THIS POST IS CONTINUED FROM PART 13, 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 !
1
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
Without the ability to
understand cause and effect, deep learning algorithms will never be able to
explain why an x-ray image suggests the presence of an ailment. In some cases,
comprehending cause and effect may seem like common sense to humans.
But
enabling AI to have the same epiphanies in reasoning would be revolutionary.
Essentially, the algorithm forms hypotheses about the causal relationships
between variables, then it tests how changing a variety of variables affects
its theories.
Through this iterative trial and error, the algorithm should be
able to start differentiating between causation and correlation. For instance,
it should still be able to recognize that cancer can be caused by smoking as
opposed to hospital visits, even though both factors are heavily related to the
situation.
The subset of machine learning relies on artificial neural networks
to simulate the way human brains learn by strengthening neural connections.
Basically, the neural network is fed and trained on data repeatedly until it
gradually adjusts its outcomes to be correct. This is how neural networks can
eventually recognize cats in photos with extreme accuracy — after seeing
hundreds of thousands of cat images, it starts to “get the picture.”
But none of this
training allows deep learning to generalize..
Correlation and
causation are often confused because the human mind likes to find patterns even
when they do not exist. We often fabricate these patterns when two variables
appear to be so closely associated that one is dependent on the other. That
would imply a cause and effect relationship where the dependent event is the
result of an independent event.
However, we cannot simply assume causation even
if we see two events We cannot simply assume causation even if we see two
events happening, seemingly together, before our eyes.happening, seemingly
together, before our eyes. One, our observations are purely anecdotal. Two,
there are so many other possibilities for an association, including:
The opposite is true: B
actually causes A.
The two are correlated,
but there’s more to it: A and B are correlated, but they’re actually caused by
C.
There’s another
variable involved: A does cause B—as long as D happens.
There is a chain
reaction: A causes E, which leads E to cause B (but you only saw that A causes
B from your own eyes).
One of the most basic
tenants of statistics is that correlation does not imply causation. In turn, a
signal’s predictive power does not necessarily imply in any way that that
signal is actually related to or explains the phenomena being predicted.
This distinction
matters when it comes to machine learning because many of the strongest signals
these algorithms pick up in their training data are not actually related to the
thing being measured.
Deep learning is
fundamentally blind to cause and effect. Unlike a real doctor, a deep learning
algorithm cannot explain why a particular image may suggest disease. This means
deep learning must be used cautiously in critical situations.
Deep learning uses
artificial neural networks to mathematically approximate the way human neurons
and synapses learn by forming and strengthening connections. Training data,
such as images or audio, are fed to a neural network, which is gradually
adjusted until it responds in the correct way. A deep learning program can be
trained to recognize objects in photographs with high accuracy, providing it
sees lots of training images and is given plenty of computing power.
But deep learning
algorithms aren’t good at generalizing, or taking what they’ve learned from one
context and applying it to another. They also capture phenomena that are
correlated—like the rooster crowing and the sun coming up—without regard to
which causes the other.
Too much of deep
learning has focused on correlation without causation, and that often leaves
deep learning systems at a loss when they are tested on conditions that aren't
quite the same as the ones they were trained on
Confusion matrix, also
known as an error matrix.
A confusion matrix is a
table that is often used to describe the performance of a classification model
(or “classifier”) on a set of test data for which the true values are known. It
allows the visualization of the performance of an algorithm.
It allows easy
identification of confusion between classes e.g. one class is commonly
mislabeled as the other. Most performance measures are computed from the
confusion matrix.
Definition of the
Terms:--
• Positive (P) :
Observation is positive (for example: is an apple).
• Negative (N) :
Observation is not positive (for example: is not an apple).
• True Positive (TP) :
Observation is positive, and is predicted to be positive.
• False Negative (FN) :
Observation is positive, but is predicted negative.
• True Negative (TN) :
Observation is negative, and is predicted to be negative.
• False Positive (FP) :
Observation is negative, but is predicted positive.
The confusion matrix is
capable of giving the researchers detailed information about how a machine
learning classifier has performed with respect to the target classes in the
dataset. A confusion matrix will demonstrate display examples that have been properly
classified against misclassified examples.
A confusion matrix is a predictive
analytics tool. Specifically, it is a table that displays and compares actual
values with the model’s predicted values. Within the context of machine
learning, a confusion matrix is utilized as a metric to analyze how a machine
learning classifier performed on a dataset. A confusion matrix generates a
visualization of metrics like precision, accuracy, specificity, and recall.
The reason that the
confusion matrix is particularly useful is that, unlike other types of
classification metrics such as simple accuracy, the confusion matrix generates
a more complete picture of how a model performed
Machine learning is the
ability of computer systems to improve their
performance through exposure to data,
without the need to follow explicitly programmed
instructions.
Algorithms are routine
processes or sequences of instructions for analysing data, solving problems, and
performing tasks.
“Self-learning” algorithms, however, are increasingly replacing programmed algorithms. Essentially, a
self-learning algorithm is programmed to refine its own performance. In the
context of machine learning, this requires a system powerful enough to process
and analyze a ton of information.
Before even creating a
model we should come up with a strategy of how to create a repeatable process
so that future data can be used to update or retrain the current model. Several
strategies are worth considering:
1. Create a new model
on a regular basis incorporating the new data and switch the new model with the
old one in production. The disadvantage of this is that retraining a model can
take quite some time and resources and by the time a new model has been
trained, it might no longer be up to date. Obviously, this depends on the size
and complexity of the model and the time needed to actually train it.
2. Implement a
self-learning algorithm that ingests batches of new data. New data can then be
added to the existing model on a regular basis. The disadvantage of this is
that there aren’t many out of the box algorithms that support this type of
retraining.
3. Implement a
self-learning algorithm that ingests new data as it becomes available. Ready to
use options for this is are also limited but you could always develop your own
custom solution.
Automatically trained
algorithms are more difficult to fine-tune, over-fitting can be a great concern
and model stability is a major issue. Your model shouldn’t be giving you
drastically different results every time it is re-trained. If this is happening
then your algorithm is not stable enough and as a result of not learning larger
trends in your underlying data.
These problems can be harder to debug and fix
with automatically re-trained models. Revising a system is
time-consuming. Having a system in place that updates machine learning models
automatically gives you peace of mind and allows systems to be accurate and
reliable in production for much longer periods of time.
Analytics is the use of
data, statistical modelling science, and algorithms to reliably generate insights,
predict outcomes, simulate scenarios, and optimise decisions.
Cognitive technologies
refer to the underlying technologies
that enable artificial intelligence
(AI). AI is the theory and development of computer systems that are able to perform tasks that normally require human
intelligence.
The ability of AI
applications to work with datasets too large for manual handling make it
possible to reveal or even predict corruption or fraud that previously was
nearly or completely impossible to detect.
AI-assisted procedures
can replace previously corruption-prone processes.
Digitisation is a
prerequisite for AI to be deployed in anti-corruption efforts.
Algorithmic bias is
often inherited from the datasets used to train the algorithm. Some systems
‘learn’ how to achieve the optimal result with no supervision. Artificial
neural networks mimic the way our brain is constructed.
Millions of
calculations are performed and sent between the nodes of the network,
generating complexity that can become impossible to explain. The ‘black box
problem’ refers to opaque calculations in complex algorithms.
Algorithm driven
chatbots reply to our questions in text or spoken language. These are deep state brainwash tools..
Machine learning models
for fraud detection can also be used to develop predictive and prescriptive
analytics software. Predictive analytics offers a distinct method of fraud
detection by analyzing data with a pre-trained algorithm to score a transaction
on its fraud riskiness.
Questions persist on
how to handle biased algorithms, our ability to contest automated decisions,
and accountability when machines make the decisions. How such systems relate to
the right to privacy, the right to explanation, and the ‘right to be forgotten‘
also remain topics of debate.
THE ILLEGAL COLLEGIUM
JUDICIARY CONTROLLED BY THE JEWISH DEEP STATE HAS BLED BHARATMATA FOR TOO LONG..
OUR KAYASTHA LAW
MINISTER PRASAD IS A MOST USELESS FELLOW.. IN 1976 PRASAD WAS THE LACKEY OF CIA
SPOOK KAYASTHA AND FELLOW BIHARI JP..
IF AI IS APPLIED WE
CAN FIND OUT THE JEWISH AMERICAN BASTARDS WHO MILKED IRAQ AFTER THE WAR.. THESE
ARE THE SAME BASTARDS WHO CAUSED THE WAR
Algorithms applied to
track transactions and the location of recipients will flag unexpected
behaviours, transactions or movements. By this the WFP can uncover attempts of
fraud or misuse. Severe criticism has risen on how a company closely related US
state security agencies controlled by Jews are to develop data systems for an
UN agency.
Black box AI systems
for automated decision making, often based on machine learning over big data,
map a user’s features into a class predicting the behavioural traits of
individuals, such as credit risk, health status, etc., without exposing the
reasons why.
This is problematic not only for lack of transparency, but also
for possible biases inherited by the algorithms from human prejudices and
collection artifacts hidden in the training data, which may lead to unfair or
wrong decisions
Machine learning
constructs decision-making systems based on data describing the digital traces
of human activities. Consequently, black box models may reflect human biases
and prejudices. Many controversial cases have already highlighted the problems
with delegating decision making to black box algorithms in many sensitive
domains, including crime prediction, personality scoring, image classification,
etc.
Predictive algorithms
tell you about the likelihood of a future outcome with scientific accuracy. Big
data is a collection of moving parts that can be smartly mixed and matched to
model hundreds of different outcomes (negative and positive) that will guide
your decision making.
Predictive analytics
identifies patterns in previous data to answer the question, “What might happen
next?”
Predictive analytics is
the practice of applying mathematical models to large amounts of data to identify
patterns of previous behavior and to predict future outcomes. The combination
of data mining, machine learning and statistical algorithms provides the
“predictive” element, allowing predictive analytics tools to go beyond simple
correlation. In business, predictive analytics has a wide variety of uses..
Predictive analytics is
not the same as predictive modeling. Predictive modeling is a technique used in
predictive analytics in which data is applied to a particular algorithmic
mathematical process (the model) to determine an outcome.
Predictive analytics is
not the same as data mining. Data mining is the process of examining and
analyzing large amounts of data to identify patterns and relationships. Making
predictions or forecasts based on those data patterns is the job of predictive
analytics.
What’s the difference
between an algorithm and a predictive model?
Algorithms are the
mathematical basis of predictive analytics. They are the series of steps, like
a recipe, executed to achieve a result or solution. Models define the way the
algorithms are applied to solve a particular problem. The model is the
framework that defines the questions, and the variables considered in answering
them. The algorithms are the steps used to weigh variables and arrive at answers.
A quick web search will
reveal that many people use the terms “algorithm” and “predictive model”
interchangeably. The word “classifier” is also used in the same context. Again,
while the terminology is fluid, “classifier” is generally used to indicate an
algorithm specifically designed for classification.
The most common models
used in predictive analytics are classification algorithms and regression
algorithms.
Classification
algorithms sort (or classify) data by category. Is this person female or male?
Is this email spam or not spam?
Regression algorithms
are used to predict a numerical outcome. Will the price go up or down?
Regression models a
target prediction value based on independent variables. It is mostly used for
finding out the relationship between variables and forecasting..
Logistic
regression model takes a linear equation as input and use logistic function and
log odds to perform a binary classification task. Regression
is based on a hypothesis that can be linear, quadratic, polynomial, non-linear,
etc. The hypothesis is a function that based on some hidden parameters and the
input values
Data scientists use a
variety of predictive models based on the type of outcome they are hoping to
achieve. The math behind each algorithm is complex and beyond the scope of this
article, but here are a few of the most popular predictive analytics algorithms
and a brief description of how they can be used.
Predictive analytics in
banking and financial services: Predictive analytics is valuable across the
spectrum of banking and financial service activities, from assessing risk to
maximizing customer relationships. Predictive analytics are used to access the
following:
Linear regression. This
compares a dependent variable with one or more independent variables. It is one
of the most common algorithms, often used for predicting an outcome or
forecasting an effect, and determining which variables have the most impact.
Random forest is a
widely-used algorithm for both classification and regression. It is an ensemble
technique (a combination of multiple algorithms) that combines multiple
decision trees to get more accurate results than a single decision tree.
Naive Bayes is a simple
but powerful algorithm often used for text categorization, including spam
filters. A Naive Bayes spam filter correlates the words in an email with spam
and non-spam emails to determine the probability of the email in question being
spam.
K-nearest neighbors
(KNN) is used to predict the characteristics of a given data point based on its
proximity to other data points. KNN could be used in credit scoring, for
example. A loan or credit card applicant with a particular set of financial
details would likely have a similar credit rating to other people with the same
financial details.
Support vector machines
(SVM) can be used for classification or regression problems. An SVM algorithm
uses training examples (known data grouped into categories by similarity) to
assign new examples to the appropriate category. SVMs have proven effective for
image classification (“Is this a tree or a person?”), providing more accurate
results than previous methods.
Boosting is an ensemble
technique designed to increase accuracy. A model is created using training data,
then a second model is created to correct the errors of the first model, then a
third to correct the errors of the second, and so on until the desired outcome
is achieved.
AdaBoost is considered
the first successful boosting algorithm, and the basis on which subsequent
models have been built.
Narrow, or weak, AI is
designed to perform a specific task, such as facial recognition or product
recommendation. General, or strong, AI aims at outperforming humans across
multiple domains.
Machine learning (ML):
There as 'the science of getting computers to act without being explicitly
programmed Machine learning is an AI
component that provides systems with the ability to automatically learn over
time, generally from large quantities of
data.
The learning process is based on observations or data, such as examples, in order to identify patterns in
data and make better predictions. An ML algorithm can be seen as an algorithm
that, from data, generates another algorithm, usually referred to as a model.
An algorithm must be
transparent if outsiders are to understand how it has been optimized. And when
it comes to systems that predict the probability of death, optimization
parameters should not be the purview of commercial businesses alone.
The
system’s developers must instead publicly disclose which goals are being
pursued with the algorithm and under what conditions it is being used. Both of
these aspects must be subject to a public social, political and ethical debate.
Moreover, it must be possible to verify the algorithm’s performance.
A number of questions
must be asked when it comes to algorithms of this type, such as:--
How reliable are their
predictions?
How often do the
results include false positives or false negatives?
Are the algorithms
truly helpful in achieving the desired goals? What those goals (e.g. improving
access to at-home palliative care or reducing costs resulting from unnecessary
treatments and interventions)?
Which framework are
they embedded in, i.e. which patient groups were they developed for?
Algorithms lack empathy
and SUBJECTIVE morality .
Recommendations about a
situation so personal and emotional as an impending death should never be made
by a computer program. Doctors can use artificial intelligence as an aid, but
they will always have to consider the entire individual as they reach their
decision on what the best way forward is.
Regardless of how
algorithms for predicting death develop in the future, guidelines must be put
in place ensuring that it is ultimately a human being – a doctor – who makes
the recommendation or decides together with the patient or family what the best
way forward is.
Chemotherapy and
surgery can be lucrative for hospitals but not for kosher INSURANCE COMPANIES greedy for profit.
Most of these automated
decision systems rely on traditional statistical techniques like regression
analysis.
Without accountability
and responsibility, the use of
algorithms and artificial intelligence leads to
discrimination and unequal access to employment opportunities.
ALGORITHMS GIVE
COMPUTERS GUIDANCE ON HOW TO SOLVE PROBLEMS.
THERE IS NO ARTIFICIAL INTELLIGENCE WITHOUT
ALGORITHMS.
ALGORITHMS ARE, IN PART, OUR
OPINIONS EMBEDDED IN CODE.
Neural networks use
“big data,” immensely large collected data sets, to analyze and reveal patterns
and trends.
The development of the
internet and advances in computer hardware have allowed programmers to take
advantage of the vast computational power and the enormous storehouses of
data—images, video, audio and text files
strewn across the internet—that, it turns out, are essential to making neural
nets work well.
For deep learning to
function, algorithms need to be fed data. Data mining uses algorithms to collect and
analyze data. Data mining consolidates massive quantities of
data generated on the internet and identifies “interpretable patterns” otherwise too subtle or complex for unaided
human discernment.
When the data is collected and relationships
are identified, it is called a
model For data mining and deep learning
to work, programmers have to translate
the problem or desired outcome “into question about the value of some target
variable.
Programmers and data
miners frequently translate ambiguous problems
into questions computers can solve by focusing on the value of a target variable. To create the model,
the algorithm is trained to behave in a
specific way by the data it is fed.
The definition of a
desirable employee is challenging because
it requires prioritization of numerous observable characteristics that make an employee “good.”
Employers tend to value action-oriented, intelligent,
productive, detail oriented employees.
This subjective
decision opens the door to potential
problems.
Essentially, what makes
a “good” employee must be defined in
ways that correspond to measurable outcomes: relatively higher sales, shorter
production time, or longer tenure
The subjective choices made both by the programmers and by
the employer in previous hiring decisions are absorbed into the algorithm by
way of the data that is used and the
subjective labels placed on specific
characteristics.
Thus, when subjective
labels are applied, the results are
skewed along the lines of those labels and the data that is utilized.
Therefore, it is possible for algorithms and artificial intelligence to
inherit prior prejudice and reflect
current prejudices.
Artificial intelligence
and algorithms rely on training data.
When these data sets are skewed as a result of bias or carelessness, the results can be
discriminatory
While datasets may be
extremely large but possible to comprehend and code may be written with
clarity, the interplay between the two
in the mechanisms of the algorithm is what yields the complexity and thus the
opacity.
AI is a term, which
consists of not only algorithms but also expert systems and formal logic. The
branch of computer science that deals primarily with symbolic, non-algorithmic
methods of problem solving.
Artificial Intelligence
(AI) refers to the creation of computer programs and devices for simulating
brain functions and activity. It also refers to the research program aimed at
designing and building intelligent artifacts. .. It covers the theory and
techniques for the development of algorithms that allow computers to show an
ability and/or intelligent activity, at least in specific domains.
Systems able to
independently react to signals from the outside world (i.e., signals not
directly controlled by programming specialists or anyone else), which therefore
cannot be foreseen, in comparison with systems based on algorithms. ..The
application of computer science such that a system can learn, reason and store
information.
Reinforcement learning,
in the context of artificial intelligence, 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
is often used for robotics, gaming and navigation. With reinforcement learning,
the algorithm discovers through trial and error which actions yield the
greatest rewards.
This type of learning has three primary components: the agent
(the learner or decision maker), the environment (everything the agent
interacts with) and actions (what the agent can do). The objective is for the
agent to choose actions that maximize the expected reward over a given amount
of time.
The agent will reach the goal much faster by following a good policy.
So the goal in reinforcement learning is to learn the best policy.
In general, there are
two types of machine learning algorithms used in fraud detection: supervised
and unsupervised learning. The former uses already annotated data – reviewed
and labeled as fraud activity by a human – to learn complex patterns in
datasets provided by a business. The latter approach deals with datasets that
have not been labeled and infers inner data structure by itself.
Data scientists have
access to a range of techniques, which can be broken down in terms of problems
they solve: classification and regression. Both can be used to analyse data and
provide the answer to whether a transaction was genuine or fraudulent. The
typical supervised machine learning algorithms used to solve these problems are
logistic regression, decision trees, random forests, and neural networks.
Data Science – How is
all the big data analyzed? Fine, the machine learns on its own through machine
learning algorithms – but how? Who gives the necessary inputs to a machine for
creating algorithms and models? No prizes for guessing that it is data science.
Data Science is a uses different methods, algorithms, processes, and systems to
extract, analyze and get insights from data.
Data science focuses on
data visualization and a better presentation, whereas machine learning focuses
more on the learning algorithms and learning from real-time data and
experience.
An algorithm is “a set
of guidelines that describe how to perform a task. Within computer science, an
algorithm is a sequence of instructions that tell a computer what to do. AI works through algorithms (neural networks
are a type of algorithm), but not all algorithms involve artificial
intelligence.
The CDC and other health
focused institutions also use machine learning to help predict and understand
the way that diseases work, and to find ways to prevent the progression of
diseases when they’re able.
The first stage of this
work is usually done through statistical analysis, which is then built upon by
implementing machine learning algorithms based on confirmed statistics.
In machine learning,
algorithms rely on multiple data sets, or training data, that specifies what
the correct outputs are for some people or objects. From that training data, it
then learns a model which can be applied to other people or objects and make
predictions about what the correct outputs should be for them.
Algorithms incentivized
to predict the majority group.
In order to maximize
predictive accuracy when faced with an imbalanced dataset, machine learning
algorithms are incentivized to put more learning weight on the majority group,
thus disproportionately predicting observations to belong to that majority
group. The next interactive example illustrates this tendency.
Decision Trees are a
type of Supervised Machine Learning (that is you explain what the input is and
what the corresponding output is in the training data) where the data is
continuously split according to a certain parameter. The tree can be explained
by two entities, namely decision nodes and leaves Decision tree algorithm falls
under the category of supervised learning. They can be used to solve both
regression and classification problems.
.
Machine learning
algorithms are computer programs that can learn from data. They gather
information from the data presented to them and use it to make themselves
better at a given task. For example, a machine learning algorithm created to
find cats in a given picture is first trained with the pictures of a cat. By
showing the algorithm what a cat looks like and rewarding it whenever it
guesses right, it can slowly process the features of a cat on its own.
The algorithm is
trained enough to ensure a high degree of accuracy and then deployed as a
solution to find cats in images. However, it does not stop learning at this
point. Any new input that is processed also contributes towards enhancing the
accuracy of the algorithm to detect cats in images. ML algorithms use various
cognitive methods and shortcuts to figure out the picture of a cat.
Today, AI has taken the
form of computer programs. Using languages, such as Python and Java, complex
programs that attempt to reproduce human cognitive processes are written. Some
of these programs that are termed as machine learning algorithms can accurately
recreate the cognitive process of learning.
These ML algorithms are
not really explainable as only the program knows the specific cognitive
shortcuts towards finding the best solution. The algorithm takes into
consideration all the variables it has been exposed to during its training and
finds the best combination of these variables to solve a problem.
This unique
combination of variables is ‘learned’ by the machine through trial and error.
There are many types of machine learning, based on the kind of training it
undergoes.
Thus, it is easy to see
how machine learning algorithms can be helpful in situations where a lot of
data is present. The more data that an ML algorithm ingests, the more effective
it can be at solving the problem at hand. The program continues to improve and
iterate upon itself every time it solves the problem.
Creating a Machine
Learning Algorithm
In order to let
programs learn from themselves, a multitude of approaches can be taken.
Generally, creating a machine learning algorithm begins with defining the
problem. This includes trying to find ways to solve it, describing its bounds,
and focusing on the most basic problem statement.
Once the problem has
been defined, the data is cleaned. Every machine learning problem comes with a
dataset which must be analyzed in order to find the solution. Deep within this
data, the solution, or the path to a solution can be found through ML analysis.
After cleaning the data
and making it readable for the machine learning algorithm, the data must be
pre-processed. This increases the accuracy and focus of the final solution,
after which the algorithm can be created. The program must be structured in a
way that it solves the problem, usually imitating human cognitive methods.
Types of Machine
Learning Algorithms
There are many ways to
train an algorithm, each with varying degrees of success and effectiveness for
specific problem statements..
Reinforcement Learning
Algorithms
RL algorithms are a new
breed of machine learning algorithms, as the method used to train them was
recently fine-tuned. Reinforcement learning offers rewards to algorithms when
they provide the correct solution and removes rewards when the solution is
incorrect.
More effective and efficient solutions also provide higher rewards to the reinforcement learning algorithm, which then optimizes its learning process to receive the maximum reward through trial and error. This results in a more general understanding of the problem statement for the machine learning algorithm.
More effective and efficient solutions also provide higher rewards to the reinforcement learning algorithm, which then optimizes its learning process to receive the maximum reward through trial and error. This results in a more general understanding of the problem statement for the machine learning algorithm.
The Difference Between
Artificial Intelligence and Machine Learning Algorithms
Even if a program
cannot learn from any new information but still functions like a human brain,
it falls under the category of AI.
For example, a program
that is created to play chess at a high level can be classified as AI. It
thinks about the next possible move when a move is made, like in the case of
humans. The difference is that it can compute every possibility, but even the
most-skilled humans can only calculate it until a set number moves.
This makes the program
highly efficient at playing chess, as it will automatically know the best
possible combination of moves to beat the enemy player. This is an artificial
intelligence that cannot change when new information is added, as in the case
of a machine learning algorithm.
Machine learning
algorithms, on the other hand, automatically adapt to any changes in the
problem statement. An ML algorithm trained to play chess first starts by
knowing nothing about the game. Then, as it plays more and more games, it
learns to solve the problem through new data in the form of moves. The
objective function is also clearly defined, allowing the algorithm to iterate
slowly and become better than humans after training.
While the umbrella term
of AI does include machine learning algorithms, it is important to note that
not all AI exhibits machine learning. Programs that are built with the
capability of improving and iterating by ingesting data are machine learning
algorithms, whereas programs that emulate or mimic certain parts of human
intelligence fall under the category of AI.
.
Mathematical models are
being used to help determine who makes parole, who’s approved for a loan, and
who gets hired for a job. If you could get access to these mathematical models,
it would be possible to understand their reasoning. But banks, the military,
employers, and others are now turning their attention to more complex
machine-learning approaches that could make automated decision-making
altogether inscrutable.
Machine learning
algorithms process vast quantities of data and spot correlations, trends and
anomalies, at levels far beyond even the brightest human mind. But as human
intelligence relies on accurate information, so too do machines. Algorithms
need training data to learn from. This training data is created, selected,
collated and annotated by humans. And therein lies the problem.
Machine and deep
learning algorithms built into automation and artificial intelligence systems
lack transparency. Many of these systems
contain an imprint of the biases of the engineers that helped to develop them. In
the context of machine learning and artificial intelligence, explainability and
interpretability are often used interchangeably
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.
If you feed a machine a
good amount of data, it will learn how to interpret, process and analyze this
data by using Machine Learning Algorithms.
A Machine Learning
process begins by feeding the machine lots of data. The machine is then
trained on this data, to detect hidden insights and patterns. These insights are used
to build a Machine Learning Model by using an algorithm in order to solve a
problem.
The training data will
be used to build and analyze the model. The logic of the model is based on the
Machine Learning Algorithm that is being implemented.
In the case of
predicting rainfall, since the output will be in the form of True (if it will
rain tomorrow) or False (no rain tomorrow), we can use a Classification
Algorithm such as Logistic Regression or Decision Tree.
Choosing the right
algorithm depends on the type of problem you’re trying to solve, the data set
and the level of complexity of the problem.
Machine Learning
Algorithms are the basic logic behind each Machine Learning model. These
algorithms are based on simple concepts such as Statistics and Probability.
TAKING ALGORITHMS TO
COURT
Citizens have the right
to know about the tools, costs, and standard practices of law enforcement
agencies that police their communities,
Humans typically select
the data used to train machine learning algorithms and create parameters for
the machines to "learn" from new data over time. Even without discriminatory
intent, the training data may reflect unconscious or historic bias.
For
example, if the training data shows that people of a certain gender or race
have fulfilled certain criteria in the past, the algorithm may
"learn" to select those individuals at the exclusion of others.
Machine Learning at its
most basic is the practice of using algorithms to parse data, learn from it,
and then make a determination or prediction about something in the world
Machine learning is the
science of getting computers to act without being explicitly programmed. It is based on algorithms that can learn from data
without relying on rules-based programming.. It can figure out how to perform important tasks
by generalizing from examples
Machine learning
research is part of research on artificial intelligence, seeking to provide
knowledge to computers through data, observations and interacting with the
world. That acquired knowledge allows computers to correctly generalize to new
settings.
Regardless of learning
style or function, all combinations of machine learning algorithms consist of
the following:--
Representation (a set
of classifiers or the language that a computer understands)
Evaluation (aka
objective/scoring function)
Optimization (search
method; often the highest-scoring classifier, for example; there are both
off-the-shelf and custom optimization methods used)
Machine learning uses
data to feed an algorithm that can understand the relationship between the
input and the output. When the machine finished learning, it can predict the
value or the class of new data point.
Algorithms are sets of rules, initially set by humans, for
computer programs to follow. Artificial intelligence can tweak these algorithms
using machine learning, so programs begin to adapt rules for themselves and
continuously self-optimize based on what they learn.
For example, predictive
analytics algorithms become smarter and faster the more they are used and the
more data they analyze.
Algorithmic Learning
Theory – is a branch of computation learning theory which, unlike statistical
learning theory, distinguishes itself by giving a non-probabilistic approach to
learning limits. This framework is highly suitable in scenarios where data is
not considered a random sample, for example learning a language.
Backpropagation
Algorithm is used in the training of
neural network models. It works by
transmitting the error gradient in a backwards direction, from the output layer
to the input layer.
The backpropagation algorithm works with optimization algorithms,
like Stochastic Gradient Descent, to solve the 'credit assignment problem,'
adjusting the weights of each neuron according to the impact they have on the
error.
Behavioral Analytics –
uses data about people’s behavior to understand their intent and predict future
actions. The upsurge in consumer data from e-commerce platforms, gaming, web
and mobile applications, and the Internet of Things feeds predictive behavioral
analytics algorithms that can enable marketing teams to target the right offerings
to the right micro-segment at the right time.
Machine Learning refers
to the processes by which machines and AI algorithmic oftware “learn” by example and/or teach
themselves to recognise patterns or reach
set goals without being explicitly programmed to do so
Reinforcement Learning
– uses a kind of algorithm that works by trial and error, where the learning is
enabled using a feedback loop of "rewards" and
"punishments". When the algorithm is fed a dataset, it treats the
environment like a game, and is told whether it has won or lost each time it
performs an action.
In this way, reinforcement learning algorithms build up a
picture of the "moves" that result in success, and those that don't.
DeepMind's AlphaGo and AlphaZero are good examples of the power of
reinforcement learning in action.
A good start at a
Machine Learning definition is that it is a core sub-area of Artificial
Intelligence (AI). ML applications learn from experience (well data) like
humans without direct programming.
When exposed to new data, these applications
learn, grow, change, and develop by themselves. In other words, with Machine
Learning, computers find insightful information without being told where to
look. Instead, they do this by leveraging
algorithms that learn from data in an iterative process.
The rapid evolution in
Machine Learning has caused a subsequent rise in the use cases, demands—and,
the sheer importance of ML in modern life. Big Data has also become a well-used
buzzword in the last few years.
This is,
in part, due to the increased sophistication of Machine Learning, which enables
the analysis of large chunks of Big Data. Machine Learning has also changed the
way data extraction and interpretation are done by automating generic
methods/algorithms, thereby replacing traditional statistical techniques.
Traditionally, data
analysis was trial and error-based, an approach that becomes impossible when
data sets are large and heterogeneous. Machine Learning provides smart
alternatives to analyzing vast volumes of data. By developing fast and
efficient algorithms and data-driven models for real-time processing of data,
Machine Learning can produce accurate results and analysis.
Generally, an algorithm
takes some input and uses mathematics and logic to produce the output. In stark
contrast, an Artificial Intelligence Algorithm takes a combination of both –
inputs and outputs simultaneously in order to “learn” the data and produce
outputs when given new inputs.
.
Unlike traditional
coding models, the outcome of an AI algorithm is very dependent on the data
used to train it as it infers results based on what it has been trained on.
Machine learning uses
an algorithm to process data, discover rules that are hidden in the data, and
that are then encoded in a "model" that can be used to make
predictions on new data.
Machine learning is a
study of computer algorithms that automatically become better through
experience. ML is one of the ways to achieve AI. Machine learning requires
large data sets to work with in order to examine and compare the information to
find common patterns.
With machine learning technologies, computers
can be taught to analyze data, identify hidden patterns, make classifications,
and predict future outcomes. The “learning” comes from these systems’ ability
to improve their accuracy over time without explicitly programmed instructions.
Machine learning typically requires technical experts who can prepare data
sets, select the right algorithms, and interpret the output. Most AI
technologies, including advanced and specialized applications such as natural
language processing and computer vision, are based on machine learning and its
more complex progeny, deep learning.
Machine learning trains
the algorithms to learn and predict answers to problems by analysing data to
make predictions on their own.
In Machine learning computer algorithms improve over time through
their experience of using data – plays an increasingly prominent role in
enterprise risk management. AI can be used to create sophisticated tools to monitor
and analyze behavior and activities in real time.
Since these systems can adapt
to changing risk environments, they continually enhance the organization’s
monitoring capabilities in areas such as regulatory compliance and corporate
governance. They can also evolve from early warning systems into early learning
systems that prevent threats materializing for real.
Machine learning can
support more informed predictions about the likelihood of an individual or
organization defaulting on a loan or a payment, and it can be used to build
variable revenue forecasting models. It is a technique which develops complex
algorithms for processing large data and delivers results to its users. It uses
complex programs which can learn through experience and make predictions.
The algorithms are
improved by itself through regular input of training data. The goal of machine
learning is to understand data and build models from data that can be
understood and used by humans. “it gives computers the ability to learn without
being explicitly programmed”.
ALGORITHMS HAVE NEVER
NEEDED TO EXPLAIN THEMSELVES TO US BEFORE BECAUSE WE WROTE THEM.
In supervised machine
learning an algorithm learns a model from training data.
The goal of any
supervised machine learning algorithm is to best estimate the mapping function
(f) for the output variable (Y) given the input data (X). The mapping function
is often called the target function because it is the function that a given
supervised machine learning algorithm aims to approximate.
In machine learning, an
algorithm is simply a repeatable process used to train a model from a given set
of training data.
You have many
algorithms to choose from, such as Linear Regression, Decision Trees, Neural
Networks, SVM's, and so on.
It is a particular
computer algorithm’s intelligence concerns solving the problems associated with
applying that algorithm to analyse the data fed into it. .
Artificial narrow
intelligence (ANI) consists of algorithms designed and/or trained to solve
particular problems. Computation is an
algorithmic and deterministic type of information processing.
Artificial Neural
Network(ANN) uses the processing of the brain as a basis to develop algorithms
that can be used to model complex patterns and prediction problems.
Google researchers have recently worked to develop an AI system
that is capable of detecting lung cancer like human radiologists. The system is
trained with a deep learning algorithm which interprets CT scans to foresee a
patient’s likelihood of possessing the disease..
The study was funded by
Google and researchers employed AI as a diagnostic tool to evaluate images and
predict disease eliminating human opinion. The AI model detected lung cancers.
Human intelligence is
not only associated with logical, algorithmic, or rational thinking. kinaesthetic and emotional intelligence in
humans. Current implementations of
emotions in machines are based on a logical, computable and deterministic
approaches, leaving out essential characteristics of emotions such as that
emotions interfere with rational processes and optimal decisions.
In fact,
these implementations are founded on the idea that emotions play an important
role in making humans more efficient, rationally speaking
TO STOP WORLD HUNGER
THE AI SUPERMACHINE WILL CULL THE POPULATION IN THIRD WORD NATION S RATHER THAN
GROWING MORE FOOD..
AI WILL BE MORE
INVOLVED IN “HOW TO PUT A JEW “ AS A RULER IN ALL NATIONS .. STARTING OFF WITH VENEZUELA , IRAN AND
SYRIA.
Natural language
understanding (NLU) is a branch of artificial intelligence (AI) that uses
computer software to understand input made in the form of sentences in text or
speech format. NLU directly enables human-computer interaction (HCI). ... NLU
uses algorithms to reduce human speech into a structured ontology.
Natural-language
understanding (NLU) or natural-language interpretation (NLI) is a subtopic of
natural-language processing in artificial intelligence that deals with machine
reading comprehension. Natural-language understanding is considered an AI-hard
problem
The umbrella term
"natural-language understanding" can be applied to a diverse set of
computer applications, ranging from small, relatively simple tasks such as
short commands issued to robots, to highly complex endeavors such as the full
comprehension of newspaper articles or poetry passages.
Natural language
understanding (NLU) is a branch of artificial intelligence (AI) that uses
computer software to understand input made in the form of sentences in text or
speech format.
NLU directly enables
human-computer interaction (HCI). NLU understanding of natural human languages
enables computers to understand commands without the formalized syntax of
computer languages and for computers to communicate back to humans in their own
languages.
NLU uses algorithms to
reduce human speech into a structured ontology. AI fishes out such things as
intent, timing, locations and sentiments.
The main drive behind
NLU is to create chat and speech enabled bots that can interact effectively
with the public without supervision. NLU is a pursuit of many start up and
major IT companies. Companies working on NLU include Medium's Lola, Amazon's
with Alexis and Lex, Apple's Siri, Google's Assistant and Microsoft's Cortana.
It requires
commonsense, understanding of context and creativity, none of which current AI
trends possess.
There is a huge gap
dividing the world of circuits and binary data and the mysteries of the human
brain. Voice transcription is one of the areas where AI algorithms have made
the most progress. In all fairness, this shouldn’t even be considered artificial
intelligence
Most current AI systems
operate as a ‘black box’, with limited interaction capabilities, human context
understanding and explanations. . Contextual AI is technology that is embedded
in and understands human context and is capable of interacting with humans.
Contextual AI does not
refer to a specific algorithm or machine learning method – instead, it takes a
human-centric view and approach to AI.. Contextual AI needs to be intelligible,
adaptive, customizable and controllable, and context-aware.
While statistical
algorithms helped with the context-awareness and adaptivity that is needed for
a Contextual AI system, they do fall short on the requirements for humans to
understand what is going on, and to customize and control it.
Data structures and
algorithms are patterns for solving problems. Developers who know more about
data structures and algorithms are better at solving problems. That's why
companies like Google, Microsoft and Amazon always include interview questions
on data structures and algorithms. .
A neural network is an
interconnected group of nodes, akin to the vast network of neurons in the human
brain.
Neural networks were
inspired by the architecture of neurons in the human brain. A simple
"neuron" N accepts input from multiple other neurons, each of which,
when activated (or "fired"), cast a weighted "vote" for or
against whether neuron N should itself activate.
Learning requires an algorithm
to adjust these weights based on the training data; one simple algorithm
(dubbed "fire together, wire together") is to increase the weight
between two connected neurons when the activation of one triggers the successful
activation of another. . Modern neural networks can learn both continuous
functions and, surprisingly, digital logical operations. .
The military uses
drones for ISR (Intelligence, Security and Reconnaissance) missions. Implementing artificial
intelligence for drones is a combination of mechanical devices, navigational
instruments, and machine vision. The AI behind the drone needs to be trained
using a supervised learning process.
First, a human operator
pilots the drone themselves to collect visual and spatial data from the cameras
and lidars; this operation is recorded. People then label objects in the
resulting recordings, such as a wall, mountain, or cliffside. The newly labeled
recordings are then run through the machine learning algorithm that is planned
to operate the drone.
This would train the drone to distinguish between objects
within the field of vision of its mounted camera. The algorithm would also
correlate instances of turns and stops to the objects that the drone sees in
its camera’s field of vision. This would in essence train the drone to stop or
turn when it encountered certain objects.
The vehicle could then
get a command to move to a new location. The algorithm behind the software
would then be able to move itself and its operational payload (for example, the
listening devices it is equipped with) safely to the determined location.
In
the case of autonomous drones, many of them utilize GPS technology and tracking
to allow operators to plot the general path of the drone’s flight. As the drone
is operating autonomously, the exact flight pattern and maneuvers would be left
to the artificial intelligence. Drones could allow
operators to make decisions without being concerned that they might be ambushed
from the rear
Many AI tools,
algorithms and platforms deployed already have transformed traditional methods
of banking and business of money. The impact can be felt across the banking
sphere, including core banking, efficiency, customer service, products and
services, and profits.
Speaking of fraud, AI security
systems are deemed better than even the most sophisticated IT platforms. AI
algorithms are designed in a way to detect fraud on the basis of predetermined
rules. It uses predictive analysis to prevent fraudulent activities
Google first started innovating
with AI in search in 2015 with the introduction of RankBrain, its machine
learning-based algorithm. .
Neural networks are a
set of algorithms, modeled loosely after the human brain, that are designed to
recognize patterns. They interpret sensory data through a kind of machine
perception, labeling or clustering raw input.
Algorithmic risks arise
from the use of data analytics and cognitive technology-based software
algorithms in various automated and semi-automated decision-making
environments. Three areas in the algorithm life cycle have unique risk
vulnerabilities:
Input data is
vulnerable to risks, such as biases in the data used for training; incomplete,
outdated, or irrelevant data; insufficiently large and diverse sample size;
inappropriate data-collection techniques; and a mismatch between the data used
for training the algorithm and the actual input data during operations.
Output decisions are
vulnerable to risks, such as incorrect interpretation of the output,
inappropriate use of the output, and disregard of the underlying assumptions.
The immediate fallouts
of algorithmic risks can include inappropriate and potential illegal decisions.
And they can affect a range of functions, such as finance, sales and marketing,
operations, risk management, information technology, and human resources.
Algorithms operate at
faster speeds in fully automated environments, and they become increasingly
volatile as algorithms interact with other algorithms or social media
platforms. Therefore, algorithmic risks can quickly get out of hand.
Algorithmic risks can
also carry broader and long-term implications across a range of risks,
including reputation, financial, operational, regulatory, technology, and
strategic risks. Given the potential for such long-term negative implications,
it’s imperative that algorithmic risks be appropriately and effectively managed.
The growing prominence
of algorithmic risks can be attributed to the following factors:--
Algorithms are becoming
pervasive
Machine learning
techniques are evolving
Algorithms are becoming
more powerful
Algorithms are becoming
more opaque
Algorithms are becoming
targets of hacking
Conventional risk
management approaches aren’t designed for managing risks associated with
machine learning or algorithm-based decision-making systems. This is due to the
complexity, unpredictability, and proprietary nature of algorithms, as well as
the lack of standards in this space.
Three factors
differentiate algorithmic risk management from traditional risk management:--
Algorithms are
typically based on proprietary data, models, and techniques..
Algorithms are complex,
unpredictable, and difficult to explain .
There’s a lack of
standards and regulations that apply to algorithms
To effectively manage
algorithmic risks, there’s a need to modernize traditional risk-management
frameworks. Organizations should develop and adopt new approaches that are
built on strong foundations of enterprise risk management and aligned with
leading practices and regulatory requirements.
Algorithms today have
the ability to absorb more data and, hence, be more accurate. As long as the
data is good and clean, feeding another million datasets to an algorithm will
inch up its accuracy. This has caused an unending hunger for well-annotated and
labelled data for AI algorithms and applications.
In recent years,
criminal justice systems in many different countries have begun to use
algorithmic risk assessment tools. All such tools automate the analysis of whatever data has been inputted
into the system.
Most of these tools
still rely on manually-inputted data from questionnaires similar to those that were part and parcel of the last
generation of risk-assessment tools,
while newer tools are fully automated and rely on information that already
exists in various government databases.
Three different kinds
of “opacity” in algorithms: (1) opacity as intentional corporate or state
secrecy, (2) opacity as technical illiteracy, and (3) opacity arising from the
characteristics of machine learning algorithms that make them useful. .
Recognizing these distinct
forms of opacity is important to determining what technical and non-technical
solutions can prevent algorithms from
causing harm.
On (1), secrecy may be essential to the proper function of an algorithm (such as to prevent it from
being gamed), but such algorithms are easily reviewable by trusted and independent auditors.
Regarding (2), the solution to technical illiteracy is simply greater public education.
Finally, (3) is
difficult because there may be a trade-off between fairness, accuracy, and interpretability. Certain AI
techniques could be avoided in fields where transparency is crucial, or new benchmarks could be
developed to assess such algorithms for discrimination and other issues.
Even though Opaque AI
algorithms such as neural networks or genetic algorithms are so powerful,
explaining how to reach the decision is almost impossible. They are almost
black boxes!
In contrast, people can
read decisions of Transparent AI algorithms such as decision tree or random
forest.
If the subject requires
legal regulations, then you might need to explain how the decide. However,
result is mostly considered more important than the means to an end.
Trusting an opaque AI algorithm requires blind confidence. You
might remember the Microsoft’s conversation bot, Tay. It is based on deep
learning system. But, it lived only 16
hours and killed by Microsoft because the bot becomes a racist ( spoke against slimy Jews ) and tweets
genocide supporting sentences..
ALGORITHMIC DECISION-MAKING:
Using outputs produced by algorithms to make decisions. One of the earliest
forms of algorithmic decision-making that is still in use today in the United
States is federal sentencing guidelines for judges. This involves nothing more
than a weighted mathematical equation, drawn
from statistics, that recommends a sentence length based on the attributes of
the crime.
A weight function is a
mathematical device used when performing a sum, integral, or average to give
some elements more "weight" or influence on the result than other
elements in the same set.
A weighted mean is a
kind of average. Instead of each data point contributing equally to the final
mean, some data points contribute more “weight” than others.
If all the weights
are equal, then the weighted mean equals the arithmetic mean (the regular
“average” you're used to).. In math and
statistics, you calculate weighted average by multiplying each value in the set
by its weight, then you add up the products and divide the products' sum by the
sum of all weights
Algorithmic
decision-making is now ubiquitous in the West, from assigning credit scores, to
identifying the best candidates for a job position, to ranking students for college admissions. Today, these algorithmic decision-making
systems are increasingly employing
machine learning, and they are spreading rapidly.
They have many of the same
problems as traditional statistical
analysis. However, the scale and reach of AI systems, the trend of rapid,
careless deployment, the immediate impact
they have on many people’s lives, and the danger of societies viewing their outputs as impartial, pose a series of
new problems.
Although it’s
comforting to imagine AI algorithms as completely emotionless and neutral, that
is simply not true. AI programmes are made up of algorithms that follow rules.
They need to be taught those rules, and this occurs by feeding the algorithms
with data, which the algorithms then use to infer hidden patterns and
irregularities.
If the training data is inaccurately collected, an error or
unjust rule can become part of the algorithm - which can lead to biased
outcomes.
Racial discrimination
in the AI used by credit agencies and parole boards.
The algorithm used by a
credit agency might be developed using data from pre-existing credit ratings or
based on a particular group’s loan repayment records. Alternatively, it might
use data that is widely available on the internet - for example, someone’s
social media behaviour or generalized
characteristics about the neighborhood in
which they live. If even a few of our data sources were biased, if they
contained information on sex, race, colour or ethnicity, or we collected data
that didn’t equally represent all the stakeholders, we could unwittingly build
bias into our AI.
If we feed our AI with
data showing the majority of high-level positions are filled by men, all of a
sudden the AI knows the company is looking to hire a man, even when that isn’t
a criteria. Training algorithms with poor datasets can lead to conclusions such
as that women are poor candidates for C-suite roles, or that a minority from a
poor ZIP code is more likely to commit a crime.
As we know from basic
statistics, even if there is a correlation between two characteristics, that
doesn’t mean that one causes the other. These conclusions may not be valid and
individuals should not be disadvantaged as a result. Rather, this implies that
the algorithm was trained using poorly collected data and should be corrected.
Fortunately, there are
some key steps we can take to prevent these biases from forming in our AI.
1. Awareness of bias
Acknowledging that AI
can be biased is the vital first step. The view that AI doesn’t have biases
because robots aren’t emotional prevents us from taking the necessary steps to
tackle bias. Ignoring our own responsibility and ability to take action has the
same effect.
2. Motivation
Awareness will provide
some motivation for change but it isn’t enough for everyone. For-profit
companies creating a product for consumers have a financial incentive to avoid
bias and create inclusive products; if company X’s latest smartphone doesn’t
have accurate speech recognition, for example, then the dissatisfied customer
will go to a competitor. Even then, there can be a cost-benefit analysis that leads
to discriminating against some users.
For groups where these
financial motives are absent, we need to provide outside pressure to create a
different source of motivation. The impact of a biased algorithm in a
government agency could unfairly impact the lives of millions of citizens.
We also need clear
guidelines on who is responsible in situations where multiple partners deploy
an AI. For example, a government programme based on privately developed
software that has been repackaged by another party. Who is responsible here? We
need to make sure that we don’t have a situation where everyone passes the buck
in a never-ending loop.
3. Ensuring we use
quality data
All the issues that
arise from biased AI algorithms are rooted in the tainted training data. If we
can avoid introducing biases in how we collect data and the data we introduce
to the algorithms, then we have taken a significant step in avoiding these
issues. For example, training speech recognition software on a wide variety of
equally represented users and accents can help ensure no minorities are
excluded.
If AI is trained on
cheap, easily acquired data, then there is a good chance it won’t be vetted to
check for biases. The data might have been acquired from a source which wasn’t
fully representative. Instead, we need to make sure we base our AI on quality
data that is collected in ways which mitigate introducing bias.
4. Transparency
The AI Now initiative
believes that if a public agency can’t explain an algorithm or how it reaches
its conclusion, then it shouldn’t be used. In situations like this, we can
identify why bias and unfair decisions are being reached, give the people the
chance to question the outputs and, as a consequence, provide feedback that can
be used to address the issues appropriately. It also helps keep those
responsible accountable and prevents companies from relinquishing their
responsibilities.
While AI is undeniably
powerful and has the potential to help our society immeasurably, we can’t pass
the buck of our responsibility for equality to the mirage of a supposedly
all-knowing AI algorithms. Biases can creep in without intention, but we can
still take action to mitigate and prevent them. It will require awareness,
motivation, transparency and ensuring we use the right data.
Modern AI propelled
wars -- As the war plane plane swooped low over the jungle, it dropped a bundle
of devices into the canopy below. Some were microphones, listening for
guerrilla footsteps or truck ignitions. Others were seismic detectors, attuned
to minute vibrations in the ground.
Strangest of all were the olfactory
sensors, sniffing out ammonia in human urine. Tens of thousands of these
electronic organs beamed their data to drones and on to computers. In minutes,
warplanes would be on their way to carpet-bomb the algorithmically-ordained
grid square
The idea of collecting data from sensors, processing them with
algorithms fuelled by ever-more processing power and acting on the output more
quickly than the enemy lies at the heart of military thinking across the
world’s biggest powers. And today that is being supercharged by new
developments in artificial intelligence ( AI).
Complex decision-making
under uncertainty is at the heart of modern economies. Whether as a consumer
deciding which products and services to consume, as an employee when it comes
to choosing the right job and career, or as a manager when running daily
operations or planning the next factory, we all face constantly and
simultaneously complex, interrelated problems for which our natural
intelligence seems to have made us particularly well equipped
AI is being used in a
surprising number of applications, making judgments about job performance,
hiring, loans, and criminal justice among many others. Most people are not
aware of the potential risks in these judgments. They should be. .
One
important example is that the right to appeal judicial decisions is weakened
when AI tools are involved. In many other cases, individuals don’t even know
that a choice not to hire, promote, or extend a loan to them was informed by a
statistical algorithm.
At an innovation
conference just outside of Silicon Valley, one of the presentations included a
doctored video of a very famous person delivering a speech that never actually
took place. The manipulation of the video was completely imperceptible.
When the researcher was
asked about the implications of deceptive technology, she was dismissive of the
question. Her answer was essentially, “I make the technology and then leave
those questions to the social scientists to work out”
IMAGINE THE FRENCH
QUEEN WAS BEHEADED FOR SOMETHING SHE DID NOT SAY-- “IF PEOPLE DON’T HAVE BREAD
THEY CAN EAT CAKE”.
Artificial intelligence
bridges the gap between the unstructured data of case law and a lawyer's keen
instinct. Algorithms can uncover new patterns in case law and measure the
impact of relevant factors on an outcome.
Instincts honed over the course of a
long career can now be quantified, and juniors don't need to wait years before
being able to develop their own instincts. Moreover, lawyers can now have
unparalleled visibility into the law and have access to the same information as
their opposition, no matter the size of the firm.
It's unlikely that
machines will ever replace lawyers, but one thing is becoming clear: lawyers
that use artificial intelligence will replace lawyers that don't.
In recent years, the
advent of alternative legal service providers (ALSPs) has created an entirely
new level of competition for law firms that traditionally practice in
particular geographic areas and for particular types of clients.
Innovative AI
developments in healthcare include the following:
Diagnostic research and
development. The ability of AI to identify disease-related risks is quickly
developing. For example, one technology company has developed an artificial
neural network (a computing system inspired by the biological neural network
that involves various machine learning algorithms working together to process
complicated data inputs) that uses retinal images to assist in the
identification of cardiovascular risk factors. Similarly, Stanford University
researchers have developed an algorithm to assist in the identification of skin
cancer using neural networks.
Do-it-yourself
diagnostics. Smartphones, wearables, and other connected personal devices will
continue to become resources for at-home diagnostics, sometimes eliminating the
need to go to a doctor’s office. For example, technology companies have
developed apps that use image recognition algorithms to identify skin cancer
risks and to diagnose urinary tract infections.
AI and medical records.
While many large health systems already use electronic medical records, the
medical records ecosystem continues to evolve.
Various companies have developed
and now offer programs that analyze unstructured patient medical records by
using AI tools like machine learning (a type of AI that involves algorithms
that can learn from data without relying on rules-based programming) and
natural language processing (a type of AI in which computers can understand and
interpret human language) to deliver meaningful and searchable data, such as
diagnoses, treatments, dosages, symptoms, etc.
Employers are increasingly
using AI to analyze job applicants and make day-to-day employment-related
decisions. For example, some employers are using AI-powered software programs
to auto-screen resumes as a traditional recruiter would, and others are using
AI recruiting assistants to communicate with applicants through messaging apps.
The information used to structure an AI algorithm could be unintentionally
biased, which could potentially lead to discrimination claims by employees
and/or applicants.
If employers are using AI either directly or indirectly to
make employment-related decisions, they may want to evaluate employment
discrimination risks and mitigate against them, if possible, by, for example,
understanding the data used to build out and/or train the AI at issue and
regularly auditing decisions made through the use of AI.
Because of the pace of
AI development and the prioritization of its growth, employers may want to
continue harnessing the opportunities AI presents while staying mindful of
legal and regulatory compliance issues.
Processes involving
algorithms, direct cause and effect or predictability are far more likely to be
assigned to purpose-built software than the less set-in-stone elements of the
legal process – and this is why it’s extremely unlikely that lawyers will ever
be replaced by robots.
There are a number of
particular skills that are required in order for a legal expert to perform well
in their role which AI simply cannot currently emulate ( lawyers point of view ).
1. Strategic and
Creative Thinking
The ability to “think
outside the box” is very human. There are thousands upon thousands of slightly
different possible outcomes that may result from every distinguishable action.
The human mind – with its ability to judge from experience which is most
likely, which is least, and what would need to happen for each to come about –
is programmed for these purposes in a far more sophisticated way than AI can
currently achieve.
2. Conflict resolution
and negotiation
With our understanding
of the complexities of human-related processes and our ability to improvise and
judge, we are far better equipped to deal with conflict than robots are ever
likely to be – and conflict is a lawyer’s bread and butter.
We can read into
statements in order to extrapolate the true intentions and priorities of each
party on either side of an argument. We can then make adjustments and offers to
the opposing party in order to satisfy those intentions and priorities. We can
judge when is the right time to push and when to concede – a robot is not capable
of these complex mental gymnastics.
3. Emotional
Intelligence and Empathy
AI may be able to
recognise faces in images, but it can rarely successfully read the feelings
those faces show. Humans – to lesser or greater degrees – are capable of the
accurate analysis of emotional subtext, the application of intuition and the
use of delicately worded or allusive language.
Through these methods,
we are able to properly judge how a person feels. This way, we can judge
whether proceedings are going well for that individual or not, and we may also
be able to determine what we may need to change in order to shift onto the
right track. We can also often tell if someone is lying or being manipulated –
important skills in the field of law.
4. The Interpretation
of Grey Areas
Robots and computers
function well when presented with quantifiable data. However, once a situation
enters a “grey area” – whether this term refers to morals, processes or
definitions – robots are more likely to falter. As lawyers, we excel at using
our judgement ( MY LEFT BALL ) when there is no “right” or “wrong” answer, while computers
generally require the existence of a definite solution to a problem in order to
function correctly.
5. Critical Thinking
Humans are capable of
responding to more indicators of “quality” than computers are. While an AI
system may be able to analyse a document according to the “true” or “false”
statements made within the text, we can judge whether or not it is well-written
and analyse the implication of the use of certain words and the overall meaning
of the content.
6. Problem Solving
AI cannot yet be
programmed to solve problems in the same way that a human mind can. We are
capable of working from experience, analysing and responding to failures or
mistakes of our own accord, navigating complications and obstacles and
understanding the complex reasons why a problem has arisen in the first place.
7. Planning
Because we are able to
predict outcomes, make informed assumptions and lay the groundwork for
complicated processes, humans are great planners. We know that schedules change
and we can create backup plans for that eventuality. We understand the
strengths, weaknesses and tendencies of every individual and process involved
in our plan, and we can prioritise tasks in order to make every step effective.
AI is not yet capable of navigating these nuanced elements.
Developments in AI are
actually more likely to create jobs in the legal sector than to displace
lawyers. With the development of new technologies, the processing of new patents,
the addition of new workplace regulations and the rise of new areas of
cyber-crime are inevitable. Because of this, cyber-law is an ever-growing
field.
The main difference is
that expert system is based on rule, but the artificial intelligence system is
based on statistical simulation. Second , the expert system is knowledge based,
artificial intelligence is generally using the algorithm to calculate and
analyse the best results
The bot and fraud
detection algorithms will improve over time, and so will the ability of the bot
to go undetected.
AN indirect threat is
the impact of AI-powered social media and search algorithms. For example, Facebook’s
algorithm determines the content of a user’s newsfeed and influences how widely
and to whom content is shared.
Google’s search algorithm indexes content, and
decides what shows up in the top of search
results. These algorithms have played a significant role in establishing
and reinforcing echo chambers, and they
ultimately risk negative impacts on media pluralism and inhibition of a
diversity of views.
Computational
propaganda has been defined as 'the use of algorithms, automation, and human
curation to purposefully distribute misleading information over social media
networks'. These activities can feed into influence campaigns: coordinated,
illegitimate efforts of a third state or non-state agent to affect democratic
processes and political decision-making, including (but not limited to)
election interference. by 2020, virtually everyone in the World will be online.
Algorithms on social
media and search engines
Algorithms are
processes in (computational) calculations or operations. Online platforms such
as Google, Facebook and Twitter use
various algorithms to predict what users are interested in seeing, spark engagement
and maximise revenues.
Based on a user's habits and history of clicks, shares
and likes, algorithms filter and prioritise the content that the user receives.
As users tend to engage more with content that sparks an emotional reaction
and/or confirms already existing biases, this type of content is prioritised.
A bot (short for robot)
is an automated account programmed to interact like a user, in particular on
social media. For disinformation purposes, illegitimate bots can be used to
push certain narratives, amplify misleading
messaging and distort online discourse. Some of the bots have been used to
spread disinformation
Responding to growing
concern about the impact of disinformation bots, Twitter suspended up to 70
million accounts between May and June 2018. Facebook removed 583 million fake
accounts in the first quarter of 2018 in
an attempt to combat false news. Experts predict that the next generation of
bots will use natural language processing, making it harder to identify them as
bots
WHY SHOULD TWITTER / FACEBOOK PREVENT A DESH BHAKT FROM TRANSMITTING HIS CRITICAL POINT OF VIEW TO HIS OWN
NATIONS PM ?
IS A BLOGGER EXPECTED ONLY TO SHOWCASE JEW
ROTHSCHILD AND DEEP STATE APPROVED NEWS?
Trolls are human online
agents, sometimes sponsored by state actors or deep state to harass other users
or post divisive content to spark controversies.
Machine-driven
communications (MADCOMs) marry artificial intelligence (AI) with machine
learning to generate text, audio and video content, making it easier to tailor
messages to individual users' personalities
and backgrounds. For example, MADCOM can use chatbots using natural
language processing to engage users in
online discussions, or even to troll and threaten people.
As deep-learning
algorithms evolve, it is becoming easier
to manipulate sound, image and video for impersonation, or to make it appear
that a person did or said something they
did not ('deep fakes'). This will make it increasingly difficult to distinguish
between real and (highly realistic) fake audiovisual content, further hampering
trust online.
The private and public
sectors are increasingly turning to artificial intelligence (AI) systems and
machine learning algorithms to automate simple and complex decision-making
processes. The mass-scale digitization of data and the emerging technologies
that use them are disrupting most economic sectors, including transportation,
retail, advertising, and energy, and other areas.
AI is also having an impact
on democracy and governance as computerized systems are being deployed to improve
accuracy and drive objectivity in government functions.
Algorithms are
harnessing volumes of macro- and micro-data to influence decisions affecting
people in a range of tasks, from making movie recommendations to helping banks
determine the creditworthiness of individuals.
In the pre-algorithm
world, humans and organizations made decisions in hiring, advertising, criminal
sentencing, and lending. These decisions were often governed by federal, state,
and local laws that regulated the decision-making processes in terms of
fairness, transparency, and equity. Today, some of these decisions are entirely
made or influenced by machines
Algorithms are
harnessing volumes of macro- and micro-data to influence decisions affecting
people in a range of tasks, from making movie recommendations to helping banks
determine the creditworthiness of individuals.
Given this, some
algorithms run the risk of replicating and even amplifying human biases,
particularly those affecting protected groups.
For example, automated risk
assessments used by U.S. judges to determine bail and sentencing limits can
generate incorrect conclusions, resulting in large cumulative effects on
certain groups, like longer prison sentences or higher bails imposed on people
of color.
Bias in algorithms can
emanate from unrepresentative or incomplete training data or the reliance on
flawed information that reflects historical inequalities. If left unchecked,
biased algorithms can lead to decisions which can have a collective, disparate
impact on certain groups of people even without the programmer’s intention to
discriminate.
The exploration of the intended and unintended consequences of
algorithms is both necessary and timely, particularly since current public
policies may not be sufficient to identify, mitigate, and remedy consumer
impacts.
..
Back-propagation is
just a way of propagating the total loss back into the neural network to know
how much of the loss every node is responsible for, and subsequently updating
the weights in such a way that minimizes the loss by giving the nodes with
higher error rates lower weights and vice versa
Neural networks are a set of algorithms, modeled loosely after the human
brain, that are designed to recognize patterns. They interpret sensory data
through a kind of machine perception, labeling or clustering raw input.
Neural networks can be
hardware- (neurons are represented by physical components) or software-based
(computer models), and can use a variety of topologies and learning algorithms.
AI generally refers to “machines
that respond to stimuli in accordance with traditional responses from humans,
giving the human capacity for meditation, judgment, and purpose.
Intentionality: Artificial intelligence algorithms are designed to make
decisions, often using real-time data. They are different from passive machines
capable of only mechanical or predetermined responses.
Using sensors, digital
data, or remote inputs, they combine information from a variety of sources,
instantly analyze the material, and act on the insights gained from that data.
With tremendous improvements in storage systems, processing speed and
analytical methods, they have excellent sophistication in analytics and
decision making.
Adaptability: AI
systems have the ability to learn and adapt when making decisions. In the
transportation area, for example, semi-autonomous vehicles have the means of
notifying drivers and vehicles about impending congestion, potholes, highway
construction or other traffic interruptions.
Vehicles can take advantage of the
experience of other vehicles on the road, without human involvement, and the
entire corpus of their “experience” is immediately and completely transferred
to other similarly configured vehicles.
Their advanced algorithms, sensors, and
cameras have experience in current operations and use dashboards and visual
displays to display information in real-time, allowing human drivers to
understand the ongoing traffic and vehicle conditions. And in the case of fully
autonomous vehicles, sophisticated systems can fully control a car or truck and
make all navigational decisions.
The importance of
identifying bias in AI algorithms
Ultimately, AI and ML
algorithms, however tech-savvy and automated they eventually become, begin as
human ideas. They are then manipulated, designed, tested and trained by humans
as well. As such, there are many ways in which human error, judgment, opinion
or experience could find a way into the outcome.
When this happens and the
model itself is faulty, it can have an even more difficult time performing amid
data that is also biased. In-house training is required to ensure that these
situations happen as infrequently as possible and when they do, the issues are
caught and reversed as quickly as possible.
Bias can creep in long
before the data is collected as well as at many other stages of the
deep-learning process." It stems
from lack of awareness of the downstream impacts of data, imperfect processes,
and operating with a lack of social context.
Then there's the grand
philosophical quandary -- "what the absence of bias should look
like." This could take the form of running algorithms alongside human
decision makers, comparing results, and examining possible explanations for
differences.
Similarly, if an organization realizes an algorithm trained on its
human decisions (or data based on prior human decisions) shows bias, it should
not simply cease using the algorithm but should consider how the underlying
human behaviors need to change
Algorithms never think
for themselves. In fact, they don’t think at all (they’re tools) so it’s up to
us humans to do the thinking for them..
Artificial intelligence
is an umbrella term that refers to computers that exhibit any form of human
cognition. It is a term used to describe the way computers mimic human
intelligence. Even by this definition of ‘intelligence’, the way AI functions
is inherently different from the way humans think..
In the provided example
of an algorithm that analyzes the images of a cat, the program is taught to
analyze the shifts in the color of an image and how the image changes. If the
color suddenly switches from pixel to pixel, it could be indicative of the
outline of the cat.
Through this method, the algorithm can find the edges of
the cat in the picture. Using such methods, ML algorithms are tweaked until
they can find the optimal solution in a small dataset.
Once this step is
complete, the objective function is introduced. The objective function makes
the algorithm more efficient at what it does. While the cat-detecting algorithm
will have an objective to detect a cat, the objective function would be to
solve the problem in minimal time. By introducing an objective function, it is
possible to specifically tweak the algorithm to make it find the solution
faster or more accurately.
The algorithm is
trained on a sample dataset with the basic blueprint of what it needs to do,
keeping in mind the objective function. .
Compounding all of this
is essentially two things:--
(1) virtually all of these AI applications can be
expected to barricade themselves with as-is warranties and other pro-developer
legal mechanisms that end users invariably agree to without actually ever
having a meaningful opportunity to understand what’s at stake; and
(2) the
algorithmic bias is shrouded in secrecy, bolstered by trade secret provisions,
non-circumvention (e.g., no reverse engineering) obligations and other features
that make it difficult to detect. –
The Emerging Irrelevance of Algorithmic
Transparency in AI. Suppose AI developers agree to be “transparent”, that they
are willing to disclose their algorithm. Ultimately, in some instances, this
willingness may dispel the allure we normally accord to transparency because
when we are dealing with machine learning AI applications, the value of the
disclosed algorithm is diminished by its age.
Stated differently, the more
iterations the AI has gone through, what the original developer can disclose
becomes more and more meaningless. So while we may be able to take a look under
the hood, our desire to understand the “why” of what happened may not be
satisfied.
This, in turn, might bring us to the (uncomfortable) conclusion that
we simply don’t understand, or at least don’t fully understand (as much as we’d
like to) why the AI produced the result that it did. With that, we will have to
learn to be satisfied that the actions of machine-learning AI applications
cannot be fully understood. This observation directly ties in with the issue of
developer liability.
The Open Algorithms
(OPAL) project may facilitate the transition to greater reliance on these
‘private’ data. OPAL aims at extracting key indicators (such as population
density, poverty, or diversity) through a secured open source platform.
It also
relies on open algorithms running on the companies’ servers, behind their
firewalls. OPAL comes with governance standards that ensure the security,
auditability, inclusivity, and relevance of the algorithms for different
scenarios.
AI systems can also
provide a useful ‘aspirational analogy’ to make future human actions more
effective. What makes current AI so impressively good at its job is the credit
assignment function.
The ability of the algorithms to identify and reinforce
the Artificial Neural Networks that most contribute to coming up with the
“right’ result through many iterations and data-fueled feedback loops. These
allow for machine learning.
In a future ‘Human AI ecosystem’, governments,
corporations or the aid sector, could apply AI tools to identify and reinforce
what contributes to ‘good policy results’, including outcomes of aid programs.
They could also better understand whether these effects are desirable in the
long run through feedbacks.
In the pre-algorithm
world, humans and organizations made decisions in hiring, advertising, criminal
sentencing, and lending. These decisions were often governed by federal, state,
and local laws that regulated the decision-making processes in terms of
fairness, transparency, and equity.
Today, some of these decisions are entirely
made or influenced by machines whose scale and statistical rigor promise
unprecedented efficiencies. Algorithms are harnessing volumes of macro- and
micro-data to influence decisions affecting people in a range of tasks, from
making movie recommendations to helping banks determine the creditworthiness of
individuals.
In machine learning, algorithms rely on multiple data sets, or
training data, that specifies what the correct outputs are for some people or
objects. From that training data, it then learns a model which can be applied
to other people or objects and make predictions about what the correct outputs
should be for them.
The revolution in
object detection algorithms brought about by convolutional neural nets and deep
neural nets it has really increased the probability of detection.
Computers must be able
to recognize an object before they can classify it. "We need to create a
labeled data set for training these algorithms [to identify] lots of different
objects," Doing so is very manpower intensive
Things will get worse
in the future as radars develop the ability to sense their environment with
artificial intelligence and machine learning, and adapt their transmission
characteristics and pulse processing algorithms to defeat attempts to jam them.
New approaches like
REAM seek to enable systems to generate effective countermeasures automatically
against new, unknown, or ambiguous radar signals in near real-time. They are
trying to develop new processing techniques and algorithms that characterize
enemy radar systems, jam them electronically, and assess the effectiveness of
the applied countermeasures.
Waveform-agile radar
systems of the future will shift frequencies quickly in a pre-programmed
electronic dance to foil electronic warfare attempts to defeat them.
The company is moving
machine-learning algorithms to the EA-18G carrier-based electronic warfare jet
to counter agile, adaptive, and unknown hostile radars or radar modes. REAM
technology is expected to join active US Navy fleet squadrons around 2025.
It specializes in EW
modeling and simulation. The company has expertise in RF and wireless circuit
and systems design; electronic board design, layout, and fabrication; embedded
hardware and software design; RF modeling and simulation; computational
electromagnetics; antennas; wireless testing; cell phone forensics; servo and
stepper motor control; algorithm and digital signal processing development;
cryptography; data compression; and RF detection
Unavoidable presence of
human bias in designing and, sometimes, training these programs can make it
difficult to completely eradicate new errors or course correct after finding a
bug.
“Locked” algorithms are
those that provide the same result each time the same input is provided. As
such, a locked algorithm applies a fixed function (e.g., a static look-up
table, decision tree, or complex classifier) to a given set of inputs
An adaptive algorithm
is an algorithm that changes its behavior at the time it is run, based on
information available and on a priori defined reward mechanism (or criterion).
Game artificial intelligence (AI) controls the decision-making process of
computer-controlled opponents in computer games.
Adaptive game AI (i.e., game
AI that can automatically adapt the behaviour of the computer players to
changes in the environment) can increase the entertainment value of computer
games.
Genetic algorithms are
computational problem-solving tools (generation over generation, they evolve
and they learn). A genetic algorithm is a heuristic search method used in
artificial intelligence and computing. It is used for finding optimized
solutions to search problems based on the theory of natural selection and
evolutionary biology.
Genetic algorithms are good for searching through
large and complex data sets. Genetic algorithms are used in artificial
intelligence like other search algorithms are used in artificial intelligence —
to search a space of potential solutions to find one which solves the problem.
In machine learning we are trying to create solutions to some problem by using
data or examples
The process of using
genetic algorithms goes like this:--
Determine the problem
and goal.
Break down the solution
to bite-sized properties (genomes)
Build a population by
randomizing said properties.
Evaluate each unit in
the population.
Selectively breed (pick
genomes from each parent)
Rinse and repeat.
A genetic algorithm is
an algorithm that imitates the process of natural selection. They help solve
optimization and search problems. ... Genetic algorithms imitate natural
biological processes, such as inheritance, mutation, selection and crossover.
Big data is extremely
beneficial. However, engineers and scientists are unable to utilize this data
without the help of complex AI algorithms.
A genetic algorithm is
based on the chootiya Darwinian principle of natural selection. Its purpose is to evolve
and quickly solve optimisation problems.
There are many
applications for Genetic Algorithms across many industries like air travel,
trading and data security.
For example…
In air travel, a
genetic algorithm optimizes shape, minimizes wing weight and optimizes fuel
weight. This all improves the overall efficiency of the airplane.
In security, GA’s are used
for encrypting sensitive data and protecting copyrights. Hackers then create a
more complex GA to beat that encryption. The cycle repeats. Possibly forever.
In robotics, a genetic
algorithm can be programmed to search for a range of optimal designs for each
specific use. It can also return results for entirely new types of robots, ones
that can perform multiple tasks and have more general applications.
WITH GENETIC ALGORITHMS
YOU ARE NEVER GUARANTEED AN OPTIMAL, OR EVEN A GOOD, SOLUTION, AND IT IS A
BLACK ART TO FIND GOOD PARAMETERS AND ENCODING SCHEMES.
ALSO, YOU OFTEN GET
SOLUTIONS THAT ARE RIDICULOUS, IMPLAUSIBLE OR INEFFICIENT BECAUSE THE GA
INTERPRETED YOUR FITNESS FUNCTION WITHOUT HUMAN COMMON SENSE.
SO SO SO ,HOW DOES A CHOOTIYA SILVER BULLET GENETIC
ALGORITHM WORK?
So in MAD MAN Darwin’s theory of Natural Selection, three
main principles necessary for evolution to happen are :--
Variation — There must
be a variety of traits present in the population or a means with which to
introduce a variation.
Selection — There must
be a mechanism by which some members of the population can be parents. Passing
down their genetic information and some do not.
Heredity — There must
be a process in place by which children receive the property of their parent
A genetic algorithm is
an algorithm that imitates the process of natural selection. They help solve
optimization and search problems. ... Genetic algorithms imitate natural
biological processes, such as inheritance, mutation, selection and crossover
This would be an
opinion based question, but in terms of how things are commonly defined – Yes,
Genetic algorithms are a part of Artificial Intelligence. ... Genetic
algorithms are computational problem-solving tools (generation over generation,
they evolve and they learn)
Genetic algorithms
search parallel from a population of points. Therefore, it has the ability to
avoid being trapped in local optimal solution like traditional methods, which
search from a single point.
Genetic algorithms use probabilistic selection
rules, not deterministic ones
The following outline
summarizes how the genetic algorithm works: The algorithm begins by creating a
random initial population. The algorithm then creates a sequence of new
populations. At each step, the algorithm uses the individuals in the current
generation to create the next population.
The main difference
between genetic algorithm and traditional algorithm is that genetic algorithm
is a type of algorithm that is based on the principle of genetics and natural
selection to solve optimization problems while traditional algorithm is a step
by step procedure to follow, in order to solve a given problem
A genetic algorithm is
a search heuristic that is inspired by MAD MAN Charles Darwin's theory of natural
evolution. This algorithm reflects the process of natural selection where the
fittest individuals are selected for reproduction in order to produce offspring
of the next generation
In genetic algorithms,
a chromosome (also sometimes called a genotype) is a set of parameters which
define a proposed solution to the problem that the genetic algorithm is trying
to solve. The set of all solutions is known as the population.
A genetic
algorithm is a search heuristic that is inspired by CUNT Charles Darwin's theory of
natural evolution.
This algorithm reflects the process of natural selection
where the fittest individuals are selected for reproduction in order to produce
offspring of the next generation. Genetic algorithms are commonly used to
generate high-quality solutions to optimization and search problems by relying
on bio-inspired operators such as mutation, crossover and selection Genetic
Algorithms and What They Can Do For You.
A genetic algorithm solves
optimization problems by creating a population or group of possible solutions to
the problem. ... After the genetic algorithm mates fit individuals and mutates
some, the population undergoes a generation change.
The main difference between
genetic algorithm and traditional algorithm is that genetic algorithm is a type
of algorithm that is based on the principle of genetics and natural selection
to solve optimization problems while traditional algorithm is a step by step
procedure to follow, in order to solve a given problem
Genetic algorithms
search parallel from a population of points. Therefore, it has the ability to
avoid being trapped in local optimal solution like traditional methods, which
search from a single point.
Genetic algorithms use probabilistic selection
rules, not deterministic ones In genetic algorithms, operators such as
selection, crossover and mutation are applied to generate the individuals of
the next generation.
Elitism refers to a method for improving the GA
performance; the basic idea is to transfer the best individuals of the current
generation to the next generation The genetic algorithm is a method for solving
both constrained and unconstrained optimization problems that is based on
natural selection, the process that drives biological evolution. The genetic
algorithm repeatedly modifies a population of individual solutions
Artificial Neural
Network - Genetic Algorithm. ... Genetic Algorithms (GAs) are search-based
algorithms based on the concepts of natural selection and genetics. GAs are a
subset of a much larger branch of computation known as Evolutionary Computation.
Genetic algorithms
(GAs) are stochastic search methods based on the principles of natural genetic
systems. They perform a search in providing an optimal solution for evaluation
(fitness) function of an optimization problem. GAs deal simultaneously with
multiple solutions and use only the fitness function values
Genetic algorithm is
the unbiased optimization technique. It is useful in image enhancement and
segmentation. GA was proven to be the most powerful optimization technique in a
large solution space. This explains the increasing popularity of GAs
applications in image processing and other fields.
Mutation (genetic algorithm)
Mutation is a genetic operator used to maintain genetic diversity from one
generation of a population of genetic algorithm chromosomes to the next. ...
Hence GA can come to a better solution by using mutation. Mutation occurs
during evolution according to a user-definable mutation probability
The main difference
between them is the representation of the algorithm/program. ... A parser also
has to be written for this encoding, but genetic programming does not (usually)
produce invalid states because mutation and crossover operations work within
the structure of the tree
Genetic operator. A
genetic operator is an operator used in genetic algorithms to guide the
algorithm towards a solution to a given problem. There are three main types of
operators (mutation, crossover and selection), which must work in conjunction
with one another in order for the algorithm to be successful.
A genetic algorithm is
a heuristic search method used in artificial intelligence and computing. It is
used for finding optimized solutions to search problems based on the theory of
natural selection and evolutionary biology. Genetic algorithms are excellent
for searching through large and complex data sets
Abstract: Genetic
Algorithm (GA) is a calculus free optimization technique based on principles of
natural selection for reproduction and various evolutionary operations such as
crossover, and mutation. Various steps involved in carrying out optimization
through GA are described
Parameters of genetic
algorithm
Population size,
stopping criteria, probability of crossover, probability of mutation and
generation gap are the parameters of genetic algorithm. ... The generation gap
is defined as the proportion of chromosomes in the population which are
replaced in each generation.
Genetic algorithms are
used in artificial intelligence like other search algorithms are used in
artificial intelligence — to search a space of potential solutions to find one
which solves the problem. In machine learning we are trying to create solutions
to some problem by using data or examples.
A fitness function is a
particular type of objective function that is used to summarise, as a single
figure of merit, how close a given design solution is to achieving the set
aims. Fitness functions are used in genetic programming and genetic algorithms
to guide simulations towards optimal design solutions
Genetic algorithms are
commonly used to generate high-quality solutions to optimization and search
problems by relying on bio-inspired operators such as mutation, crossover and
selection.
Convergence is a
phenomenon in evolutionary computation. It causes evolution to halt because
precisely every individual in the population is identical. Full convergence
might be seen in genetic algorithms (a type of evolutionary computation) using
only crossover (a way of combining individuals to make new offspring)
When & How to Solve
Problems with Genetic Algorithms
Determine the problem
and goal.
Break down the solution
to bite-sized properties (genomes)
Build a population by
randomizing said properties.
Evaluate each unit in
the population.
Selectively breed (pick
genomes from each parent)
Rinse and repeat.
Artificial Neural
Network - Genetic Algorithm. ... Genetic Algorithms (GAs) are search-based
algorithms based on the concepts of natural selection and genetics. GAs are a
subset of a much larger branch of computation known as Evolutionary Computation
Genetic algorithms (GA)
are a family of heuristics which are empirically good at providing a decent
answer in many cases, although they are rarely the best option for a given
domain. ... Genetic methods are well suited for multicriteria optimization when
gradient descent is dedicated to monocriteria optimization ...
A genetic algorithm
solves optimization problems by creating a population or group of possible
solutions to the problem. ... The genetic algorithm similarly occasionally
causes mutations in its populations by randomly changing the value of a
variable.
Genetic algorithm is
the unbiased optimization technique. It is useful in image enhancement and
segmentation. GA was proven to be the most powerful optimization technique in a
large solution space. This explains the increasing popularity of GAs
applications in image processing and other fields.
A genetic operator is
an operator used in genetic algorithms to guide the algorithm towards a
solution to a given problem. There are three main types of operators (mutation,
crossover and selection), which must work in conjunction with one another in
order for the algorithm to be successful.
Mutation (genetic
algorithm) Mutation is a genetic operator used to maintain genetic diversity
from one generation of a population of genetic algorithm chromosomes to the
next. ... Hence GA can come to a better solution by using mutation. Mutation
occurs during evolution according to a user-definable mutation probability.
Genetic algorithm
programs are a machine learning model that is loosely based on biological
evolution. In biological evolution, only a few individuals will survive in a
certain environment out of the thousands that attempted to live there.
Similarly, in genetic programming, hundreds or thousands of potential solutions
will be tested to superior ones, such as, in this case, locations in need of a
finer mesh grid.
Genetic algorithms are
designed to offer good solutions, rather than perfect ones.
Genetic algorithms do
not guarantee the best solution, but they do guarantee finding better solutions
faster.
In real life, genetic
algorithms are applied in situations that are complex for humans to resolve
manually
As evolution
increasingly explains various non-biological processes impacting our lives, the
schema theorem details the underlying mechanism of how these societal
transformations take place. It is fascinating that a theorem that was initially
formulated to describe the process of genetic algorithms has achieved truly
global influence, relevance, and applicability.
SO SO SO-- What is Genetic
Algorithm ?
This is what you get..
1.An algorithm that
mimics the genetic concepts of natural selection, combination, selection, and
inheritance.
2.A probabilistic
search technique for attaining an optimum solution to combinatorial problems
that works in the principles of genetics.
3.An iterative
meta-heuristic based on the evolution of species, that handles a population of
solutions of the optimization problem that have a survive probability
proportional to the quality of the respective solution, and makes combinations
of solutions based on crossover and mutation operators. 4.The concept of basic
genetics is applied to form a meta-heuristic optimization search technique.
5.A systematic method
used to solve search and optimization problems and apply to such problems the
principles of biological evolution, namely, selection of the ‘fittest’, sexual
reproduction (crossover) and mutation.
6.The approximation
algorithm based on the evolutional process.
7.Search technique to
find exact or approximate solution to search problems.
8.A type of
evolutionary computation algorithm in which candidate solutions are represented
typically by vectors of integers or bit strings, that is, by vectors of binary
values 0 and 1
9.Global search method
based on a simile of the natural evolution.
10.A search heuristic
used in computing to find true or approximate solutions to global optimization
problems.
11.A special type of
evolutionary technique where the potential solutions are represented by means
of chromosomes (usually either binary or real sets of values). Each gene (or
set of genes) represents a variable or parameter within the global solution.
12.A search technique
used in computing to find exact or approximate solutions to optimization and
search problems
13.A heuristic method
for finding solutions to an optimization problem that takes advantage of
evolutionary principles; different possible solutions to the problem are
iteratively subjected to “replication”, “mutation” and “selection” processes.
In order to illuminate its general principles a simple instance of the method
is described below. In the context of RNA folding, the genetic algorithm might
start with a randomly generated set of conformations that are compatible with
the RNA sequence being folded. Then, in each iteration of the algorithm,
multiple copies of each conformation are made (the replication step); more
copies are made for conformations with lower free energies. The copying process
is not perfect, but it introduces “mutations”, which may involve the
creation/destruction of base pairs or entire helices. Following replication and
mutation, a subset of the resulting conformations is selected based on their
free energies and subsequently subjected to the next round of replication,
mutation, and selection. Eventually, the obtained conformations would be
enriched for those with free energies approaching the lowest possible free
energy for the RNA sequence being folded.
14.Stochastic global
optimization method, based on biological evolution and inspired by Darwin’s
theory of “survival of the fittest”.
15.A search algorithm
to enable you to locate optimal binary strings by processing an initial random
population of binary strings by performing operations. Learn more in: A
Bayesian Network Model for Probability Estimation
16.Belongs to the
larger class of evolutionary algorithms and is a search heuristic inspired on
process of natural selection and is routinely used to generate useful solutions
to optimization and search problems.
17.Abstraction and
implementation of evolutionary principles and theories in computational
algorithms to search optimal solutions to a problem.
18.Genetic algorithm
(GA), one of the most popularly used evolutionary tools among soft computing
paradigm, is mainly devised to solve real world ill-defined, and imprecisely
formulated problems requiring huge computation. It is the power of GA to
introduce some heuristic methodologies to minimize the search space for optimal
solution(s) without sticking at local optima. Due to the inherent power, GA
becomes one of the most successful heuristic optimization algorithms. It is
widely used to solve problems of diversified fields ranging from engineering to
art.
19.A genetic algorithm
is a population-based metaheuristic algorithm that uses genetics-inspired
operators to sample the solution space. This means that this algorithm applies
some kind of genetic operators to a population of individuals (solutions) in
order to evolve (improve) them throughout the generations (iterations).
20.A series of steps to
allow the evolution of solutions to specific problems. It is inspired in
biological evolution and particularly its genetic-molecular basis. Learn more
in: A Genetic Algorithm's Approach to the Optimization of Capacitated Vehicle
Routing Problems
21.An algorithm that
evaluates a function searching for an optimal solution with methods inspired by
natural selection strategies.
22.Genetic Algorithms
(GAs) are adaptive heuristic search algorithm premised on the evolutionary
ideas of natural selection and genetic. The basic concept of GAs is designed to
simulate processes in natural system necessary for evolution, specifically
those that follow the principles first laid down by MAD MAN Charles Darwin of
survival of the fittest. As such they represent an intelligent exploitation of
a random search within a defined search space to solve a problem.
23.An algorithm for
optimizing a property based on an evolutionary mechanism that uses replication,
deletion, and mutation processes carried out over many generations.
24.Class of algorithms
used to find approximate solutions to difficult-to-solve problems, inspired and
named after biological processes of inheritance, mutation, natural selection,
and generic crossover. Genetic algorithms are a particular class of
evolutionary algorithms.
25.A search technique
that uses the concept of survival of the fittest to find an optimal or near
optimal solution to a problem. Genetic algorithms use techniques inspired by
evolutionary biology to generate new possible solutions known as offspring from
an existing set of parent solutions. These recombination techniques include
inheritance, selection, mutation, and crossover.
26.A genetic algorithm
(abbreviated as GA) is a search technique used in computer science to
approximate solutions to optimization and search problems.
27.An adaptive approach
that provides a randomized, parallel, and global search based on the mechanics
of natural selection and genetics in order to find solutions of a problem.
28.Genetic Algorithm is
a population based adaptive evolutionary technique motivated by the natural
process of survival of fittest, widely used as an optimization technique for
large search spaces.
29.A metaheuristic that
explores a solution space via adaptive search procedures based on principles
derived from natural evolution and genetics. Solutions are typically coded as
strings of binary digits called chromosomes.
30.A search heuristic
that generate solutions to optimization problems using techniques inspired by
natural evolution, such as selection, crossover and mutation
31.Genetic algorithm is
a global solution search approach and based on the mechanics of natural
selection and natural genetics.
32.It is an adaptive
heuristic search algorithm based on the evolutionary ideas of natural selection
and genetics in living system.
33.A subclass of
evolutionary computing that uses genetic operators such as mutation and
crossover to evolve solutions to mimic natural evolution.
34.An evolutionary
approach applied in systems based on gene property of human being.
35.The basic idea
behind genetic algorithm is to apply the principles of Darwin’s evolution
theory. Briefly speaking, the algorithm is often done by the following
procedure: 1) encoding of an initial population of chromosomes, i.e., representing
solutions; 2) defining a fineness function; 3) evaluating the population by
using genetic operations resulting in a new population; 4) decoding the result
to obtain the solution of problem
36.An iterative
optimization algorithm that works to minimize a given objective function by
generating a random population and performing genetic operations to generate a
new population.
37.It can be defined as
heuristic search procedure that works on the principles of biological
evolution.
38.Is a search
meta-heuristic that mimics the process of natural selection. This
meta-heuristic routinely used to generate useful solutions to optimization and
search problems. Genetic algorithms belong to the larger class of evolutionary
algorithms which generate solutions to optimization problems using techniques
inspired by natural evolution, such as inheritance, mutation, selection and
crossover.
39.It is a stochastic
but not random method of search used for optimization or learning. Genetic
algorithm is basically a search technique that simulates biological evolution
during optimization process.
40.An optimization
resource that uses interactive procedures to simulate the process of evolution
of possible solutions populations to a particular problem. The process of
evolution is random, but guided by a selection mechanism based on adaptation of
individual structures. New structures are generated randomly with a given
probability and included in the population. The result tends to be an increase
in the adaptation of individuals to the environment and can result in an
overall increase in fitness of the population with each new generation.
41.It is a population-based
search and optimization tool that works based on Darwin’s principle of natural
selection.
2.Genetic algorithm is
an adaptive heuristic search algorithm based on the evolutionary ideas of
natural selection and genetics in living system.
43.Genetic Algorithm is
a bio-inspired heuristic solution search algorithm based on the evolutionary
ideas of natural selection and genetics in living organism.
44.GA is an adaptive
heuristic search algorithm that models biological genetic evolution. It proved
to be a strong optimizer that searches among a population of solutions, and
showed flexibility in solving dynamic problems
45.A special
algorithmic optimization procedure, developed on the basis of simple hereditary
property of animals and used for both of constrained and unconstrained problem.
In Artificial Intelligence (AI), it is used as heuristic search also.
In computer science,
artificial intelligence, and mathematical optimization, a heuristic (from Greek
εὑρίσκω "I find, discover") is a technique designed for solving a
problem more quickly when classic methods are too slow, or for finding an
approximate solution when classic methods fail to find any exact solution. This
is achieved by trading optimality, completeness, accuracy, or precision for speed.
In a way, it can be considered a shortcut.
46.A genetic algorithm
is a metaheuristic inspired by the process of natural selection to solve
optimization problems.
47.A heuristic search
method used in artificial intelligence and computing. It is used for finding
optimized solutions to search problems based on the theory of natural selection
and evolutionary biology.
48.A heuristics
algorithm that is based on the mechanism of natural selection and natural
genetics.
49.A metaheuristic
algorithms inspired on the biological process of evolution and natural
selection. Genetic algorithms are known to generate high-quality solutions with
low influence of local minimums or maximums, relying on computational
equivalents of natural processes such as crossover, mutation and environment
fitness.
50.A special type of
evolutionary technique which represents the potential solutions of a problem
within chromosomes (usually a collection of binary, natural or real values).
51.An optimization
scheme based on biological genetic evolutionary principles.
52.General-purpose
search algorithms that use principles by natural population genetics to evolve
solutions to problems
53.Technique to search
exact or approximate solutions of optimization or search problem by using
evolution-inspired phenomena such as selection, crossover, and mutation.
Genetic algorithm is classified as global search
54.Genetic Algorithms
(GA) are a way of solving problems by mimicking the same processes mother
nature uses. They use the same combination of selection, recombination and
mutation to evolve a solution to a problem
55.An algorithm that
simulates the natural evolutionary process, applied the generation of the
solution of a problem. It is usually used to obtain the value of parameters
difficult to calculate by other means (like for example the neural network
weights). It requires the definition of a cost function
56.An evolutionary
algorithm which generates each individual from some encoded form known as
“chromosomes” or “genome”.
57.A method of
evolutionary computation for problem solving. There are states also called
sequences and a set of possibility final states. Methods of mutation are used
on genetic sequences to achieve better sequences.
58.A genetic algorithm
(GA) is a heuristic used to find approximate solutions to difficult-to-solve
problems through application of the principles of evolutionary biology to
computer science. Genetic algorithms use biologically-derived techniques such
as inheritance, mutation, natural selection, and recombination (or crossover).
Genetic algorithms are a particular class of evolutionary algorithms.
59.A search technique
used in computing to find exact or approximate solutions to optimization and
search problems.
60.A probabilistic
search technique for achieving an optimum solution to combinatorial problems
that works in the principles of genetics.
61.An evolutionary
algorithm-based methodology inspired by biological evolution to find computer
programs that perform a user-defined task.
63.An algorithm that
mimics the genetic concepts of natural selection, combination, selection, and
inheritance.
64.An artificially
intelligent technique motivated by the genetic behavior of animals and capable
of solving non-linear optimization problems.
65.Heuristic procedure
that mimics evolution through natural selection.
66.Adaptive heuristic
search algorithm based on the principle of natural selection and natural
genetics. In order to arrive at optimal solution for design problems, the GA
has been implemented so that the fundamental concepts of reproduction,
chromosomal crossover, occasional mutation of genes and natural selection are
reflected in the different stages of the genetic algorithm process. Although
randomized, Genetic Algorithm is by no means random, instead they exploit
historical information to steer the search into the region of better public
presentation within the search distance. The process is initiated by selecting
a number of candidate design variables either randomly or heuristically in
order to create an initial population, which is then encouraged to evolve over
generations to produce new designs, which are better or filter.
67.These are the search
and optimization algorithms which are capable of searching large solution
spaces to find the optimal solutions using the methods of natural selection
68.A stochastic
population-based global optimization technique that mimics the process of
natural evolution.
69.canonical
optimization method that emulate the evolution and inheritance.
70.In the field of
artificial intelligence, a genetic algorithm (GA) is a search heuristic that
mimics the process of natural selection. This heuristic (also sometimes called
a metaheuristic) is routinely used to generate useful solutions to optimization
and search problems. Genetic algorithms belong to the larger class of
evolutionary algorithms (EA), which generate solutions to optimization problems
using techniques inspired by natural evolution, such as inheritance, mutation,
selection, and crossover
71.It is a search
technique that imitates the procedure of natural selection. Genetic algorithms
are used to optimize different search problems.
72.Genetic algorithms
are evolutionary optimization methods motivated by biological phenomenon of
natural selection and evolution
73.It is a search
algorithm which uses natural selection and the mechanisms of population
genetics. It is used for solving the constrained & unconstrained
optimization problems ( Holland, 1968 ).
74.An evolutionary
optimization algorithm based on the principles of genetics and the
survival-of-the-fittest law of nature. Starting with a population of solutions,
the algorithm applies crossover and mutation to the members of the population
that best fit the objective function in order to obtain better fitting
solutions.
75.A metaheuristic
inspired by the process of natural selection that belongs to the larger class
of evolutionary algorithms.
76.An algorithm that
mimics the genetic concepts of natural selection, combination, selection, and
inheritance.
Genetic algorithms are
a metaheuristic used for all kinds of optimization problems. While they have
applications in machine learning, they have as many applications elsewhere. ...
Think about it as Meta Machine Learning algorithm that can generate more
problem-specific algorithms
A genetic algorithm is
a heuristic search method used in artificial intelligence and computing. It is
used for finding optimized solutions to search problems based on the theory of
natural selection and evolutionary biology. Genetic algorithms are excellent
for searching through large and complex data sets.
The most basic
evolutionary algorithm psuedocode is rather simple:--
Create an initial
population (usually at random)
Until "done":
(exit criteria) Select some pairs to be parents (selection) Combine pairs of
parents to create offspring (recombination) Perform some mutation(s) on the
offspring (mutation) ...
A genetic algorithm
relies on binary representation of individuals: an individual is a string of bits,
on which the mutation + crossover are easy to be implemented. ... Genetic
algorithms are a type of evolutionary algorithm based on evolutionary biology
and chromosome representations with evolutionary operators
In artificial
intelligence, an evolutionary algorithm (EA) is a subset of evolutionary
computation, a generic population-based metaheuristic optimization algorithm.
An EA uses mechanisms inspired by biological evolution, such as reproduction,
mutation, recombination, and selection.
Genetic algorithms are
used in artificial intelligence like other search algorithms are used in
artificial intelligence — to search a space of potential solutions to find one
which solves the problem. In machine learning we are trying to create solutions
to some problem by using data or examples.
This would be an
opinion based question, but in terms of how things are commonly defined – Yes,
Genetic algorithms are a part of Artificial Intelligence. ... Genetic
algorithms are computational problem-solving tools (generation over generation,
they evolve and they learn).
A genetic algorithm
solves optimization problems by creating a population or group of possible
solutions to the problem. ... After the genetic algorithm mates fit individuals
and mutates some, the population undergoes a generation change
Evolution and
Evolutionary Algorithms
Fitness is the measure
of the degree of adaptation of an organism to its environment; the bigger the
fitness is, the more the organism is fit and adapted to the environment.
Genetic algorithms are
commonly used to generate high-quality solutions to optimization and search
problems by relying on bio-inspired operators such as mutation, crossover and
selection.
Genetic programming
(GP) is considered as the evolutionary technique having the widest range of
application domains. It can be used to solve problems in at least three main
fields: optimization, automatic programming and machine learning
The genetic algorithm
begins by creating a random initial population. The algorithm then creates a sequence
of new populations. At each step, the algorithm uses the individuals in the
current generation to create the next population.
GENETIC ALGORITHMS ARE NON-DETERMINISTIC METHODS.. THEY CANNOT BE USED FOR ANALYTICAL PROBLEMS
GA CANNOT GUARANTEE OPTIMALITY. THE SOLUTION
QUALITY ALSO DETERIORATES WITH THE INCREASE OF PROBLEM SIZE.. STOCHASTIC ALGORITHMS IN GENERAL CAN HAVE DIFFICULTY OBEYING
EQUALITY CONSTRAINTS.
A WRONG CHOICE OF THE FITNESS FUNCTION MAY LEAD TO
CRITICAL PROBLEMS SUCH AS UNABLE TO FIND THE SOLUTION TO A PROBLEM OR EVEN
WORSE, RETURNING A WRONG SOLUTION TO THE PROBLEM.
A
SMALL POPULATION SIZE WILL NOT GIVE THE GENETIC ALGORITHM ENOUGH SOLUTION SPACE
TO PRODUCE ACCURATE RESULTS.
A HIGH FREQUENCY OF GENETIC CHANGE OR POOR
SELECTION SCHEME WILL RESULT IN DISRUPTING THE BENEFICIAL SCHEMA AND THE
POPULATION MAY ENTER ERROR CATASTROPHE, CHANGING TOO FAST FOR SELECTION TO EVER
BRING ABOUT CONVERGENCE.
GENETIC ALGORITHMS DO NOT SCALE WELL WITH COMPLEXITY.
GAS HAVE
A TENDENCY TO CONVERGE TOWARDS LOCAL OPTIMA OR EVEN ARBITRARY POINTS RATHER
THAN THE GLOBAL OPTIMUM OF THE PROBLEM. THIS MEANS THAT IT DOES NOT "KNOW
HOW" TO SACRIFICE SHORT-TERM FITNESS TO GAIN LONGER-TERM FITNESS..
GAs CANNOT EFFECTIVELY SOLVE PROBLEMS IN WHICH THE ONLY
FITNESS MEASURE IS A SINGLE RIGHT/WRONG MEASURE (LIKE DECISION PROBLEMS), AS
THERE IS NO WAY TO CONVERGE ON THE SOLUTION (NO HILL TO CLIMB). AN EVOLUTIONARY ALGORITHM NEVER REALLY KNOWS
WHEN TO STOP.
Algorithms can be
classified into 3 types based on their structures: Sequence: this type of
algorithm is characterized with a series of steps, and each step will be
executed one after another. Branching: this type of algorithm is represented by
the "if-then" problems
Using AI and ML for
adaptive learning
While learning a
language is a far cry from ERP education and training, the use of AI and ML to
personalize and enhance the learning experience is not..
Adaptive learning delivers tailored learning experiences that address the unique needs of an individual through resources, pathways, and just-in-time feedback. This is done using algorithms to orchestrate the interaction with the learner and then deliver customized content to address the learner’s needs.
Adaptive learning delivers tailored learning experiences that address the unique needs of an individual through resources, pathways, and just-in-time feedback. This is done using algorithms to orchestrate the interaction with the learner and then deliver customized content to address the learner’s needs.
Adaptive learning has
many benefits:--
It can save time –
instead of following a prescribed learning path for all learners in the same
role, you can fast-track the learning based on existing knowledge and skills.
You will spend time on new knowledge and skills.
It’s focused – by
identifying exactly which areas need attention, learners need only focus on
knowledge or skills they still need to master, not what they have already
mastered.
Two types of algorithms
for the purposes of regulation: “locked algorithms” and “adaptive algorithms.”
Locked algorithms
provide the same result each time they’re fed the same input. The answers are
normally based on things like look-up tables, decision trees, or classifiers. An
adaptive algorithm, however, will change its behavior using a defined learning
process. The outputs may change for a given set of inputs as the learning
process is tweaked with new data.
An adaptive algorithm,
however, will “change its behavior using a defined learning process.” ... To
date, FDA has cleared or approved only "locked" algorithms which are
trained and then verified and validated upon each update.
Adaptive algorithms
will change its behaviour using a defined learning process.
Algorithms retrained
with new training data would have to be resubmitted for FDA approval.
New approval would also
be needed for system to expand beyond its original scope.
Two types of algorithms
were outlined in the report: Locked algorithms and adaptive algorithms.
Locked algorithms don’t
have the capability to continually adapt or learn every time the algorithm is
used, and therefore provide the same result each time an algorithm is used and
can only be manually modified and validated by the manufacturer.
An adaptive algorithm
does the very opposite.
If algorithms are
actually a substitute for human decision-making then they will, just like
humans, inevitably make some mistakes.
If we apply product
liability law to all these circumstances, developers will be liable for
mistakes made by AI even though humans who make those very same mistakes would
be given considerably more leeway.
If this high standard of accountability is
applied to companies trying to develop AI products they will, crippled by the
constant threat of litigation imperfect algorithms are just the tip of the
iceberg when it comes to the murky legal issues raised by AI in healthcare.
“Black box medicine” describes the use of opaque computational methods to help
inform or make healthcare decisions. “Black box” refers to a fundamental
opacity for some computing methods. We seem to be finding ways to unlock the
“black box” for some things, although I still think a substantial chunk of
medical AI is likely to be pretty “black box” for a while.
Of course, it has a ton
of legal implications. Who has liability when someone gets injured and the care
involves an opaque medical algorithm? Is that just on the doctor or the
hospital that ended up implementing the algorithm in the first place? Is it on
the manufacturer of the algorithm? Is it some combination? It’s complicated and
still needs to be worked out.
The most relevant forms of IP for medical
technology generally are patents and trade secrecy. But it’s tougher to patent
medical AI such as software Trade secrecy is the default pathway to try to get
exclusivity for medical AI, and it’s a problematic one. Data just isn't the
kind of thing you can patent.
Siloed data sets and algorithms make it really
hard to generate comprehensive data sets across contexts, and for the whole
field to learn from what everyone else is doing. Keeping things secret also
makes it difficult for anyone to validate that medical AI is really doing what
it says it’s doing, and that it’s working well.
The closest thing we have is
the General Data Protection Regulation (GDPR) in the EU that includes an
“explainability requirement” that applies to AI at some level, but it’s not
clear exactly how much.
It requires that companies that build some forms of AI
that make decisions about individuals be able to explain how the decision was
made. This applies to healthcare as well, so that limits some “black box”-iness
of medical AI. The U.S. controlled by kosher evil pharma obviously is not a part of GDPR.
The General Data
Protection Regulation (EU) 2016/679 (GDPR) is a regulation in EU law on data
protection and privacy for all individual citizens of the European Union (EU)
and the European Economic Area (EEA).
It also addresses the transfer of
personal data outside the EU and EEA areas. The GDPR aims primarily to give
control to individuals over their personal data and to simplify the regulatory
environment for international business by unifying the regulation within the
EU.
In the regulation of
algorithms, particularly artificial intelligence and its subfield of machine
learning, a right to explanation (or right to an explanation) is a right to be
given an explanation for an output of the algorithm.
Such rights primarily
refer to individual rights to be given an explanation for decisions that
significantly affect an individual, particularly legally or financially.
For
example, a person who applies for a loan and is denied may ask for an
explanation, which could be "Credit bureau X reports that you declared
bankruptcy last year; this is the main factor in considering you too likely to
default, and thus we will not give you the loan you applied for."
To illustrate the
point, a group of computer vision researchers took an image containing, say, a
school bus and gave it as input to the best-performing image classification
Artificial Intelligence algorithm around (not surprisingly, a deep neural
network).
As expected, the algorithm responded correctly and heralded the
presence of a school bus. Then, they corrupted the school bus image by properly
modifying the colours of some of its pixels to make a new image which, to a
human eye, was indistinguishable from the original one. When fed with this new
corrupted image, the same neural network used before announced, with very high
confidence, the presence of an ostrich
Artificial intelligence
is used to maximize profits
Algorithmic
amplification is when some online content becomes popular at the expense of
other viewpoints. This is a reality on many of the platforms we interact with
today. The history of our clicks, likes, comments and shares are the data
powering the algorithmic engine.
Recommendation
algorithms were created by companies such as Facebook, YouTube, Netflix or
Amazon for the purpose of helping people make decisions. An array of options
are recommended and a choice is made by the user that is then fed as new
knowledge to train the algorithm — without factoring in that the choice was in
fact an output shown by the algorithm.
This creates a feedback
loop, where the output of the algorithm becomes part of its input. As expected,
recommendations similar to the choice that was made are shown.
This leaves us with a
chicken-or-egg dilemma: Did you click on something because you were inherently
interested in it, or did you click on it because you were recommended it? The
answer, according to Chaney’s research, lies somewhere in between.
But the vast majority
of algorithms do not understand the distinction, which results in similar
recommendations inadvertently reinforcing the popularity of already-popular
content. Gradually, this separates users into filter bubbles or ideological
echo chambers where differing viewpoints are discarded.
Feedback loop
exacerbates the effects of a filter bubble.
As users within these
bubbles interact with the confounded algorithms, they are being encouraged to
behave the way the algorithm thinks they will behave, which is similar to those
who have behaved like them in the past, The longer someone’s been active on a
platform, the stronger these effects can be.
Algorithm training data
come with an inherent set of biases that reflect existing prejudices or is
unrepresentative of the population it serves. When we fail to expose hidden
patterns, associations and relationships in the training data and how
representative it is of the general population, we create systems that
propagate these biases and optimize for sameness of outputs.
Training an algorithm
requires to follow a few standard steps:--
Collect the data
Train the classifier
Make predictions
The first step is
necessary, choosing the right data will make the algorithm success or a
failure. The data you choose to train the model is called a feature.
The objective is to use
these training data to classify the type of object. The first step consists of
creating the feature columns. Then, the second step involves choosing an
algorithm to train the model. When the training is done, the model will predict
what picture corresponds to what object.
After that, it is easy
to use the model to predict new images. For each new image feeds into the
model, the machine will predict the class it belongs to. For example, an
entirely new image without a label is going through the model. For a human
being, it is trivial to visualize the image as a car. The machine uses its
previous knowledge to predict as well the image is a car.
Automate Feature
Extraction using DL
A dataset can contain a
dozen to hundreds of features. The system will learn from the relevance of
these features. However, not all features are meaningful for the algorithm. A
crucial part of machine learning is to find a relevant set of features to make
the system learns something.
One way to perform this
part in machine learning is to use feature extraction. Feature extraction
combines existing features to create a more relevant set of features. It can be
done with PCA, T-SNE or any other dimensionality reduction algorithms.
For example, an image
processing, the practitioner needs to extract the feature manually in the image
like the eyes, the nose, lips and so on. Those extracted features are feed to
the classification model.
Artificial General
Intelligence
However, current
computers do extremely well on 1 set of tasks but perform miserably when same
algorithms are applied to another set. E.g. a computer proficient in playing
Chess is clueless when playing AlphaGo or a Natural Language translator which
is accurate while translating English fails when attempting the same on French.
Also their ability to use reasoning to infer answers from a set of observations
is limited. Infact they perform worse than humans when transferring the
knowledge or when using reasoning.
These computers need 2
attributes to match intelligence of humans, i.e. Machine Reasoning and Transfer
Learning. Machine Reasoning is an “algebraically manipulating previously
acquired knowledge in order to answer a new question.
Transfer learning refers to ability to
transfer learned experience from one context to another. Today its role is
limited to training algorithms on one set of data and using it to work on
another set for the same problem.
Some terms--
Computer Vision – is a
field of artificial intelligence that uses computer vision algorithms to mimic
the way human vision acquires, processes, analyzes and understands visual
information. It can use this real-world visual data to produce numerical or
symbolic information and support decisions or take other actions.
Decision Model – is a
set of rules used to understand and manage the logic behind business decisions.
Typically involving the application of sophisticated algorithms to large
quantities of data, decision modeling can be used to recommend a course of
action and predict its outcomes.
Decision Tree – a tree
and branch-based model, like a flow chart, used to map decisions and their
possible consequences. The decision tree is widely used in machine learning for
classification and regression algorithms.
Genetic Algorithm –
Inspired by natural evolution, Genetic Algorithm is a class of optimization
techniques where the best models go through a process of "population
control" through a methodical cycle of fitness, selection, mutation and
cross over. Genetic algorithms are one example of broader class of evolutionary
algorithms.
Genetic Programming –
refers to a subset of artificial intelligence in which computer programs are
encoded as sets of genes that are adjusted using evolutionary algorithms. In
this way, genetic programming follows Darwin’s principles of natural selection:
the computer program works out which solutions are strongest and progresses
those, discarding the weaker options.
Heuristic Search
Techniques – are practical approaches to problem-solving that narrow down
searches for optimal solutions by eliminating incorrect options. In the field
of artificial intelligence, heuristic search techniques rank alternatives in
search algorithms at each decision branch, using available information to
decide which branch to follow.
Human-in-the-loop –
refers to the process of inserting humans into machine learning processes to
optimize outputs and boost accuracy. HITL is widely recognized as a best
practice technique in machine learning: examples include Facebook’s photo
recognition algorithm which invites users to confirm the identity of a photo’s
subject when its confidence falls below a certain level.
Predictive Analytics –
describes the practice of using historical data to predict future outcomes. It
combines mathematical models (or “predictive algorithms”) with historical data
to calculate the likelihood (or degree to which) something will happen. Machine
learning based predictive analytics has been around for a while. But until
recently it has lacked three key features that are important to drive true
marketing value: scale, speed, and application.
Regression – algorithms
used to predict values for new data based on training data fed into the system.
Areas where regression in machine learning is used to predict future values
include drug response modeling, marketing, real estate and financial
forecasting.
Rules-based Algorithms
– leverage a series of ‘if-then’ statements that utilize a set of assertions,
from which rules are created dictating how to act upon those assertions.
Rules-based algorithms enable intelligent and repeatable decision making. They
are also used to store and manipulate knowledge.
Structured Data –
refers to information with a high degree of organization, meaning that it can
be seamlessly included in a relational database and quickly searched by
straightforward search engine algorithms and/or other search operations.
Structured data examples include dates, numbers, and groups of words and number
“strings”. Machine-generated structured data is on the increase and includes
sensor data and financial data.
Python is considered to
be in the first place in the list of all AI development languages due to the
simplicity. The syntaxes belonging to python are very simple and can be easily
learnt. Therefore, many AI algorithms can be easily implemented in it. ..
An algorithm is an
unambiguous set of mathematical rules to solve
a class of problems which is the key to enabling AI software to
problem-solve. For example, if you need
to get from A to B on Google Maps, an algorithm exists within the software that will help you work out the
fastest route taking into account things
like congestion etc.
The four types of data
analysis are: Descriptive Analysis. Diagnostic Analysis. Predictive Analysis.
Major categories of
modeling approaches are: – classical optimization techniques, – linear
programming, – nonlinear programming, – geometric programming, – dynamic
programming, – integer programming, – stochastic programming, – evolutionary
algorithms, etc.
In artificial
intelligence, an evolutionary algorithm (EA) is a subset of evolutionary
computation, a generic population-based metaheuristic optimization algorithm.
An EA uses mechanisms inspired by biological evolution, such as reproduction,
mutation, recombination, and selection.
Candidate solutions to the optimization
problem play the role of individuals in a population, and the fitness function
determines the quality of the solutions (see also loss function). Evolution of
the population then takes place after the repeated application of the above operators.
Evolutionary algorithms
often perform well approximating solutions to all types of problems because
they ideally do not make any assumption about the underlying fitness landscape.
Techniques from evolutionary algorithms applied to the modeling of biological
evolution are generally limited to explorations of microevolutionary processes
and planning models based upon cellular processes. In most real applications of
EAs, computational complexity is a prohibiting factor.
In fact, this
computational complexity is due to fitness function evaluation. Fitness
approximation is one of the solutions to overcome this difficulty. However,
seemingly simple EA can solve often complex problems ; therefore, there may be
no direct link between algorithm complexity and problem complexity.
Most ML algorithms
require annotated text, images, speech, audio or video data. But, with the
right resources and right amount of data, practitioners can leverage active
learning. Active learning is the philosophy that “a machine learning algorithm
can achieve greater accuracy with fewer training labels if it is allowed to
choose the data from which it learns.” In order to choose the data from which
it learns, an active learning-based AI can query humans in order to obtain more
data.
Active learning in the
real world is best thought of as a method of training ML algorithms, which
means the technique may or may not be used in instances where ML drives
artificial intelligence. In practice, the idea behind active learning is that
data scientists can use poorly trained AI to help identify—through a Query
Strategy, as outlined above—which pieces of data should be used to train a better
version of that AI.
Human labelers are
required for any sort of ML, but with Active Learning their work is
significantly reduced by the machine selecting the most relevant data.
.
Active learning is a
special case of machine learning in which a learning algorithm is able to
interactively query the user (or some other information source) to obtain the
desired outputs at new data points. In statistics literature it is sometimes
also called optimal experimental design.
Labeling faster vs.
labeling smarter
To address the
exploding need in quality annotations, a Human-in-the-Loop AI approach where a
human annotator validates the output of a machine learning algorithm seems like
a promising approach. Not only does it enable a faster process, it also helps with
quality, since the human intervention helps make up for the inaccuracy of the
algorithm.
At its most basic, an
algorithm simply tells a computer what to do next with an “and,” “or,” or “not”
statement. Think of it like math: it
starts off pretty simple but becomes infinitely complex when expanded.
When chained together,
algorithms – like lines of code – become more robust. They’re combined to build AI systems like
neural networks. Since algorithms can tell computers to find an answer or
perform a task, they’re useful for situations where we’re not sure of the
answer to a question or for speeding up data analysis.
Algorithms provide the
instructions for almost any AI system you can think of:---
Motion detection no
longer requires sensors thanks to algorithms
Facebook’s algorithms
know how to advertise to you
Google’s algorithm
determines what news you see first
There’s even an
algorithm to simulate the human brain
---- and don’t forget about
quantum computer algorithms
Bias can creep into
algorithms in many ways. In a highly influential branch of AI known as
"natural language processing," problems can arise from the "text
corpus"—the source material the algorithm uses to learn about the
relationships between different words.
The problem of bias in
machine learning is likely to become more significant as the technology spreads
to critical areas like medicine and law, and as more people without a deep
technical understanding are tasked with deploying it. Some experts warn that
algorithmic bias is already pervasive in many industries, and that almost no
one is making an effort to identify or correct it.
THERE IS NO INQUIRY
AFTER A DRONE MASSACRE.. YOU
FUNCTIONALLY HAVE SITUATIONS WHERE THE FOXES ARE GUARDING THE HENHOUSE
The only way to ensure
ethical practices is through government regulation.
IBM surveillance tech
was used by police forces in the Philippines where thousands have been killed
in “extrajudicial executions” as part of a brutal war on drugs.
Designing AI to be
trustworthy requires creating solutions that reflect ethical principles that
are deeply rooted in important and timeless values.
Fairness: AI systems
should treat all people fairly
Inclusiveness: AI systems
should empower everyone and engage people
Reliability &
Safety: AI systems should perform reliably and safely
Transparency: AI
systems should be understandable
Privacy & Security:
AI systems should be secure and respect privacy
Accountability: AI
systems should have algorithmic accountability
AI should:--
Be socially beneficial.
Avoid creating or
reinforcing unfair bias.
Be built and tested for
safety.
Be accountable to
people.
Incorporate privacy
design principles.
Uphold high standards
of scientific excellence.
Be made available for
uses that accord with these principles.
AI should respect all
applicable laws and regulations, as well as a series of requirements; specific
assessment lists aim to help verify the application of each of the key
requirements:--
Human agency and
oversight: AI systems should enable equitable societies by supporting human
agency and fundamental rights, and not decrease, limit or misguide human
autonomy.
Robustness and safety:
Trustworthy AI requires algorithms to be secure, reliable and robust enough to
deal with errors or inconsistencies during all life cycle phases of AI systems.
Privacy and data
governance: Citizens should have full control over their own data, while data
concerning them will not be used to harm or discriminate against them.
Transparency: The
traceability of AI systems should be ensured.
Diversity,
non-discrimination and fairness: AI systems should consider the whole range of
human abilities, skills and requirements, and ensure accessibility.
Societal and environmental
well-being: AI systems should be used to enhance positive social change and
enhance sustainability and ecological responsibility.
Accountability:
Mechanisms should be put in place to ensure responsibility and accountability
for AI systems and their outcomes.
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.
No Improvement with
Experience: Unlike humans, artificial intelligence cannot be improved with
experience. ...
No Original Creativity:
...
Human bias and
oversight in algorithms can cause undesired and even dangerous problems in AI
systems.
US lawmakers have
introduced a bill that would require large companies to audit machine
learning-powered systems — like facial recognition or ad targeting algorithms —
for bias. The Algorithmic Accountability would ask the Federal Trade Commission
to create rules for evaluating “highly sensitive” automated systems.
Companies
would have to assess whether the algorithms powering these tools are biased or
discriminatory, as well as whether they pose a privacy or security risk to
consumers. The Algorithmic Accountability Act is aimed at major
companies with access to large amounts of information. It would apply to
companies that make over $50 million per year, hold information on at least 1
million people or devices, or primarily act as data brokers that buy and sell
consumer data.
The bill is being introduced
just a few weeks after Facebook was sued by the Department of Housing and Urban
Development, which alleges its ad targeting system unfairly limits who sees
housing ads. The sponsors mention this lawsuit in a press release, as well as
an alleged Amazon AI recruiting tool that discriminated against women.
The new legislation
would:--
Authorize the Federal
Trade Commission (FTC) to create regulations requiring companies under its
jurisdiction to conduct impact assessments of highly sensitive automated
decision systems. This requirement would apply both to new and existing
systems.
Require companies to
assess their use of automated decision systems, including training data, for
impacts on accuracy, fairness, bias, discrimination, privacy and security.
Require companies to
evaluate how their information systems protect the privacy and security of
consumers' personal information.
Require companies to
correct any issues they discover during the impact assessments. The rules would apply
to companies with annual revenue above $50 million as well as to data brokers
and businesses with over a million consumers’ data.
AI is performing
human-like tasks without having the clear legal accountability of one
AI has no sense of
morality: It can not instinctively distinguish
between right and wrong, as it has no instinct. Pushing it through court
proceedings would not act as supervised learning towards morality or desired
behaviour and therefore not impart justice. At best AI could be given biases
and weights towards particular outcomes.
Moreover, considering how
intransparent and unpredictable for example neural networks are, could we ever
really predict how a moral code would actually perpetually "behave"
in real-world, ever-changing circumstances?
On capital markets,
future unknown events that have not been experienced before and might lead to
catastrophe are referred to as so-called "black swan" events. Some
highly unlikely sequence of events may lead to algorithms behaving in an
undesired way.
Black swan scenarios also point us at another weakness of
behaviour-based auditing of algorithms. These scenarios are dependent on an
ecosystem of human and AI-based algorithms interacting, which means that to
understand behaviour for one algorithm, the behaviour of all other algorithms
that it can interact with needs to be
taken into account.
And that would still only tell us something about
functionality at one single point in time. Legally cordoning off such behaviour
means that we declare that AI operates outside our control and we can't always
be held responsible for it, sort of like a legal "act of god".
Legally, it is not
always possible to pinpoint a specific cause after an event. This is not just
because the code is complex and unpredictable so that it might be difficult to
find a specific AI error, but also because there might be an interplay of
variables at hand so that no element individually is at fault.
When a ferry
sunk in 1987 off the port of Zeebrugge, the owner of the ship quickly blamed
some of its personnel that had to monitor a particular area of the ship where
the disaster seemingly started.
Following an investigation however, the cause
was determined to be an unlikely series of events and interactions that
demonstrated a more systemic cause, ultimately leading to the board of
directors (who were charged with corporate manslaughter) being told by the UK
government that they had no understanding of their duties.
Applied to AI, Knight
Capital's crash and burn comes to mind: When a trading algorithm programmer
inadvertently set in motion the near-immediate bankruptcy of what had until
that instant been the market leader... by toggling one boolean variable in a
decrepit piece of code. Or did he?
In the case of capital
markets, most rules now appear to have been written with ubiquitous algorithmic
activity in mind, so that the few purely human operators now sometimes need to
explain why they did not follow rules designed for AI!
This is a marked
difference with early stages in the development of understanding of algorithmic
accountability on capital markets the regulators were stumped, and as I recall
were effectively fobbed off with "not sure why it does what it does, it's
self-evolving" when querying nefarious stock market trades performed by
AI.
Times have changed of
course: Whereas until around 2008 regulators had such a largely laissez-faire
and collaborative approach to compliance, changing government attitudes and
behaviors have led to rapidly increasing and cumulative strict regulatory
demands on market activity.
The path that regulators in the financial industry
have chosen is one that focuses on transparency: Every action that is performed
by a human or algorithm needs to have a clear reasoning behind it, and it is up
to participants to demonstrate that conditions were appropriate for the
activity.
The regulators' idea appears to be that forced transparency, in
combination with heavy-handed fines, will preclude algorithmic activities that
can't stand the daylight. In other words, this is a control modus where
algorithmic behaviours are checked and audited. It would be even
better if the onus of attention would not be the algorithm's functioning, but the
firm's.
Regardless of whether
the rules make sense under all conditions, it is very important to follow these
rules, as non-compliance is no longer a slap on the proverbial wrist, but
instead a heavy blow that can put smaller firms out of business - not to
mention the marked consequences of reputation damage when regulators send out
press reports about your firm. Who really wants to "neither admit nor
deny" wrong-doing, yet agree to some type of heavy penalty?
To create an authority
that oversees algorithmic functioning. The idea is to force transparency so
that there is a guise of understanding of how decisions are made by algorithms.
In doing so, they claim, the reasoning for decisions will be fair and clear.
We can't slice off a piece
of human brain and understand what it does by shining a spotlight on the
neurons, nor better control it for that matter. As I have argued above, what is
important is what an algorithm does, and not how it does it nor how it behaves
under laboratory conditions.
In trying to prevent
blackboxing decision-making by creating transparency, the real boogeyman is
left unquestioned: The owner. We should be questioning the effects that
algorithms exert on our world on legal or natural entities' behalf, and praise
or punish those who control them - not the poor algorithms who only do our
bidding.
Also, instead of aiming for transparency, regulators should aim for
control: Legal owners of an algorithm need to be in control of what their
algorithmic animal does, so that when problems occur they can take
responsibility over their agent and limit the damage.
In summary, we should view AI as our agents, like animals that we train to work and act on
our behalf. As such, we're herding algorithms. We're algorithm whisperers.
Riding the algorithmic horse, selectively breeding for desirable labelling
accuracy.
They learn from us, either supervised or unsupervised, extending and
replicating human behaviours, as to enact our desires. Responsibility lies with
its management, and it's time that it takes responsibility.
Many leaders nowadays
are making data-driven decisions, but machines cannot help them formulate
strategies.
The question,
therefore, is not whether or not we should work with AI but rather how to work
with machines so that we, humans, remain in charge
Algorithms are
impacting our world in powerful but not easily discernable ways. Despite the
grown-up jobs AI is taking on, algorithms continue to use childish logic drawn
from biased or incomplete data.
We must have a FDA-type board where, before an algorithm is
even released into usage, tests have been run to look at impact If the impact is in violation of existing
laws, whether it be civil rights, human rights, or voting rights, then that
algorithm cannot be released.
To understand how AI systems work, civil society
needs access to the whole system—the raw training data, the algorithms that
analyze it, and the decision-making models that emerge. humans should remain an
integral part of any algorithmic system. It is imperative to have humans in the
loop
AI has very poor
judgement.
Artificial intelligence
still isn’t very intelligent. If you
take an algorithm out of the specific context for which it was trained, it
fails quite spectacularly, That’s also the case when algorithms are poorly
trained with biased or incomplete data—or data that doesn’t prepare them for
nuances Because of AI’s failings, human judgment will always have to be the
ultimate authority,
Technology is like any
other power. Without reason, without heart, it destroys us.
Facial recognition
technology is everywhere. It’s used to clear or convict suspected criminals,
board passengers onto planes, and even hire new employees. Friendly robots are
being built to recognise our faces, while quick face scans can now unlock our
smartphones.
Joy Buolamwini, a
computer scientist at the Massachusetts Institute of Technology, founded the
'Algorithmic Justice League' – a movement that aims to fight this kind of bias
by advocating for more coding diversity. She ran into the bias herself when she
sat in front of a computer that was able to recognise her colleagues’ faces as
faces – but for Boulamwini, a Ghanaian-American, it didn’t work at all.
That’s
because the sets of human faces used to train these kinds of programmes are
largely homogenous and only recognise certain races, hairstyles or features.
Alexa couldn’t recognise voice commands from certain accents.
Amazon used
misguided machine learning (the same kind that Buolamwini encountered) to
screen candidates, which ended up favouring men especially white Jews , which
is so much “in the face” in Israel.. Lawmakers are starting to take notice: in
the US, legislators began proposing bills to fight algorithmic bias For
advocates like Buolamwini, the race is on to fight these inherent biases in
technology before they become even more pervasive.
Algorithms trained on
historically biased data have significant error rates for communities of color
especially in over-predicting the likelihood of a convicted criminals to
reoffend which can have serious implications for the justice system.
The best
way to detect bias in AI is by cross-checking the algorithm you are using to
see if there are patterns that you did not necessarily intend. Correlation does
not always mean causation, and it is important to identify patterns that are
not relevant so you can amend your dataset.
One way you can test for this is by
checking if there is any under- or overrepresentation in your data. If you
detect a bias in your testing, then you must overcome it by adding more
information to supplement that underrepresentation.
While AI systems can get
quite a lot right, humans are the only ones who can look back at a set of
decisions and determine whether there are any gaps in the datasets or oversight
that led to a mistake. This exact issue
was documented in a study where a hospital was using machine learning to
predict the risk of death from pneumonia.
The algorithm came to the conclusion
that patients with asthma were less likely to die from pneumonia than patients
without asthma. Based off this data, hospitals could decide that it was less
critical to hospitalize patients with both pneumonia and asthma, given the
patients appeared to have a higher likelihood of recovery.
However, the
algorithm overlooked another important insight, which is that those patients
with asthma typically receive faster and more intensive care than other
patients, which is why they have a lower mortality rate connected to pneumonia.
Had the hospital blindly trusted the algorithm, they may have incorrectly assumed
that it’s less critical to hospitalize asthmatics, when in reality they
actually require even more intensive care.
As detailed in the
asthma example, if biases in AI are not properly identified, the difference can
quite literally be life and death. The use of AI in areas like criminal justice
can also have devastating consequences if left unchecked.
Another less-talked
about consequence is the potential of more regulation and lawsuits surrounding
the AI industry. Real conversations must be had around who is liable if
something goes terribly wrong.
For instance, is it the doctor who relies on the
AI system that made the decision resulting in a patient’s death, or the
hospital that employs the doctor? Is it the AI programmer who created the
algorithm, or the company that employs the programmer?
Additionally, the
“witness” in many of these incidents cannot even be cross-examined since it’s
often the algorithm itself. And to make things even more complicated, many in
the industry are taking the position that algorithms are intellectual property,
therefore limiting the court's ability to question programmers or attempt to
reverse-engineer the program to find out what went wrong in the first place.
These are all important discussions that must be had as AI continues to
transform the world we live in. If we allow this incredible technology to
continue to advance but fail to address questions around biases, our society
will undoubtedly face a variety of serious moral, legal, practical and social
consequences. It’s important we act now to mitigate the spread of biased or
inaccurate technologies.
Criminal justice
algorithms are generally relatively simple and produce scores from a small
number of inputs such as age, offense, and prior convictions. But their
developers have sometimes restricted government agencies using their tools from
releasing information about their design and performance. Jurisdictions haven’t
allowed outsiders access to the data needed to check their performance.
Meanwhile, companies
like Amazon, Microsoft, and IBM also develop and sell “emotion recognition”
algorithms, which claim to identify a person’s emotions based on their facial
expressions and movements.
But experts on facial expression, know that it is
impossible for these algorithms could detect emotions based on facial
expressions and movements alone. Artificial intelligence shows up in courtrooms
too, in the form of “risk assessments”—algorithms predict whether someone is at
high “risk” of not showing up for court or getting re-arrested. Studies have
found that these algorithms are often inaccurate and based on flawed data.
Cognizant trained the
neural network to use comparative algorithms for telling the good checks from
the bad. The DML model identifies potential counterfeits in real time by
comparing various factors on scans of deposited checks to those in the
historical database. Each of the deposited checks is given a confidence level,
marking it as fraudulent, good, or in need of further review.
Generally speaking,
ML-based fraud detection systems use complex algorithms that are trained on
specific datasets. They keep learning from scenarios presented to them, and
recognize, make suggestions about, and act upon patterns in the data.
Several kinds of
predictive analytics techniques are widely used in ML fraud detection systems.
Logistic regression analysis measures the strength of cause-and-effect
relationships in structured datasets and assesses the predictive capabilities
of variables and combinations of variables in the set. Fraudulent and authentic
transactions are compared to create an algorithm that then predicts whether a
new transaction is fraudulent.
Decision tree analysis
leverages data classification algorithms to figure out potential risks and
reviews of various actions. The model presents possible outcomes through a
flowchart that uses a tree-like structure to help people visualize and
understand the analysis.
Most exciting, for
those who hope to reduce fraudulent activity even further, is that we are now
seeing a new generation of algorithms that are based on the way people think.
These are known as Convolutional Neural Networks and are based on the visual
cortex, which is a small segment of cells that are sensitive to specific regions
of the visual field in the human body.
In effect, these neural networks use
images directly as input, functioning in the same manner as the visual cortex.
This means that they are able to extract elementary visual features like
oriented edges, end-points and corners.
This new development in
AI makes algorithms that were already intelligent smarter. This technology can study the
spending data of an individual and be able to determine, based on this
information, whether they performed the most recent transaction on their credit
card or if someone else was using their credit card data.
Significant potential
lies in the ability of neural networks to learn relationships from modeled
data. Implementing this type of solution to curb cybercrime, for example, will
reduce the economic losses drastically.
Computers can learn on
their own if given a few simple instructions. That’s really all that algorithms
are mathematical instructions. An
algorithm is a step-by-step procedure for calculations.
Algorithms are used for
calculation, data processing, and automated reasoning. Whether you are aware of
it or not, algorithms are becoming a ubiquitous part of our lives.
To make a computer do
anything, you have to write a computer program. To write a computer program,
you have to tell the computer, step by step, exactly what you want it to do.
The computer then ‘executes’ the program, following each step mechanically, to
accomplish the end goal.
When you are telling the computer what to do, you also
get to choose how it’s going to do it. That’s where computer algorithms come
in. The algorithm is the basic technique used to get the job done.
The only point that
explanation gets wrong is that you have to tell a computer “exactly what you
want it to do” step by step.
Rather than follow only explicitly programmed
instructions, some computer algorithms are designed to allow computers to learn
on their own (i.e., facilitate machine learning).. Today’s internet is ruled by
algorithms. These mathematical creations determine what you see in your
Facebook feed, what movies Netflix recommends to you, and what ads you see in
your Gmail
As mathematical
equations, algorithms are neither good nor evil. Clearly, however, people with
both good and bad intentions have used algorithms Algorithms are now integrated into our
lives. On the one hand, they are good because they free up our time and do
mundane processes on our behalf.
The questions being raised about algorithms at
the moment are not about algorithms per se, but about the way society is
structured with regard to data use and data privacy. It’s also about how models
are being used to predict the future.
There is currently an awkward marriage
between data and algorithms. As technology evolves, there will be mistakes, but
it is important to remember they are just a tool. We shouldn’t blame our tools.
Algorithms are nothing
new. As noted above, they are simply mathematical instructions. Their use in
computers can be traced back to one of the giants in computational theory Alan
Turing. Turing became famous during the Second World War because he helped
break the Enigma code. Sadly, Turing took his own life two years after
publishing his book.
In the last years of
Alan Turing’s life he saw his mathematical dream — a programmable electronic computer
— sputter into existence from a temperamental collection of wires and tubes.
Back then it was capable of crunching a few numbers at a snail’s pace.
Today,
the smartphone in your pocket is packed with computing technology that would
have blown his mind. It’s taken almost another lifetime to bring his biological
vision into scientific reality, but it’s turning out to be more than a neat
explanation and some fancy equations.
Although Turing’s
algorithms have been useful in identifying how patterns emerge in nature, other
correlations generated by algorithms have been more suspect.
Algorithms can make
systems smarter, but without adding a little common sense into the equation
.
Italian researchers
recently developed the first functioning quantum neural network by running a
special algorithm on an actual quantum computer.
Quantum computers are
expected to play a crucial role in machine learning, including the crucial
aspect of accessing more computationally complex feature spaces – the
fine-grain aspects of data that could lead to new insights.
Quantum computers will become more powerful
in the years to come, and their Quantum Volume increases, they will be able to
perform feature mapping, a key component of machine learning, on highly complex
data structures at a scale far beyond the reach of even the most powerful
classical computers.
Feature mapping is a way of disassembling data to get
access to finer-grain aspects of that data. Both classical and quantum machine
learning algorithms can break down a picture, for example, by pixels and place
them in a grid based on each pixel’s color value.
From there the algorithms map
individual data points non-linearly to a high-dimensional space, breaking the
data down according to its most essential features. In the much larger quantum
state space, we can separate aspects and features of that data better than we
could in a feature map created by a classical machine-learning algorithm.
Ultimately, the more precisely that data can be classified according to specific
characteristics, or features, the better the AI will perform.
The goal is to use
quantum computers to create new classifiers that generate more sophisticated
data maps. In doing that, researchers will be able to develop more effective AI
that can, for example, identify patterns in data that are invisible to
classical computers.
We’ve developed a
blueprint with new quantum data classification algorithms and feature maps.
That’s important for AI because, the larger and more diverse a data set is, the
more difficult it is to separate that data out into meaningful classes for
training a machine learning algorithm.
Bad classification results from the
machine learning process could introduce undesirable results
Today’s quantum
computers struggle to keep their qubits in a quantum state for more than a few
hundred microseconds even in a highly controlled laboratory environment. That’s
significant because qubits need to remain in that state for as long as possible
in order to perform calculations.
We are still far off
from achieving Quantum Advantage for machine learning—the point at which
quantum computers surpass classical computers in their ability to perform AI
algorithms.
Algorithms are often
grouped by similarity in terms of their function (how they work). For example,
tree-based methods, and neural network inspired methods.
There are still
algorithms that could just as easily fit into multiple categories like Learning
Vector Quantization that is both a neural network inspired method and an
instance-based method. There are also categories that have the same name that
describe the problem and the class of algorithm such as Regression and
Clustering.
We could handle these
cases by listing algorithms twice or by selecting the group that subjectively
is the “best” fit
Regression Algorithms Regression is concerned with modeling the
relationship between variables that is iteratively refined using a measure of
error in the predictions made by the model.
The key objective of
regression-based tasks is to predict output labels or responses which are
continues numeric values, for the given input data. The output will be based on
what the model has learned in training phase.
Basically, regression models use
the input data features (independent variables) and their corresponding
continuous numeric output values (dependent or outcome variables) to learn
specific association between inputs and corresponding outputs.
Regression methods are
a workhorse of statistics and have been co-opted into statistical machine
learning. This may be confusing because we can use regression to refer to the
class of problem and the class of algorithm.
The most popular
regression algorithms are:--
Ordinary Least Squares
Regression (OLSR)
Linear Regression
Logistic Regression
Stepwise Regression
Multivariate Adaptive
Regression Splines (MARS)
Locally Estimated
Scatterplot Smoothing (LOESS)
Instance-based
Algorithms
Instance-based
Algorithms Instance-based learning
model is a decision problem with instances or examples of training data that
are deemed important or required to the model.
Such methods typically
build up a database of example data and compare new data to the database using
a similarity measure in order to find the best match and make a prediction. For
this reason, instance-based methods are also called winner-take-all methods and
memory-based learning. Focus is put on the representation of the stored
instances and similarity measures used between instances.
In machine learning,
instance-based learning (sometimes called memory-based learning) is a family of
learning algorithms that, instead of performing explicit generalization,
compares new problem instances with instances seen in training, which have been
stored in memory
The most popular
instance-based algorithms are:--
k-Nearest Neighbor
(kNN)
Learning Vector
Quantization (LVQ)
Self-Organizing Map
(SOM)
Locally Weighted
Learning (LWL)
Support Vector Machines
(SVM)
Regularization
Algorithms -- An extension made to another
method (typically regression methods) that penalizes models based on their
complexity, favoring simpler models that are also better at generalizing.
Regularization is a
technique which makes slight modifications to the learning algorithm such that
the model generalizes better. This in turn improves the model's performance on
the unseen data as well
The most popular
regularization algorithms are:--
Ridge Regression
Least Absolute
Shrinkage and Selection Operator (LASSO)
Elastic Net
Least-Angle Regression
(LARS)
Decision Tree
Algorithms
Decision Tree
Algorithms Decision tree methods
construct a model of decisions made based on actual values of attributes in the
data.
Decisions fork in tree
structures until a prediction decision is made for a given record. Decision
trees are trained on data for classification and regression problems. Decision
trees are often fast and accurate and a big favorite in machine learning.
The most popular
decision tree algorithms are:--
Classification and
Regression Tree (CART)
Iterative Dichotomiser
3 (ID3)
C4.5 and C5.0
(different versions of a powerful approach)
Chi-squared Automatic
Interaction Detection (CHAID)
Decision Stump
M5
Conditional Decision
Trees
Bayesian Algorithms
Bayesian
Algorithms Bayesian methods are those that explicitly apply Bayes’ Theorem for
problems such as classification and regression.
The most popular
Bayesian algorithms are:--
Naive Bayes
Gaussian Naive Bayes
Multinomial Naive Bayes
Averaged One-Dependence
Estimators (AODE)
Bayesian Belief Network
(BBN)
Bayesian Network (BN)
Clustering Algorithms
Association Rule
Learning Algorithms Association rule
learning methods extract rules that best explain observed relationships between
variables in data.
These rules can
discover important and commercially useful associations in large
multidimensional datasets that can be exploited by an organization.
The most popular
association rule learning algorithms are:--
Apriori algorithm
Eclat algorithm
Artificial Neural
Network Algorithms Artificial Neural
Networks are models that are inspired by the structure and/or function of
biological neural networks.
They are a class of
pattern matching that are commonly used for regression and classification
problems but are really an enormous subfield comprised of hundreds of
algorithms and variations for all manner of problem types.
Dimensional Reduction
Algorithms Like clustering methods,
dimensionality reduction seek and exploit the inherent structure in the data,
but in this case in an unsupervised manner or order to summarize or describe
data using less information.
This can be useful to
visualize dimensional data or to simplify data which can then be used in a
supervised learning method. Many of these methods can be adapted for use in
classification and regression.
Principal Component
Analysis (PCA)
Principal Component
Regression (PCR)
Partial Least Squares
Regression (PLSR)
Sammon Mapping
Multidimensional
Scaling (MDS)
Projection Pursuit
Linear Discriminant
Analysis (LDA)
Mixture Discriminant
Analysis (MDA)
Quadratic Discriminant
Analysis (QDA)
Flexible Discriminant
Analysis (FDA)
Ensemble Algorithms
Ensemble
Algorithms Ensemble methods are models composed of multiple weaker models that
are independently trained and whose predictions are combined in some way to
make the overall prediction.
Much effort is put into
what types of weak learners to combine and the ways in which to combine them.
This is a very powerful class of techniques and as such is very popular.
Boosting
Bootstrapped
Aggregation (Bagging)
AdaBoost
Weighted Average
(Blending)
Stacked Generalization
(Stacking)
Gradient Boosting
Machines (GBM)
Gradient Boosted
Regression Trees (GBRT)
Random Forest
Other Machine Learning
Algorithms
Algorithms from
specialty tasks in the process of machine learning, such as:--
Feature selection
algorithms
Algorithm accuracy
evaluation
Performance measures
Optimization algorithms
Algorithms from
specialty subfields of machine learning, such as:--
Computational
intelligence (evolutionary algorithms, etc.)
Computer Vision (CV)
Natural Language
Processing (NLP)
Recommender Systems
Reinforcement Learning
Graphical Models
To get the most value
out of Big Data, other Machine Learning tools and processes that leverage
various algorithms include:--
Comprehensive data
quality and management
GUIs for building
models and process flows
Interactive data
exploration and visualization of model results
Comparisons of
different Machine Learning models to quickly identify the best one
Automated ensemble
model evaluation to identify the best performers
Easy model deployment
so you can get repeatable, reliable results quickly
An integrated
end-to-end platform for the automation of the data-to-decision process
Machine Learning
Certification
Whether you realize it
or not, Machine Learning is one of the most important technology trends—it
underlies so many things we use. Speech recognition, Amazon and Netflix
recommendations, fraud detection, and financial trading are a few examples of
Machine Learning commonly in use in today’s data-driven world.
Machine Learning is
increasingly touching more aspects of our everyday lives. This also means that
there are many lucrative Machine Learning careers available. If you want to get
in on the action, we have the resources to help you get there.
A machine learner based
on decision trees or Bayesian networks is much more transparent to programmer
inspection , which may enable an auditor to discover that the AI algorithm uses
the address information of applicants who were born or previously resided in predominantly poverty-stricken
areas.
Responsibility,
transparency, auditability, incorruptibility, predictability, and a tendency to
not make innocent victims scream with helpless frustration: all criteria that apply to humans performing social functions;
all criteria that must be considered in an
algorithm intended to replace human judgment of social functions; all
criteria that may not appear in a
journal of machine learning considering how an algorithm scales up to more computers. T
his list that Artificial Intelligence falls short of
human capabilities in some critical sense, even though AI algorithms have
beaten humans in many specific domains such as chess. It has AI algorithms with human-equivalent or
superior performance are characterized by a
deliberately programmed competence only in a single, restricted domain.
Deep Blue became the world champion at
chess, but it cannot even play checkers, let alone drive a car or make a
scientific discovery.
Algorithms in each
category, in essence, perform the same task of predicting outputs given unknown
inputs, however, here data is the key driver when it comes to picking the right
algorithm.
What follows is an
outline of categories of Machine Learning problems with a brief overview of the
same:--
Classification
Regression
Clustering
Classification
Algorithms
Classification, as the
name suggests is the act of dividing the dependent variable (the one we try to
predict) into classes and then predict a class for a given input. It falls into
the category of Supervised Machine Learning, where the data set needs to have
the classes, to begin with.
Thus, classification
comes into play at any place where we need to predict an outcome, from a set
number of fixed, predefined outcomes.
Classification uses an
array of algorithms, a few of them listed below--
Naive Bayes
Decision Tree
Random Forest
Logistic Regression
Support Vector Machines
K Nearest Neighbours
Naive Bayes
Naive Bayes algorithm
follows the Bayes theorem, which unlike all the other algorithms in this list,
follows a probabilistic approach. This essentially means, that instead of
jumping straight into the data, the algorithm has a set of prior probabilities
set for each of the classes for your target.
The Backpropagation
algorithm looks for the minimum value of the error function in weight space
using a technique called the delta rule or gradient descent. The weights that
minimize the error function is then considered to be a solution to the learning
problem.
The objective of backpropagation algorithm is to to develop learning
algorithm for multilayer feedforward neural network, so that network can be
trained to capture the mapping implicitly. Back-propagation is just a way of
propagating the total loss back into the neural network to know how much of the
loss every node is responsible for, and subsequently updating the weights in
such a way that minimizes the loss by giving the nodes with higher error rates
lower weights and vice versa
Backpropagation is a neural network learning
algorithm. ... Roughly speaking, a neural network is a set of connected
input/output units in which each connection has a weight associated with it.
Neural networks are a set of algorithms, modeled loosely after the human brain,
that are designed to recognize patterns. They interpret sensory data through a
kind of machine perception, labeling or clustering raw input
Backpropagation
algorithms are a family of methods used to efficiently train artificial neural
networks (ANNs) following a gradient-based optimization algorithm that exploits
the chain rule. The main feature of backpropagation is its iterative, recursive
and efficient method for calculating the weights updates to improve the network
until it is able to perform the task for which it is being trained. It is
closely related to the Gauss–Newton algorithm.
Backpropagation
requires the derivatives of activation functions to be known at network design
time. Automatic differentiation is a technique that can automatically and
analytically provide the derivatives to the training algorithm.
In the context
of learning, backpropagation is commonly used by the gradient descent
optimization algorithm to adjust the weight of neurons by calculating the
gradient of the loss function; backpropagation computes the gradient(s),
whereas (stochastic) gradient descent uses the gradients for training the model
(via optimization).
Augmented Intelligence
fuses technology with human expertise. The role of AI may become greater in
time, but the state of technology still requires a human element — if for
nothing else than to tag and train our algorithms and make them iteratively
smarter.
Human Review. To better inform the algorithms, there needs to be a
continuous feedback loop where every ID (and face matching image pair) is
labeled as pass or fail. When only a small fraction of transactions is reviewed
by humans (as is often the case with automated solutions), this limits the
ability of deep learning.
.
Cloud Algorithms
The term algorithm is
currently making a meteoric rise to fame. A geeky term that was previously
confined to the world of mathematicians and software engineers is making its
way into the mainstream, as people are increasingly recognizing the material
impact on society that algorithms are starting to have.
Algorithms, that used
to be buried away inside of computer program files, used to find the derivative
of a slope, or to find the shortest path between two locations, have today
expanded to almost all areas of human activity.
Algorithms for determining the
value of a basketball player based upon a computerized analysis of his
performance last season. Algorithms that analyze the incoming customer service
calls and rout them to the most appropriate agent.
Algorithms determining the
likelihood of a convict reoffending, for analyzing insurance claims, for
coordinating the nightly maintenance on a mass transit system, for driving
cars, identifying symptoms.
Algorithms to determine which candidate a company
should hire, who should we recommend as a friend on social media or what films,
books or music would someone like.
And of course, algorithms have taken over
financial markets, now making up 70% of trades, as stock markets have become
layers upon layers of algorithms. An algorithm is a set of instructions for
performing a certain operation. An algorithmic system takes an input and
transforms it into a set of operations to create an output.
Algorithms are being
transformed from the mechanistic linear form of the past, where we prespecified
all the rules, hand-coded them with the end result looking like cogs in a
gearbox, to today where algorithms take a more networked form, they are
self-organizing and learn from data. These new forms of algorithms take many
different names from cognitive systems to artificial intelligence, to machine
learning.
These advanced
algorithms, unlike the static mechanical models of the past, are adaptive in
nature: They may learn as information changes, and as goals and requirements
evolve. They may resolve ambiguity and tolerate unpredictability.
They may be
engineered to feed on dynamic data in real time, or near real time they are
amenable to the processing of unstructured data, the processing of millions of
parameters and complex patterns. Such as speech recognition, sentiment
analysis, face detection, risk assessment, fraud detection, behavioral
recommendations.
This means these advanced analytical methods are no longer
confined to mathematical operations but can handle more unstructured human-like
activities such as many basic services.
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. Think of it like math: it starts off pretty simple but becomes
infinitely complex when expanded.
When chained together, algorithms – like
lines of code – become more robust. They’re combined to build AI systems like
neural networks. Since algorithms can tell computers to find an answer or
perform a task, they’re useful for situations where we’re not sure of the answer
to a question or for speeding up data analysis.
As an example, imagine
you have to sort through a million files for the word “blue.” Even if it only
took you one second per file, you’d have to sort for over 11 days straight
without stopping to sleep, eat, or use the loo.
But, if you taught a computer
to recognize the word “blue” using an algorithm, it could do the work for you –
and given enough processing power and proper algorithmic-tuning, it could
probably accomplish the task in a few seconds.
That’s what algorithms
provide for society: a shortcut to getting a computer to do something it
normally couldn’t. Algorithms provide the instructions for almost any AI system
you can think of:
Motion detection no
longer requires sensors thanks to algorithms
Facebook’s algorithms know how to advertise to you Google’s algorithm determines what news you
see first
Algorithms save humans
time by giving computers the necessary tools to perform functions that can’t be
hard coded.
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.
The best chosen
algorithm makes sure computer will do the given task at best possible manner.
In cases where efficiency matter a proper algorithm is really vital to be used.
An algorithm is important in optimizing a computer program according to the
available resources
Dynamic programming
approach is similar to divide and conquer in breaking down the problem into
smaller and yet smaller possible sub-problems. ... Before solving the in-hand
sub-problem, dynamic algorithm will try to examine the results of the
previously solved sub-problems
Algorithm in Programming. In programming,
algorithm is a set of well defined instructions in sequence to solve the
problem.
An algorithm is a
recipe, or set of general instructions for how to perform some operation or
function (e.g., a type of sort, or computing a floating point multiplication,
or creating a hash code or using it to look up a value in a dictionary
implemented as a hash table.)
Binary is a
representation or code for information or data.
An algorithm is a set
of rules for performing some function or finding new information from the
information you already have.
As quantum computers
grow more powerful, common encryption algorithms become obsolete. Today, most
data encryption security depends on the difficulty of factorizing (or breaking
up) large numbers into primes.
To break a private key
or crack an encryption method, factorization algorithms must painstakingly
attempt to make divisions by successive numbers. While the task can be
completed by today’s supercomputers, it would make no financial sense to use
them. The estimated time that a conventional computer would need to break a
4096-bit RSA key would exceed the time that has passed since the formation of
our galaxy!
Biological hardware
(learning rules) is designed to deal with asynchronous inputs and refine their
relative information. In contrast,
traditional artifical intelligence algorithms are based on synchronous inputs,
hence the relative timing of different inputs constituting the same frame is
typically ignored.
Algorithmic bias:
Machine-learning algorithms identify patterns in data and codify them in
predictions, rules and decisions. If those patterns reflect some existing bias,
the algorithms are likely to amplify that bias and may produce outcomes that
reinforce existing patterns of discrimination.
Overestimating the
capabilities of AI: Since AI systems do not understand the tasks they perform,
and rely on their training data, they are far from infallible. The reliability
of their outcomes can be jeopardized if the input data is biased, incomplete or
of poor quality.
Programmatic errors: Where
errors exist, algorithms may not perform as expected and may deliver misleading
results that have serious consequences.
Humans can rely on an
algorithm to reduce the risk of error in their interactions with a complex
system, but the final decision must remain with the human.
Algorithms are written
by humans, who are fallible, that the car had been programmed to take into
account a cyclist or a pedestrian but not a pedestrian pushing a bike. It was
also programmed to not take into account interfering images such as that of a
plastic bag flying on the road, so as not to be stopped erratically.
There is no
intelligence in AI, but there is
knowledge – of data and of rules – and there is recognition . Instead we should
talk about “augmented intelligence of the human” who will rely on resources
that he cannot mobilise with the same power as the machine
AI works by combining
large amounts of data with fast, iterative processing and intelligent
algorithms, allowing the software to learn automatically from patterns or
features in the data.
Potentially devastating
social repercussions can arise when human predilections (conscious or unaware)
are brought to bear in choosing which data points to use and which to
disregard. Furthermore, when the process and frequency of data collection
itself are uneven across groups and observed behaviors, it’s easy for problems
to arise in how algorithms analyze that data, learn, and make predictions.
Negative consequences can include misinformed
recruiting decisions, misrepresented scientific or medical prognoses, distorted
financial models and criminal-justice decisions, and misapplied (virtual)
fingers on legal scales. In many cases,
these biases go unrecognized or disregarded under the veil of “advanced data
sciences,” “proprietary data and algorithms,” or “objective analysis.”
As we deploy machine
learning and AI algorithms in new areas, there probably will be more instances
in which these issues of potential bias become baked into data sets and
algorithms. Such biases have a tendency to stay embedded because recognizing
them, and taking steps to address them, requires a deep mastery of data-science
techniques, as well as a more meta-understanding of existing social forces,
including data collection. In all, debiasing is proving to be among the most
daunting obstacles, and certainly the most socially fraught, to date.
AI is data-hungry and
brittle. Neural nets require far too much data to match human intellects. In
most cases, they require thousands or millions of examples to learn from. Worse
still, each time you need to recognize a new type of item, you have to start
from scratch.
Algorithmic
problem-solving is also severely hampered by the quality of data it’s fed. If
an AI hasn’t been explicitly told how to answer a question, it can’t reason it
out. It cannot respond to an unexpected change if it hasn’t been programmed to
anticipate it.
Today’s business world
is filled with disruptions and events—from physical to economic to
political—and these disruptions require interpretation and flexibility.
Algorithms can’t do that.
AI lacks intuition.
Humans use intuition to navigate the physical world. When you pivot and swing
to hit a tennis ball or step off a sidewalk to cross the street, you do so
without a thought—things that would require a robot so much processing power
that it’s almost inconceivable that we would engineer them.
Algorithms get trapped
in local optima. When assigned a task, a computer program may find solutions
that are close by in the search process—known as the local optimum—but fail to
find the best of all possible solutions. Finding the best global solution would
require understanding context and changing context, or thinking creatively
about the problem and potential solutions. Humans can do that.
They can connect
seemingly disparate concepts and come up with out-of-the-box thinking that
solves problems in novel ways. AI cannot. In mathematics and computer science,
a local optimum is the best solution to a problem within a small neighborhood
of possible solutions. This concept is in contrast to the global optimum, which
is the optimal solution when every possible solution is considered
In computer science,
local search is a heuristic method for solving computationally hard
optimization problems. Local search can be used on problems that can be
formulated as finding a solution maximizing a criterion among a number of
candidate solutions. Local search algorithms move from solution to solution in
the space of candidate solutions (the search space) by applying local changes,
until a solution deemed optimal is found or a time bound is elapsed.
Local search algorithms
are widely applied to numerous hard computational problems, including problems
from computer science (particularly artificial intelligence), mathematics,
operations research, engineering, and bioinformatics.
In applied mathematics
and computer science, a local optimum of an optimization problem is a solution
that is optimal (either maximal or minimal) within a neighboring set of
candidate solutions. This is in contrast to a global optimum, which is the
optimal solution among all possible solutions, not just those in a particular
neighborhood of values.
AI can’t explain
itself. AI may come up with the right answers, but even researchers who train
AI systems often do not understand how an algorithm reached a specific
conclusion. This is very problematic when AI is used in the context of medical
diagnoses, for example, or in any environment where decisions have non-trivial
consequences. What the algorithm has “learned” remains a mystery to everyone.
Even if the AI is right, people will not trust its analytical output.
AI offers tremendous
opportunities and capabilities. But it can’t see the world as humans do.
Instead, it provides the potential for humans to focus on more meaningful
aspects of work that involve creativity and innovation. As automation replaces
more routine or repetitive tasks, it will allow workers to focus more on
inventions and breakthroughs, which ultimately fuels an enterprise’s success.
The ethics of
artificial intelligence is the part of the ethics of technology specific to
robots and other artificially intelligent beings. It is typically divided into roboethics, a concern with the moral behavior of humans as
they design, construct, use and treat artificially intelligent beings, and
machine ethics, which is concerned with the moral behavior of artificial moral
agents (AMAs).
In October 2017, the
android Sophia was granted "honorary" citizenship in Saudi Arabia,
though some observers found this to be more of a publicity stunt than a meaningful
legal recognition. Some saw this gesture as openly denigrating of human rights
and the rule of law.
Explainability is
technically valuable. Developers need to be able to determine whether a system is
solving the right problem. There are many examples of AI systems that “cheated”
to arrive at the desired outcome.
If AI systems in high stakes fields ultimately solve the wrong
problem, the outcome could be life threatening.
Because explainability
is necessary for the adoption of AI in certain fields, in some ways the quest
for explainability is spurring AI
innovation. For both ethical and technical reasons, academics and major AI
companies alike are devoting significant
effort toward explainability, and they are making serious progress.
Explainability is
knowing why AI rejects your credit card charge as fraud, denies your insurance
claim, or confuses the side of a truck with a cloudy sky. Explainability is
necessary to build trust and transparency into AI-powered software. The power
and complexity of AI deep learning can make predictions and decisions difficult
to explain to both customers and regulators.
As our understanding of potential
bias in data sets used to train AI algorithms grows, so does our need for
greater explainability in our AI systems. To meet this challenge, enterprises
can use tools like Low Code Platforms to put a human in the loop and govern how
AI is used in important decisions.
Imagine stupid Indian
judiciary wants AI in our system.. Our “ loser lawyer turned judges” are the
worst on the planet.. Software has even been allowed to predict future
criminals, ultimately controlling human freedom by shaping how parole is denied
or granted to prisoners. In this way, the minds of judges are being shaped by
decision-making mechanisms they cannot understand because of how complex the
process is and how much data it involves.
We humans are not
merely cut off from the decisions that machines are making for us but deeply
affected by them in unpredictable ways. Instead of being central to the system
of decisions that affects us, we are cast out in to its environment. We have
progressively restricted our own decision-making capacity and allowed
algorithms to take over. We have become artificial humans, or human artefacts,
that are created, shaped and used by the technology.
The more you use the
web and social networks, the more Google, Facebook and other internet companies
know about you. Then, of course, there are the reams of data collected via more
conventional means -- voter rolls, driver’s licenses, magazine subscriptions,
credit card purchases -- that can be cross-linked with online information to
paint a complete profile of individuals. Data itself isn’t inherently
discriminatory.
The problem arises in how it’s used and interpreted --
especially when algorithms characterize people via correlations or “proxy”
data. When data is misused, software can compound stereotypes or arrive at
false conclusions. If you were to check out a homosexual porn site to protect
you own motherland , you may be branded as a homosexual.
Algorithms function by drawing on past data while also
influencing real-life decisions, which makes them prone, by their very nature,
to repeating human mistakes and perpetuating them through feedback loops.
Often, their implications can be unexpected and unintended
An algorithm is a sequence of steps
• to perform a task
• given an initial situation (i.e., the input)
They work to provide a path between a start point and an end
point in a consistent way, and provide the instructions to follow it
A computer program is an implemented algorithm
In the job market,
excessive reliance on technology has led some of the world’s biggest companies
to filter CVs through software, meaning human recruiters will never even glance
at some potential candidates’ details. Not only does this put people’s
livelihoods at the mercy of machines, it can also build in hiring biases that
the company had no desire to implement, as happened with Amazon. Jews are
always the gainers.
In news, what’s known
as automated sentiment analysis analyses positive and negative opinions about
companies based on different web sources. In turn, these are being used by
trading algorithms that make automated financial decisions, without humans
having to actually read the news.
91 % of all trading in
the foreign exchange markets is conducted by algorithms alone. The growing
algorithmic arms race to develop ever more complex systems to compete in these
markets means huge sums of money are being allocated according to the decisions
of machines. Jews laugh all the way to the kosher bank.
On a small scale, the
people and companies that create these algorithms are able to affect what they
do and how they do it. But because much of artificial intelligence involves
programming software to figure out how to complete a task by itself, we often
don’t know exactly what is behind the decision-making. As with all technology,
this can lead to unintended consequences that may go far beyond anything the
designers ever envisaged.
TAKE THE 2010 “FLASH CRASH” OF THE DOW JONES INDUSTRIAL
AVERAGE INDEX. THE ACTION OF ALGORITHMS HELPED CREATE THE INDEX’S SINGLE
BIGGEST DECLINE IN ITS HISTORY, WIPING NEARLY 9% OFF ITS VALUE IN MINUTES
(ALTHOUGH IT REGAINED MOST OF THIS BY THE END OF THE DAY). A FIVE-MONTH
INVESTIGATION COULD ONLY SUGGEST WHAT SPARKED THE DOWNTURN (AND VARIOUS OTHER
THEORIES HAVE BEEN PROPOSED).
BUT THE ALGORITHMS THAT AMPLIFIED THE INITIAL PROBLEMS
DIDN’T MAKE A MISTAKE. THERE WASN’T A BUG IN THE PROGRAMMING. THE BEHAVIOUR
EMERGED FROM THE INTERACTION OF MILLIONS OF ALGORITHMIC DECISIONS PLAYING OFF
EACH OTHER IN UNPREDICTABLE WAYS, FOLLOWING THEIR OWN LOGIC IN A WAY THAT
CREATED A DOWNWARD SPIRAL FOR THE MARKET.
THE CONDITIONS THAT MADE THIS POSSIBLE OCCURRED BECAUSE,
OVER THE YEARS, THE PEOPLE RUNNING THE TRADING SYSTEM HAD COME TO SEE HUMAN
DECISIONS AS AN OBSTACLE TO MARKET EFFICIENCY.
BACK IN 1987 WHEN THE US STOCK
MARKET FELL BY 23 %, SOME WALL STREET BROKERS SIMPLY STOPPED PICKING UP THEIR
PHONES TO AVOID RECEIVING THEIR CUSTOMERS’ ORDERS TO SELL STOCKS. THIS STARTED
A PROCESS THAT HAS ENDED WITH COMPUTERS ENTIRELY REPLACING THE PEOPLE.
ALGORITHMS DELIBERATELY DO “BACK SWINGS” TO PUMP AND DUMP IN MILLI SECONDS ..
The financial world has
invested millions in superfast cables and microwave communications to shave
just milliseconds off the rate at which algorithms can transmit their
instructions. When speed is so important, a human being that requires a massive
215 milliseconds to click a button is almost completely redundant. Our only
remaining purpose is to reconfigure the algorithms each time the system of
technological decisions fails.
As new boundaries are
carved between humans and technology, we need to think carefully about where
our extreme reliance on software is taking us. As human decisions are
substituted by algorithmic ones, and we become tools whose lives are shaped by
machines and their unintended consequences, we are setting ourselves up for
technological domination. We need to decide, while we still can, what this
means for us both as individuals and as a society.
ALTHOUGH ALGORITHMS ARE USED IN GLOBAL FINANCE AND OTHER
WAYS THAT IMPACT SOCIETY, THERE IS NO LEGAL RECOURSE OR OFFICIAL AUTHORITY TO
HOLD A COMPANY RESPONSIBLE FOR THE ACTIONS OF ITS ALGORITHMS.
THAT’S PARTIALLY BECAUSE ALGORITHMS ARE OFTEN DEVELOPED AND
IMPLEMENTED IN SECRET TO AVOID HACKING AND REVERSE ENGINEERING.
UNDERSTANDING ALGORITHMS AND THEIR IMPACT ON HUMAN LIFE GOES
FAR BEYOND BASIC DIGITAL LITERACY
IT HAS BEEN MADE TOO CONVENIENT FOR PEOPLE TO FOLLOW THE
ADVICE OF AN ALGORITHM (OR, TOO DIFFICULT TO GO BEYOND SUCH ADVICE), TURNING
THESE ALGORITHMS INTO SELF-FULFILLING PROPHECIES, AND USERS INTO ZOMBIES OF
GEORGE ORWELL’S 1984.
WE KNOW WHAT HAPPENED TO BOEING 737 SUPERMAX PASSENGER PLANE
WHEN AUTOMATION VETOES THE HUMAN PILOT.. EVEN TODAY THE INQUIRY INTO THE CRASH
HAS NOT GONE BEYOND TO WHAT I PREDICTED IN THE POST BELOW AS SOON AS THE CRASH
HAPPENED —
THE ALGORITHMS ARE NOT IN CONTROL; PEOPLE CREATE AND ADJUST
THEM TO HIJACK THE SYSTEM FOR EVIL PURPOSE OR PROFITS
GOOGLE, FACEBOOK, TWITTER, QUORA ETC USE ALGORITHMS THAT CAN SINK MY BLOG POSTS WHICH
EXHUME CRITICAL BURIED TRUTHS..
THESE JEWISH DEEP STATE AGENTS , USE FILTER BUBBLES AND
TAILOR UPDATES AND CONTENT TO EACH USER, WHICH COULD KEEP USERS FROM RECEIVING
INFORMATION OR NEWS FROM SOURCES THAT CHALLENGE THEIR WORLDVIEW..
CAPT AJIT VADAKAYIL’S FOLLOWERS CANNOT EVEN SEND A CRITICAL ADVISE/ WARNING
TO OUR OWN NATIONS PM
WHEN YOU REMOVE THE HUMANITY FROM A SYSTEM WHERE PEOPLE ARE
INCLUDED, THEY BECOME VICTIMS
SHYLOCK WINS-- THE COMMON GOOD HAS BECOME A DISCREDITED,
OBSOLETE RELIC OF THE PAST..
ARTIFICIAL
INTELLIGENCE IS ONLY AS SMART AS THE DATA SETS SERVED
THE POWER TO CREATE AND CHANGE REALITY WILL DELIBERATELY BE
INSERTED IN BLACK BOX TECHNOLOGY THAT
ONLY A FEW TRULY UNDERSTAND..
THIS BLOGSITE WILL NOT ALLOW HUMANS TO BE AT THE TENDER
MERCIES OF KOSHER BIG BROTHER WHO CONTROLS THE TECHNOLOGY.
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--
-
CONTINUED FROM 1-
BRAIN DEAD CUNT AL-KHWARIZMI DEVELOPED THE CONCEPT OF THE ALGORITHM IN MATHEMATICS -WHICH IS A REASON FOR HIS BEING CALLED THE GRANDFATHER OF COMPUTER SCIENCE ( SIC ).. THEY SAY THAT THE WORD “ALGORITHM” IS ACTUALLY DERIVED FROM A LATINIZED VERSION OF AL-KHWARIZMI’S NAME BRAAAYYYYYYY.
ALGORITMI DE NUMERO INDORUM IN ENGLISH AL-KHWARIZMI ON THE HINDU ART OF RECKONING GAVE RISE TO THE WORD ALGORITHM DERIVING FROM HIS NAME IN THE TITLE. THE WORK DESCRIBES THE HINDU PLACE-VALUE SYSTEM OF NUMERALS BASED ON 1, 2, 3, 4, 5, 6, 7, 8, 9, AND 0. THE FIRST USE OF ZERO AS A PLACE HOLDER IN POSITIONAL BASE NOTATION WAS DUE TO AL-KHWARIZMI IN THIS WORK.
ANOTHER IMPORTANT WORK BY AL-KHWARIZMI WAS HIS WORK SINDHIND ZIJ ON ASTRONOMY. THE WORK, DESCRIBED IN DETAIL IN , IS BASED IN INDIAN ASTRONOMICAL WORKS..
THE MAIN TOPICS COVERED BY AL-KHWARIZMI IN THE SINDHIND ZIJ ARE CALENDARS; CALCULATING TRUE POSITIONS OF THE SUN, MOON AND PLANETS, TABLES OF SINES AND TANGENTS; SPHERICAL ASTRONOMY; ASTROLOGICAL TABLES; PARALLAX AND ECLIPSE CALCULATIONS; AND VISIBILITY OF THE MOON. A RELATED MANUSCRIPT, ATTRIBUTED TO AL-KHWARIZMI, ON SPHERICAL TRIGONOMETRY IS DISCUSSED..
PTOLEMY’ ENTIRE WORKS ARE LIFTED FROM KODUNGALLUR UNIVERSITY KERALA OWNED BY THE CALICUT KING. AL-KHWARIZMI'S TABLES WERE CAST ON PTOLEMY’S TABLES.
AL-KHWARIZMI WROTE ON THE ASTROLABE AND SUNDIALS ,WHICH ARE HINDU INSTRUMENTS
THERE IS A STATUE OF MUHAMMAD IBN MUSA AL-KHWARIZMI HOLDING UP AN ASTROLABE IN FRONT OF THE FACULTY OF MATHEMATICS OF AMIRKABIR UNIVERSITY OF TECHNOLOGY IN TEHRAN . HE GOT AN ASTROLABE INSTRUMENT AND TRANSLATED INTO ARABIC NOTES OF THE MANUAL ( BOTH CONSTRUCTION AND OPERATIONAL ) FOR GOLD .. HIS ASTROLABE INSTRUMENT HAD PLATES FOR MECCA/ ISTANBUL/ ALEXANDRIA.
ASTROLABE BRASS INSTRUMENTS WERE SOLD BY KODUNGALLUR UNIVERSITY PROFESSORS AT THE LIBRARY OF CORDOBA IN SPAIN..
THESE SIMPLE BRASS DEEP SEA NAVIGATION INSTRUMENTS WERE PRODUCED MUCH BEFORE THE COMPLICATED ANTIKYTHERA AUTOMATIC ( PERPETUAL MOTION ) MECHANISM..
THE DEEP SEA NAVIGATING SHIPS OF QUEEN DIDO , A KERALA THIYYA PRINCESS WHO TAUGHT AT THE UNIVERSITY OF ALEXANDRIA IN 1600 BC ( ON DEPUTATION FROM KODUNGALLUR UNIVERSITY ) CARRIED THESE INSTRUMENTS..
http://ajitvadakayil.blogspot.com/2019/05/the-ancient-7000-year-old-shakti.html
ASTROLABE IT IS AN ELABORATE INCLINOMETER, HISTORICALLY USED BY ASTRONOMERS AND NAVIGATORS TO MEASURE THE ALTITUDE ABOVE THE HORIZON OF A CELESTIAL BODY, DAY OR NIGHT.
IT CAN BE USED TO IDENTIFY STARS OR PLANETS, TO DETERMINE LOCAL LATITUDE GIVEN LOCAL TIME (AND VICE VERSA), TO SURVEY, OR TO TRIANGULATE. ASTROLABE WAS CALLED SITARA YANTRA..
http://ajitvadakayil.blogspot.com/2019/09/onam-our-only-link-to-planets-oldest.html
AN ASTROLABE (SOLD IN CORDOBA SPAIN ) WAS EXCAVATED FROM THE WRECK SITE OF A PORTUGUESE ARMADA SHIP AS THE OLDEST IN THE WORLD. THEY ALSO CERTIFIED A SHIP'S BELL -- DATED 1498 -- RECOVERED FROM THE SAME WRECK SITE ALSO AS THE OLDEST IN THE WORLD.
DONT EVER THINK THAT VASCO DA GAMA AND COLUMBUS NAVIGATED ON WESTERN TECHNOLOGY.. THEY USED ANCIENT DEEP SEA NAVIGATING INSTRUMENTS OF ANCIENT KERALA THIYYA NAVIGATORS..
DIOPHANTUS STUDIED IN KODUNGALLUR UNIVERSITY. HE IS THE AUTHOR OF A SERIES OF BOOKS CALLED ARITHMETICA, ALL LIFTED FROM KERALA SCHOOL OF MATH.
THIEF DIOPHANTUS WAS THE FIRST GREEK MATHEMATICIAN WHO RECOGNIZED FRACTIONS AS NUMBERS; THUS HE ALLOWED POSITIVE RATIONAL NUMBERS FOR THE COEFFICIENTS AND SOLUTIONS.
IN MODERN USE, DIOPHANTINE EQUATIONS ARE USUALLY ALGEBRAIC EQUATIONS WITH INTEGER COEFFICIENTS, FOR WHICH INTEGER SOLUTIONS ARE SOUGHT. DIOPHANTUS WAS A BRAIN DEAD FELLOW WHO STOLE HIS ALGEBRA FROM THE KERALA SCHOOL OF MATH.
MEDIOCRE BRAIN JEW ALBERT EINSTEIN WAS A THIEF… HE STOLE FROM PART TWO ( BRAHMANAS ) AND PART THREE ( ARANYAKAS ) OF THE VEDAS..
http://ajitvadakayil.blogspot.com/2018/11/albert-einstein-was-thief-plagiarist.html
LIES WONT WORK.. A BROWN BLOGGER IS IN TOWN !
Capt ajit vadakayil
..
THIS POST IS NOW CONTINUED TO PART 15 BELOW--
CAPT AJIT VADAKAYIL
..
REVELATION?
ReplyDeleteWHY HAS THE WORLD GOT A SIX MILLION FIXATION OF JEWS KILLED .. WHEN HITLER WAS A JEW?
WHY HAVE OUR NCERT BOOKS NOT REVEALED THAT ONE MILLION STRONG ANGLO INDIAN POPULATION ARE DESCENDANTS OF RAPE ?--
FOUL RAPE BY WHITE SOLDIERS ( SANCTIONED BY THE QUEEN ) ON UNWILLING INDIAN GIRLS BELOW THE AGE OF 13 --WHO HAS JUST HAD HER FIRST MENSES --AND MARRIED OFF TO ON IDOL OF MONKEY HANUMAN?
http://ajitvadakayil.blogspot.com/2014/02/devadasi-system-immoral-lie-of-temple.html
WHY IS IT NOT TAUGHT IN NCERT BOOKS THAT INDIANS WERE SHIPPED ABROAD AS SLAVES WITH FAMILY-- FOR REFUSING TO GROW OPIUM FOR ROTHSCHILD ? THEY WERE SUBJECTED TO DELIBERATE FAMINE .. JEW GANDHI PLAYED BALL.
http://ajitvadakayil.blogspot.com/2010/04/indentured-coolie-slavery-reinvented.html
http://ajitvadakayil.blogspot.com/2011/09/amartya-sen-gets-nobel-prize-for.html
http://ajitvadakayil.blogspot.com/2019/07/how-gandhi-converted-opium-to-indigo-in.html
WHEN KAYASTHA AMARTYA SEN WON THE NOBEL PRIZE ONE MILLION ADORING BONGS DANCED ON THE STREETS OF CALCUTTA..
LET ME SEE HOW MANY BONGS ATTEND THIS BASTARDs FUNERAL.. MIND YOU HE HAS A ROTHSCHILD WIFE..
capt ajit vadakayil
..
ReplyDeleteXXXXXXXX
Thu, Feb 27, 11:46 PM (9 hours ago)
to me
Why is Amartya Sen always spewing venom against Narendra Modi sitting in US?
So, here is the answer.... Read it carefully.
When UPA government inaugurated Nalanda University in Bihar in 2007, Amartya Sen was made the first Chancellor of the university. A very important feature of his appointment was that he had all powers in the name of "autonomy".... So much so that he did not even have to provide the account of money spent on anything to the government....
.
Imagine a public servant spending any amount of taxpayers' money and yet exempt from any kind of accountability.... Not only that, he was withdrawing a salary of ₹ 5 lakh per month - a university chancellor of a government university drawing a salary more than any other public servant. Apart from that, he had unlimited foreign trips allowances on taxpayers money by the virtue of being Nalanda University Chancellor.
.
The story doesn't end here. During the 7 years (2007-2014), Amartya Sen spent ₹ 2730 CRORE on a university which still was not fully functional.... Yes...a whopping ₹ 2730 CRORE.....
Since it was by law (made by UPA) exempt from any kind of accountability, we can never know what happened to that money and yet it will remain legal.
.
Now, coming to appointments....
Even appointments made by Amartya Sen were exempt from any kind of accountability.
So who did he appoint ?
The first 4 faculties were :
1. Dr. Upinder Singh
2. Anjana Sharma
3. Nayanjot Lahiri
4. Gopa Sabharwal.
.
Who were they ??
Dr. Upinder Singh is the Daughter of former PM Manmohan Singh.
The other 3 are close associates/friends of Dr. Upinder Singh.
.
Amartya Sen then appointed 2 more "GUEST" faculties-
1. Daman Singh
2. Amrit Singh
.
Who are they ?
Middle and youngest DAUGHTER of ex-PM Manmohan Singh.
.
What's unique about the appointment of Daman Singh and Amrit Singh is that they REMAINED in the USA all along 7 years... But were drawing a huge salary as a guest faculty. What salary they were withdrawing, only God knows.... The reason, again, is that Nalanda University had been made exempt from any kind of accountability to government.
.
So, the summary....
1. The University had hardly one building.
2. It had just 7 faculty members & a few guest faculties (who NEVER came) - all relatives/friends of Manmohan Singh/Amartya Sen.
3. There were hardly a hundred students.
4. There was no expenses on costly reagents or equipments as no scientific research was going on.
5. Still, the expenses was ₹ 2730 CRORE
.
In short, Amartya Sen had access to unlimited government fund without any accountability.
.
When Modi came to know about what all was going in the name of university, he kicked this leech out of the university in 2015 and cancelled all the appointments he had made. Amartya Sen had splurged more than ₹ 2700 CRORE on himself and his associates. He lived in USA and was drawing 5 Lakh per month and enjoying all allowances from India's taxpayers' money without doing anything.
.
Just because someone is a Nobel laureate doesn't mean that he is totally clean or doesn't have any ulterior intention. Nobel prize or a big degree is no indication of people's nature.
Even Manmohan had a PhD degree. That didn't mean he was the best in governance. His government turned out to be the worst in India's independent history.
.
We can never take action against Amartya Sen or technically call him corrupt because he was merely following the "rules" and the rules had been made in such a way by the UPA Government that he had the powers to spend as much he wanted without being accountable. That's why he will remain protected and can never be dragged to court. This was a LEGALISED PLUNDER of ₹ 2730 Crore by Amartya Sen.
Source: https://twitter.com/bhartijainTOI/status/1122408575731507200?s=17
DeleteIT IS A LIE THAT NALANDA UNIVERSITY WAS A BUDDHIST UNIVERSITY..
THERE WAS NOT A SINGLE BUDDHIST TEACHER OR STUDENT HERE..EVER
http://ajitvadakayil.blogspot.com/2019/06/deliberately-buried-truths-about-buddha.html
I AM ON TO MY BLOG NO 1600.
ReplyDeleteEVERY TRIED COUNTING TILL 1600?
TRY IT !
IF YOU PASTE THESE BLOGS ON A4 SIZE SHEETS-- DO YOU HAVE AN IDEA HOW MANY LAKHS OF SHEETS IT WILL TAKE..
NOBODY ON THIS PLANET PAST AND PRESENT ( AND IN FUTURE TILL SUN GOES SUPERNOVA ) HAS WRITTEN SO MUCH CONTENT BY QUANTITY/ QUALITY AND SUBJECT MATTER..
THIS IS THE WORK DONE BY TWO FINGERS ON A LAPTOP.. IN JUST A DECADE..
THERE ARE MORE THAN 10,000 REVELATIONS , ALL ORIGINAL ..
NOWADAYS I HOLD MY REVELATIONS LIKE A DAM RESERVOIR , ALLOWING LIGHTER ONES TO JUST FLOW OVER THE WALL AND VANISH..
MY REVELATIONS STAND AT 60 %..
THE REAL REVELATIONS WILL START ONLY AT 97%..
AND WHEN ( IF ) IT HAPPENS THE BALLS AND TWATS OF THE ENTIRE PLANET WILL GO TRRRRR PRRRRRR BRRRRRR
( APOLOGIES TO SWARA BHASKAR WHO HAS PERFECTED AND PATENTED THESE ORIFICE SOUNDS )
capt ajit vadakayil
..
MY TWO FINGERS ARE WORN OUT !
DeleteDear captain sir, please take good care of yourself..
DeleteWe need you.... India needs you...
Captain I tried to copy your only Sanatan dharma blog series. I found it so difficult only in copying and pasting. The word file became unresponsive in opening at one stage. We all know how much exertion you are taking and writing. And the most difficult is thinking which you do while writing. Unexplainable, out of the world. Hats off to you captain sir.
DeleteNo words sir...
Deletewriting is one thing...there can be no other person like you in past and future till sun goes supernova.
Thank You!!
You are the person who has changed the course of time for many other human beings. Feels very disheartening when you stop your posts. Even your comments are always awaited in the day to get the perspective correction. I think you get into Arjun's mode sometime and leave your Astra and Shastra. But Please do remember the Krishna's saying in Gita. Change maker and Adharm nashak blogs will rule the world. Thanks Captain for all the humungus effort that you have done it's like moving a mountain.
DeleteNamaskar Guruji, Your writings have inspired us a lot. That's why we are not ready to leave you. Daily we watch the Bhagavath Gita series of Swami Chinmayananda Swamiji, beautiful and simple explanation by Swamiji. Writing is indeed a difficult job.
DeleteHi captain
DeleteI always have wellbeing of my family and you in my wish to god.
Dear Captainji,
DeleteNo amount of gratitude can surpass what you have done, Sir. I thank God for the day I bumped into your blog couple of years ago when I was looking for spiritual articles to read.
Thank you Sir for opening our blinded eyes to show us what actually transpired historically and what is happening. We are a much learned bunch because of you, Sir.
Thank you thank you thank you, Sir.
Regards,
Bunga
No ordinary human can do the greatest feat that you have done.which ordinary human can write on so many subjects that too with greater detail and with over 10,000 revelations?you are a super human.No one can Match your intelligence and perception till sun goes supernova.Thanks for everything that you are doing for this planet,mankind and for our sanathana dharma.
DeleteLove you always....
Namaste Captain Ajitji,
DeleteWe believe in you.
We trust you.
We are concerned about you.
We love you.
We salute you.
We are with you.
Always
You have youself said
"It is no measure of health to be well adjusted to this profoundly sick society"
Hello Captain,
DeleteThank you for all you efforts in awakening us from slumber.
Please keep on guiding us and aiding us for our country!
Kind regards,
Somtimes when I look at ur pics.. With friends n fmaikt.. I wonder.. He seems like a normal family man.. Beer n cuss words.. D crs handsome n classy..
DeleteBut what u reside inside of u and what u have done it.. It gives on hope one can be simple yet be creative and a huge impact on the world..
I am grateful to God who brought me to ur blog.. It was the illuminati one.. Still rmeb people raving on YouTube about u.. Thats whwre I searched u after reading about u
I wish u superbbb health.. Clarity.. Guidance by god.. To do the right in the best possible way..
Dear Capt Ajit sir,
DeleteYour thoughts are electric, emotions are magnetic and unbiased good intention based karma to write endlessly with patience is an eternal sacrifice....we owe you a lot for all your revelations and taking us progressively forward to be aware and beware of current events and share the right perception which is dynamic and changing constantly in this transformative world we live in.....bow down to you in gratitude..._/\_
Dear Captain,
DeleteWords cannot describe when you come back after a gap. It is a deep emotional happiness. This time when i read that your fingers are worn out my happiness did not last. Though i had not opened your blog for a week or two in the last month my mind was worrying about you every day.
Give your fingers rest. Please try using speech to text converter and check whether it is comfortable to you or not.
You are so much filled with abundance. Taking so much effort for the country! I have been hoping we get a good patriotic leader who can implement everything you have been saying! So amazing it would be! Personally I am grateful to you and shall be always :)
Deletedear captain, one birth is not enough to comprehend the wisdom expressed in blogs.
DeleteHi Sir,
DeleteIts good to see ure taking intervals between ur next blogs. Pls take rest sir. Ur health is important as ur words. We will be happy if u are in great shape and mind. Take sufficient relaxation and time off sir. Thanks again for taking ur time to give us time again ur precious knowledge and insights.
I wonder why none of world leaders request you to do practically whatever you have suggested. By giving you enough powers and resources.
DeleteDont they realise there will be be never again a soul like u in near future... This opportunity never gonna come again soon...
At back of my mind...their sub concious their conscience won't let them ignore you... They will be born again( no idea about exception cases) in this world.
Just a hope that someone makes practical changes at world level in a positive way soon.
I wonder where would we be without you?
DeleteYour guidance has helped us a lot in life.
Dearest Guruji,
DeleteMy brain and eyes has been burned out partially in translation work of your just one blog. So you have created all impossible 1600+ blog post directly from your mind with just two fingers. These are original and unique. I am suggesting you that please use any "speech to text" software. I am busy in forwarding your comments nowadays and I am fighting corona paramedic with you, doctors, police, army and others.
I agreed with all above reader's comments and their comments are mine also.
You are a MAHAPURUSHA.
You have appeared to world on right time otherwise all humans would have been disappeared some years ago or back to stone age.
With deepest gratitude
Nishant
Dear captain , please take rest for few days.. 🙏🏻
DeleteHi Capt. please check this 4 minute video...
DeleteHow to use Voice Typing in Google Docs - All you need to know
https://www.youtube.com/watch?v=tW8EwD77FI4
Might take a while to get used to. Hope it works out..:)
https://twitter.com/IAmSudhirMishra/status/1233039430362923009
ReplyDeleteBLINDING AN IB OFFICER AND STABBING HIM 400 TIMES IS INDEED SELF DEFENCE..
HANG THE TRAITOR JUDGES..
https://timesofindia.indiatimes.com/videos/city/delhi/delhi-riots-when-catapults-became-weapons-of-destruction/videoshow/74374268.cms
ReplyDeleteBOYCOTT MOVIES LIKE THAPPAD WHICH IS CULTURAL TERRORISM TARGETING OUR PRICELESS CULTURE..
ReplyDeletePranam Ajit uncle https_/\_
ReplyDeleteLooks like someone is picking up ideas & details from ur revelation and gonna patent it as this own discovery.
Check this news below:
https://futurism.com/scientists-discover-protein-meteorite
I just don't understand how could these guy be so ungrateful.
Regards
Jeet
Dear Capt Ajit sir,
ReplyDeleteYou are absolutely right....Rothschild run Wikipedia is distorting history of recent NE Delhi riots to...showing biased info of Anurag Thakur, Kapil Mishra behind Hindu riots...not muslim riots....exactly like Godhra...they must be punished and asked to apologise.
https://www.opindia.com/2020/02/delhi-riots-wikipedia-article-biased-anti-hindu/
Pranaam Captain
ReplyDeleteNarendra Modi has decided to give up his social media accounts by this Sunday in his public announcement on twitter.. what could be the reason? Is it because of the censorship they follow. Please guide sir
With Regards
Sagar
Respected Sir,
ReplyDeleteI am sending this mail with Gratitude.
Thank you for your immense help which helped my brain tremendously.
I suffer from a severe mental health problem which I don't share with anyone.
The only people who know about them are the doctor who treats me, 2 family members.
I hated myself for getting this mental health problem.
There were times when I cried I should have suffered from cancer and died.
Then over a period of time I came across your blog and things changed.
You wrote about the mental health problem which I suffer and explained that medications should be taken and doctor's advice should be followed.
It helped me immensely. Later I also asked you how many years should a person continue
taking medicines.
You took time and replied for that question which was very helpful for me.
Every year my doctor makes a note of changes happened.
This year he made a note and he shared with me
He told that After many years he observed that
I became mentally strong.
I stopped telling that I worry how to face typical family situations. I stopped telling how I am afraid to face certain family members.
He told many positive things.
Sir, All this happened to me because of reading your blogs.
Thank You for helping me who is a mental health patient to become mentally strong and live like normal person with courage.
Sir, Thank You very much for coming back.
Sir, It gives immense happiness to see you again.
With Gratitude
Corona virus an economic weapon? Lots of panic selling of stocks world wide. When the dust settles, who sold what to whom at what price?
ReplyDeleteVirus detection kits will be a big business, face masks , sanitizers etc. This is just superficial layer.
Bitcoin no longer banned says SC undoing RBI.
ReplyDeleteHoli has been jinxed. Not water savers this time , it's the virus
May God Bless u Ajit Sahab its an honour to read your Blog You Have written Such Truths of History that may be in our whole lifes it could have possible to no it, It is beyound doubt that you r the One who is making clear to the world that when Kalki comes we shud not any doubt to figure it out.(Only who or Getting lights in der herats they Can understand the Final Avatar of God Vishnu) Thanks and Gratitude. Om Peace Peace Peace unto you and your Family.
ReplyDelete
ReplyDeletehttps://www.theweek.in/news/india/2020/03/06/in-violation-of-law--hoardings-with-photos-of-anti-caa-protester.html
""In violation of law, hoardings with photos of anti-CAA protesters put up in Lucknow""
Turf war, me thinks... 'as according to SC only judges, of high court or claim commissioner can calculate damages'...
Captain, What is the Buddhis concept of Shunyata? If you won't reveal this nobody can until sun goes supernova.
ReplyDeleteGuruji,
ReplyDeleteIt seems Babu's found a new anecdote for readers piling up their complaints.
Arey yeh toh ek blog hai bhaiyya constitution thodi hai ????
for registration number : MINHA/E/2019/05791
Grievance Concerns To
Name Of Complainant
Vikramaditya
Date of Receipt
17/08/2019
Received By Ministry/Department
Home Affairs
Grievance Description
https://timesofindia.indiatimes.com/city/mumbai/parliament-not-absolute-ruler-says-mark-tully/articleshow/70695073.cms
INDIAN COLLEGIUM JUDICIARY IS ILLEGAL..
READ ALL 8 PARTS OF THE POST BELOW--
http://ajitvadakayil.blogspot.com/2019/01/justice-be-damned-enforce-law-not-any.html
REVOKE THE PADMA BHUSHAN AWARD GIVEN TO MARK TULLY.. REVOKE HIS INDIAN VISA..
MARK TULLYs JEWISH ANCESTORS WERE ALL JEW ROTHSCHILDs OPIUM AGENTS ..I HAVE DONE ENOUGH RESEARCH AND I WILL POST A BLOG SOON..
MARK TULLYs GRANDFATHER WORKED UNDER GEORGE ORWELLs FATHER AT CHAMPARAN, EXPORTING OPIUM FROM INDIA TO CHINA ....
http://ajitvadakayil.blogspot.com/2019/07/how-gandhi-converted-opium-to-indigo-in.html
MARK TULLYs JEWISH GREAT GRANDFATHER WAS A VERY POWERFUL MAN.. DERIVING POWER FROM JEW ROTHSCHILD..
I KNOW MORE ABOUT MARK TULLYs ANCESTORS THAN TULLY BABY HIMSELF..
http://ajitvadakayil.blogspot.com/2010/12/dirty-secrets-of-boston-tea-party-capt.html
http://ajitvadakayil.blogspot.com/2010/11/drug-runners-of-india-capt-ajit.html
BBC KNEW IN ADVANCE THAT INDIRA GANDHI WOULD BE MURDERED.. THEY WERE THERE TO WITNESS IT LIVE..
MARK TULLY DID NOT GIVE INDIA THE TIME TO MOVE TO PLAN B.. HE ANNOUNCED THE INDIRA GANDHI MURDER ON BBC.. THIS IS SEDITION BY LAWS OF ANY NATION..
HIS BOOK FOUR FACES IS RIDICULOUS BULL. JESUS CHRIST NEVER EXISTED..
BIBLE / CHRISTIANITY/ JESUS / MOSES/ ABRAHAM/ GABRIEL/ NOAH/ ADAM ETC WAS COOKED UP BY JEWESS HELENA ( MOTHER OF ROMAN EMPEROR CONSTANTINE THE GREAT ) IN 325 AD, AT THE FIRST COUNCIL OF NICEA..
EVERY WORK ON MARK TULLY IS A LIE.. IT WAS DIFFICULT TO READ THROUGH HIS RIDICULOUS LIES..
MARK TULLYs WATERLOO WILL HAPPEN SOONER THAN LATER.. HE HAS BLED BHARATMATA ENOUGH..
MARK TULLY IS NOT A FRIEND OF INDIA SAYS CAPT AJIT VADAKAYIL..HE IS A WOLF IN SHEEPs CLOTHING.. NOT ANY MORE
capt ajit vadakayil
This grievance is not a suggestion or mere whitewash of formalities. NO MERRY GO ROUND (THIS IS NOT THIS DEPARTMENT S DIRTY WORK. statements will not go down well with public. Save the nation from enemies. Ignoring such complaints will have severe repercussions.
JAI BHARAT MATHA .
Current Status
Case closed
Date of Action
18/03/2020
Reason
Others
Remarks
Quoting from someone s blog does not constitute a grievance. This is personal view of blog writer.
Officer Concerns To
Officer Name
S K Shahi
Officer Designation
Joint Secretary
Contact Address
Email Address
vb.dubey@gov.in
Contact Number
23092722
Dear Captain,
ReplyDeleteYou have mentioned that Corona virus harbours in throat , mouth etc and activated charcoal helps to destroy them. Can mouthwashes and brushing teeth frequently and drinking hot water help too for those who can't get activated charcoal ?
Commenting after a long time Captain. I believe I have fully uncovered the Opium nexus between Rothschild and desh-drohis of India. I think you will tell us about Kacchwa Rajputs who were the most loyal Mughal supporters and supported British and conducted mass-murders of other Hindus.
ReplyDeleteWill wait for you to reveal and see if I am correct.
What I wanted to write was that I have basically finished up reading history of Maratha Empire. I always wondered which Empire of Chattrapati Shahu Maharaja these Peshwas gobbled up because the propaganda spread by Chitpavans was that Jew Peshwas expanded the Maratha Empire and that Kshatriya Marathas were mere mindless sidekicks. In fact the Maratha Empire was already upto Malwa, Gujarata and down to Karnataka and Tamil Nadu.
Raghoji Bhonsale was the first Maratha warrior who battled the Bengal establishments of British and Alivardi Khan, and they have turned him into a mass-rapist who killed lakhs of innocent Bengali brahmins and peasants which is a complete lie. In fact the Hindu population of East Bengal was shifted to West Bengal under his rule. Please tell us if this is correct or not. If he was a hero he has been the most insulted Hero in Indian history. After Sambhaji Maharaja who is also called a alcoholic rapist by that Jadunath Sarkar bastard.
Also, I think you have mixed up Mahar-Maratha ethnic groups. Mahars are Gondi Tribals who were part of Raghoji Bhonsale's Kingdom of Nagpur which he took over after their king had no heir. Shahuji Maharaja was going to adopt Raghoji's son as his heir but he died suddenly and these Chitpavan jews took over Maratha Empire. Some of them were heroic no doubt, but Balaji was a proper traitor who destroyed the Angrian Navy which could have protected India from British vessels. Even after this Sambhuji Angre remained independent.
That's why there is confusion but Bhonsales are not Mahars, it is this Eastern Branch that you can get confused with. Otherwise it is the normal Marathas only that engaged pitched in battles, even those at World Wars, my own Great-Grandfather did and faced off against Panzers. Mahars were men-at-arms, fortkeepers and generic enlisted troops.
Grievance Status for registration number : DBIOT/E/2020/00034
ReplyDeleteGrievance Concerns To
Name Of Complainant
Vikramaditya
Date of Receipt
13/03/2020
Received By Ministry/Department
Bio Technology
Grievance Description
ajitvadakayil.blogspot.com/2020/02/coronavirus-deaths-nano-gold-colloids.html
WE ASK THE MODI GOVT TO SANCTION FUNDS TO IMMEDIATELY DEVELOP GOLD NANO COLLOID BASED ANTIBODY TEST KITS FOR CORONAVIRUS.
WE ARE IN THE AGE WHERE WOMEN CAN FIND OUT OF THEY ARE PREGNANT USING A TEST STRIP AT HOME.
WE CAN QUALITATIVELY DETECT IgG AND IgM ANTIBODIES OF COVID-19 IN HUMAN SERUM, PLASMA OR WHOLE BLOOD IN VITRO.
GOLD NANO COLLOIDS IS THE ANSWER FOR CORONAVIRUS DETECTION, PREVENTION AND CURE.
capt ajit vadakayil
This is very crucial for saving and protecting our country from the deadly pandemic
Current Status
Case closed
Date of Action
17/03/2020
Remarks
Govt. of India (Union Health Ministry)/ICMR is already making efforts to address the issue.
Officer Concerns To
Officer Name
DR. VINITA CHAUDHARY
Officer Designation
SC. E
Contact Address
Email Address
vinita.chaudhary@nic.in
Contact Number
SOMEBODY ASKED ME..
ReplyDeleteCAPTAIN--
HOW IS IT POSSIBLE FOR ROTHSCHILD TO CREATE MASS FAMINE IN CHINA..
THE SECRET LIES IN LEADING BOTH SIDE IN A BATTLE, AS JEW ROTHSCHILD ALWAYS DOES....
DURING THIS TAIPING REBELLION CONFLICT, BOTH SIDES TRIED TO DEPRIVE EACH OTHER OF THE RESOURCES WHICH THEY NEEDED IN ORDER TO CONTINUE THE WAR..
IT BECAME STANDARD PRACTICE FOR EACH SIDE TO DESTROY THE OPPOSING SIDE'S AGRICULTURAL AREAS, BUTCHER THE POPULATIONS OF CITIES, AND GENERALLY EXACT A BRUTAL PRICE FROM THE INHABITANTS OF CAPTURED ENEMY LANDS IN ORDER TO DRASTICALLY WEAKEN THE OPPOSITION'S WAR EFFORT ..
THIS SCORCHED EARTH TACTICS WERE CAREFULLY MANAGED BY THE MERCENERIES / MAFIA IN ROTHSCHILDs PAYROLL AND MICROMANAGED GRID BY GRID BY ROTHSCHILDs AGENTS.
DESPERATE CHINSE STARTED EATING DOGS, RATS , SNAKES ETC .. THIS DNA MEMORY CONTINUES EVEN TODAY..
WARS MUST BE WON BY BRAINS NOT BRAWN.. AS CHANAKYA WROTE 2300 YEARS AGO.
https://ajitvadakayil.blogspot.com/2020/03/chinese-slavery-capt-ajit-vadakayil.html
Capt ajit vadakayil
..
Dear Ajit Sir,
DeleteFinally I got my answer after wondering a lot post the outbreak of COVID-19.
What made these CHINESE to carve for such Halloween items as food?!!!
The answer is this desperation to survive & hence to eat anything.
This is also a damn revelation.
We all know DNA memory for anything but how that memory got infused will always be a mystery unless unlocked by you, as the way you did here.
WARNING: CRITICAL MESSAGE ABOUT COVID-19..
ReplyDeleteJUST BEFORE PEOPLE DIE OF COVID-19 THEY LOSE THEIR SENSE OF TASTE AND SMELL, EVEN WITH CONCENTRAED VINEGAR .. THIS IS SURE SHOT ….
I WONDER WHY WORLD HEALTH ORG IS HIDING THIS TRUTH..
THE WHO CHIEF MUST RESIGN OR HE MUST BE KICKED OUT ..
INSIDE THE BRAIN , PIRIFORM CORTEX, A COLLECTION OF NEURONS LOCATED JUST BEHIND THE OLFACTORY BULB THAT WORKS TO IDENTIFY THE SMELL. SMELL INFORMATION ALSO GOES TO THE THALAMUS, A STRUCTURE THAT SERVES AS A RELAY STATION FOR ALL OF THE SENSORY INFORMATION COMING INTO THE BRAIN..
THE GUSTATORY CORTEX IS THE AREA OF THE BRAIN RESPONSIBLE FOR THE SENSATION OF TASTE. TASTE IS THE RESULT OF A COMPLEX NETWORK OF NERVES AND NERVE IMPULSES THAT TRAVEL BETWEEN THE MOUTH AND BRAIN.
THE MOST CHARACTERISTIC SYMPTOM OF PATIENTS WITH COVID‐19 IS RESPIRATORY DISTRESS, AND MOST OF THE PATIENTS ADMITTED TO THE INTENSIVE CARE COULD NOT BREATHE SPONTANEOUSLY. THIS IS THE TIME A SIMPLE AMBU BAG CAN SAVE THE PATIENT..
CORONAVIRUSES ARE NOT CONFINED TO THE RESPIRATORY TRACT ..THEY MAY INVADE THE CENTRAL NERVOUS SYSTEM… THE BRAINSTEM GETS INFECTED..
SACK ALL WORKING FOR CDC ( THE CENTERS FOR DISEASE CONTROL AND PREVENTION ).. THEY HAVE FAILED
DO RESEARCH ON NANO GOLD COLLOIDS FOR CURE / PREVENTION ( BLOOD BRAIN BARRIER ) AND MEDICAL CANNABIS FOR RELIEF TO THE AFFECTED PATIENT ON VERGE OF LUNG COLLAPSE .. ..
https://ajitvadakayil.blogspot.com/2020/02/coronavirus-deaths-nano-gold-colloids.html
capt ajit vadakayil
..
PUT ABOVE MESSAGE IN WEBSITES OF--
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SPREAD BY WHATS APP
Sent to the below ids
DeleteDate: Sat, 4 Apr 2020 at 19:43
Subject: WARNING: CRITICAL MESSAGE ABOUT COVID-19..
To: , , , , , , , Hony Secretary General , , , , , , , , , , , , , , , , , , , , , , , , <17akbarroad@gmail.com>, <38ashokroad@gmail.com>, , , , , , , , , , , , Ram Madhav , , , , , , , , , , , , , , , , ,
https://twitter.com/shree1082002/status/1246463877509832713
DeleteRe-tweeted above.
DeleteCaptain,
DeleteSouth Korea identified among 30% of asymptomatic cases consists of loss of taste among teen groups and quarantined them from contracting elders. Unfortunately no other country has identified this as a criteria for testing the Covid 19 patients. Doctors are under the assumption that the prolonged flu/viral fever can also cause loss of taste and hence they are paying keen attention to this symptom.
The SK's top doctor gave very good inputs how they contain the community spread, pre-planning of test kits, massive testing, the mechanism of aerosol etc. It is a good watch.
https://www.youtube.com/watch?v=gAk7aX5hksU
Posted and Verified
Deletehttps://twitter.com/IndiaDST/status/1245932792543764482?s=20
DONALD THE TRUMP
ReplyDeleteYOU HAVE FUCKED IT UP OLD MAAAN..
WHAT A DISGRACE, YOU COULD NOT CONTAIN COVID-19..
AMERICAN "EXCEPTIONALISM " CHICKENS ARE COMING HOME TO ROOST .. THERE IS NO ESCAPE..
EVEN IN THIS HOUR OF PANDEMIC YOU HAVE NOT LIFTED SANCTIONS ON NORTH KOREA , VENEZUELA AND IRAN..
IF MADURO CAN BE REPLACED BY JEW GUAIDO, VENEZUELAs INFLATION WILL COME DOWN FROM 210,000 % TO ZERO OVERNIGHT..
BUT AS LONG AS PUTIN SUPPORTS MADURO YOU CAN DO FUCK ALL ABOUT IT.. TWO MISSILES TO THE PANAMA CANAL LOCKGATES CAN TURN AMERICA INTO A FOOD STAMP NATION.. NORTH KOREA HAS 5 MORE SUBMARINES THAN USA.. THEY CAN DO IT ANY TIME..
FOUR GENERATIONS OF NORTH KOREANS HAVE NOT SEEN PROPER FOOD.. SO IS KIM STARVING HIS PEOPLE?... BALLS..
http://ajitvadakayil.blogspot.com/2017/03/regime-change-in-north-korea-rare.html
EVEN A SMALL CHILD IN USA KNOWS THAT AMERICA STARVES NORTH KOREA, VENEZUELA ETC BY ECONOMIC SANCTIONS..
IRAN WAS A GOOD NATION AS LONG AS JEW SHAH PEHLAVI RULED..
http://ajitvadakayil.blogspot.com/2017/04/the-most-evil-journalist-capt-ajit.html
IN THIS INTERNET AGE, BIG BROTHER JEW ROTHSCHILDs MEDIA CANNOT BURY TRUTHS..
BULLY UNCLE SAMs EXCEPTIONAL SH1T DOES NOT SMELL, OH YEAH ? …
AMERICAN EXCEPTIONALISM IS NOW CONSTRUED AS NAKED IMPERIALISM, LAYING DOWN THEIR OWN LAWS , STRIKING UNILATERALLY AND RIDING ROUGH-SHOD OVER PEOPLE'S CULTURES AND BELIEFS…
BULLY HILLY BILLY AMERICANS REVEL IN THE MINDLESS EXCEPTIONALISM. … THE ROOT CAUSE IS TERRIFIC IGNORANCE…
ALL THIS NONSENSE OF AMERICA BEHAVING AS A "NATION ABOVE NATIONS" IMPOSING ITS HEGEMONY ON THE REST OF THE WORLD, ACTING IN ITS OWN INTEREST, WITH NO CONCERN FOR OTHERS, WILL SOON COME TO A GRINDING HALT…..
SPARE US THIS “WE DROP ONLY GOOD BOMBS SHIT “!!
HERE COMES THE EPITAPH ON THE TOMBSTONE OF THE GRAVE OF AMERICIAN EXCEPTIONALISM... THIS FROM CAPT AJIT VADAKAYIL, THE NO 1 ON SOCIAL MEDIA …… AARRGGHH FUCK1N’ PTTHHEEOOOYYYY !....
INDIA IS THE ONLY NATION HAVING MORAL AUTHORITY ON THIS PLANET.. THE WHOLE WORLD KNOWS THIS…
REPEAL SANCTIONS ON VENEZUELA, NORTH KOREA AND IRAN-- NOW !
capt ajit vadakayil
..
PUT ABOVE COMMENT IN WEBSITES OF--
DeleteTRUMP
PUTIN
BORIS JOHNSON
ANGELA MERKEL
MACRON
EVERY RULER OF EU
EVERY RULER OF EVERY MAJOR NATION ON THE PLANET
KIM
XI
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Wrote to Trump using:
Deletehttps://www.whitehouse.gov/contact/
https://twitter.com/Nishant81565744/status/1246447103812833280?s=20
Delete@realDonaldTrump
@KremlinRussia_E
@BorisJohnson
@DFB_Team_EN
#AngelaMerkel
@EmmanuelMacron
#France #Europe #European
@EP_President
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@ChinaDaily
#XiJingping
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@BBCWorld
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@IndiaUNNewYork
@antonioguterres
#AlexJones #RENSE
@PMOIndia
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MESSAGE TO MODI FROM CAPT AJIT VADAKAYIL
ReplyDeleteCOME ON TV LIVE
WEAR A MASK
SPEAK THROUGH THE MASK
DONT APE STUPID FELLOW TRUMP..
WHO AND CDC ARE GUILTY OF MISLEADING THE WORLD OVER MASKS
capt ajit vadakayil
PUT ABOVE COMMENT IN WEBSITES OF--
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Sent
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PMOPG/E/2020/0287076
Sent to PMO....couldn't note down ack....no email confirmation came either
Deletehttps://twitter.com/Nishant81565744/status/1246442761156354048?s=20
DeleteHOPE.. This catches some attention
Deletehttps://twitter.com/Mohit_b_Handa/status/1246456084463669248
Your Registration Number is : PMOPG/E/2020/0287625
DeleteTweeted sir
DeleteDear Capt Ajit sir,
Deletehttps://twitter.com/IwerePm/status/1246666375562592256?s=20
Posted in
DeleteBiplab
IAS association
Pema kundu
Surah prabhu
Devendra
Pmo
Amit shaha
MODi
Mandar
WE DONT WANT PRANNOY JAMES ROY WITH A WHITE JEWESS MOTHER TO SAVE INDIA..
ReplyDeletehttps://twitter.com/ANI/status/1246430163820146688
ReplyDeletePEACEFUL RELIGION HAI BHAIYYA
MARTYRDOM KE BAAD 72 HOURIS MILENGA
Master Ji,
DeletePakistan dirty act with corona
https://mobile.twitter.com/kannanlp/status/1246459027376103426
Thanks
https://ajitvadakayil.blogspot.com/2020/03/chinese-slavery-capt-ajit-vadakayil.html
ReplyDeleteSOMEBODY ASKED ME..
CAPTAIN, HOW WERE CHINESE CONVERTED TO CHRISTIANITY.. HOW DID THEY BECOME SLAVES IN AMERICA..
IF YOU CONVERT TO CHRISTIANITY IN CHINA DURING THE TAIPING REBELLION, YOUR FAMILY CAN SURVIVE THE DELIBERATE FAMINE ..
ROTHSCHILDSs MERCENERIES DEIVERED RATION BAGS OF RICE AT YOUR DOORSTEP.. THE BIGGEST INCENTIVE IS THAT YOU WONT BE GRABBED TO BE SLAVES IN FOREIGN LANDS..
IN INDIA WE CALL THEM “RICE BAG CONVERTS”..
THESE CONVERTED HINDUS DID NOT FALL FOR THE CHARMS OF MESSIAH JESUS CHRIST ( WHO NEVER EXISTED )..
http://ajitvadakayil.blogspot.com/2010/04/indentured-coolie-slavery-reinvented.html
THE TERM "RICE CHRISTIANS" APPEARS IN HARPER LEE'S BOOK TO KILL A MOCKINGBIRD, IN CHAPTER 13. SCOUT'S AUNT ALEXANDRA USES THE WORD REFERRING TO MISSIONARY SOCIETY REFRESHMENTS DURING "LONG REPORTS ON RICE CHRISTIANS." TO KILL A MOCKINGBIRD WAS FIRST PUBLISHED IN 1960.
ARUNDHATI ROY PLAIGIARISED “ TO KILL A MOCKINGBIRD “ FOR HER BOOK “GOD OF SMALLTHINGS”.
QUOTE: [A large number of Paravans converted to Chrisitianity. ] As added incentive they were given a little food and money. They were known as the Rice Christians. As a special favor they were even given their own separate Pariah Bishop.UNQUOTE
AN EXAMPLE OF "RICE CHRISTIANITY" WAS USED IN A.J. CRONIN'S NOVEL THE KEYS TO THE KINGDOM, ABOUT A SCOTTISH PRIEST WHO DOES MISSIONARY WORK IN CHINA
THE NAXAL RED CORRIDOR TRIBALS HAVE THOUSANDS OF “RICE CHRISTAINS”.. THEIR LIFE BECAME UNBEARABLE BECAUSE OF “BAUXITE MINING “ DONE BY THE CHURCH.. THESE CHRISTIANS WERE ASKED TO RETAIN THEIR HINDU NAMES, OR SUFFER THE PENALTY OF NO RICE..
OUR JUDGES IN FOREIGN PAYROLL AND THEY LACKEY MEDIA PLAYED KOSHER BALL.
http://ajitvadakayil.blogspot.com/2012/09/bauxite-mining-naxalite-menace-joshua.html
capt ajit vadakayil
..
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Guruji, Stephen Knapp replied to the comment "Iskcon Depriving Small Children Of Garlic And Onion" I mailed him
Delete"
Namaste,
What is this person's spiritual authority? Who is his spiritual master, and what lineage is he associated with to have any credit to make such comments about anyone? A person should have some legitimacy before they can speak like this. Simply being a captain is not a necessary qualification in such matters.
Hari Om,
Sri Nandanandana dasa (Stephen Knapp) "
Sounds to me like he is jealous of you
Please give him a suitable reply if you feel it's necessary
Thanks And Regards
Debdoot Sarkar
I ASK MY READERS TO RESPOND SUITABLY TO MASKA TASKARA NAY-- NANDAN ANDANA DASA..
Delete#############################################3
http://ajitvadakayil.blogspot.com/2017/08/rajnikanth-entry-into-politics-babaji.html
TO AVOID LINEAGE ALL YOU NEED IS TO PRETEND TO GO TO HIMALAYAS ( YOU COULD BE HIDING IN YOUR HOME ) AD TELL THAT YOU SAW BABAJI... RAJNIKANTHs GURU
AFTER THAT YOU DONT NEED ANY LINEAGE .. TEE HEEEEEEE
FROM NOW ON STEPHEN KNAPP IS CONSIDERED TO BE AN ENEMY OF SANATANA DHARMA AND INDIA.. A WHITE MAN DASA..
HE IS BEING DROPPED FROM MY MAILING LIST..
WE DONT NEED WHITE SKINNED DASAS ( BHAKTI MOVEMENT PARTY ) TO TEACH INDIA ABOUT OUR OWN CULTURE AND SPIRITUALITY..
############################
COPY MY COMMENT ABOVE TO PM MODI, SO THAT HE CAN GIVE STEPHEN KNAPP PADMA VIBHUSHAN ..AND FOR AJIT DOVAL TO PLACE HIM IN OUR DATABASE..
Dear Captain,
DeleteAbove comment mailed to PMO.
Your Registration Number is : PMOPG/E/2020/0287677
Regards,
Swapnil Panchal
the whiteman has survived on this bullshit argument like
Delete"person's spiritual authority?,Who is his spiritual master, and what lineage is he associated with"
the white man gains authority by making someone a guru, then start an institute with his name. Start a 21 day certificate in the name of veda,tantra,yoga,ayurveda. They themselves become a guru and become authorised to create so called lineage.
White man can only give such stupid arguments. Like have you taken your medicine.
They think attending a 21 day seminar or 300hr course makes them an authority.
Responded suitably to srinandan@aol.com, narendramodi1234@gmail.com
DeleteResponded to Stephen Knapp through srinandan@aol.com..
Deletedear captain, tweeted reply to nandan andana dasa https://twitter.com/rakeshsivan/status/1246602207241179137
DeleteWe dont need these white men to teach us what sanathana dharma is.we know how these white invaders bleeded our glorious sanathana dharma by creating hundreds of fake mutts.This nandanandana DASA profile shows that he is the follower of ROTHSCHILD created character,BHAKTI vedanta swami prabhupada,who tom tommed fake radha.
DeleteSent email to us, russian, england, chinese embassy, chinese governor, borisjohnson, dgps, igs, cms, governors, ajit doval, ministers, education and defence ministry, president and vp.
DeleteALL THESE WHITE SKINNED DASAS TRACE THEIR LINEAGE TO SOME ANAL SEX RECEIVING GURU..
DeletePMOPG/E/2020/0288307
DeleteMail send to stephen napp at srinandan@aol.com
What Spiritual authority u were asking about?what lineage?who gave u authority to even talk of Captan Ajit Vadakayil in this manner?just because u can read sanskrit doesn't mean u can understand it.Captain has shown millions of hindus the right path ,their past glory and culture which the likes of u can't do in a thousand janams.he is the torch bearer of hinduism in this time..u were talking about legitmacy.open the link and read the blog
https://www.google.com/url?sa=t&source=web&rct=j&url=http://ajitvadakayil.blogspot.com/2017/12/testosterone-boosted-by-onion-and.html%3Fm%3D1&ved=2ahUKEwjaqpDRo9DoAhVSzTgGHQCkDBUQFjAAegQIARAB&usg=AOvVaw12s77BARXxzxEgkhyCPJ9B
All reasons are there but ur mediocre brain and evil heart will stop u from even reading it..this is what u worth.we Hindus have woken up.
Master Ji,
DeleteSent mail to srinandan@aol.com, narendramodi1234@gmail.com
Replied
Read the Blogs - http://ajitvadakayil.blogspot.com/2017/12/testosterone-boosted-by-onion-and.html?m=1
Do you want the Lineage check on this - Could you even write or touch10% of this Blogs
http://ajitvadakayil.blogspot.com/2017/02/we-hindu-indians-are-proudest-people-on.html?m=1
YOU ARE ENEMY OF SANATANA DHARMA AND INDIA
Thanks
Sent mail to
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Thanks
Shraddheya Shri Gurudev
DeleteStephen Knapp mailed back this reply --
________________________________________
""
Dear Raktim,
Your words about me come far too late. Anyone who knows me already knows my dedication to India and Sanatana Dharma, which I follow, and my respect for the great Rishis of India who gave all this knowledge to humanity, for which I am invited as a guest speaker to many conferences in India, and author of 47 books on the topic. I am certainly not an enemy of India, nor to Sanatana Dharma. I don't know why you talk like this.
Hari OM,
Stephen Knapp
""
_____________________________________
I was late in opening the mail , he replied earlier in the morning.
Gurudev these ICSKON org is a big fraud organisation , they are big land grabber and the people there know zilch about sanatana dharma and as they call themselves krishna devotee , they have zero perception to understand Srimad Bhagavat Gita or krishna . Crooks of the first order .
Pranam
Namaste Capt Ajit Sir,
DeleteReply to Stephen Knapp
https://drive.google.com/open?id=1pxdhTFm1EsaeK7-bRwSYsAx6anK490mi
-----------------------------------------------------------------------
Hello Mr Stephen Knapp,
We know that entire Iskcon organisation is fake and is making a religion out of Krishna, We wont let that happen, We know that Radha was fake poison injected in scriptures.
I know this right when I saw ISKCON for the first time. By declaring onion and garlic, ISKCON is depriving Indians of immunity. They have shown their colors.
We know the propaganda you are running and trying to make money out of you books. We know every white skin who is trying the same trick and his luck to become famous and rich.
We know our Indian DNA and know that you are totally opposite of it by trying to sell your irrelevant books to Indian audience.
By one look at Ajit Vadakayil's blogposts, you should have realized what you have been missing and never understood. People have been tagging you with information but your irresponsible and inconsiderate response has exposed you big time.
And then you questions one's lineage.
TO AVOID LINEAGE ALL YOU NEED IS TO PRETEND TO GO TO HIMALAYAS ( YOU COULD BE HIDING IN YOUR HOME ) AND TELL THAT YOU SAW BABAJI... RAJNIKANTHs GURU
AFTER THAT YOU DONT NEED ANY LINEAGE .. TEE HEEEEEEE
You have done your Karma. Ever Heard of Karma.
Check this
https://ajitvadakayil.blogspot.com/2017/08/rajnikanth-entry-into-politics-babaji.html
https://ajitvadakayil.blogspot.com/2017/12/testosterone-boosted-by-onion-and.html
You are an enemy of SANATANA DHARMA & INDIA..
WE DONT NEED WHITE SKINNED DASAS ( BHAKTI MOVEMENT PARTY ) TO TEACH INDIA ABOUT OUR OWN CULTURE AND SPIRITUALITY.."
We know our culture better than anyone because its in our DNA.
-------------------------------------------------------------------
Dear Capt Ajit sir,
Deletehttps://twitter.com/IwerePm/status/1246767570369179654?s=20
Pranam Ajit uncle _/\_
ReplyDeletehttps://rense.com/general96/another-black-death-haunts-italy.php
As pointed out by a supporter of the remarkably insightful Captain Ajit Vadakayil in India.
The writer Yoichi Shimatsu once again acknowledged & gave credits to you for ur revelation about Chinese slavery and the pigeon dropping causing chronic lung infection is an underlying condition among millions of Italians affected by COVID19.
Hope the WHO wakes up sooner and follow your advice and save millions of life.
Regards
Jeet
INDIA MUST EXTERMINATE EXCESSIVE PIGEONS..
DeleteI CONSIDER THEM AS PESTS..
IN CITIES PEOPLE CANNOT SLEEP DURING DAYTIME.. THESE SHAMELESS PIGEONS WHEN THEY FUCK RAISE SO MUCH OF NOISE..
CHILDREN SUFFER FROM ALLERGIES AND FUNGAL FLU DUE TO PIGEON SHIT..
CRYPTOCOCCAL MENINGITIS CAUSED BY PIGEON SHIT IS A SERIOUS INFECTION OF THE BRAIN AND SPINAL COLUMN
FUNGUS C. NEOFORMANS CAUSES MOST CASES OF CRYPTOCOCCAL MENINGITIS. THIS SPECIES IS SPREAD VIA PIGEON DROPPINGS.
CHINESE SLAVES DIED ENMASSE WHILE MINING BIRD SHIT AT ISLAND OF PERU.. MOST BECAME BLIND..
https://ajitvadakayil.blogspot.com/2020/03/chinese-slavery-part-2-capt-ajit.html
IF YOU STAY IN BANGALORE AND YOU ARE LOSING YOUR EYESIGHT, YOU MUST STAY AWAY FROM PIGEONS..
ITALY AND SPAIN EAT SQUAB ( PIGEON MEAT )..
THE MOST COMMON PATHOGENS WHICH CAN CAUSE DISEASE TRANSMITTED FROM PIGEONS TO HUMANS ARE:
E. COLI. ...
ST. LOUIS ENCEPHALITIS. ...
HISTOPLASMOSIS. ...
CANDIDIASIS. ...
SALMONELLOSIS.
THESE PIGEONS LIKE MONKEYS ARE NEITHER USEFUL WHEN ALIVE OR DEAD..
AND HULLO--
HANUMAN IS NOT A GOD IN RAMAYANA.. JEW ROTHSCHILD CONVERTED HANUMAN TO GOD..
http://ajitvadakayil.blogspot.com/2014/02/devadasi-system-immoral-lie-of-temple.html
capt ajit vadakayil
..
.
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https://twitter.com/rakeshsivan/status/1246649133328265217
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https://twitter.com/rakeshsivan/status/1246649333832806400
Sent to CM'S DGP'S BJP Speakers Health Minister BJP President, Alex Jones And BBC
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Posted on their twitter Accounts:
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pranaam cptain
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furthurmore earlier msg sent to stphen napp.his reply-
DeleteAman Thakur
8:26 AM (3 hours ago)
to srinandan
What Spiritual authority u were asking about?what lineage?who gave u authority to even talk of Captan Ajit Vadakayil in this manner?just because u can read sanskrit doesn't mean u can understand it.Captain has shown millions of hindus the right path ,their past glory and culture which the likes of u can't do in a thousand janams.he is the torch bearer of hinduism in this time..u were talking about legitmacy.read his blog on testerone boosted by onion and garlic
All reasons are there but ur mediocre brain and evil heart will stop u from even reading it..this is what u worth.we Hindus have woken up.
srinandan@aol.com
10:03 AM (1 hour ago)
to me
Aman Thakur,
I understand the article, having read it in spite of your cruel comments that I would not. But my point was his blanket comment about Iskcon and the dietary standards that they follow, when we can see similar dietary standards or restrictions among the Sikhs, Jains, Buddhists, and others. So, if he is making such judgements, why does he not give the same blame on them? Otherwise, why not let organizations provide free food programs for children, such as Iskcon's Akshay Patra, often in areas where the children do not have access to nutritious meals?
My question was a legitimate question. If he wants to criticize any spiritual organization in such a way, then what standard is he basing his judgement on? And why not similarly place blame on the Sikhs, Jains etc? Anyone should be able to ask that question if he is similarly going to be so derogatory toward others and their spiritual practice. He may have his reasons for saying what he has, just as other spiritual organizations have their purposes for what they do, which he obviously seems just as uninterested in understanding. His criticism was extremely harsh, not something that was asking for understanding between anyone. And if I am asking the questions I did, then obviously others are, too.
Furthermore, I did not ask to be put on this thread, but someone sent it to me anyway, which gives me the right to respond. So, if they did not want to hear from me, then they should have kept me off the thread. Plus, anyone who knows me already knows that I am no enemy of India, nor to Sanatana Dharma, which I respect and follow. Which is why I get invited to many conferences in India as a guest speaker, and I have been encouraging more Hindus to wake up and protect their culture. But arguments among Hindus like this and such disrespect for their own traditions or other sects only shows how much disunity they actually have.
Hari OM,
Stephen Knapp
Aman Thakur
10:32 AM (1 hour ago)
to srinandan
wow.You said u read the blog.After reading it, u didn't say that the scientific reasons he gave were wrong or right.Probably because either u have nil understanding of the subject or u didn't want to shown an iota of appreciation for him for his unprecedented efforts to revive sanatana dharma in modern time.You said the same diet is among jains,buddhists etc.So what.Sanatana dharma is based on science.Every ritual is based on fundamental laws of universe.The Bhartiya culture developed within the boundary of sanatana dharma also have every ritual and practice based on science.Sanatana dharma is not jainism or buddhism.In sanatana dharma,onion and garlic are shunned only for those who are very serious about spiritual growth.Not for all.This is plain common sense
ISKCON is doing all this in the name of Hinduism.Let them do on the name of some food science meant for their cult.Iskcon is just derogating hinduism.This is the problem.none of what i have written is unknown to you.We live and honour our traditions.But we Hindus will not accept any tradition imposed on us by the white invaders.Better change.Karma will catch on.
no reply yet
https://twitter.com/DvSathvik/status/1246710798564470785
Deletesent to
Your Registration Number is : PMOPG/E/2020/0289192
pd.ncert@nic.in,
cbm.ncert@nic.in
AYUSH/E/2020/00884
DeleteDHRES/E/2020/00342
DPHAM/E/2020/00179
DHLTH/E/2020/02921
Thanks
Hello Mr Dasa
DeleteAjit Vadakayil is not some ordinary Captain, for starters read his blog introduction to have a better clarity about his identity and achievements, he is an all-rounder with a remarkable perception which very few in the planet has, he is on the plane of Shri Adi Shankaracharya - the last real guru of the planet, what he is doing to heal the wounds inflicted on santana Dharma and India by the Hilly Billy white men invaders, no guru in the present can even dream of doing, his blog is not just a mere blog but an encyclopedia full of treasures read by millions and it is all free for seekers and aspirants, his teachings are not mindless dogmas but are full of wisdom.
Being sponsored, Attending some fancy seminar or changing name to some dasa or baba, selling courses doesn't make someone a guru.
In Sanatana Dharma one doesn't automatically become a guru by following a guru like fictional babaji from Himalayas, there is absolutely no space for blind bhakti in it, you can only become guru by your worth, selfless karma and sacrifice.
I understand for white men, selfless and consciousness are alien concepts, even if they know the terms they are far from grasping the concepts truly. Its a genetic thing.
So my advice to you would be to,
Lay Off Hinduism And Never Dare To Speak Like That About Our Guru
From now onwards You are dropped from our mailing list.
P.S If you really want to do something for Bharatmata and Santana Dharma start with asking your sponsers to not deprive Bharatmata's children of garlic and onion which are necessary for building body immunity.
Regards
Pupil Of Captain Ajit Vadakayil
This idiot nandanandana DASA(Stephen knapp)and his gora gand ilk like koenraad elst,david frawley...knows nothing about hinduism or sanathana dharma.what a pathetic situation india is in!we are inviting these fools as guest speakers to speak on Hinduism.Iskon displaced position of rukmini, beloved wife of krishna with fake radha.In all its organisations fake radha is placed beside krishna.what kind of spiritual organisation is this? Can this ISKON organisations worldwide gets established without blessings of ROTHSCHILD?These organisation is allowed to spread all over the world by ROTHSCHILD only because it tom tommed fake radha character and maligned lord krishnas character as "womaniser" with fake stories of gopikas.what type of hindu organisation is this?
DeleteThese gora gand idiot knows nothing on how these Buddhist,jain and Sikhs religions originated.school going children need onion and garlic compulsorily in their diet as it boosts their immunity. ROTHSCHILD deprived these ISKON cult of onion and garlic so that they remain subservient and like slaves and tattus.
This idiot DASA talk of dietary habits.ha ha ha..what can be a biggest joke than this? This is like rat lecturing a lion.A man CAPTAIN AJIT VADAKAYIL born on land of vedas,land of ayurveda and who has the best perception in the world and who realised sanathana dharma as quantum physics is a man of his own. CAPTAIN AJIT VADAKAYIL dont need any DASA gurus lineage.
Hello Captain,
DeleteI have sent on twitter to the following links:
https://twitter.com/manntharkan/status/1246757952322707456
https://twitter.com/manntharkan/status/1246758336026083328
https://twitter.com/manntharkan/status/1246758529882562560
https://twitter.com/manntharkan/status/1246758788411076608
https://twitter.com/manntharkan/status/1246759073468514313
https://twitter.com/manntharkan/status/1246759934659878913
Kind regards,
Guruji
DeleteWhite Baba Replied
Namaste,
Some people feel that my questioning Capt. Ajit Vadakayil is because of a difference in opinion about the use of onions and garlic. That is hardly the case. Everyone has an opinion on these items, so what? What I objected to was his blanket statement about Iskcon, which I found to be extremist, harsh, outright racist, and ridiculously wrong. And I'm expected to stay silent about that? There are so many organizations and sects within Hinduism that work in various ways and also have dietary restrictions for various reasons, but to show such bias and to say what he did against one particular organization is a prime example of how Hindus, at a time when they should be united and work together for the future of India and the Hindu / Vedic cause, are still as disunited as ever. What a pity.
It is just like, at one time or another, I have heard heavy criticism against almost everyone that was listed on the original thread. So does that mean I should stop supporting and working with them? Some people may view it that way, and even try to convince me of that. But that is not the way I see it, even if we have some differences of opinion. What we have in common is far greater. Similarly, I have also worked with so many organizations in and outside of India, such as the RSS, HSS, VHP, Vivekananda Kendra, Kalyan Ashrama, Iskcon, Vedic Friends Association, Swadhyaya, and others. And I will continue to work together with those who sincerely try to help protect and perpetuate the Hindu / Vedic cause. My life has been greatly enhanced by doing that. And I will question anyone who tries to convince me against doing that, and what basis or authority they have to do so.
Hari OM,
*By everyone he means cc to.
infinity.foundation.india@gmail.com
swamy39@gmail.com
vedicinstitute@gmail.com
fgautier26@gmail.com
info@ishafoundation.org
secretariat@artofliving.org
info@vhp.org
abvpkendra@gmail.com
svaradarajan@gmail.com
WE HINDUS HAVE ABANDONED VIVEKANANDA-- TELL STEPHEN KNAPP THIS..
DeleteFREEMASON VIVEKANANDA WAS ROTHSCHILDs AGENT.. PROPAGATING BHAKTI DOGMA INSTEAD OF SHRADDA..
https://ajitvadakayil.blogspot.com/2020/03/sanatana-dharma-hinduism-exhumed-and.html
http://ajitvadakayil.blogspot.com/2019/08/kathiawari-jain-jew-gandhi-and-kayastha.html
WHAT UPSET US WAS YOUR QUERY " WHAT IS CAPT AJIT VADAKAYILs LINEAGE"?? ..
YOU HAVE NO IDEA WHO DANAVA VADAKAYIL IS.. THE PEOPLE WHO HELD THE HEAD SIDE OF THE VASUKI SNAKE DURING SAMUDRA MANTHAN.. FROM WHOSE LAND SANATANA DHRAMA WAS BORN..
http://ajitvadakayil.blogspot.com/2019/09/onam-our-only-link-to-planets-oldest.html
VADAKAYIL IN 40 YEARS AT SEA , HAS VISITED MANY ISKCON CENTRES..WORLDWIDE.. WHERE THEY DANCE IN FRONT OF CHAITANYA MAHAPRABHU WHO NEVER EXISTED..
capt ajit vadakayil
..
Sent.
DeleteThank You _/\_
Shraddheya Shri Gurudev
DeleteThe Nanda Dasa Person replied to the above message , but it seems , it is either being auto-generated or copy paste message to all ..
_______________________________________
Namaste,
Some people feel that my questioning Capt. Ajit Vadakayil is because of a difference in opinion about the use of onions and garlic. That is hardly the case. Everyone has an opinion on these items, so what? What I objected to was his blanket statement about Iskcon, which I found to be extremist, harsh, outright racist, and ridiculously wrong. And I'm expected to stay silent about that? There are so many organizations and sects within Hinduism that work in various ways and also have dietary restrictions for various reasons, but to show such bias and to say what he did against one particular organization is a prime example of how Hindus, at a time when they should be united and work together for the future of India and the Hindu / Vedic cause, are still as disunited as ever. What a pity.
It is just like, at one time or another, I have heard heavy criticism against almost everyone that was listed on the original thread. So does that mean I should stop supporting and working with them? Some people may view it that way, and even try to convince me of that. But that is not the way I see it, even if we have some differences of opinion. What we have in common is far greater. Similarly, I have also worked with so many organizations in and outside of India, such as the RSS, HSS, VHP, Vivekananda Kendra, Kalyan Ashrama, Iskcon, Vedic Friends Association, Swadhyaya, and others. And I will continue to work together with those who sincerely try to help protect and perpetuate the Hindu / Vedic cause. My life has been greatly enhanced by doing that. And I will question anyone who tries to convince me against doing that, and what basis or authority they have to do so.
Hari OM,
Sri Nandanandana dasa (Stephen Knapp)
______________________________________________
Pranam
Pranam Ajit uncle _/\_
ReplyDeleteFinally Trump announces new face mask recommendation.. hopefully Indian Government stop following Jewish agenda and get sufficient face mask and declare strictly to wear face mask
https://edition.cnn.com/2020/04/03/politics/trump-white-house-face-masks/index.html
Regards
Jeet
Dear Captain,
ReplyDeleteRegistration Number is : PMOPG/E/2020/0287659
Regards,
Swapnil Panchal
SOMEBODY CALLED ME UP AND ASKED --
ReplyDeleteCAPTAIN WHY WERE YOU SO VEHEMENT WHEN MODI APOLOGIZED TO THE NATION FOR COVID-19 LOCKOUT..
CAPT AJIT VADAKAYIL IS A LIVING LEGEND AT SEA..
HE CHANGED MANY DEEP ROOTED , "ENGRAVED ON ROCK" PRACTICES AND CULTURE AT SEA..
MANY OF MY JUNIOR OFFICERS WHEN THEY SAW ME AGAIN ASHORE TOLD ME THIS..
THEIR CAPTAINS WOULD ASK " HAVE YOU SAILED UNDER THE COMMAND OF CAPT AJIT VADAKAYIL?"
THE REASON WAS THESE VADAKAYIL EFFECT MOLDED OFFICERS WHO NEVER GIVE AN ORDER WITH THE WORD "PLEASE" ATTACHED.
THEY WERE TRAINED THAT WAY.. THEY KNEW CAPT VADAKAYIL MONITORS WALKIE TALKIE CONVERSATIONS FOR TWO THINGS ..
1) NO PLEASE ATTACHED TO AN ORDER.. THIS COULD BE A JUNIOR OFFICER TELLING SOMETHING TO A SENIOR OFFICER.. SAY OPEN TANK NO 4 VALVE IMMEDIATELY..
2) EVERY ORDER MUST BE REPEATED , AS SOON AS IT IS HEARD ..
IF THIS WAS NOT DONE , AFTER THREE WARNINGS-- I WOULD SACK HIM..
I DONT RUN MY SHIP ON SENTIMENTS..
############
THERE WAS A TIME WHEN MY SENIOR A BONG CAME ON MY SHIP AS PILOT..
IT WAS DURGA PUJA TIME AND HE GAVE ME A BOOKLET AND SAID " AJIT , I SAW YOUR BOAT STATIONS CARD ON THE WAY UP.. YOUR CREW IS BENGALI HINDU.. TELL THEM TO GIVE CHANDA (DONATION) "
I TOLD HIM KNEE JERK " TILL TODAY , WHEN I TOLD MY CREW / OFFICERS TO DO SOMETHING, I NEVER GAVE THEM A CHOICE.. TODAY FOR THE FIRST TIME , I HAVE TO REQUEST THEM ON YOUR BEHALF.. GIVING THEM A IDEA (WHICH COULD BE PLANTED IN THEIR SUBCONSCIOUS MIND )---
-- IT IS ALL RIGHT IF YOU SAY "NO" TO THE CAPTAIN..
I TOLD MY SENIOR , I CAN ALLOW THIS.. PERIOD..
ALL CORRESPONDENCE BY WRITTEN NOTICES WOULD CHANGE
EXAMPLE:
OLD : PLEASE NOTE THAT CLOCKS WILL BE RETARDED TODAY BY 60 MINUTES
NEW : CLOCKS WILL BE RETARDED TODAY BY 60 MINUTES
I WAS THE ONE WHO CHANGED THE ABANDON SHIP SIGNAL TO A FIRM ORDER IN CAPTAINS VOICE ON LOUD SPEAKER " ABANDON SHIP" " ABANDON SHIP" "ABANDON SHIP"...
BEFORE IT WAS SOS ON THE GENERAL ALARM/ WHISTLE , WITH CAPTAIN SAYING PLEEEAASSEEEEE ABANDON SHIP..
ORDERS ON THE BRIDGE TO HELMSMAN HAS TO BE LOUD AND CLEAR..THE ORDER HAS TO BE REPEATED LOUDLY BY THE PERSON PRIOR EXECUTING THE ORDER ..HE HAS TO REPORT BACK AFTER HE HAS DONE THE JOB..
HARD A STARBOARD .. IN A LOUD FIRM MANLY VOICE
THERE CAN BE NO "HARD A STARBOARD PLEASE " IN CHICKOO GIRLY VOICE-- ON MY SHIP..
capt ajit vadakayil
..
Comment Sent
DeletePMOPG/E/2020/0287771
connect@mygov.nic.in
info.nia@gov.in
Dear Captain,
DeleteModi was apologetic to migrant workers only not to the nation, as they had resorted to walk hundreds of kilometers with small children, pregnant wives etc. When Western media started to malign Modi's intentions, the Home Ministry jumped quickly and allotted 2.4K crores to feed and shelter them and issued executive order to quarantine them wherever they are. Delhi's CM played politics here to save money and even these laborers decided to go to the villages to save on rent. This was a sudden turn of events and administration was not expecting. I think the central administration dealt another calamity well in timely fashion and serve your appreciation.
On another note, Captain, is it okay to have humility for a leader? The modern management seminars are advocating this for their success. Please advise.
Regards..
https://www.instagram.com/p/B-PCGhxAb7V/?igshid=1b216r2ldad3z
ReplyDeletelook at media doing alllle l e le le beta taimur on live tv.
is this the same media asking the government to be serious
navika kumar at her best.
Sir,
ReplyDeleteI have emailed to President Trump on white house website.
Pranam
Dear captain
ReplyDeleteWho Made this Coronavirus?
Was It the U.S., Israel or China Itself?
American Jew in China. Lieber.
DeleteAAA - GOOGLE HAS BEEN SINKING MY IMPORTANT POSTS ..
ReplyDeletegoogle for the post below--
" Secrets of the ROMAN PANTHEON , inaugurated by Kerala Thiyya king Rama , the founder of Rome on 21st April 850 BCE revealed- capt ajit vadakayil "
IT HAS BEEN SUNK..
But Google for the link below--
https://ajitvadakayil.blogspot.com/2019/08/secrets-of-roman-pantheon-inaugurated.html
THE SUNK POST WILL POP UP..
THIS IS WHAT ROTHSCHILDs AGENT INDIAN HINDU SUNDAR PICHAI IS WORTH..
WHAT A DISGRACE ..
GOOGLE/ QUORA/ TWITTER/ FACEBOOK ALLOWS PAKISTANI ISI MUSLIM AGENTS WITH HINDU NAMES TO ABUSE INDIAN AND HINDU GODS..
BUT IF A DESH BHAKT HINDU / INDIAN TRIES TO REUDIATE OR EXPOSTULATE JEWSIH DEEP STATE TOOLS GOOGLE/ QUORA/ TWITTER/ FACEBOOK SINKS IT ON THE INTERNET OR DELETES IT..
THIS BLOGSITE HAS COMPLAINED TO PM MODI/ PMO/ I&B MINISTER JAVEEKAR/ LAW MINISTER / RSS ETC MORE THAN 100 TIMES. NOBODY CARES..
#####################
EXAMPLE BBB--
IF YOU GOOGLE FOR " AGHORIS , THE CORPSE EATERS OF INDIA - CAPT AJIT VADAKAYIL "
NOTHING COMES UP ..
BUT IF YOU GOOGLE FOR THE LINK, MY POST COMES UP..
http://ajitvadakayil.blogspot.com/2014/09/aghoris-corpse-eaters-of-india-capt.html
#############
CCC- IF YOU GOOGLE FOR :---google for my post below--
" REPEATED BLASPHEMY AGAINST HINDU GODS BY QUORA CEO ADAM D ANGELO, FOR WHICH PUNISHMENT UNDER INDIAN LAW IS LONG TERM IMPRISONMENT "
NOTHING COMES UP....
BUT IF YOU GOOGLE FOR THE LINK, IT SHOW UP
https://ajitvadakayil.blogspot.com/2019/04/repeated-blasphemy-against-hindu-gods_9.html
#######################
WE WONDER WHO THE PEOPLE RULING INDIA ARE ? ARE THEY TRAITORS IN FOREGN PAYROLL?
capt ajit vadakayil
..
Captain, All of these are showing as No 1 result on DuckDuckGo but not using Google.
DeleteThe Raziman's comment from Quora is being displayed as number 1 for these searches, which proves that this guy is their sponsor and anti-Indian.
Google has sunk another important post.what can be best example other than this to show that google is an agent of rothschild controlled deep state?Beacuse of traitors like sundar pichai we are slaves for 800 years.
Deletehttps://www.worldometers.info/coronavirus/
ReplyDeletePORTUGAL IS UNDER DECLARING DEATHS
Capt
DeleteIndia's tests per million is ultra low in comparison to developed countries, for now.
We are testing people only with symptoms.
https://twitter.com/CMOMaharashtra/status/1246390033189187585
ReplyDeleteSABKA VISHWAAAAS
Elder Figure, Shraddha, Strength and Honor to your 60.32% revelation.
ReplyDeletehttps://www.youtube.com/watch?v=Vpe65KGk97U&feature=youtu.be
ReplyDeleteMY ELDER SON GETS HIS DAILY EXERCISE BY PLAYING THIS GAME.. IN THIS LOCKDOWN PERIOD WHEN GYM IS CLOSED..
Respected Sir,
DeleteNice game!!
You get the gross feeling of a warrior.
Love and regards
See, Rana Ayyub:
ReplyDeletehttps://twitter.com/Dix5a/status/1246588898710556673?s=19
https://twitter.com/Dix5a/status/1246588898710556673?s=19
DeleteHER DNA HAS BEEN TESTED.. BY A STRAND OF HAIR..
MUSLIMS HAVE ALWAYS WONDERED IF SHE IS A JEWESS..
HER GRANDFATHER IS A PASHTUN JEW ..
HER FATHER IS A KHAN JEWB ( COMMIE ) EMPLOYED BY RK KARANJIA OF BLITZ.. A MEMBER OF JEW ROTHSCHILDs PROGRESSIVE WRITERS ASSOCIATION..
CAPT AJIT VADAKAYIL IS DIGGING UP SHEHLA RASHID SHORAs ANCESTRY..
SEND THIS TO AFREEN ANSARI
https://twitter.com/rakeshsivan/status/1246730578876960770
DeletePosted:
Deletehttps://twitter.com/Avii02379630/status/1246735804468047874?s=19
Hello Captain,
DeleteI hope you and family are well during lockdown.
Here is the screenshot sent on twitter.
https://twitter.com/manntharkan/status/1246751333933355010
Kind regards,
Captain Sir,
ReplyDeleteRegarding ola/uber drivers putting on ac and using gloves, got this reply from Karnataka covid-19 handle.
https://twitter.com/DIPR_COVID19/status/1246519161963995136
Respected Captain,
ReplyDeleteNamaskaram. Pls allow me to ask this. As you repeatedly say Israel has become a tapeworm in India's stomach, is India becoming the next US to be a puppet in the hands of Zionists with Namo's love for White Jews. Thank you.
Respected Sir,
ReplyDeleteIs it ok to consume veggies and fruits planted in polluted soil eg--dog shit.
Can one get tapeworm/parasite infection.kindly reply.
Thank you
MOST DISEASES IN INDIA IS DUE TO DOG SHIT.. EVERY TIME WE TRY TO CULL STRAY DOGS KOSHER EVIL PHARMA USES MANEKA GANDHI TO PROTEST..
DeleteIN SNAKE GROVES NO ANIMALS DARE ENTER.. THE HUMUS LADEN SOIL GROWS EFFECTIVE AYURVEDIC HERBS.. AND CERTAIN FRUITS / VEGGIES WHICH ACT AS PREVENTIVE MEDICINE..
MY ELDER SON ORDERED PIZZA.. AFTER A MONTH OF COOKING AT HOME.. HE WORKS AT HOME..
ReplyDeletePIZZA DELIVERY BOY CAME TO THE DOOR STEP IS SPACE SUIT..
THEY USED DISPOSABLE GLOVES TO TAKE THE PACKAGE.. TONGS TO REMOVE THE PIZZA AND HEAT ( NOT WARM ) IN MICROWAVE ..
ReplyDeletePRIYADARSHAN SIRASApril 5, 2020 at 1:29 PM
RESPECTED CAPT. AJIT VADAKAYIL SIR, Would you please give DEEKSHA ..digital mantra for Corona. Sir, you have changed our lives, our thinking,our thought process,our personality and the whole world. Thanks for everything.
Yes sir...will do it for whole world
DeleteWHY WERE WE SLAVES FOR 800 YEARS?
ReplyDeleteAPNA KYA JAATHA HAI ATTITUDE.. WHAT IS THERE IN IT FOR ME PERSONALLY..
WHEN QUORA ATTACKED MY WIFE WITH FOUL SEXUAL INNUENDO , NOT A SINGLE READER DOWN VOTED THAT VILE COMMENT. LEAVE ALONE REPUDIATE.. THEY WERE ALL ENJOYING INWARDLY..
MIND YOU , HUNDREDS OF MY READERS GURUJEEEE ME DAILY AND TREAT ME AS THEIR PERSONAL DOCTOR IN OLD POSTS-- AND BEG ME TO DELETE THESE COMMENTS AFTER THEIR PROBLEM IS SOLVED.
STEPHEN KNAPP ABUSED ME.. DEMANDING MY LINEAGE ..
I KNOW AFTER 48 HOURS OF THIS EVEN EVENT, HOW MANY OF MY READERS HAVE EVEN OBJECTED , DESPITE MY CALL TO DO IT.
ALL SELFISH BASTARDS ..MY READERS ..
I HAVE COMMANDED UNITED NATIONS CREW.. THEY HAVE GRATITUDE UNLIKE BASTARD CHOOTIYA INDIANS
capt ajit vadakayil
..
Captain,
DeleteYou have written that people were even threatening to kill you for writing the truths till 2016 and now they have fallen way aside.
If anyone attempt to kill you then i will search and hunt him.
Regards,
Muthu Swamynathan.
Dear Ajit Sir,
DeleteI have replied as below:
1/2...
ISKCON preacher Stephen Knapp,
What credibility do you Albino Skinned fellow Stephen Knapp hold that you are invading the surviving HINDU space which didnot fall to Islamic Sword & Christian Missionary sops???
Stop you GUTTER philosophy which you pass through the lens of ISKCON.
HINDUISM does not subscribe to any CULT based on Dogmas like the way your ISKCON does.
Hinduism ain't any CULT & certainly this ISKCON which is a cult is not at all Hinduism.
Top positions occupied by individuals who manage ISKCON, spin money by their pseudo-philosophy moulded to sound like Hinduism but rests it's inspiration from Messiah based faith.
Fake Radha above Krishna by ISKCON is axing at the base of Hinduism.
Where are Brahma & Shiva in ISKCON?
Have they evaporated?
Read Nasadiya Sukta, penned by Captain Ajith Vadakayil in the most simplest language for a layman like you to understand.
Below is the link:
http://ajitvadakayil.blogspot.com/2014/07/nasadiya-sukta-rig-veda-5000-bc.html?m=1
You Stephan Knapp, First learn the basics of Hinduism & for you as a starter go through the profound knowledge of Sanatana Dharma series penned by Captain Ajith Vadakayil & it is very well explained in English for you people's understanding & understanding on a Global platform.
2/2...
DeleteCaptain Ajith Vadakayil has not restricted himself to one topic.
He has covered almost all topic whether it's related to Spiritualism, Science meeting Spiritualism(Quantum mechanics), Medicine, Technology, innovation, forensics, Legal reformation, Humanity, psychology, anthropology, History, Administrative ideas, Military strategy, Political science etc etc.
When you are seeking his Spiritual authority, better know that he has not met any of his followers but has transformed their mind.
Can this be done by any spiritual master in your knowledge or sphere of influence?
All you people do is meet people as person & by cherry picked verses you people try to hijack the person's subconscious mind.
That's an extreme case of cheating & is against HINDUISM.
Hinduism doesnot teach to cheat people by charm or by conning them.
HINDUISM is all about giving space for a person to think, throw questions & seek answers by reasoning.
It has got nothing to do with following a single book.
Bhagvata Gita is not the only single holy book in Hinduism as the way ISKCON preaches and markets it with their tampered version.
Also ISKCON is guilty of criminally feeding school children a diet devoid of GARLIC & ONION which is a much needed supplement to enhance the body's immunity & helps as an inflammatory agent.
ISKCON is a ploy by evil Pharmaceutical Industry.
ISKCON has been trying to wedge itself apart, establish it's supremacy, rival the original HINDUISM & make it ditto like a Messiah cult as the Abrahmic religion but Captain Ajith Vadakayil will not allow the dream of ISKCON to become a reality.
When you have asked his ardent readers that "what lineage is he associated with to have any credit to make such comments about anyone?"
Then you better know that to AVOID LINEAGE all you need to do is to pretend to go to Himalayas(you may prefer to hide in your home), then tell everyone that you saw BABAJI(Invisible Guru of South Indian superstar Rajanikanth but visible to him & some person like him).
After the above declaration that you saw BABAJI, you need not need to prove your LINEAGE.
Till date Captain Ajith Vadakayil never asked anyone to be his follower or believe him.
He left the space open.
Anyone wants to believe him, fine and if one doesnot believe, may move on.
There was no force feeding or shoving anything inside the head of a person.
For more clarification do check the Mission Statement of Captain Ajith Vadakayil in his Blogspot if you still hold any apprehension.
Thank You.
Regards
Reader of Captain Ajith Vadakayil
WHY WERE WE SLAVES FOR 800 YEARS?
ReplyDeleteAPNA KYA JAATHA HAI ATTITUDE.. WHAT IS THERE IN IT FOR ME PERSONALLY..
WHEN QUORA ATTACKED MY WIFE WITH FOUL SEXUAL INNUENDO , NOT A SINGLE READER DOWN VOTED THAT VILE COMMENT. LEAVE ALONE REPUDIATE.. THEY WERE ALL ENJOYING INWARDLY..
MIND YOU , HUNDREDS OF MY READERS GURUJEEEE ME DAILY AND TREAT ME AS THEIR PERSONAL DOCTOR IN OLD POSTS-- AND BEG ME TO DELETE THESE COMMENTS AFTER THEIR PROBLEM IS SOLVED.
STEPHEN KNAPP ABUSED ME.. DEMANDING MY LINEAGE ..
I KNOW AFTER 48 HOURS OF THIS EVEN EVENT, HOW MANY OF MY READERS HAVE EVEN OBJECTED , DESPITE MY CALL TO DO IT.
ALL SELFISH BASTARDS ..MY READERS ..
I HAVE COMMANDED UNITED NATIONS CREW.. THEY HAVE GRATITUDE UNLIKE BASTARD CHOOTIYA INDIANS.. EVEN PAKISTANIS MUSLIMS ARE BETTER THAN INDIAN HINDUS..
capt ajit vadakayil
..
emailed capt. long ago.. -- { Srinandan@aol.com }
Deleteif anyone found his twitter handle report here..
#########################
below David Frawley praises stephen knapp:-
https://twitter.com/davidfrawleyved/status/811957518897291264?s=20
below Francis Gautier ask Modi why u not give any award to koenraad elst:-
https://twitter.com/fgautier26/status/1223261412497088513?s=20
below, when i tweeted Jew R ruled india.. koenraad elst gonna mad:-
https://twitter.com/Koenraad_Elst/status/1223081012781027328?s=20
most of this type gora gand are hindu only for milking money by selling their shitty books on amazon.
To Prevent this sort of shit from happening again i suggest people mmake a database of required peoples ID ,twitter,gmail etc ,Alternatively someone post the required Id here itself , if many send at the same time we may have a better impact
DeleteReg
D
Captain Sir,
DeleteI leave any suggestions , changes in my statement etc upto you
Reg
Div
Dear Capt Ajit sir,
Deletehttps://twitter.com/IwerePm/status/1246767570369179654?s=20
CRITICAL MESSAGE FROM CAPT AJIT VADAKAYIL TO PM MODI..
ReplyDeleteWE THE PEOPLE WILL NOT BE PATIENT WITH YOUR NON-PERFORMANCE AND INSUFFERABLE HOT AIR ANY MORE..
BE WARNED..
###########################################
FOOD PRODUCTION AND NONPERISHABLE STORAGE IN INDIA MUST CONTINUE DURING THIS PANDEMIC ..IN CONTROLLED CONDITIONS.. TIMING IS EVERYTHING ..
OZONE MUST BE USED TO PRESERVE GRAIN/ PULSES/ VEGGIES/ FRUITS..
WE HAVE WASTED 38% FOOD IN MODIs TENURE OF 5 YEARS ( AVERAGE ).. THIS IS CRIMINAL..
THIS OZONE POST BELOW HAS BEEN SENT TO MODI/ PMO MORE THAN 50 TIMES..IT WAS IGNORED BECAUSE MY NAME IS NOT ANUSHKA SHARMA..
http://ajitvadakayil.blogspot.com/2013/06/unsung-ozone-for-instant-prevention-and.html
CRIMINALS USING FORMALIN TO PRESERVE FISH ( SAVING ELECTRICITY COSTS FOR FREEZER ) MUST FACE TEN YEARS IN JAIL..
http://ajitvadakayil.blogspot.com/2012/08/formalin-as-fish-preservative-and.html
WE ASK MODI-- STOP HANKERING FOR EGO MASSAGE...
STOP VOTE MILKING NUSKE..
WORK FOR BHARATMATA , NOT YOUR JEWISH MASTERS..
WE WATCH
capt ajit vadakayil
..
PUT ABOVE COMMENT IN WEBSITES OF--
DeletePMO
PM MODI
EVERY MP OF INDIA
EVERY MLA OF INDIA
EVERY COLLECTOR OF INDIA
DEFENCE MINISTER/ MINISTRY
ALL 3 ARMED FORCE CHIEFS
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E SREEDHARAN
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SADGURU JAGGI VASUDEV
BABA RAMDEV
SRI SRI RAVISHANKAR
SPREAD ON WHATS APP
SEND THIS MESSAGE TO ANUSHKA SHARMA AND VIRAT KOHLI
DeleteANUSHKA -JI -- CAN YOU FORWARD THE ABOVE MESSAGE TO MODI.. USE YOUR NAME AT THE BOTTOM OF THE MESSAGE..
MODI IS IN BHRAMMM.. HE WILL LISTEN TO YOU.. AS YOU ARE A GREAT VOTE CATCHER
I WROTE MY POST ON FORMALIN IN 2012
DeleteKERALA GOVT HAS ACTED...
https://www.thebetterindia.com/147613/news-kerala-formalin-fish-rapid-detection-kits/
capt ajit vadakayil
..
DOFPD/E/2020/00427
DeleteDOFPI/E/2020/00047
MINHA/E/2020/03557
DBIOT/E/2020/00134
DFSHR/E/2020/00038
DOAAC/E/2020/03299
DOARE/E/2020/00169
DOFPD/E/2020/00428
Sent to webmaster.indianarmy@nic.in,
Deletenarendramodi1234@gmail.com,
connect@mygov.nic.in,
contact@amitshah.co.in,
amitshah.mp@sansad.nic.in,
info.nia@gov.in,
information@cbi.gov.in,
dr.harshvardhan@sansad.nic.in,
proiaf.dprmod@nic.in,
pronavy.dprmod@nic.in,
yogiadityanath72@gmail.com,
yogi.adityanath@sansad.nic.in,
dirhealthdhs@gmail.com
REG
D
Any views on Declaring emegency hang judial traitors?
PMOPG/E/2020/0289668
DeleteSent emails to:
minister.fpi@gov.in
adhindsa@incometax.gov.in
ajit.kaur@nic.in
mos-fpi@nic.in
secy.mofpi@nic.in
as-mofpi@gov.in
prakashr@cag.gov.in
amitshah.mp@sansad.nic.in
amitabh.kant@nic.in
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sois-mha@nic.in
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deo.bangaloreu@gmail.com
Deo.bangalorer3@gmail.com
collector.mumbaisuburb@maharashtra.gov.in
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acm1.lu-up@gov.in
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acm4.lu-up@gov.in
dm-jal-rj@nic.in
divcomksh-jk@nic.in
dc-shi-hp@nic.in
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adviser-chd@nic.in
dm-patna.bih@nic.in
acpatna1@gmail.com
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dmgwalior@nic.in
collector-sur@gujarat.gov.in
rdc-sur@gujarat.gov.in
collector.nagpur@maharashtra.gov.in
ac1-south.goa@nic.in
ac2-south.goa@nic.in
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dckzk.ker@nic.in
dctvm@kerala.nic.in
collrchn@nic.in
jgtl@telangana.gov.in
jc-jgtl@telangana.gov.in
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dm-khurda@nic.in
raipur.cg@nic.in
rdc.pune-mh@gov.in
collector.nashik@maharashtra.gov.in
collectorchittoor@gmail.com
collector_vsk@ap.gov.in
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dc-ran@nic.in
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dehdm@ua.nic.in
tehdm@ua.nic.in
hardm@ua.nic.in
baksa@nic.in
kokrajhar@nic.in
kamrup@nic.in
Sent to
DeleteCM's DGP's BJP Speakers Health Minister BJP President
connect@mygov.nic.in
ramvilas.paswan@sansad.nic.in
17akbarroad@gmail.com
webmaster.indianarmy@nic.in
PRO IAF • proiaf.dprmod@nic.in
pronavy.dprmod@nic.in
contact@amitshah.co.in
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PMOPG/E/2020/0289652 MINHA/E/2020/03559
DEPOJ/E/2020/01515 MOIAB/E/2020/00947
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DBIOT/E/2020/00135 DHRES/E/2020/00344
DSEHE/E/2020/01463 DMAFF/E/2020/00186
AYUSH/E/2020/00889 MODEF/E/2020/01071
Dear Capt AJit sir,
Deletehttps://twitter.com/IwerePm/status/1246770515382951938?s=20
What's Shashi Tharoor's story? He's got those pale eyes. Ladies love him. Potential murderer. Worked at UN.
DeleteIs he like a Jame Bond character?
Dear Capt Ajit sir,
Deletehttps://twitter.com/IwerePm/status/1246770140164681728?s=20
Dear Capt Ajit sir,
Deletehttps://twitter.com/IwerePm/status/1246770515382951938?s=20
Poster to Anuskha and Virat and other Key twitter address
Deletehttps://twitter.com/kannanlp/status/1246815840151859200
https://twitter.com/kannanlp/status/1246815612396949505
Thanks
https://twitter.com/rakeshsivan/status/1246828310186745856
Deletehttps://twitter.com/rakeshsivan/status/1246828558070116354
https://twitter.com/rakeshsivan/status/1246828979585044486
Posted in other celibraty as well after changing name
DeleteDear Capt Ajit sir,
ReplyDeleteThyrocare is going to test sooner for Covid-19...this is ridiculous, numbers will be out of control, they will fool Indians most n more...we need to expose them...as they are charging Rs 4500/- per anti-body test as per Dr Velumani. He also said that a kind of assurance test like pregnancy test will be done on the table, will give results in 10 mins, other one lab test will be given in 6 hours.
a funny propaganda clip
ReplyDeletehttps://twitter.com/PawanDurani/status/1246766894683574287?s=19
QUESTION FROM CAPT AJIT VADAKAYIL TO DR SANJAY GUPTA ( OF CNN USA )..
ReplyDeleteARE YOU AN AGENT OF KOSHER EVIL PHARMA?
https://ajitvadakayil.blogspot.com/2020/02/coronavirus-deaths-nano-gold-colloids.html
capt ajit vadakayil
..
PUT ABOVE COMMENT IN WEBSITES OF--
DR SANJAY GUPTA
TRUMP
CNN USA
ALEX JONES
RENSE
ANDERSON COOPER
BRIAN STELTER
WOLF BLITZER
CHRIS CUOMO
SEND TO IN THE LINK BELOW ( PICK OUT MAJOR ONES )--
https://edition.cnn.com/specials/tv/anchors-and-reporters
SEND TO BBC GANG ( CHOOSE MAJOR ONES )
https://en.wikipedia.org/wiki/Category:BBC_newsreaders_and_journalists
Poster to above address and BBC Gangs
Deletehttps://twitter.com/kannanlp/with_replies
https://twitter.com/rakeshsivan/status/1246834992207912960
Deletehttps://twitter.com/rakeshsivan/status/1246835035304411136
sent to
Deletesanjay@yorkcardiology.co.uk trump@trumporg.com cnn.feedback@cnn.com community@cnn.com contactrense@earthlink.net
sengt to wolf blitzer and chris cuomo
https://twitter.com/colkt/status/1246662339304153088
ReplyDeleteASK THIS DORK COL TIWARI, IF HE HEARD OF TED KACZYNSKI WHO MAKE A CHOOT OUT OF HIS THESIS PROFESSOR..
DOES DORK TIWARI KNOW OF DHARMAWIKI SPONSORED BY SWAMY WHO SOAKED OUR PURANAS IN SEMEN..
http://ajitvadakayil.blogspot.com/2019/02/mistakes-deliberate-or-inadvertant-in_7.html
SWAMY DOES NOT KNOW THE BASICS OF ECONOMICS..
READ QUESTIONS TO QUORA 686 TO 820 OF THE POST BELOW..
http://ajitvadakayil.blogspot.com/2019/06/archived-questions-to-quora-from-capt.html
HAVE YOU HEARD WHY PM CHANDRASHEKAR SOLD OUR PRISTINE GOLD TO JEW ROTHSCHILD?
DO YOU KNOW WHO SPONSORED SWAMYs SIKH TURBAN IN 1976?
capt ajit vadakayil
..
PUT ABOVE COMMENT IN WBEITS OF--
CUNT TIWARI
ALL THREE ARMED FORCE CHIEFs CDS
PMO
PM MODI
Dear Capt Ajit sir,
Deletehttps://twitter.com/IwerePm/status/1246808455203000320?s=20
https://twitter.com/rakeshsivan/status/1246836723964731392
Deletehttps://twitter.com/rakeshsivan/status/1246837238853296130
Captain you deserve the best. After reading your comment of ingrates my only question is what can I do to not be a balless tatuu
ReplyDeleteI have sent message to trump and Alex jones will Facebook be just as good as Twitter or is email the only thing big brother cannot control?
https://twitter.com/TIinExile/status/1246785696104247296?s=19
ReplyDeleteAll readers, when dealing with whites just abuse them badly (there is plenty of info from Captain's blog) but never introduce them to the blog's as they will get nuske's of life and medical gyaan. The contents of all those emails sent to knapp should instead be spread among Indians/Hindus who follow such white charlatans.
ReplyDeletePranaam Captain
ReplyDeleteI got this reply from Stephen Knapp
Namaste,
Some people feel that my questioning Capt. Ajit Vadakayil is because of a difference in opinion about the use of onions and garlic. That is hardly the case. Everyone has an opinion on these items, so what? What I objected to was his blanket statement about Iskcon, which I found to be extremist, harsh, outright racist, and ridiculously wrong. And I'm expected to stay silent about that? There are so many organizations and sects within Hinduism that work in various ways and also have dietary restrictions for various reasons, but to show such bias and to say what he did against one particular organization is a prime example of how Hindus, at a time when they should be united and work together for the future of India and the Hindu / Vedic cause, are still as disunited as ever. What a pity.
It is just like, at one time or another, I have heard heavy criticism against almost everyone that was listed on the original thread. So does that mean I should stop supporting and working with them? Some people may view it that way, and even try to convince me of that. But that is not the way I see it, even if we have some differences of opinion. What we have in common is far greater. Similarly, I have also worked with so many organizations in and outside of India, such as the RSS, HSS, VHP, Vivekananda Kendra, Kalyan Ashrama, Iskcon, Vedic Friends Association, Swadhyaya, and others. And I will continue to work together with those who sincerely try to help protect and perpetuate the Hindu / Vedic cause. My life has been greatly enhanced by doing that. And I will question anyone who tries to convince me against doing that, and what basis or authority they have to do so.
Hari OM,
Sri Nandanandana dasa (Stephen Knapp)
With Regards
Sagar
I have been copied the same reply.
Deletehttps://drive.google.com/open?id=1tw5Eo1yCEcqdtWQTAjmhaDqtBC7Lxauq
Master Ji,
DeleteGot the same message as reply
Thanks
Dear Captain,
ReplyDeleteSent all your critical comments on twitter to concerned people.
Is PM Modi invoking the power of collective consciousness to fight this pandemic? What mantra would be best to chant during this collective prayer at 9.00 pm?
Regards.
Gautam
Guruji Pranaam,
ReplyDeletePlease find below reply from him
SUBJECT : CHECK YOUR LINEAGE FIRST
HELLO,
Who are you Maskara jaskara faskara dasa or Nandanadana Dasa? Just a spritiual guru you call yourself. Right ?LET ME TELL YOU SANATANA DHARMA DOES NOT NEED ANY GURU LIKE YOU. STOP ATTACKING ON OUR PRITINE CULTURE. Who gave you authority to touch our pristine Sanatana Dharma? Why don't you write on your thieves westerners ancestors. I am sure all dasas like you trace their lineage to some **** receiving guru. Before asking for someone's lineage first check yours.
Our respected Captain Ajit Vadakayil rightly said about the dietary of ISKCON' Akshay Patra what problem do you have in this? Our captain has showed right path to millions of people. His perception is incredible, due to him we hindus know the real truth and real history which was buried by Rothschild agents.
People like dont have courage to face the truth thats why you have not allowed comments anywhere. Why do you hide like poor senile woman. Dont call yourself a spritual guru, what you do or spread is bhakti and there is no place for bhakti in sanatana dharma. BETTER CALL YOURSELF A BHAKTI GURU. You better hide in your home. You are enemy of sanatan dharma, we will not tolerate your any attack on hinduism. Mind you! we hindus are no more self loathing we have woken up, we will blast you with more power.
BE WARNED, KEEP YOUR STORIES WITHIN YOURSELF. LAY OFF SANATANA DHARMA.
REGARDS FROM A WOKEN HINDU,
Shailender Parmar
REPLY FROM HIM :
Namaste,
Some people feel that my questioning Capt. Ajit Vadakayil is because of a difference in opinion about the use of onions and garlic. That is hardly the case. Everyone has an opinion on these items, so what? What I objected to was his blanket statement about Iskcon, which I found to be extremist, harsh, outright racist, and ridiculously wrong. And I'm expected to stay silent about that? There are so many organizations and sects within Hinduism that work in various ways and also have dietary restrictions for various reasons, but to show such bias and to say what he did against one particular organization is a prime example of how Hindus, at a time when they should be united and work together for the future of India and the Hindu / Vedic cause, are still as disunited as ever. What a pity.
It is just like, at one time or another, I have heard heavy criticism against almost everyone that was listed on the original thread. So does that mean I should stop supporting and working with them? Some people may view it that way, and even try to convince me of that. But that is not the way I see it, even if we have some differences of opinion. What we have in common is far greater. Similarly, I have also worked with so many organizations in and outside of India, such as the RSS, HSS, VHP, Vivekananda Kendra, Kalyan Ashrama, Iskcon, Vedic Friends Association, Swadhyaya, and others. And I will continue to work together with those who sincerely try to help protect and perpetuate the Hindu / Vedic cause. My life has been greatly enhanced by doing that. And I will question anyone who tries to convince me against doing that, and what basis or authority they have to do so.
Hari OM,
Sri Nandanandana dasa (Stephen Knapp)
Ditto same thing Stephen Knapp replied to me.
DeleteSeems copying & pasting.
I will reply him back screwing him more.
As soon, Goras get eliminated from becoming the mouth piece of Hinduism, that much better it would be for INDIA as a Nation.
They still charm the naive gullible hindus by their White Skin pretending to be more Hindu than we Hindus are
Money spinning by getting donations & selling their tampered text which they call Bhagavata Geeta is the only job they do.
ISKCON & Spiritualism!!! poles apart.
Tell this bastard that Guruji made many muslims go to shiva temples for shiva lingam nowadays and made many Christians not to attend church prayers. These are impossible tasks. Can this old white goat do himself otherwise he can be screwed very badly and bloody. He is a wolf hidden in white sheep's clothes. I just prayed lord shiva to take care of him. If he is in my city, i will beat him up badly.
DeleteCOLLECTIVE CONSCIOUSNESS WORKS BOTH WAYS..
ReplyDeletehttp://ajitvadakayil.blogspot.com/2010/05/collective-consciousness-and-milling.html
http://ajitvadakayil.blogspot.com/2010/05/when-crowd-tuns-into-mob-capt-ajit.html
ABOVE POSTS I WROTE TEN YEARS AGO. .
MY ELDER SON FLIES FROM USA TO MANCHESTER TO BE PART OF THE MANCHESTER UNITED HOOLIGAN MOB.. HE JUST EXPERIENCES THE MOB ENERGY..
Pranaam Guruji
DeleteLit Diya, Candle
Played Hum Honge Kaamyaab
Thanks And Regards
Debdoot Sarkar
Dear Captain,
DeleteThank you for the reply.
I hope this collective effort will be a sight to please the Gods too. I really pray that this quantum collective consciousness becomes a benchmark for entire world and proves the purity and science of Sanatan Dharma once and for all. And India occupies the place of Jagat Guru.
I prayed that entire nation and world come out of this difficult period soon. I also prayed that the divinity of Gau mata shines through during this period and she occupies the high pedestal she deserves. I also prayed that world awakens to Gau mutra, nano gold colloids and marijuana as the Brahmastra for all pandemics.
Pray that you, your family and all readers pass through this phase unscathed.
Regards.
Gautam
this TrueIndology is such a propagandist
ReplyDeletehttps://twitter.com/TIinExile/status/1181648600062779392?s=19
Dear Sir,
ReplyDeletePlayed song as suggested by you
Reg
D
Stay safe
Dear sir apso burst crackers as support
ReplyDeleteDear Captain,
ReplyDeleteReporting that my family lit lamps and played Hum honge Kamyab on loudspeaker in Bangalore. All the neighbors lot lamps as well. Shared your link to many of my friends as well.
https://www.facebook.com/503018155/posts/10158227473133156/?d=n
In gratitude,
Anish.
capt. ji,
ReplyDeletebelow Kollur Mookambika temple Karnataka--
https://twitter.com/Indsamachar/status/1246832985543860224?s=20
is this truth
ReplyDeletehttps://www.youtube.com/watch?v=fS8KwZcxVZg&feature=youtu.be
please enlighten us guruji
PMOPG/E/2020/0290622
ReplyDeletewebmaster.indianarmy@nic.in administrator.ids@nic.in
mssdmedia.ids@gov.in Narendramodi1234@gmail.com
connect@mygov.nic.in
proiaf.dprmod@nic.in
sorry captain.i couldn't find him on any other place other than twitter.couln,t post this reply on twitter because of excess characters.i m new to twitter so don't know if i m doing something wrong.if anyone know has any other info about him.kindly tell
pronavy.dprmod@nic.in
https://timesofindia.indiatimes.com/india/is-2-years-jail-term-enough-for-malignant-act-to-spread-life-threatening-disease/articleshow/74997026.cms
ReplyDeleteWE DONT WANT DESH DROHI JUDICIARY INVOLVED.. JUST BREAK HIS KNEE CAPS WITH A HAMMER.
HemaV
ReplyDeleteNamaste Sir,
Though I am not in India now my father and my relative participated in this Collective Conciousness at 9.00 pm and sent the videos now and saw your video too.I felt the energy of people of Bharatmata to fight the Coronavirus.I am too praying for wellbeing of all people.
With Gratitude
HemaV
ReplyDeleteRajeev April 5, 2020 at 10:11 PM
Thank you captain sir. People are awakening due to your efforts. Clearely you have made a dent in the universe through your efforts. God bless you and your family.
ReplyDeleteSavithurApril 5, 2020 at 9:40 PM
My Dear Captain,
Did light the candles and lamp with family members and blasted the music...cheers for the Nation and the world and for your kind guidance.A true Quantum
Event,the pulse which could be felt. With Gratitudes,pranam.
ReplyDeleteKarthikeyan YelumalaiApril 5, 2020 at 9:39 PM
Pranam Guru Ji
Your one of the Best thing that ever happened in this world, until my last breath you will remain a Eternal Flame in our soul.
I bow my head to your feet.
I pray and wish the God to be with you always and forever.
With Love
Karthikeyan Yelumalai
ReplyDeleteCapt. Ajit VadakayilApril 5, 2020 at 2:52 PM
WHY WERE WE SLAVES FOR 800 YEARS?
APNA KYA JAATHA HAI ATTITUDE.. WHAT IS THERE IN IT FOR ME PERSONALLY..
WHEN QUORA ATTACKED MY WIFE WITH FOUL SEXUAL INNUENDO , NOT A SINGLE READER DOWN VOTED THAT VILE COMMENT. LEAVE ALONE REPUDIATE.. THEY WERE ALL ENJOYING INWARDLY..
MIND YOU , HUNDREDS OF MY READERS GURUJEEEE ME DAILY AND TREAT ME AS THEIR PERSONAL DOCTOR IN OLD POSTS-- AND BEG ME TO DELETE THESE COMMENTS AFTER THEIR PROBLEM IS SOLVED.
STEPHEN KNAPP ABUSED ME.. DEMANDING MY LINEAGE ..
I KNOW AFTER 48 HOURS OF THIS EVEN EVENT, HOW MANY OF MY READERS HAVE EVEN OBJECTED , DESPITE MY CALL TO DO IT.
ALL SELFISH BASTARDS ..MY READERS ..
I HAVE COMMANDED UNITED NATIONS CREW.. THEY HAVE GRATITUDE UNLIKE BASTARD CHOOTIYA INDIANS.. EVEN PAKISTANIS MUSLIMS ARE BETTER THAN INDIAN HINDUS..
capt ajit vadakayil
..
ReplyDelete
Replies
Amardeep Singh MannApril 5, 2020 at 6:50 PM
Hi Captain,
Sent message to Steven Knapp by email to him.
Declared his lineage as non, due to it being iskcon.
Kind regards,
ReplyDeletePRIYADARSHAN SIRASApril 5, 2020 at 1:29 PM
RESPECTED CAPT. AJIT VADAKAYIL SIR, Would you please give DEEKSHA ..digital mantra for Corona. Sir, you have changed our lives, our thinking,our thought process,our personality and the whole world. Thanks for everything.
Hi Sir,
ReplyDeleteHad Flashlight and put the SOng "Hum Monge". Strangely i heard the song from many places. Couldn't believe it.I guess there are many more who just reads your blog with out mentioning in Hyderabad.
Thanks,
Srini.
ReplyDeleteJayasree Narayanan PillayApril 5, 2020 at 10:56 PM
Namaskar guruji, we also celebrated the diya lighting and also blasted your favorite song Hum honge kamayab. Thank you for everything what a timing and a spectacular to see the United India. Love you always:-)
ReplyDeleteMohit HandaApril 5, 2020 at 10:47 PM
Namaste Captain Ajitji,
We cannot take any insult for you. Please do not consider us weak from heart. Please forgive us for any delay in our responses.
I understand the immense disappointing sentiments when you say its a curse on us to think "Apna kya jata hai".
I have been grappling with this all life be it with family or friends. So can sense somewhat the amount of frustration you must be going. It brings tears thinking how you would be feeling.
This sickening environment is changing our genes may be. Its a big fight going within us in our mind daily
Let me share a story please.
When Draupadi asked Bhisma Pitamah that why didnt he protect her when she was being disrobed, he told her a story.
Once a rishi visited a king. The king welcomed him and took great care and asked him to take rest that night in palace and continue journey next day.
The rishi's guest room was close to the queens room.
In midnight the rishi suddenly got up and went to queens room and picked the jewelry on the table and brought back to his room and slept back.
In the morning when rishi got up, he was stunned at what he had done. He immediately told the king what happened. He asked the king to investigate with the cook how the food was prepared. The cook replied that it was the usual same style of preparation. After some more investigation, the king came to know that the previous day, the kings army had captured a gang of dacoits and acquired lot of stuff which also had many vegetables. These vegetables were used that night for food preparation and served to rishi.
The rishi pointed out this to the king for the act which he did of getting jewelry from queens room.
After narrating this, Pitamah bhism then said to Draupadi that consistently eating food prepared in Duryodhans palace, he has also been impacted like that rishi and become like this.
I see lack of consciousness in most of the people I see. First it took much time to me to understand consciousness. You have helped us immensely with tbis.
I keep thinking that this worldly materialistic poison injected all around us keeps pushing us back into the hole. But we will not give up this fight so easily.
https://twitter.com/rahulroushan/status/1246833321444659201
ReplyDeleteNamaste capt ji..
ReplyDeletefirst i lit candles, chant om for 5 min, then pray, my body shuddered while praying.
capt it is called generating scalar waves or electro magnetic waves ?
Dear Captain,
ReplyDeleteI switched off the lights and lit two ghee lamp outside my house. My mother was on call for a long time so i could not play the song. But i was thinking about you and unity of our country.
Regards,
Muthu Swamynathan.
Namaste caption...tried counting continuously to 1600 ..........it took me around 27 minutes to just count upto 1-1600.
ReplyDeleteDear Capt Sir Namaskaram Today is my Wedding Anniversary.
ReplyDeleteSeeking your and Amma blessings.
With Gratitude RVK