wont-do-his-wifes-laundry-1192201.html
HAVING SAILED AROUND THE WORLD FOR 40 YEARS , I KNOW WE ARE LUCKY TO HAVE A PRICELESS CUTURE..
NURTURE IT !
MY ELDER SON CAME TO INDIA A FEW DAYS AGO WITH HIS WIFE .. HE SAW THE FIRST T20 BANGLADESH MATCH IN DELHI WITH HIS WIFE .. HE FLEW IN TO ATTEND HIS CLASSMATEs WEDDING.. HE STAYED IN A 7 STAR DELHI HOTEL FOR A WEEK AND SAW TAJ MAHAL THROUGH THE SMOG .
capt ajit vadakayil
..
Dear sir,
DeleteIn Bihar woman keep fast on teej , almost similar but they don’t t wait to see the moon. pandit ji comes to our home and do puja reciting Vedic mantra. When we grew up watching the movies we got to know about karwa chauth.
Same as my parents.
DeleteDidnot surprise me reading the above, neither did it do before when I read the Karva chauth post.
What surprises me is separation, I get headache when I read someone's ordeal about domestic violence or the story of divorce(inauspicious word)
Tasleema Nasreen had tweeted yesterday that why do one needs to marry
Didnot feel she was doing any NLP.
She is a lost soul.
Many of her tweets are directly or indirectly about loneliness.
Possibly split personality, conflicting emotions, inside the feeling of being lonely while outside pretending to be unfazed.
Dear captain,
DeleteYou are a wonderful husband,wonderful father,and a exceptional human being....your blog awakened and moulded me.No wonder you have a moksha jatakam.Had I not found your blog,I would have missed a trove of treasure in this birth,which can uplift the soul..you are a guru and fatherly figure to me...your family is a perfect example of ideal indian family where home is filled with happiness,care,love...May god shower his blessing always on your family and may you reach moksha in this birth..
With lots of love and gratitude
Charishma
Dear captain,
ReplyDeleteGreat relief to have you back. Great photo can we have a smiling photo for a change.
Sudhir.
https://www.indiatoday.in/india/story/iit-madras-suicide-case-in-note-student-blames-professor-for-harassing-her-1618535-2019-11-13
ReplyDeleteIITs ARE ELITE TECHNICAL COLLEGES
WHAT IS THIS BULLSHIT HUMANITIES STUDIES ?
Neither are IIMs of any prestige.
DeleteA fellow in one of PPT presentation to a topic started by uttering "Kind of" ; used it more than the contents of his PPT & ended with "Kind of"
The "kind of" was ridiculous to hear from that duffer.
He was from IIM Calcutta.
I was in the audience listening to the PPT.
I kept wondering if these are the products from IIM.
Totally crap(sorry for the language)
ReplyDeleteBuddha ZorbaNovember 15, 2019 at 10:28 PM
Hello Sir,
This woman is literally speaking from your blog. Really. Its a long audio please go through it randomly if possible. Some really sick rituals of jews is mentioned.
After being posted here this audio might get deleted, so we might save it.
Pornography: Weaponized Degeneracy
https://redice.tv/red-ice-radio/pornography-weaponized-degeneracy
Jeanice Barcelo, M.A., is a birth doula and independent childbirth educator, specializing in the prevention and healing of birth trauma. She is the author of Birth Trauma and the Dark Side of Modern Medicine. Through her radio and television shows, Jeanice has worked tirelessly to expose the evils of pornography, pedophile rings, Jewish supremacism, and more.
We begin by discussing Trump’s recent victory. Jeanice tells us that she believes this is not only a sign that the elite’s power is waning, but that there is a positive, spiritual force propelling Trump. We talk about the fact that many within the conspiracy community still think Trump is controlled opposition; this, according to Jeanice, is a poor, defeatist attitude. Transitioning to our main topic, pornography and sexuality, we discuss the ways in which sexual deviancy are used to undermine the positive, life-affirming values traditionally found in the West. Jeanice explains to us that this is merely part of a greater, Satanic attack on life itself. The first hour also touches on Jewish sexual mores, Sigmund Freud, and the question of transexuality.
In the members’ hour, we delve deeper into the topic of pornography. Jeanice urges us to see pornography as a weapon, rather than an idle amusement. We discuss the fact that sexuality exists to bring new life into world, and that fornication, being rooted in carnality, therefore lies outside of the natural order. Jeanice then outlines what she believes to be the truth behind pornography, which includes occult rituals and demonic forces. We discuss how pornography and sexual degeneracy have been snuck into the mainstream with very minimal pushback from conservative organizations. The members’ hour also explores the various ways in which sexual degeneracy is pushed on children.
guruji, what is the last animal body your soul takes before taking a first ever birth as a human ? is it elephant ?
ReplyDeleteELEPHANT/ BONOBOS/ DOLPHINS
DeleteCaptain, does the concept of evolution apply to physical-&-mental attributes as well ?
DeleteSay in the first-life as a human, the soul is given a dull-mind but normal body so that it can pick up basic human-functions and do physical-activity (blue-collar-jobs) to earn a living, since it is easy transition because all previous lives too were physical activity based rather than mental. But with every next-life it gets more intelligent-bodies.
So does this mean that a smart person could be a soul which has had many previous human lives ? Is increased-intelligence-&-consciousness an evolution/promotion based on seniority of a soul ? Or does Karma have the right to overrule such evolution based on the soul's actions over it's multiple previous lives?
YOUR KARMA DECIDES INTO WHICH WOMB YOU ARE BORN IN YOUR NEXT LIFE..
DeleteIF YOU DONT CREMATE AFTER DEATH, THE SOUL IS TRAPPED ON EARTH--LONELY AND UNHAPPY FOREVER, HANGING AROUND THE BURIED GRAVE..
WHAT IS THE CONCEPT OF DARGAH?
CHRISTIANS ALL OVER THE PLANET HAVE STOPPED BURIALS AND STARTED CREMATION..
http://ajitvadakayil.blogspot.com/2012/11/rip-impossible-with-burial-world-is.html
Happy to see you back Ajit sir.
ReplyDeleteCaptain now the latest craze is of canada PR,every tom dick and harry is going to canada through express entry and then posting pictures on fb and instaa,in real time they are living hand to mouth in extreme cold weather conditions.i think even IELTS exam is a money minting scheme where hundreds of pounds are lost just to score perfect bands,kindly tell everyone about about IELTS EXAM fraud.
ReplyDeleteI HAVE MANY BATCHMATES ( AMONG MY 125 ON THE TRAINING SHIP) WHO HAVE US AND CANADIAN PASSPORTS..
DeleteTHEY COME TO INDIA FOR OUR GET TOGETHERS AND PRETEND THEY ARE WELL OFF.
IN REALITY-- ALL ARE HAND TO MOUTH DESPITE BEING SHIP CAPTAINS... I HAVE DONE EXTENSIVE RESEARCH USING MY US AND CANADIAN SOURCES...
Captain,
Deletespeking of canada...
i remember when YOU tried to contact a certain calicut u-toober based in canada with awkward mouth expressions and four eyed goggles on his face....then later you said he is not goood...
well he is scoring some big interviews with BIIGIES discussin NEFARI0US propagaanda....(ther is interview with da wife-k1ller thaar00r on his front page)
and oh yes there is this video which u might want to check out----->> https://www.youtube.com/watch?v=LcLRWb4Ofn0
what is he upto captain ??
the only good thing abt canADA IS CLEAN AIR AND WATER
DeleteCaptain, is it possible that the words Secular and Socialist were introduced by Indira-Gandhi as a preventive-measure for future ?
ReplyDeleteMaybe she feared that in State-Govt or even Central-Govt a day may come where the majority is non-Hindu and hence she introduced the word secular so that Hindus do not face sufferings again ? And for Socialist maybe she did so that no future Govt focuses on USA style economics where poor are neglected ?
Is this a plausible theory ? I ask because you have written that she was a desh-bhakt.
http://ajitvadakayil.blogspot.com/2014/12/remove-word-secular-from-indian.html
Deleteplease apne bachchon ki kasam mat khayen . thats not good . may god shower all happiness on you and your family !
ReplyDeletetake care always as you you know so much of the untold history .
THERE ARE BIMBETTES WHO THINK THAT MY WIFE IS A WOMAN WITHOUT SPIRIT , A DEHAATI BEHENJI.. IN REALITY SHE IS MORE WELL READ THAN ME..
DeleteWELL-- MY WIFE CAN KICK THEIR EMPTY HEADS AND PURULENT TWATS .
IN THE POST BELOW YOU CAN SEE HER BEATING 25 YOUNG SAILORS WHO ARE TALLER THAN HER IN THE "HIGH KICK" CONTEST...
http://ajitvadakayil.blogspot.com/2012/06/equator-crossing-ceremony-at-sea-capt.html
THIS WAS A CEREMONY ON THE WAY TO CURACAO..
capt ajit vadakayil
..
Captain, please read this.
ReplyDeleteQUOTE=== On Section 4, Chandrachud wrote, "Since the Constitution had conferred a limited amending power on the Parliament, the Parliament cannot under the exercise of that limited power enlarge that very power into an absolute power. Indeed, a limited amending power is one of the basic features of our Constitution and therefore, the limitations on that power can not be destroyed. In other words, Parliament can not, under Article 368, expand its amending power so as to acquire for itself the right to repeal or abrogate the Constitution or to destroy its basic and essential features. The donee of a limited power cannot by the exercise of that power convert the limited power into an unlimited one." The ruling was widely welcomed in India, and Gandhi did not challenge the verdict. The Supreme Court's position on constitutional amendments laid out in its judgements in Golak Nath v. State of Punjab, Kesavananda Bharati v. State of Kerala and the Minvera Mills case, is that Parliament can amend the Constitution but cannot destroy its "basic structure". ===UNQUOTE
https://en.wikipedia.org/wiki/Forty-second_Amendment_of_the_Constitution_of_India
Does this mean India is forever stuck with this Constitution ? Any major reform in the Constitution to improve governance of India can be declared "unconstitutional" and be cancelled by the above claims. Is this a bluff on part of the courts or can the Govt of any Nation overcome their Constitutions, maybe even discard the old ones and introduce a new one ?
I ask because constitutions in all countries is being treated with a dogmatic-attitude as if it were some holy-book which can never be changed/replaced (unless there is a sponsored-revolution). Isn't it more desirable for it to be more like a manual/rulebook which should be easy to amend, upgrade and even replaced if required ?
CONSTITUTION IS A DYNAMIC ARTICLE.. IT MUST BE AMENDED TO MOVE WITH THE TIMES.. LIKE HOW TECHNICAL BOOKS HAVE NEW EDITIONS EVERY YEAR ..
DeleteWHAT IS THE USE OF STUDYING A NCERT SCIENCE BOOK IN THIS DNA AGE, WHICH BRAINWASHES US THAT MAN EVOLVED FROM MONKEY?
EVOLUTION IS "SOUL EVOLUTION"..
BALLS TO FARHAN AKTHAR " ZINDAGI NA MILEGI DOOBAARA"..
FOR HINDUS ZINDAGI ZAROOR MILEGI DOOBAARA ( UNLESS YOU HAVE A MOKSHA JATAKAM LIKE YOURS TRULY )..
FOR SUBJECTS LIKE ARTIFICIAL INTELLIGENCE -- THE CORE CHANGES EVERY THREE YEARS .. THIS IS WHY I AM WRITING A TEN PART POST ON AI..
WE HAVE HALWAI DOCTOR CONMAN WHO IS UNEMPLOYED FOR SEVEN YEARS TEACHING DATA MINING AND ML / AI BY CONDUCTING HIS BULLSHIT PRIVATE SEMINARS..
CHANDRACHUD / NARIMAN ARE DARLINGS OF THE JEWISH DEEP STATE..LIKE OUR KAYASTHA MINISTERS PRASAD AND JAVEDEKAR..
Dear Ajit Sir,
DeleteYou mentioned: "Zindegi zaroor milegi doobara"
What a profound statement!!!
यही बात आपकी है जो हमारा दिल जीत लिया है
It's such a bliss when we read all these from you.
So crystal clear
Wisdom Wisdom Wisdom
Your replies are as if you are not typing but talking to the person infront of you.
So much realistic & apt.
Wowww...!!!
@abcindiagogo
DeleteArt 368 of constitution empowers parliament to amend any part of the constitution with(depending on which part of constitution is being amended)
Simple majority,
2/3rds majority,
2/3rds majority with consent of half of the states
"The basic structure doctrine is laid out by the supreme court".Again what constitutes the basic structure is interpreted by SC itself.
Indian constitution is the largest constitution in the world.
Collegium system is not part of the constitution. It was laid out by SC in two judges case.
Recently SC has dismissed review plea of second judges case that created collegium system
https://www.thehindu.com/news/national/plea-to-review-second-judges-case-order-dismissed/article29902091.ece
The above opinion by chandrachud is his own interpretation based on the previous rulings..we know where his interests lie..
INDIA CANNOT HAVE A CAPITAL WITH SMOG ( TEMPERATURE INVERSION PROBLEMS ) IF WE HAVE TO THIS PLANETs NO 1 SUPERPOWER IN 14 YEARS..
ReplyDeleteSHIFT THE CAPITAL OF INDIA AWAY FROM DELHI AND PAKISTAN ..
IT IS A LIE THAT INDRAPRASTHA WAS DELHI 6000 YEARS AGO..
THE SLUMS OF DELHI ARE THE WORST ON THE PLANET.
A FEW DAYS AGO MY ELDER SON AND WIFE TRAVELLED FROM DELHI TO AGRA BY TRAIN, TO SEE THE TAJ MAHAL..
HE COULD NOT HAVE A REGULAR TAJ MAHAL PHOTO FROM FRONT FACADE SITTING ON THE USUAL BENCH , AS SMOG CAUSES TAJ TO BE INVISIBLE..
ON THE RAIL TRIP-- WESTERN TOURISTS INSIDE THE TRAIN WERE TAKING VIDEOS OF THE FILTH ON EITHER SIDE OF THE RAILWAY TRACKS ..
THE WHOLE RAILWAY TRACKS ON EITHER SIDE WERE LITTERED WITH PLASTIC.. WHEREVER THERE WAS WATER, IT WAS EITHER YELLOW/ GREEN OR BLACK ( DEPENDING ON HOW OLD THE SHIT IS )..
MY SON AND WIFE WENT TO THE FAMOUS STREET FOR STREET FOOD IN DELHI AT MIDNIGHT ( IT WAS RUSH HOUR ) --AT KABABS AND HAD DYSENTRY FOR THREE DAYS..
THE BEST PART WAS NO OLA / UBER CAB WOULD COME..
FINALLY AFTER WALKING ON THE DESERTED STREETS THEY MANAGED TO BRIBE AN AUTO DRIVER.. THE FELLOW HAD NO IDEA WHERE CHANAKYA PURI AND LEELA HOTEL IS, AND MY SON HAS TO GUIDE HIM USING GOOGLE MAPS..
ON THE WAY FROM USA TO DELHI, THEY TOURED SCOTLAND AND CAMPED AT LOCHNESS LAKE SHORE AT NIGHT.. I GUESS THE MONSTER STORY IS BULLSHIT.. SCOTLAND WAS CLEAN --
MY SON SAID , ONCE THEY SAT OUTDOORS WITHIN LEELA HOTEL COMPOUND-- WITHIN TEN MINUTES THE EYES BECAME RED AND THROAT BURNED AND THEY RAN BACK INSIDE THE HOTEL..
HE SENT ME VIDEOS OF THE SUITE THEY STAYED -- SHEER OPULENCE..
MY SON HAS STAYED IN THE BEST HOTELS , ALL OVER USA ON COMPANY ACCOUNT.. COMPARED TO LEELA THEY ARE SHIT..
https://en.wikipedia.org/wiki/Loch_Ness_Monster
https://www.youtube.com/watch?v=4lRsqxQbAKQ
DeleteMY SON SAID THAT IN HIS LEELA PALACE SUITE THE TOILET SHOWER WAS LIKE RAIN FROM TOP ..
AT THE AIRPORT THERE IS ONE PERSON TO RECEIVE YOU, THEN HE HANDS YOU OVER TO ANOTHER UNIFORMED MAN TO TAKE YOU TO THE PARKING LOT-- THEN ANOTHER MAN TO ACCOMPANY YOU ON THE BNW LUXURY RIDE ..
AS SOON AS YOU ENTER THE HOTEL, DOZENS OF EMPLOYEES FAWN OVER YOU, TREATING YOU LIKE ROYALTY..
IN HIS SEVEN DAY STAY AT LEELA HE HAD COMPLIMENTARY BREAKFAST ONLY ONCE. THE HALL HAD THE CHOICEST SPREAD --WHICH YOU DONT GET IN USA.. EVEN IN THE TOP THREE HOTELS AT LAS VEGAS
Sad to see Nairs selling this iconic brand to Brookfield (Kneeda)
DeleteCapt Ajitji
DeleteNever knew Hotel leela in the video above has a copy of 'Bhagavad Gita as it is' and ' Bible' in the drawer.
Video timestamp 3:00
ReplyDeletehttps://timesofindia.indiatimes.com/business/india-business/govt-mulls-raising-insurance-cover-on-bank-deposits-to-above-rs-1-lakh-fm/articleshow/72074060.cms
BY THE TIME MODI FINISHES HIS TENURE AS PM, BHARATMATA WILL AGAIN BE ENSLAVED BY ROTHSCHILD..
ALREADY JEWISH FOREIGN BANKS AND INSURANCE COMPANIES ( FRONTS OF ROTHSCHILD ) ARE ON THE DRIVERS SEAT..
MODIs SOLE AIM IS TO FORCE INDIANS TO LOSE TRUST IN INDIAN BANKS..SO THAT THEY SHIFT THEIR HARD EARNED DEPOSITS TO ROTHSCHILDs BANKS..
https://www.tribuneindia.com/news/punjab/pmc-bank-scam-maharashtra-sikhs-unable-to-go-on-kartarpur-pilgrimage/859447.html
WHEN AN INDIAN PUTS HIS MONEY IN A GOVERNMENT BANK-- IT IS AKIN TO LENDING MONEY TO THE ELECTED PM OF INDIA...
ROTHSCHILD MURDERED INDIRA GANDHI FOR NATIONALIZING HIS BANKS -- USING CRYPTO JEW KHALISTANIS WITH PALE EYES..
ROTHSCHILD USED INDIANS ( WEARING SIKH TURBAN FANCY DRESS IN 1976 ) TO KICK OUT INDIRA GANDHI..
EVEN I HAVE LOST TRUST IN INDIAN BANKS..
THIS SAYS IT ALL.
capt ajit vadakayil
..
But Guruji,if this is a propaganda,should we fall prey to it.Or we should still keep our money in our Indian Banks only.Because I remember ,that your Russian crew didnt trust their home banks.Please comment on this.
Delete
DeleteMediclaim insurance Claims are being arbitrary deducted by insurance companies based on a clause "Reasonable and Customary Charges".
No definition has been clearly defined of same.
Definition of reasonable and customary is left to insurance companies imagination.
Patients intimate the insurance company within 24 hours of admission with the possible diagnosis.
Then, Why are the citizens not informed of such reasonable charges payable at time of admission?
Till now insurance companies would target hospitals, now they are targeting citizens: knowing that most citizens would not go to consumer courts against such deductions.
The squeeze is on. We are being slowly harvested.
I WAS HEART BROKEN WHEN MY SERBIAN CHIEF ENGINEER AND ELECTRICAL OFFICER DID OT SEND MONTHLY ALLOTMENT BY BANKING ROUTE ..
DeleteINSTEAD THEY PREFERRED TO COLLECT ALL THEIR HARD EARNED MONEY IN 100 DOLLAR NOTES AND PUT IT IN A HIDDEN MONEY BELT AROUND THEIR WAIST WHILE SIGNING OFF
CHIEF ENGINEER TOLD ME THAT ALL SERBIAN BANKS ARE JEWISH AND NONE CAN BE TRUSTED..
ROTHSCHILD IS THRILLED WITH MODI
DeleteHIS MEDICAL INSURANCE COMPANIES IN INDIA HAVE GONE FROM TOE HOLD TO FOOT HOLD TO DRIVERs SEAT..
https://timesofindia.indiatimes.com/india/ldf-govt-cpm-do-u-turn-on-entry-of-women-at-sabarimala/articleshow/72079244.cms
ReplyDeleteJEWISH DEEP STATE DARLINGS JUDGES CHANDRACHUD AND NARIMAN MUST KNOW THIS--
SABARIMALA IS A PILGRIMAGE ..THE PLANETs LARGEST ..
DONT EVER TRY TO COMPARE THIS WITH BOHRA / PARSI / MUSLIM/ CHRISTIAN PLACES OF WORSHIP AND PUT FOG ...
http://ajitvadakayil.blogspot.com/2018/11/5900-year-old-sabarimala-this-planets.html
PUT ABOVE COMMENT IN WEBSITES OF--
DeletePUT ABOVE COMMENT IN WEBSITES OF--
JANAM TV
MARUNADAN TV
CM PINARAYI VIJAYAN
ALL KERALA MINISTERS
ALL KERALA MLAs
ALL KERALA COLLECTORS
DGP BEHERA
RAMAN SRIVASTAVA
HIGH COURT CHIEF JUSTICE KERALA
PMO
PM MODI
AJIT DOVAL
CBI
NIA
ED
IB
HOME MINISTRY
AMIT SHAH
LAW MINISTER
LAW MINISTRY
NEW CJI
INDU MALHOTRA
ROHINGTON NARIMAN
CHANDRACHUD
KHANWILKAR
ALL SUPREME COURT JUDGES
TOM VADAKKAN
GOVERNOR OF KERALA
PRESIDENT OF INDIA
VP OF INDIA
SPEAKER LOK SABHA
SPEAKER RAJYA SABHA
PC GEORGE MLA
RAHUL EASHWAR
SRIDHARAN PILLAI
PARASARAN
SAI DEEPAK
VIDYASAGAR GURUMURTHY
I&B DEPT/ MINISTER
AMITABH KANT
NITI AYOG
RSS
VHP
AVBP
SPREAD ON SOCIAL MEDIA
Sir, Posted screenshot on twitter to almost all of the above handles.
DeleteThank you very much for coming back.
Gratitude.
https://twitter.com/kkarthikeyan09/status/1195576487719473152
Deletehttps://twitter.com/kkarthikeyan09/status/1195577448227692544
Tweets:
Deletehttps://twitter.com/AghastHere/status/1195580359863394304?s=20
https://twitter.com/AghastHere/status/1195580450749763584?s=20
Handles:
@tvjanam @marunadannews @vijayanpinarayi @NIA_India @dir_ed @AmitShah @HMOIndia @OfficeOfRSP @rsprasad @RSSorg @VHPDigital @ABVPVoice @NITIAayog @amitabhk87 @MIB_India @PrakashJavdekar @jsaideepak @parasaran @RahulEaswar @rashtrapatibhvn @MVenkaiahNaidu @VPSecretariat @loksabhaspeaker @KeralaGovernor @TomVadakkan2
sent to all the major news handles on twitter, but most of the tweets were blocked by twitter
Deleteonly these two tweets could be verified:
https://twitter.com/tvjanam/status/1193048610360840192
https://twitter.com/surendranbjp/status/1195639941864153090
Yin and Yang.
ReplyDeleteMake yin more like yang under guise of empowerment.
Make yang more like yin under brolly if gender equality .
Both become neutral fertility drops automatically along with other mental traits creating a bunch of servile people. Good depopulation strategy also works towards destroying traditional family values ..
Suicide of IIT Madras student is sailing with political winds..
Reminds me of a similar case of a doctor who was allegedly ragged to death by her seniors in Maharashtra.
The doctors were jailed for over a month without bail .
Meanwhile the distressed telecom sector has got public sector banks to pressure the govt saying loan default of 100000 crores is possible due to this sector in distress.
IIT FEMALE STUDENT SUICIDE
Delete##############################
THE MALAYALI MOTHER OF THIS FEMALE MUSLIM STUDENT LIED ON TV THAT HER DAUGHTER WAS AFRAID OF HINDU PROFESSORS IN IIT MADRAS AND SHE DID NOT EVEN DARE TO PUT THE FACE SCARF AND REVEAL THAT SHE IS A MUSLIM..
THIS STUDENTS NAME IS FATHIMA LATHEEF ... IS THIS NAME OPAQUE?
WE KNOW THAT IT IS NATURAL FOR A DISTRAUGHT MOTHER TO LASH OUT WHEN HER DAUGHTER COMMITS SUICIDE--
BUT WHY DRIVE COMMUNAL WEDGES?..
LOT OF FOREIGN FUNDED NGOs HANG AROUND IIT MADRAS , TO HELP COMMIE ATHEIST AND MUSLIM STUDENTS COOK UP STORIES AGAINST THEIR ELITE HINDU PROFESSORS WHO REJECT THEIR PROJECTS / THESIS BECAUSE IT HAS BEEN PLAGIARIZED FROM THE INTERNET ..
WE ASK PM MODI TO DEAL WITH THIS PROBLEM..
IN CORNELL IF THE STUDENT PLAGIARIZES THE PROJECTS -- HE/ SHE IS CHUCKED OUT IMMEDIATELY AFTER A SUMMARY INQUIRY BY A BOARD..
THIS INQUIRY ABOUT STUDENT MENTAL INTEGRITY IS VERY FAIR..
SOMETIMES CORNELL STUDENTS COPY FROM OTHERS.. THE INTELLIGENT STUDENT WHO ALLOWS A WEAK STUDENT TO COPY IS ALSO PENALIZED EQUALLY..
http://ajitvadakayil.blogspot.com/2010/11/my-son-at-cornell-university-capt-ajit_25.html
capt ajit vadakayil
..
PUT ABOVE COMMENT IN WEBSITES OF--
DeletePMO
PM MODI
EDUCATION MINISTER MINISTRY CENTRE / STATES
ALL IIT DEANS
AJIT DOVAL
CBI
NIA
ED
RAW
AMIT SHAH
HOME MINISTRY
NITI AYOG
AMITABH KANT
NEW CJI
ATTORNEY GENERAL
Posted on twitter to central ministries and IITs..
Deletekoshy@iitm.ac.in
Deletecontact@amitshah.co.in
PMOPG/E/2019/0660958
Informed above people and PMO
Visvanathan
Is kosher vinegar the slow death kryptonite of Jews? R can kill for money while most ppl cannot that is the advantage he has. But his family's power over so many centuries means that he has been granted so sort of boon (wish); but like any other boon there must be something that can counter it. What is it? who will do it and when?
ReplyDeletehttps://www.latimes.com/projects/la-me-oxycontin-part3/
ReplyDeleteThey have never stopped dealing in opium. Still maximum earning potential.
MOST OF THE BOLLYWOOD KHANS ARE DESCENDANTS OF OPIUM RETAIL STREET DRUG RUNNERS..
DeleteRABINDRANATH TAGOREs KABILLIWALAH STORY IS BASED ON SUCH A PASHTUN KHAN JEW..
BISHNOIS ( WHO ARE AFTER SALMAN KHANs ASS ) WERE KILLER HITMEN OF OPIUM DRUG RUNNING MARWARIS --AGENTS OF ROTHSCHILD..
http://ajitvadakayil.blogspot.com/2018/04/salman-khan-chinkara-poaching-jail-term.html
hail Captain!
ReplyDeleteYou're back with a bang!
Pranamams,
Sriganesh
Dear Ajit Sir,
ReplyDeleteSunanda Vasisth has spoken for the cause of Kashmiri Pandits in tandem with the cause of Jews of Israel as their right to have their homeland.
In Washington DC for Congressional hearing on Human Rights, the short video clips of Sunanda making the round ups has very cleverly & conveniently captured the statement where she has mentioned the rights of Jews in sync with Kasmiri Pandits & their ethnic cleansings.
Jews will never come out of their mischiefs.
Every opportunity they find, they just tag themselves exclusively.
On Social Media, having exchanged hints, the Western World is thoroughly aware of Jews but they don't have the balls to go against them.
How come Israel couldn't gather information regarding massive Rocket strikes from Gaza which happened 3 days back?
With such huge catche of Rockets, it takes time to assemble them & upon that the Canister Launcher itself is more than enough to guess that there would be a massive Rocket strike.
Is it a False Flag attack?
Having been searching, couldn't get any answer...
Captain sir,
ReplyDeletehttps://thewire.in/government/andhra-pradesh-bureaucracy-accountability-cmo
AP Chief Secretary LV Subramanyam was transferred to an obscure post by CM YS Jagan because the CS took strict action against non-hindu TTD[Tirumala Tirupathi Devasthanam] employees and even threatened them with surprise inspection at their houses who submitted a wrong declaration to conceal their religion for the sake of getting a job in the TTD.
As soon as he was transferred Christian organizations celebrated it as their victory
https://twitter.com/PVSIVAKUMAR1/status/1191584974098403328/photo/1
Jagan seems to be giving in too much leeway to 'Dalit' Christians just like his papa.
Regards,
Sriganesh
NETFLIX IS JUST ANOTHER TOOL OF THE JEWISH DEEP STATE TO DO PRO-JEWISH PROPAGANDA LIKE HOLLYWOOD/ HISTORY CHANNEL/ WIKIPEDIA/ QUORA ETC..
ReplyDeleteTHE NETFLIX SERIES "NARCOS" IS GENUINE..
THE NETFLIX SERIES " PABLO ESCOBAR" IS JUST A JEWISH PROPAGANDA MELODRAMA .., EVEN JEETENDRAs DAUGHTER EKTA KAPOOR CANNOT MATCH THE SOAP MELODRAMA..
THIS SERIES WAS ORIGINALLY 115 EPISODES--LATER REDUCED TO 75..
AS SOON AS I STARTED SEEING THE SERIES, I TOLD MY WIFE THAT THIS IS JEWISH PROPAGANDA OF THE EL SPECTATOR NEWSPAPER WHO DID EXTREME YELLOW JOURNALISM TO MAKE PABLO ESCOBAR MENTALLY DISINTEGRATE..
https://ajitvadakayil.blogspot.com/2019/11/paradox-redemption-victory-in-defeat.html
EVERY FALSE FLAG ATTACK OF BOMBING PUBLIC SPACES WAS ATTRIBUTED WRONGLY TO PABLO ESCOBAR MAKING HIM LOSE PUBLIC SUPPORT.. IMAGINE IN THE END PABLO WAS HIDING LIKE A LAK EVEN IN HIS MEDELLIN WHERE ONE HE WAS "ROBIN HOOD"..
TODAY MY WIFE LET OUT A WHOOP.. SHE DID RESEARCH ON HER OWN.
SHE SAID I AM RIGHT.. THIS NETFLIX SERIES "PABLO ESCOBAR" WAS PRODUCED BY THE DESCENDANTS OF THREE ANTI-PABLO JEWISH FORCES.
THE SERIES WAS CREATED BY CAMILO CANO AND JUANA URIBE WHO ARE BOTH CLOSELY TIED WITH PABLO ESCOBAR.
1) CAMILO CANO IS THE SON OF GUILLERMO CANO WHO WAS THE PUBLISHER OF NEWSPAPER EL ESPECTADOR AND WHO WAS MURDERED BY ESCOBAR IN DECEMBER 1986.
2) JUANA URIBE IS THE VICE PRESIDENT OF CARACOL TV AND ALSO THE SERIES' PRODUCER. SHE IS THE DAUGHTER TO MARUJA PACHÓN WHO WAS KIDNAPPED BY PABLO ESCOBAR ON 7 NOVEMBER 1990 AND LATER RELEASED.
3) JUANA IS ALSO THE NIECE TO PRESIDENTIAL CANDIDATE LUIS CARLOS GALÁN WHO WAS KILLED BY ESCOBAR IN AUGUST 1989.
WE ARE MADE TO BELIEVE THAT THESE JEWISH FAMILIES ARE THE SALT NAY BOUNTY OF THE PLANET..
https://en.wikipedia.org/wiki/Pablo_Escobar,_The_Drug_Lord
capt ajit vadakayil
..
Namaste Captain,
ReplyDeleteGlad to see you're back.
Charanasparsham
- Biju
Pranaam
SOMEBODY CALLED ME UP AND SAID --
ReplyDeleteIN THE NETFLIX SERIAL "PABLO ESCOBAR ", PABLO IS SHOWN NAKED WITH A FEMALE VOLLEYBALL PLAYER MARIA ON THE BED..
WELL YOU WILL NOTICE THAT HE DID NOT HAVE HIS PRICK INSIDE HER CUNT..
SHE WAS IN TWO PIECE BIKINI LYING ON TOP OF HIM HEART TO HEART ..
YOU WILL NOTICE THAT PABLO WAS SPEAKING TO HIS WIFEs BROTHER AND GUNMEN WHO BROUGHT HER TO HIS HIDEOUT..
PABLO WAS ILL AND WEAK.. WHEN THE POLICE HELICOPTERS STARTED SHOOTING, HE TELLS MARIA TO RUN, BUT HE CONTINUES LYING DOWN..
HIS WIFEs BROTHER DIES IN THIS SHOOTOUT..
JET A FEW EPISODES BEFORE THIS INCIDENT , PABLOs SMALL DAUGHTER WAS VERY ILL... HER EAR WAS DAMAGED BY A BOMB BLAST BY CALI CARTEL..
PABLO TELL HIS WIFE.. REMOVER MY DAUGHTERS CLOTHES AND PUT HER ON TOP OF ME HEART TO HEART..
HE EXPLAINS TO HIS WIFE THAT HE IS GIVING HER HIS OWN PRANA.. ( LIFE FORCE )..
##########
ONCE I WAS NEARLY BRANDED AS A DIRTY OLD MAN.
ME AND MY WIFE WERE CLIMBING THE DARK WINDING STAIRS OF CHARMINAR IN HYDERABAD..
MY WIFE BEING A REIKI CHANNEL IS VERY SENSITIVE TO NEGATIVE ENERGY..
AT A PARTICULAR SPOT SHE WAS OVERWHELMED .. SHE CRIED OUT TO ME " I AM FEELING FAINT"
I CARRIED HER TO THE NEXT STAIR LANDING JUST TWO METRES AWAY.
THEN I DID BODY TO BODY REIKI.. JUST HUGGING..
YOU SHOULD HAVE SEEN THE FACES OF SOME WOMEN WHO SAW THIS -- DIRTY OLD SEXUAL PREDATOR DOING IT IN A PUBLIC SPACE.. THEY DID NOT KNOW SHE IS MY WIFE..
IN THREE MINUTES FLAT MY WIFE WAS OK.. I PUMPED PRANA INTO HER..
WESTERN CHILDLESS WOMEN UNDERSTAND THIS .. SPERM IS ELECTRICALLY CHARGED .. IT WAS TO SWIM UPWARDS TO CONCEIVE.. ( A healthy adult male can release 1.2 billion sperm cells in a single ejaculation. only one sperm is required for a baby ).
WHEN YOU HAVE SEX , HEART CHAKRA TO HEART CHAKRA MAKES THE SPERM SWIM STRONG UPWARDS..
https://www.youtube.com/watch?v=R-lrEBevJ60
POOR ATHEISTS
WHAT DO THEY KNOW !
http://ajitvadakayil.blogspot.com/2017/02/my-visit-to-bhagyalaxmi-nagar-or-modern.html
capt ajit vadakayil
..
Do you think sylicon implants create an aditional blockage in this regard?
Deletebtw I read this book *the female brain* and it seems to be prooven fact that as female babies (unlike boys) have an enhanced activity in the brain zones connected wirh facial recognition mothers who have lip implant of any sort wind up affecting them at a psycho-neurological level in the long run.
https://ajitvadakayil.blogspot.com/2018/11/5900-year-old-sabarimala-this-planets.html
ReplyDeletehttps://ajitvadakayil.blogspot.com/2018/11/save-5900-year-old-sabarimala.html
http://ajitvadakayil.blogspot.com/2018/07/menstruating-women-and-sabarimala.html
POOR MODI/ BJP/ RSS/ AMIT SHAH
THEIR DESIGNS FOR SABARIMALA FAILED..
THESE IMMORAL PEOPLE/ ORGS WANTED TO USE SABARIMALA TO GRAB POWER IN KERALA..
PINARAYI VIJAYAN UPSET THE BJP / RSS APPLE CART..
HE HAS GIVEN STRICT ORDERS THAT POLICE MUST RESPECT PILGRIMS AND ADDRESS THEM AS "SWAMY"..
JEWISH DEEP STATE DARLINGS CHANDRACHUD / NARIMAN AND BENAMI MEDIA MUST KNOW THIS..
SABARIMALA IS A PILGRIMAGE--NOT SOME WEE BOHRA OR PARSI TEMPLE..
capt ajit vadakayil
..
PUT ABOVE COMMENT IN WEBSITES OF--
DeleteJANAM TV
MARUNADAN TV
CM PINARAYI VIJAYAN
ALL KERALA MINISTERS
ALL KERALA MLAs
ALL KERALA COLLECTORS
DGP BEHERA
RAMAN SRIVASTAVA
HIGH COURT CHIEF JUSTICE KERALA
PMO
PM MODI
AJIT DOVAL
CBI
NIA
ED
IB
HOME MINISTRY
AMIT SHAH
LAW MINISTER
LAW MINISTRY
NEW CJI
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ROHINGTON NARIMAN
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TOM VADAKKAN
GOVERNOR OF KERALA
PRESIDENT OF INDIA
VP OF INDIA
SPEAKER LOK SABHA
SPEAKER RAJYA SABHA
PC GEORGE MLA
RAHUL EASHWAR
SRIDHARAN PILLAI
PARASARAN
SAI DEEPAK
VIDYASAGAR GURUMURTHY
I&B DEPT/ MINISTER
AMITABH KANT
NITI AYOG
RSS
VHP
AVBP
SPREAD ON SOCIAL MEDIA
The women/activists who try and enter do they do the 41 day vrat? Is it applicable to women even? Pilgrimage rules must be followed. All temples have their specific practices. If u don't like then don't go. No one is forcing u to enter a temple nor is it mandatory.
DeleteBJP version 2 is going all out in Hindu extinction. From NRC is Assam identifying mainly Hindu illegals, to AP and Northeast doing mass conversions - there is no stopping the juggernaut (Jagannath).
DeleteSir, sent emails..
DeleteHave u heard of this pre-Incan Goddess called Pachamama? There must be some Hindu/Vedic connection.
ReplyDeletehttps://en.wikipedia.org/wiki/Pachamama
JEWISH DEEP STATE DARLINGS MODI/ AMIT SHAH/ RSS/ BJP / CHANDRACHUD/ NARIMAN WANT INVADERS RELIGION STANDARDS TO BE APPLIED TO A HINDU PILGRIMAGE, SABARIMALA.. THIS PLANETs LARGEST ..
ReplyDeleteIT WONT WORK..
KERALA CM PINARAYI VIJAYAN HAS SCREWED ALL OF THEM WITHOUT GREASE..
https://ajitvadakayil.blogspot.com/2018/11/5900-year-old-sabarimala-this-planets.html
capt ajit vadakayil
..
PUT ABOVE COMMENT IN WEBSITES OF--
JANAM TV
MARUNADAN TV
CM PINARAYI VIJAYAN
ALL KERALA MINISTERS
ALL KERALA MLAs
ALL KERALA COLLECTORS
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SPREAD ON SOCIAL MEDIA
Sir, Tweeted to most of them.
DeleteThat's why they try an equate Islam, Christianity and Judaism to Sanatana Dharma. It's not the same; they are intentionally categorizing it as such - DECEPTION.
DeleteWelcome Back, Captain! Missed the daily dose of strength n valor. Great to have you back again!! After years in IT, it all seems a sheet waste. Need your guidance on a way out.
ReplyDeletehttps://twitter.com/shree1082002/status/1195726946740523013
ReplyDeleteWelcome back Captain _/\_. It was a great relief for us to see you and your blogs.
ReplyDeleteRegards,
K N Rao
Namaste captain,
ReplyDeleteYou had told you will be writing about karl marx and what he actually meant in his book Das Kapital.
Plz if you could in your next post.
Gratitude
Aditya
Dear captain,
ReplyDeletePlease tell us about recent brics summit and new development bank.
By any means this bank is beneficial to india.
Please clarify.
Thanks
Radhakrishnan kundayil
Jai hind.
My Dear Captain,
ReplyDeleteWelcome back and many congratulations on your Catharsis journey and the flashpoints. Was concerned and your last post Captain is all alone also had got me worried but then again got to know you are fine..
:) and was waiting for your come back.Now we have a Reninvented and Rejuvenated Captain. Pranams.
The bloodline of Chatrapati Shivaji ended with Emperor Shahu, because the Chitpavan Peshwas indirectly helped the enemy and betrayed Maratha people. Now the Shav Sena, controlled by "The Gang of Four", is repeating the same mistake by betraying the popular mandate.
ReplyDeletefor all their hoopla around being a maratha party the thackerays are all ckps. it seems only fair that a party which was created to unleash a reign of strong armed thuggery style terror against the labour unions and immigrant communities in Mumbai meets an opportunists fate who can't see the big picture and overplayed his hand. Being pro hindu was just a facade for them.
DeleteIndia resumes buying Malaysian palm oil as Kuala Lumpur offers discount:
ReplyDeleteToday most of savouries packed and sold are made from palmolein as they have long shelf life.
very good question ! need to stop this evil
ReplyDeleteCaptain,
ReplyDeleteWhat is your opinion on the recent spat between cristiano ronaldo and msurizio sarri of Juventus.
https://www.google.com/amp/s/www.express.co.uk/sport/football/1204852/cristiano-ronaldo-juventus-sarri-portugal-hat-trick/amp
today in navi mumbai katy perry concert happened. all chutney mary with cellulite filled were roaming nanga punga after the concert.
ReplyDeleteKaty Perry and Lipa Dua's Mumbai concert: Katy Perry and Dua Lipa enthrall Mumbai with their mesmerising performances
https://timesofindia.indiatimes.com/entertainment/english/music/news/katy-perry-and-lipa-duas-mumbai-concert-katy-perry-and-dua-lipa-enthrall-mumbai-with-their-mesmerising-performances/articleshow/72088180.cms
Welcome back captain! The path is lit again.
ReplyDeleteCaptain Sahib,
ReplyDeleteCan you kindly throw some light on this so called Kushwaha caste. Seems bit like a Kayastha thingy.
Sincerely,
G. Rawat
https://timesofindia.indiatimes.com/india/karma-tends-to-explain-everything-justice-nariman/articleshow/72091029.cms
ReplyDeletePARSI PRIEST , AND LIBERAL DEEP STATE DARLING SUPREME COURT JUDGE ROHINTON NARIMAN KNOWS NOTHING ABOUT KARMA..
THE WORD KARMA APPEARS IN THE RIG VEDA , ATHARVA VEDA AND THE BRHADARANYAKA UPANISAD PENNED 7000 YEARS AGO .. THE CONCEPT OF KARMA APPEARS STRONGLY IN THE BHAGAVAD GITA PENNED 6000 YEARS AGO .
KARMA IS NOT RELIGION OR EVEN SPIRITUALITY..THIS IS ABOUT CONSCIOUSNESS AND ADVANCED QUANTUM PHYSICS. THERE IS NO DOGMA OR SUPERSTITION HERE..
THE UNIVERSE IS WOVEN FROM CONSCIOUSNESS .. IT KNOWS WHAT GOES ON IN YOUR MIND..
IDIOTS LIKE NARIMAN , MUST NOT TALK ABOUT KARMA ( BUTTERFLY EFFECT ) , WHICH IS ADVANCED QUANTUM PHYSICS.. A HUMAN ACTION CREATES AN INVISIBLE QUANTUM MOTION OF FORCE AT THE SUBATOMIC LEVEL.
KARMA IS "INTENTION BASED " NOT ACTION BASED.. IF YOU DO A BAD DEED WITH GOOD INTENTION, YOU DONT GET BAD KARMA-- AND VICE VERSA. .
YOU CAN HIDE YOUR INTENTIONS FROM OTHERS BUT NOT FROM YOURSELF OR THE UNIVERSE. YOU ARE A SMALL COG WITHIN MANY OTHER COGS. WHEN YOU MOVE, YOU MOVE ALL THE OTHER COGS.
KARMA IS THAT CATALYST THAT CONNECTS ACTIONS AND THOUGHTS WITH THE QUANTUM RIPPLES OF ENERGY THAT IN ESSENCE CREATES THIS DYNAMISM IN LIFE HERE AND HEREAFTER.
THIS IS WHY INTENTION IS SUCH A POWERFUL THING. IT MOVES THE COSMOS.
KARMA CATCHES UP BEHIND CLOSED DOORS..KARMA IS A UNIVERSAL LAW; IT CANNOT BE BROKEN.
THE DOCTRINE OF KARMA TEACHES: "DO NOT BLAME ANYBODY WHEN YOU SUFFER. DO NOT ACCUSE GOD. BLAME YOURSELF FIRST. YOU WILL HAVE TO REAP WHAT YOU HAVE SOWN IN YOUR PREVIOUS BIRTH.
AN INDIVIDUAL’S KARMA IS BASED ON THEIR THOUGHTS, WORDS, AND INTENTIONAL ACTIONS AND THE CHOICES THEY MAKE.
WE HINDUS WERE THE FIRST TO PRAY ON THIS PLANET.. WE WERE THE FIRST TO UNDERSTAND THE MEANING OF INTENTION AND CONCSIOUSNESS
KARMA IS NOT PUNISHMENT OR RETRIBUTION BUT SIMPLY AN EXPRESSION OR CONSEQUENCE OF NATURAL ACTS.
KARMA IS LIKE A SEED, MOST OF THE TIMES KARMA DOES NOT FRUCTIFY IMMEDIATELY AFTER THE SEED IS SOWN.
“LAW OF KARMA” IS LIKE A UNIVERSAL LAW SAY “LAW OF GRAVITY” - IT APPLIES WHETHER YOU ACCEPT OR REJECT THE LAW.
KARMA MEANS YOU CREATE YOUR OWN LIFE. KARMA IS AN UNBREAKABLE LAW OF THE COSMOS. YOU DESERVE EVERYTHING THAT HAPPENS TO YOU, GOOD OR BAD. YOU CREATED YOUR HAPPINESS AND YOUR MISERY.,
THE WHOLE UNIVERSE IS OBSERVING YOU..
KARMA TREATS EVERYONE EQUALLY. YOU WON’T GET SPECIAL TREATMENT.
KARMA PLANTS A SEED. OVER TIME IT WILL GROW. AT JUST THE RIGHT MOMENT, THE EXACT MOMENT, YOU WILL RECEIVE YOUR KARMA. KARMA MAKES YOU EXPERIENCE WHAT YOU HAVE DONE TO OTHERS.
THE DOCTRINE OF KARMA ONLY CAN BRING SOLACE, CONTENTMENT, PEACE AND STRENGTH TO THE AFFLICTED AND THE DESPERATE. IT SOLVES OUR DIFFICULTIES AND PROBLEMS OF LIFE.
IT GIVES ENCOURAGEMENT TO THE HOPELESS AND THE FORLORN. IT PUSHES A MAN TO RIGHT THINKING, RIGHT SPEECH AND RIGHT ACTION
THE MOST IMPORTANT PART OF YOUR LIFE WILL BE THE UNSEEN RANDOM ACTS OF KINDNESS YOU PERFORMED.
THE THING WITH KARMA IS THAT IT DOESN’T ALWAYS HAPPEN IMMEDIATELY. SOMETIMES WE HAVE TO WAIT FOR YEARS. SOMETIMES OUR KARMA ARRIVES WITHOUT US EVEN KNOWING IT, OR KNOWING WHAT FORM IT HAS TAKEN.
REMEMBER THE PURPOSE OF KARMA IS NOT REVENGE. THE UNIVERSE IS NOT VENGEFUL. THE PURPOSE OF KARMA IS TO HELP YOU BECOME A BETTER PERSON..
THERE CAN BE NO MOKSHA EVEN IF YOU HAVE AN IOTA OF EGO WITHIN YOU.
AS EGO REDUCES , KARMIC BAGGAGE LIGHTENS, AND YOUR SOUL FREQUENCY INCREASES..
KARMA IS SELF-BALANCING DIVINE JUSTICE; IT IS 100% FAIR AND ABSOLUTELY INFALLIBLE. IT DOESN'T MATTER IF A CRIMINAL SEEMS TO "GET AWAY WITH IT", BECAUSE THERE IS NO GETTING AWAY WITH IT – KARMA WILL EVENTUALLY CATCH UP WITH HIM.
KARMA IS SIMPLY AN OPPORTUNITY TO MAKE GOOD – IT NEITHER PUNISHES NOR REWARDS; IT SIMPLY GUIDES.
LAWS OF KARMA GIVE HOPE..
CONTINUED TO 2---
CONTINUED FROM 1--
DeleteTHE AIM OF EVERY RELIGION IS TO GIVE HOPE.. BUT THE SINGLE MESSSIAH / SINGLE HOLY BOOK RELIGIONS PUT YOU UNDER SERVILITY. THE MULLAHS AND POPES WANT TO SAVE YOU--BECAUSE THEY HAVE A HOT LINE WITH GOD
IN SANATANA DHARMA THERE IS NO MIDDLE MAN BETWEEN YOU AND GOD..
THE PURPORT OF THE TERM KARMA IS TO MAKE INDIVIDUALS TAKE OWNERSHIP FOR THEIR CONSCIOUS ACTIONS
THE CONSEQUENCES OF YOUR ACTIONS GOOD OR BAD MAY WILL FOLLOW YOU INTO THE NEXT LIFE. . ZINDAGI NA MILEGI DOOBARA HEDONISM IS A CONCEPT OF SINGLE MESSIAH BURIAL RELIGIONS..
THE DOCTRINE OF KARMA BRINGS HOPE TO THE HOPELESS, HELP TO THE HELPLESS, JOY TO THE CHEERLESS AND NEW STRENGTH TO THE WEAK. IT BRACES UP A SUNKEN MAN. IT IS AN IDEAL "PICK-ME-UP" FOR THE DEPRESSED AND GLOOMY.
THE DOCTRINE OF KARMA TEACHES: "DO NOT BLAME ANYBODY WHEN YOU SUFFER. DO NOT ACCUSE GOD. BLAME YOURSELF FIRST. YOU WILL HAVE TO REAP WHAT YOU HAVE SOWN IN YOUR PREVIOUS BIRTH.
YOUR PRESENT SUFFERINGS ARE DUE TO YOUR OWN BAD KARMA IN YOUR PAST LIFE. YOU ARE YOURSELF THE AUTHOR OF THE PRESENT STATE.
GUILT IS A FORM OF PUNISHMENT. GUILT DEPRIVES MAN OF REM SLEEP. PAINFUL RESULTS IN YOUR LIFE COME FROM ACTIONS YOU TOOK THAT CLASHED WITH THE LAWS OF THE UNIVERSE. THE PAIN DRIVES HOME THE LESSON.
KARMA IS THE PHYSICAL MANIFESTATION OF THE LAW OF BALANCE AND HARMONY, AS IT APPLIES TO THE RESULTS OF DECISIONS REACHED AND ATTITUDES HELD BY BEINGS CAPABLE OF FREE WILL AND CHOICE
YOUR INTENTIONAL ACTIONS CREATE YOUR FUTURE. WHAT YOU ARE EXPERIENCING RIGHT NOW IS WHAT KARMA WANTS YOU TO EXPERIENCE
YOU CAN’T ESCAPE FROM YOUR PAST, BUT LEARNING FROM IT WILL CHANGE YOUR FUTURE.
KARMA IS REAL. KARMA IS YOUR BOND WITH THE PAST. KARMA TEACHES YOU AND MAKES YOUR KNOWLEDGE OF THE WORLD MORE COMPLETE.
YOUR PRESENT SUFFERINGS ARE DUE TO YOUR OWN BAD KARMA IN YOUR PAST LIFE. YOU ARE YOURSELF THE AUTHOR OF THE PRESENT STATE.
OUR INDIAN JUDICIARY HAS NEVER FOLLOWED SANATANA DHARMA WHICH IS ENMESHED WITH KARMA.. KARMA GIVES US HOPE, IN THIS LIFETIME..
DHARMA IS ABOVE WE THE PEOPLE, THE WATAN AND THE CONSTITUTION..
http://ajitvadakayil.blogspot.com/2018/01/sanatana-dharma-hinduism-exhumed-and_21.html
capt ajit vadakayil
..
Suprabhat Capt Ajitji
DeleteOnce a king was performing a grand offering of food to people in his kingdom. While the king was serving food to a brahmin, an eagle went above them holding a snake which was its prey. Accidentally, a little poison from the snake spilt in the food the king was serving and the Brahmin died.
So whom should the laws of karma be applied for the death of Brahmin. The king, the eagle or the snake?.
The karma went to a lady who was making spiteful comments to the people about the king that all this offering is a farce and show off.
I think that ladys name could be Rohini Chatterjee.
Superbly explained.
DeleteKarma is cause and effect where intention generated on base of inherent beliefs generates cause which is never visible.
Effect and it's consequences are.
Laws of physics govern this.
Good or bad is ego intellect complex at work w R t societal mores prevailing at that time.
Masterpiece...thanks, Capt.
DeleteSharing on Social Media.
POISON ON FOOD WONT KILL THE BRAHMIN -- UNLESS HE HAS LOT OF ALKALINE CHOONA.
Delete@mohit bhai,
DeleteAs i understand what is written above, the intention of neither party is set towards what conspired. The law hence applies to the past life karma of the brahmin, animals are bereft of karma as guruji has explained earlier. Hence eagle and snake are out of scope. Only king and brahmin remain in karmic scope, the king with the intention of offering food to the people is a good deed with a good intention, hence he is also not afflicted. As guruji points out "we are the architects of our own state of existence". Thus, the karma of the brahmin delivered his fruit to him at the right time.....
Captain Ajitji's info on choona is too good.
DeleteOne more story. This is just a story for conversation.
Once Yudhistra was very saddened and felt unbelievable to see Pitahmah Bhism on a bed of arrows as he had performed many great deeds in his life and why should he bear through this pain.
So he performed meditation and went through past 100 life's of Pitamah Bhism. He couldn't find any misdeed that Bhism would have done. He was very furious and called his God Father Yaksha and demanded explanation.
On this Yaksha God appeared and said why did you stop only till 100 previous lives. In one of the earlier life he caught a snake and threw it in bushes full of thorn which injected the snake all over. That is why he is facing this pain in this life.
On hearing this Yudhistra got annoyed and asked Yaksha God that couldn't he get any better time to punish Pitamah Bhism than this life.
He replied that his deeds were so good that I couldn't punish him.
So Yudhistra asked that why did you choose this life. To which Yakhsa God replied that this is Pitamah Bhism's last life after which he will attain MOKSHA. I had to settle all karma before he attains Moksha.
Finally Yudhistra understands and pays his respects to Yaksha GOD.
Respected Sir,
DeleteKindly pardon me if am wrong.
It is said that free will is directly proportional to awareness.and also that one's vasanas and karma determine our present environment and experiences.with so many factors influencing our current life how much of free will do we really have percentage wise to shape our future.is not life and events predetermined in such a case.
Is it that only when one is self-realized do we have 100% free will and not bound by karma and it's effects.
Also when Lord Krishna says consider me as the doer of all your actions and not the ego what are we to understand.
Thank you
Thanks you Master Ji
DeleteHello Sir,
DeleteWhat you have done for me and all the readers which can't be described in words.
Today i shape my life on my terms. But i feel i can't help my near and dear ones.
This question is related to karma. Can we at all help our near and dear ones ?
It is like they are possessed and can't even take the best advice even from this blog.
Do we leave them to their own karma and not think too much into it ?
Now I truely understand why a mightly warrior like Arjuna faced depression during Mahabharata war.
Earlier i used to wonder is Arjuna so weak to go under depression. Raising weapon against own blood and gurus was really hard on him.
Did war of Mahabharata reduce arjuna's karma ? even with Krishna on his side he does not get Moksha ??????
Makes me sometimes wonder what else simple people like us have to do ?
Also i understand when u say karmically women can't achive moksha.
Does following dharma totally alienate you ? or changes your company with the ones who follow your path ?
Do we leave our friends who are still stuck in the old rut ( and we know they have chosen not to get out of it even though they suffer ) ?
Glad to see you back Kaptaan Sahib, hope you are recharged. Was W.D Gann a genius or did he do partial ripoff of Vedic Astrology and was still one of the greatest stock market predictors.
ReplyDeleteAll Gann Theory, Elliot Wave analysis is based on astrology and fractal theory respectively which is lifted from India.
DeleteCaptain has already mentioned about Fibonacci series and the golden ratio which was lifted from India is used in stock markets and is a critical element of Elliot wave theory.
I know this because I perform regular analysis with these.
It may be a tough decision to shift capital Dilli out of inversion layer.
ReplyDeleteLots of vested interests will try n abort it.
No other solution in sight? What else will disruptor the inversion layer.
High voltage electron beams???
It is good to see you blogging again captain
ReplyDeletenamaste captain ji,
ReplyDeletethanks & welcome for coming back.
om
Nobody can explain KARMA as you have done! Thanks a TON dear Captain.
ReplyDeleteGrateful to you always!
https://timesofindia.indiatimes.com/home/sunday-times/jnu-its-not-about-freebies-its-about-freedom/articleshow/72092659.cms
ReplyDeleteJEW FAIZ AHMED FAIZ WAS A ROTHSCHILD COMMIE.. HIS WIFE WAS A WHITE SKINNED JEWISH HONEY TRAP ALYS ...
NEHRUs HAF BROTHER JEW SHEIKH ABDULLAH WHOSE WIFE IS THE DAUGHTER OF FRENCH JEW MICHAEL HARRY NEDOU MARRIED OFF FAIZ..
ALYS WAS THE AUNT OF JEW SALMAN TASEER WHOSE HAS SEX WITH TAVLEEN SINGH AND PRODUCED JEW AATISH TASEER....
SOMEBODY ASKED ME --
ReplyDeleteCAPTAIN, ARE THE GERMANS IN SOUTH AMERICA , CHRISTIANS?
SORRY--
GERMAN IN SOUTH AMERICA ARE ALL JEWS-- OR GERMAN CHRISTIANS WITH JEWISH WIVES..
AFTER WW2 JEW HITLER AND HIS JEWISH THUGS ESCAPED TO SOUTH AMERICA BY SUBMARINES..
http://ajitvadakayil.blogspot.com/2015/10/if-zionist-jews-created-isis-who.html
THEY WERE WARNED BY THE ZIONISTS -- PRETEND TO BE CHRISTIANS AND CHANGE YOUR NAMES.. KOSHER WORSHIP MUST BE DONE ONLYBIN SECRET AT HOME .. NEVER ATTEND ANY PUBLIC SYNAGOGUES..
ONE JEW ADOLF EICHMANN IGNORED THIS STRICT INSTRUCTION AND MOSSAD MADE AN EXAMPLE OUT OF HIM..
THE ENTIRE MEDIA OF SOUTH AND CENTRAL AMERICA IS JEWISH..( LIKE THE REST OF THE PLANET )..
ENEMY OF JEWS IS ENEMY OF THE NATION..
THESE JEWISH MEDIA BARONS HAVE KILLER ORGS TO CREATE NEWS, PLANT BOMBS IN PUBLIC SPACES, ASSASSINATE POLICE..
THESE JEWISH MEDIA ARE CONTROLLED BY JEWISH OLIGARCHS WHOSE TENTACLES ARE IN EVERY GOVT INSTITUTIONS..
WHEN IT CAME TO PABLO ESCOBAR, THE JEWISH DEEP STATE CONTROLLED THE LEFT AND RIGHT WING GUERRILLAS..
THIS IS BASED ON ROTHSCHILDs DIKTAT- " IF YOU WANT TO CONTROL THE OPPOSITION LEAD THEM YOURSELF"..
GANDHI AND INDIAN NATIONAL CONGRESS WAS CONTROLLED BY JEW ROTHSCHILD..
TILL TRUMP CAME ON SCENE NOBODY BELIEVED ME..
ROTHSCHILDs CANDIDATE HILLARY CLINTON WAS GIVEN PRESIDENTIAL DEBATE QUESTIONS IN ADVANCE..
HAVE YOU SEEN HOW THE JEWISH MEDIA HAS LAID SIEGE ON TRUMP TODAY? PEOPLE NOW SMELL A RAT..
THE US JUDICIARY WAS CONTROLLED BY THE JEWISH DEEP STATE STILL TRUMP MANAGED TO TILT IT A WEE BIT AWAY FROM JEWS..
http://ajitvadakayil.blogspot.com/2018/10/second-defeat-for-deep-state-capt-ajit.html
THE RULING FAMILIES OF AMERICA ARE ALL JEWISH DESCENDANTS OF ROTHSCHILDs DRUG RUNNERS..
http://ajitvadakayil.blogspot.com/2010/12/dirty-secrets-of-boston-tea-party-capt.html
MODI / AJIT DOVAL / RAW KNOW SHIT ABOUT WORLD INTRIGUE..
BHARATMATA IS RACING TO BE THIS PLANETs NO 1 SUPERPOWER IN 14 YEARS.. WITH PEOPLE LIKE JEW DARLING MODI IN CHARGE WHO KNOWS?
HESS ( NO 2 RANK IN GERMANY ) PARACHUTED TO ENGLAND ONE DAY BEFORE START OF WW2 TO TELL THE BRITISH KING THAT HITLER IS A JEW..
HE GOT A SHOCK OF HIS LIFE WHEN HE CAME TO KNOW THE HARD WAY, THAT THE BRITISH ROYALTY ARE OF GERMAN JEW BLOOD-- THAT CHURCHILL/ ROOSEVELT/ STALIN/ EISENHOWER ARE ALL JEWS.
http://ajitvadakayil.blogspot.com/2011/11/rudolf-hess-honourable-man-who-was.html
capt ajit vadakayil
..
COMMENTS IN THIS BLOG POST ARE NOW CLOSED..
ReplyDeletehttps://m.timesofindia.com/business/india-business/air-india-bharat-petroleum-corporation-to-be-sold-by-march-fm/articleshow/72090771.cms
ReplyDeleteDear Captainji,
ReplyDeleteSo happy today, Sir. Welcome back.
Regards,
Bunga