Ajit Vadakayil: WHAT ARTIFICIAL INTELLIGENCE CANNOT DO , a grim note to the top 100 intellectuals of this planet , Part 5

Wednesday, November 20, 2019

WHAT ARTIFICIAL INTELLIGENCE CANNOT DO , a grim note to the top 100 intellectuals of this planet , Part 5 - Capt Ajit Vadakayil

THIS POST IS CONTINUED FROM PART 4, BELOW--

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

Deep learning is a type of machine learning (ML) and artificial intelligence (AI) that imitates the way humans gain certain types of knowledge.

Deep learning is an important element of data science, which includes statistics and predictive modeling. It is extremely beneficial to data scientists who are tasked with collecting, analyzing and interpreting large amounts of data; deep learning makes this process faster and easier.

Deep learning represents the very cutting edge of artificial intelligence (AI). Instead of teaching computers to process and learn from data (which is how machine learning works), with deep learning, the computer trains itself to process and learn from data.

Deep learning (DL) is a subset of ML, in which multiple levels of models work together at more complex tasks, with each level of model relying on outputs from a prior level to perform a higher-level function. .

Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on artificial neural networks. Learning can be supervised, semi-supervised or unsupervised.

The most important difference between deep learning and traditional machine learning is its performance as the scale of data increases. When the data is small, deep learning algorithms don't perform that well. This is because deep learning algorithms need a large amount of data to understand it perfectly

You need to learn Machine Learning first then you can plan for Deep Learning or AI. Machine Learning is mandatory to learn deep learning or AI

Deep learning is a field of study that gives computers the ability to learn without being explicitly programme – while adding that it tends to result in higher accuracy, require more hardware or training time, and perform exceptionally well on machine perception tasks that involved unstructured data such as blobs of pixels or text.

We refer to ‘deep learning’ because the neural networks have various (deep) layers that enable learning. Just about any problem that requires “thought” to figure out is a problem deep learning can learn to solve.

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

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

Machine learning does not require the same costly, high-end machines and high-performing GPUs that deep learning does.

In the end, many data scientists choose traditional machine learning over deep learning due to its superior interpretability, or the ability to make sense of the solutions. Machine learning algorithms are also preferred when the data is small.

At its simplest, deep learning can be thought of as a way to automate predictive analytics. While traditional machine learning algorithms are linear, deep learning algorithms are stacked in a hierarchy of increasing complexity and abstraction.

A type of advanced machine learning algorithm, known as artificial neural networks, underpins most deep learning models. As a result, deep learning may sometimes be referred to as deep neural learning or deep neural networking.

Because deep learning models process information in ways similar to the human brain, they can be applied to many tasks people do. Deep learning is currently used in most common image recognition tools, natural language processing and speech recognition software. These tools are starting to appear in applications as diverse as self-driving cars and language translation services.

Deep learning is a subset of machine learning. Usually, when people use the term deep learning, they are referring to deep artificial neural networks, and somewhat less frequently to deep reinforcement learning.

Deep learning is a computer software that mimics the network of neurons in a brain. -- it is called deep learning because it makes use of deep neural networks.

The machine uses different layers to learn from the data. The depth of the model is represented by the number of layers in the model.

Deep learning is the new state of the art in term of AI. In deep learning, the learning phase is done through a neural network. A neural network is an architecture where the layers are stacked on top of each other

The biggest limitation of deep learning models is they learn through observations. This means they only know what was in the data on which they trained. If a user has a small amount of data or it comes from one specific source that is not necessarily representative of the broader functional area, the models will not learn in a way that is generalizable.

Deep learning requires large amounts of data. Furthermore, the more powerful and accurate models will need more parameters, which, in turn, requires more data.

Once trained, deep learning models become inflexible and cannot handle multitasking. They can deliver efficient and accurate solutions, but only to one specific problem. Even solving a similar problem would require retraining the system.

Any application that requires reasoning -- such as programming or applying the scientific method -- long-term planning and algorithmic-like data manipulation is completely beyond what current deep learning techniques can do, even with large data.

Deep learning is a particular kind of machine learning that achieves great power and flexibility by learning to represent the world as nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones.”

This is because deep learning algorithms need a large amount of data to understand it perfectly. On the other hand, traditional machine learning algorithms with their handcrafted rules prevail in this scenario.

Deep learning is a subset of machine learning that differentiates itself through the way it solves problems. Machine learning requires a domain expert to identify most applied features. On the other hand, deep learning learns features incrementally, thus eliminating the need for domain expertise.

This makes deep learning algorithms take much longer to train than machine learning algorithms, which only need a few seconds to a few hours. However, the reverse is true during testing. Deep learning algorithms take much less time to run tests than machine learning algorithms, whose test time increases along with the size of the data.

The key difference between deep learning vs machine learning stems from the way data is presented to the system. Machine learning algorithms almost always require structured data, whereas deep learning networks rely on layers of the ANN (artificial neural networks).

The biggest advantage Deep Learning algorithms as discussed before are that they try to learn high-level features from data in an incremental manner. This eliminates the need of domain expertise and hard core feature extraction

The difference between neural networks and deep learning lies in the depth of the model. Deep learning is a phrase used for complex neural networks. ...

Deep learning is a Neural Network consisting of a hierarchy of layers, whereby each layer transforms the input data into more abstract representations

Deep-learning networks are distinguished from the more commonplace single-hidden-layer neural networks by their depth; that is, the number of node layers through which data must pass in a multistep process of pattern recognition.

Earlier versions of neural networks such as the first perceptrons were shallow, composed of one input and one output layer, and at most one hidden layer in between. More than three layers (including input and output) qualifies as “deep” learning. So deep is not just a buzzword -- It is a strictly defined term that means more than one hidden layer.

Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.

In addition to scalability, another often cited benefit of deep learning models is their ability to perform automatic feature extraction from raw data, also called feature learning.

Deep learning algorithms seek to exploit the unknown structure in the input distribution in order to discover good representations, often at multiple levels, with higher-level learned features defined in terms of lower-level features

Deep learning methods aim at learning feature hierarchies with features from higher levels of the hierarchy formed by the composition of lower level features. Automatically learning features at multiple levels of abstraction allow a system to learn complex functions mapping the input to the output directly from data, without depending completely on human-crafted features.

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction.

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. The patterns they recognize are numerical, contained in vectors, into which all real-world data, be it images, sound, text or time series, must be translated.

In deep-learning networks, each layer of nodes trains on a distinct set of features based on the previous layer’s output. The further you advance into the neural net, the more complex the features your nodes can recognize, since they aggregate and recombine features from the previous layer.

Instances where deep learning becomes preferable include situations where there is a large amount of data, a lack of domain understanding for feature introspection or complex problems, such as speech recognition and natural language processing.

Deep learning and neural networks are picking up steam in applications like self-driving cars, radiology image processing, supply chain monitoring and cybersecurity threat detection.

The difference between neural networks and deep learning lies in the depth of the model. ... The architecture has become more complex but the concept of deep learning is still the same. Albeit there's now an increased number of hidden layers and nodes that integrate to estimate the output(s)

Generative adversarial networks (GANs) are deep neural net architectures comprised of two nets, pitting one against the other (thus the “adversarial”). Generative adversarial networks, or GANs, are a powerful type of neural network used for unsupervised machine learning.

Made up of two competing models which run in competition with one another, GANs are able to capture and copy variations within a dataset Generative artificial intelligence and deep machine learning are set to build on the progress that basic AI has already made across business and personal computing and daily life

Generative modeling is an unsupervised learning task in machine learning that involves automatically discovering and learning the regularities or patterns in input data in such a way that the model can be used to generate or output new examples that plausibly could have been drawn from the original dataset.

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

Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to human experts.

Deep is a technical term. It refers to the number of layers in a neural network. A shallow network has one so-called hidden layer, and a deep network has more than one. Multiple hidden layers allow deep neural networks to learn features of the data in a so-called feature hierarchy, because simple features (e.g. two pixels) recombine from one layer to the next, to form more complex features (e.g. a line).

Nets with many layers pass input data (features) through more mathematical operations than nets with few layers, and are therefore more computationally intensive to train. Computational intensivity is one of the hallmarks of deep learning, and it is one reason why a new kind of chip call GPUs are in demand to train deep-learning models.

A deep neural network (DNN) is an artificial neural network (ANN) with multiple hidden layers of units between the input and output layers.

In exchange for large quantities of data and processing power, deep neural networks have yielded models that provide state of the art predication capabilities in many fields. However, a lack of strong guarantees on their behaviour have raised concerns over their use in safety-critical applications

To achieve an acceptable level of accuracy, deep learning programs require access to immense amounts of training data and processing power, neither of which were easily available to programmers until the era of big data and cloud computing.

Because deep learning programming can create complex statistical models directly from its own iterative output, it is able to create accurate predictive models from large quantities of unlabeled, unstructured data. This is important as the internet of things (IoT) continues to become more pervasive, because most of the data humans and machines create is unstructured and is not labeled.

Neural networks come in several different forms, including recurrent neural networks, convolutional neural networks, artificial neural networks and feedforward neural networks -- and each has benefits for specific use cases. However, they all function in somewhat similar ways, by feeding data in and letting the model figure out for itself whether it has made the right interpretation or decision about a given data element.

Neural networks involve a trial-and-error process, so they need massive amounts of data on which to train. It's no coincidence neural networks became popular only after most enterprises embraced big data analytics and accumulated large stores of data.

Because the model's first few iterations involve somewhat-educated guesses on the contents of an image or parts of speech, the data used during the training stage must be labeled so the model can see if its guess was accurate.

This means, though many enterprises that use big data have large amounts of data, unstructured data is less helpful. Unstructured data can only be analyzed by a deep learning model once it has been trained and reaches an acceptable level of accuracy, but deep learning models can't train on unstructured data.

Various different methods can be used to create strong deep learning models. These techniques include learning rate decay, transfer learning, training from scratch and dropout.---

Learning rate decay. The learning rate is a hyperparameter -- a factor that defines the system or sets conditions for its operation prior to the learning process -- that controls how much change the model experiences in response to the estimated error every time the model weights are altered. Learning rates that are too high may result in unstable training processes or the learning of a suboptimal set of weights. Learning rates that are too small may produce a lengthy training process that has the potential to get stuck.

The learning rate decay method -- also called learning rate annealing or adaptive learning rates -- is the process of adapting the learning rate to increase performance and reduce training time. The easiest and most common adaptations of learning rate during training include techniques to reduce the learning rate over time.

Transfer learning. This process involves perfecting a previously trained model; it requires an interface to the internals of a preexisting network. First, users feed the existing network new data containing previously unknown classifications. Once adjustments are made to the network, new tasks can be performed with more specific categorizing abilities. This method has the advantage of requiring much less data than others, thus reducing computation time to minutes or hours.

Training from scratch. This method requires a developer to collect a large labeled data set and configure a network architecture that can learn the features and model. This technique is especially useful for new applications, as well as applications with a large number of output categories. However, overall, it is a less common approach, as it requires inordinate amounts of data, causing training to take days or weeks.

Dropout. This method attempts to solve the problem of overfitting in networks with large amounts of parameters by randomly dropping units and their connections from the neural network during training. It has been proven that the dropout method can improve the performance of neural networks on supervised learning tasks in areas such as speech recognition, document classification and computational biology.

Use cases today for deep learning include all types of big data analytics applications, especially those focused on natural language processing, language translation, medical diagnosis, stock market trading signals, network security and image recognition.

Specific fields in which deep learning is currently being used include the following:----

Customer experience. Deep learning models are already being used for chatbots. And, as it continues to mature, deep learning is expected to be implemented in various businesses to improve the customer experiences and increase customer satisfaction.

Text generation. Machines are being taught the grammar and style of a piece of text and are then using this model to automatically create a completely new text matching the proper spelling, grammar and style of the original text.

Aerospace and military. Deep learning is being used to detect objects from satellites that identify areas of interest, as well as safe or unsafe zones for troops.

Industrial automation. Deep learning is improving worker safety in environments like factories and warehouses by providing services that automatically detect when a worker or object is getting too close to a machine.

Adding color. Color can be added to black and white photos and videos using deep learning models. In the past, this was an extremely time-consuming, manual process.

Medical research. Cancer researchers have started implementing deep learning into their practice as a way to automatically detect cancer cells.

Computer vision. Deep learning has greatly enhanced computer vision, providing computers with extreme accuracy for object detection and image classification, restoration and segmentation.

Limitations and challenges--

The issue of biases is a major problem for deep learning models. This bias I call ( BIAS2 ) has nothing to do with bias related to variance . I will go into great detail later..

If a model trains on data that contains biases, the model will reproduce those biases in its predictions. This has been a vexing problem for deep learning programmers, because models learn to differentiate based on subtle variations in data elements.

Often, the factors it determines are important are not made explicitly clear to the programmer. This means, for example, a facial recognition model might make determinations about people's characteristics based on things like race or gender without the programmer being aware.

The learning rate can also become a major challenge to deep learning models. If the rate is too high, then the model will converge too quickly, producing a less-than-optimal solution. If the rate is too low, then the process may get stuck, and it will be even harder to reach a solution.

The hardware requirements for deep learning models can also create limitations. Multicore high-performing graphics processing units (GPUs) and other similar processing units are required to ensure improved efficiency and decreased time consumption.

However, these units are expensive and use large amounts of energy. Other hardware requirements include random access memory (RAM) and a hard drive or RAM-based solid-state drive (SSD).

Deep reinforcement learning has emerged as a way to integrate AI with complex applications, such as robotics, video games and self-driving cars. The primary difference between deep learning and reinforcement learning is, while deep learning learns from a training set and then applies what is learned to a new data set, deep reinforcement learning learns dynamically by adjusting actions using continuous feedback in order to optimize the reward.

A reinforcement learning agent has the ability to provide fast and strong control of generative adversarial networks (GANs). The Adversarial Threshold Neural Computer (ATNC) combines deep reinforcement learning with GANs in order to design small organic molecules with a specific, desired set of pharmacological properties.

GANs are also being used to generate artificial training data for machine learning tasks, which can be used in situations with imbalanced data sets or when data contains sensitive information.

GANs are a clever way of training a generative model by framing the problem as a supervised learning problem with two sub-models: the generator model that we train to generate new examples, and the discriminator model that tries to classify examples as either real (from the domain) or fake (generated). The two models are trained together in a zero-sum game, adversarial, until the discriminator model is fooled about half the time, meaning the generator model is generating plausible examples.

Unlike machine learning, deep learning uses multiple layers and structures algorithms such that an artificial neural network is created that learns and makes decisions on its own!

Human brains are made up of connected networks of neurons. ANNs seek to simulate these networks and get computers to act like interconnected brain cells, so that they can learn and make decisions in a more humanlike manner.

ANN that is made up of more than three layers – i.e. an input layer, an output layer and multiple hidden layers – is called a ‘deep neural network’, and this is what underpins deep learning. A deep learning system is self-teaching, learning as it goes by filtering information through multiple hidden layers, in a similar way to humans.

As you can see, the two are closely connected in that one relies on the other to function. Without neural networks, there would be no deep learning.

Artificial Neural Networks (ANNs) were inspired by information processing and distributed communication nodes in biological systems. ANNs have various differences from biological brains. Specifically, neural networks tend to be static and symbolic, while the biological brain of most living organisms is dynamic (plastic) and analog.

Perceptron is a single layer neural network and a multi-layer perceptron is called Neural Networks.

Perceptron is a linear classifier (binary). Also, it is used in supervised learning. It helps to classify the given input data.

The single-layer Perceptron is the simplest of the artificial neural networks (ANNs).

Deep Learning is a collection of statistical machine learning techniques used to learn feature hierarchies based on the concept of artificial neural networks.”

A Deep neural network consists of the following layers:

The Input Layer

The Hidden Layer

The Output Layer

The first layer is the input layer which receives all the inputs

The last layer is the output layer which provides the desired output

All the layers in between these layers are called hidden layers.

There can be n number of hidden layers and the number of hidden layers and the number of perceptrons in each layer will entirely depend on the use-case you are trying to solve.

Deep Learning is used in highly computational use cases such as Face Verification, self-driving cars, and so on. Let’s understand the importance of Deep Learning by looking at a real-world use case.

Deep Learning is based on the functionality of a biological neuron, so let’s understand how we mimic this functionality in the artificial neuron (also known as a perceptron):

In a biological neuron, dendrites are used to receive inputs. These inputs are summed in the cell body and through the Axon, it is passed on to the next neuron.

Similar to the biological neuron, a perceptron receives multiple inputs, applies various transformations and functions and provides an output.

The human brain consists of multiple connected neurons called a neural network, similarly, by combining multiple perceptrons, we’ve developed what is known as a Deep neural network.

A perceptron is a simple model of a biological neuron in an artificial neural network. Perceptron is also the name of an early algorithm for supervised learning of binary classifiers.

The most popular artificial neural network algorithms are:---

Perceptron

Multilayer Perceptrons (MLP)

Back-Propagation

Stochastic Gradient Descent

Hopfield Network

Radial Basis Function Network (RBFN)

US psychologist Frank Rosenblatt from Cornell, invented the ‘perceptron’, an algorithm that ran on specific neuron-mimicking hardware and was capable of learning similarly to a neural network: by strengthening or weakening the connections between neighbouring, interconnected neurons.

The perceptron was the ancestor of artificial neural networks and deep learning, or what we today – 60 years later – understand as the big idea behind ‘artificial intelligence’.

A perceptron is a simple model of a biological neuron in an artificial neural network. The perceptron is a mathematical model of a biological neuron. While in actual neurons the dendrite receives electrical signals from the axons of other neurons, in the perceptron these electrical signals are represented as numerical values. ... As in biological neural networks, this output is fed to other perceptrons.

In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. A binary classifier is a function which can decide whether or not an input, represented by a vector of numbers, belongs to some specific class. It is a type of linear classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector.

Rosenblatt’s pioneering invention provided an alternative approach, one that enabled computers to go beyond logic, and venture into solving a really hard problem: perception.

Perceptron is a single layer neural network and a multi-layer perceptron is called Neural Networks. Perceptron is a linear classifier (binary). Also, it is used in supervised learning. It helps to classify the given input data.

Backpropagation is a supervised learning algorithm, for training Multi-layer Perceptrons (Artificial Neural Networks). 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.

A Perceptron is a single layer neural network that is used to classify linear data. The perceptron has 4 important components:---

Input

Weights and Bias

Summation Function

Activation or transformation Function

Perceptron helps to classify the given input data.. In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. ... It is a type of linear classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector.

One possible strategy is to use a local greedy algorithm which works by computing the error of the perceptron for a given weight vector, looking then for a direction in weight space in which to move, and updating the weight vector by selecting new weights in the selected search direction. The purpose of the learning rule is to train the network to perform some task. ...

The learning rule is then used to adjust the weights and biases of the network in order to move the network outputs closer to the targets. The perceptron learning rule falls in this supervised learning category

A single perceptron can only be used to implement linearly separable functions. It takes both real and boolean inputs and associates a set of weights to them, along with a bias We learn the weights, we get the function. Let's use a perceptron to learn an OR function.

The Perceptron is inspired by the information processing of a single neural cell called a neuron. A neuron accepts input signals via its dendrites, which pass the electrical signal down to the cell body. .

If the decision boundary is non-linear, you really can’t use the Perceptron The perceptron is the basic unit powering what is today known as deep learning. It is the artificial neuron that, when put together with many others like it, can solve complex, undefined problems much like humans do.

Understanding the mechanics of the perceptron (working on its own) and multilayer perceptrons (working together) will give you an important foundation for understanding and working with modern neural networks. A perceptron helps to divide a set of input signals into two parts—“yes” and “no”.

But unlike many other classification algorithms, the perceptron was modeled after the essential unit of the human brain—the neuron and has an uncanny ability to learn and solve complex problems.

A perceptron is a very simple learning machine. It can take in a few inputs, each of which has a weight to signify how important it is, and generate an output decision of “0” or “1”. However, when combined with many other perceptrons, it forms an artificial neural network. A neural network can, theoretically, answer any question, given enough training data and computing power.

What Is a Multilayer Perceptron?

A multilayer perceptron (MLP) is a perceptron that teams up with additional perceptrons, stacked in several layers, to solve complex problems.

Each perceptron in the first layer on the left (the input layer), sends outputs to all the perceptrons in the second layer (the hidden layer), and all perceptrons in the second layer send outputs to the final layer on the right (the output layer).. sends multiple signals, one signal going to each perceptron in the next layer. For each signal, the perceptron uses different weights.

Every line going from a perceptron in one layer to the next layer represents a different output. Each layer can have a large number of perceptrons, and there can be multiple layers, so the multilayer perceptron can quickly become a very complex system. The multilayer perceptron has another, more common name—a neural network.

A three-layer MLP, is called a Non-Deep or Shallow Neural Network. An MLP with four or more layers is called a Deep Neural Network.

One difference between an MLP and a neural network is that in the classic perceptron, the decision function is a step function and the output is binary.

In neural networks that evolved from MLPs, other activation functions can be used which result in outputs of real values, usually between 0 and 1 or between -1 and 1. This allows for probability-based predictions or classification of items into multiple labels.

A convolutional neural network (CNN) is a specific type of artificial neural network that uses perceptrons, a machine learning unit algorithm, for supervised learning, to analyze data. CNNs apply to image processing, natural language processing and other kinds of cognitive tasks.

Convolutional neural networks (CNN) are one of the most popular models used today. This neural network computational model uses a variation of multilayer perceptrons and contains one or more convolutional layers that can be either entirely connected or pooled.

These convolutional layers create feature maps that record a region of image which is ultimately broken into rectangles and sent out for nonlinear processing.

The CNN model is particularly popular in the realm of image recognition; it has been used in many of the most advanced applications of AI, including facial recognition, text digitization and natural language processing. Other uses include paraphrase detection, signal processing and image classification.

Deconvolutional neural networks utilize a reversed CNN model process. They aim to find lost features or signals that may have originally been considered unimportant to the CNN system's task. This network model can be used in image synthesis and analysis.

Modeled in accordance with the human brain, a Neural Network was built to mimic the functionality of a human brain. The human brain is a neural network made up of multiple neurons, similarly, an Artificial Neural Network (ANN) is made up of multiple perceptrons

A neural network consists of three important layers:--

Input Layer: As the name suggests, this layer accepts all the inputs provided by the programmer.

Hidden Layer: Between the input and the output layer is a set of layers known as Hidden layers. In this layer, computations are performed which result in the output.

Output Layer: The inputs go through a series of transformations via the hidden layer which finally results in the output that is delivered via this layer.

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.

To understand neural networks, we need to break it down and understand the most basic unit of a Neural Network, i.e. a Perceptron.

Like Logistic Regression, the Perceptron is a linear classifier used for binary predictions. This means that in order for it to work, the data must be linearly separable.

Although the Perceptron is only applicable to linearly separable data, the more detailed Multilayered Perceptron can be applied to more complicated nonlinear datasets. This includes applications in areas such as speech recognition, image processing, and financial predictions just to name a few.

Neural networks are a specific set of algorithms that have revolutionized machine learning. They are inspired by biological neural networks and the current so-called deep neural networks have proven to work quite well.

Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data. Deep learning, a powerful set of techniques for learning in neural networks.

Deep learning is the reigning monarch of AI. In the six years since it exploded into the mainstream, it has become the dominant way to help machines sense and perceive the world around them.

Deep Learning is a machine learning technique that uses structures called “neural networks” that are inspired by the human brain. These consist of a set of layered units, modeled after neurons. Each layer of units processes a set of input values and produces output values that are passed onto the next layer of units.

Neural networks often consist of more than 100 layers, with a large number of units in each layer to enable the recognition of extremely complex and precise patterns in data.

To explore this further, consider image recognition software that utilizes neural networks to identify a picture of an elephant. The first layer of units might look at the raw image data for the most basic patterns, perhaps that there is a thing with what appears to be four legs. Then the next layer would look for patterns within these patterns, perhaps that it is an animal. Then maybe the next layer identifies the trunk.

This process would continue throughout many layers, recognizing increasingly precise patterns in the image, until the network is able to identify that it is indeed a picture of an elephant.

Progress in deep learning has been the cause for much of the optimism about AI due its ability to process and find patterns in massive amounts of data with accuracy Whereas early ML typically uses a decisiontree structure, deep learning has become the dominant technique. It is often used to power specific ML approaches such as machine vision and natural language processing.

Neural Networks are a class of models within the general machine learning literature. Neural networks are a specific set of algorithms that have revolutionized machine learning. They are inspired by biological neural networks and the current so-called deep neural networks have proven to work quite well.

The term “Neural Net” refers to both the biological and artificial variants, although typically the term is used to refer to artificial systems only.

Mathematically, neural nets are nonlinear Artificial neural networks are parallel computational models (unlike our computers, which have a single processor to collect and display information). ... The basis for these networks originated from the biological neuron and neural structures – every neuron takes in multiple unique inputs and produces one output.

Neural Networks are themselves general function approximations, which is why they can be applied to almost any machine learning problem about learning a complex mapping from the input to the output space.

Deep learning is a powerful tool to make prediction an actionable result. Deep learning excels in pattern discovery (unsupervised learning) and knowledge-based prediction. Big data is the fuel for deep learning. When both are combined, an organization can reap unprecedented results in term of productivity, sales, management, and innovation.

Deep learning can outperform traditional method. For instance, deep learning algorithms are 41% more accurate than machine learning algorithm in image classification, 27 % more accurate in facial recognition and 25% in voice recognition...

There is a category of AI algorithms that are both a part of ML and AI but are more specialized than machine learning algorithms. These are known as deep learning algorithms, and exhibit characteristics of machine learning while being more advanced.

In the human brain, any cognitive processes are conducted by small cells known as neurons communicating with each other. The entire brain is made up of these neurons, which form a complex network that dictates our actions as humans. This is what deep learning algorithms aim to recreate.

They are created with the help of digital constructs known as neural networks, which directly mimic the physical structure of the human brain in order to solve problems. While explainable AI had already been a problem with machine learning, explaining the actions of deep learning algorithms is considered nearly impossible today.

Deep learning algorithms may hold the key to more powerful AI, as they can perform more complex tasks than machine learning algorithms can. It learns from itself as more data is fed to it, like machine learning algorithms. However, deep learning algorithms function differently when it comes to gathering information from data.

Deep learning uses the concept of neural networks to solve complex problems. It is mainly used to deal with high dimensional data. It is based o the concept of Neural Networks and is often used in object detection and image processing.

AI, Machine Learning and Deep Learning are interconnected fields. Machine Learning and Deep learning aids Artificial Intelligence by providing a set of algorithms and neural networks to solve data-driven problems.

Artificial Intelligence is not restricted to only Machine learning and Deep learning. It covers a vast domain of fields including, Natural Language Processing (NLP), object detection, computer vision, robotics, expert systems and so on.

Deep learning algorithms run data through several “layers” of neural network algorithms, each of which passes a simplified representation of the data to the next layer. Most machine learning algorithms work well on datasets that have up to a few hundred features, or columns.

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

Neural networks help us cluster and classify. You can think of them as a clustering and classification layer on top of the data you store and manage. They help to group unlabeled data according to similarities among the example inputs, and they classify data when they have a labeled dataset to train on.

Neural networks can also extract features that are fed to other algorithms for clustering and classification; so you can think of deep neural networks as components of larger machine-learning applications involving algorithms for reinforcement learning, classification and regression.

The following are the limitations of Machine Learning:----

Machine Learning is not capable of handling and processing high dimensional data.

It cannot be used in image recognition and object detection since they require the implementation of high dimensional data

Another major challenge in Machine Learning is to tell the machine what are the important features it should look for in order to precisely predict the outcome. This very process is called feature extraction. Feature extraction is a manual process in Machine Learning.

The above limitations can be solved by using Deep Learning.

Deep learning is one of the methods by which we can overcome the challenges of feature extraction. This is because deep learning models are capable of learning to focus on the right features by themselves, requiring minimal human interventions.

Deep Learning mimics the basic component of the human brain called a brain cell or a neuron. Inspired from a neuron an artificial neuron was developed..

PayPal is no stranger to fraud. As one of the Internet’s first online payment services, PayPal has been exposed to every type of wire fraud imaginable (and some beyond imagination).

Sometimes the fraudsters had the upper hand, but now, thanks to deep learning (DL) models running on high performance computing (HPC) infrastructure, PayPal is leveraging its vast repository of fraud data to keep the fraudsters on the run.

Consider how PayPal uses Deep Learning to identify any possible fraudulent activities. PayPal processed over $235 billion in payments from four billion transactions by its more than 170 million customers.

Considering that the company processed $235 billion in transactions for its 170 billion customers last year, the $300 million that it spent fighting fraud would seem to be a good investment.

PayPal used Machine learning and Deep Learning algorithms to mine data from the customer’s purchasing history in addition to reviewing patterns of likely fraud stored in its databases to predict whether a particular transaction is fraudulent or not.

Running a payment processing system on the Internet is no easy business, and PayPal faces substantial financial and reputational risks from hackers, conmen, organized crime, and money launderers alike. In fact, the company struggled mightily with fraud in its early days, and incurred heavy losses.

According to the 2006 book “The PayPal Wars,” PayPal once had fraud losses amounting to 120 basis points (or 1.2% of the value of transactions), and lost $2,300 per hour to fraud.

PayPal stepped up its fraud-fighting game by using more advanced technologies, such as neural networks and Gradient Boosted Trees (GBTs).

The new deep learning models are voracious consumers of data.

PayPal has one big advantage over the fraudsters: a huge repository of data. This data repository was important for training classical machine learning models, but it’s absolutely critical for training a deep learning model.

The new deep learning approach lets PayPal vary the types of data that it’s using to predict which users, transactions, or locations are likely to be fraudulent, or associated with fraud.

Deep learning is also bolstering PayPal’s ability to utilize technology developed in other domains and transfer it to the fraud-detection business

AI is an algorithm that allows a system to learn from its environment and available inputs. In advanced applications, such as self-driving cars, AI is trained using an approach called deep learning, which relies solely on large volumes of sensor data without human involvement. However, these machine learning systems can fail when the training data misleads the decision making process.

For many applications, an AI failure is merely an inconvenience as long as the AI works as expected most of the time. However, for applications, such as self-driving cars and medical diagnosis in which failure can result in death or catastrophe, repeated unexpected failures are not tolerated.

Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Similarly to how we learn from experience, the deep learning algorithm would perform a task repeatedly, each time tweaking it a little to improve the outcome.

The neural networks have various (deep) layers that enable learning. Just about any problem that requires “thought” to figure out is a problem deep learning can learn to solve.

DL is a set of algorithms in machine learning that attempt to learn in multiple levels, where the lower-level concepts help define different higher-level concepts.

ANN takes data samples rather than entire data sets to arrive at solutions

There are multiple types of neural network, each of which come with their own specific use cases and levels of complexity. The most basic type of neural net is something called a feedforward neural network, in which information travels in only one direction from input to output. A feedforward neural network is an artificial neural network wherein connections between the nodes do not form a cycle

A more widely used type of network is the recurrent neural network, in which data can flow in multiple directions. These neural networks possess greater learning abilities. A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior.

Unlike feedforward neural networks, RNNs can use their internal state (memory) to process sequences of inputs. This makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition

There are also convolutional neural networks, Boltzmann machine networks, and a variety of others. Picking the right network for your task depends on the data you have to train it with, and the specific application you have in mind. In some cases, it may be desirable to use multiple approaches, such as would be the case with a challenging task like voice recognition.

A Boltzmann Machine is a network of symmetrically connected, neuron- like units that make stochastic decisions about whether to be on or off. Boltz- mann machines have a simple learning algorithm that allows them to discover interesting features in datasets composed of binary vectors

There were a few drawbacks in Machine learning that led to the emergence of Deep Learning..

Deep learning is self-education for machines; you feed an AI huge amounts of data, and eventually it begins to discern patterns all by itself

Deep learning, and most machine learning (ML) methods for that matter, learn patterns or associations from data. On its own, observational data can only possibly convey associations between variables -- the familiar adage correlation does not imply causation.

Machines don’t evolve on their own. The next step in deep learning will not come from a machine iterating on itself. It will come when humans iterate on their previous work. For now, we are still the agents hiding in the apparatus. We still make the moves.

Current approaches to NLP are based on deep learning, a type of AI that examines and uses patterns in data to improve a program's understanding. Deep learning models require massive amounts of labeled data to train on and identify relevant correlations, and assembling this kind of big data set is one of the main hurdles to NLP currently.

Deep learning doesn’t understand human language. Period!

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

Deep learning algorithms can automatically translate between languages. This can be powerful for travelers, business people and those in government.

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

Deep learning is being used for facial recognition .. The challenges for deep-learning algorithms for facial recognition is knowing it’s the same person even when they have changed hairstyles, grown or shaved off a beard or if the image taken is poor due to bad lighting or an obstruction.

The more experience deep-learning algorithms get, the better they become. . The benchmark for AI is the human intelligence regarding reasoning, speech, and vision. This benchmark is far off in the future.

Deep learning is the breakthrough in the field of artificial intelligence. When there is enough data to train on, deep learning achieves impressive results, especially for image recognition and text translation. .

Deep learning uses huge neural networks with many layers of processing units, taking advantage of advances in computing power and improved training techniques to learn complex patterns in large amounts of data. Common applications include image and speech recognition.

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

The "deep" in "deep learning" refers to the number of layers through which the data is transformed. .

Deep learning-based image recognition has become "superhuman", producing more accurate results than human contestants.

Deep learning has been shown to produce competitive results in medical application such as cancer cell classification, lesion detection, organ segmentation and image enhancement

The United States Department of Defense applied deep learning to train robots in new tasks through observation.

Cloud computing facilitates speedy, enhanced development of advanced AI capabilities, such as deep learning. Some current deep learning applications make security cameras smarter by spotting patterns that could indicate trouble.

Such technology can also categorize images. The deep learning technology inside self-driving cars differentiates between people and road signs.

A shallow network has one so-called hidden layer, and a deep network has more than one. Multiple hidden layers allow deep neural networks to learn features of the data in a so-called feature hierarchy, because simple features (e.g. two pixels) recombine from one layer to the next, to form more complex features (e.g. a line).

Nets with many layers pass input data (features) through more mathematical operations than nets with few layers, and are therefore more computationally intensive to train.

Computational intensivity is one of the hallmarks of deep learning, and it is one reason why a new kind of chip call GPUs are in demand to train deep-learning models.

Deep learning algorithms must be trained on big datasets. Once trained, inference puts a trained model to work, to draw conclusions or make predictions

GPU(Graphics Processing Unit) is considered as heart of Deep Learning, a part of Artificial Intelligence. It is a single chip processor used for extensive Graphical and Mathematical computations which frees up CPU cycles for other jobs

The individual accelerator chip is designed to perform the execution side of deep learning rather than the training part. Engineers generally measure the performance of such “inferencing” chips in terms of how many operations they can do per joule of energy or millimeter of area

A GPU, or graphics processing unit, is used primarily for 3D applications. It is a single-chip processor that creates lighting effects and transforms objects every time a 3D scene is redrawn. These are mathematically-intensive tasks, which otherwise, would put quite a strain on the CPU

The GPU, or graphics processing unit, is a part of the video rendering system of a computer. The typical function of a GPU is to assist with the rendering of 3D graphics and visual effects so that the CPU doesn't have to

Highly specialized chips designed for a specific computational task can outperform GPU chips that are good at handling many different kinds of computation.

Hardware design, rather than algorithms, will help us achieve the next big breakthrough in AI.

A tensor processing unit (TPU) is an AI accelerator application-specific integrated circuit (ASIC) developed by Google specifically for neural network machine learning

A neural processor or a neural processing unit (NPU) is a specializes circuit that implements all the necessary control and arithmetic logic necessary to execute machine learning algorithms, typically by operating on predictive models such as artificial neural networks (ANNs) or random forests (RFs).

NPUs sometimes go by similar names such as a tensor processing unit (TPU), neural network processor (NNP) and intelligence processing unit (IPU) as well as vision processing unit (VPU) and graph processing unit (GPU).

Executing deep neural networks such as convolutional neural networks means performing a very large amount of multiply-accumulate operations, typically in the billions and trillions of iterations.

A large number of iterations comes from the fact that for each given input (e.g., image), a single convolution comprises of iterating over every channel and then every pixel and performing a very large number of MAC operations. And many such convolutions are found in a single model and the model itself must be executed on each new input (e.g., every camera frame capture).

Unlike traditional central processing units which are great at processing highly serialized instruction streams, Machine learning workloads tend to be highly parallelizable, much like a graphics processing unit.

Moreover, unlike a GPU, NPUs can benefit from vastly simpler logic because their workloads tend to exhibit high regularity in the computational patterns of deep neural networks. For those reasons, many custom-designed dedicated neural processors have been developed.

Tensor Processing Units (TPUs) are Google’s custom-developed application-specific integrated circuits (ASICs) used to accelerate machine learning workloads. Cloud TPU resources accelerate the performance of linear algebra computation, which is used heavily in machine learning applications.

TPUs minimize the time-to-accuracy when you train large, complex neural network models. Models that previously took weeks to train on other hardware platforms can converge in hours on TPUs.

AI analyzes more and deeper data using neural networks that have many hidden layers. Building a fraud detection system with five hidden layers was almost impossible a few years ago. All that has changed with incredible computer power and big data. You need lots of data to train deep learning models because they learn directly from the data. The more data you can feed them, the more accurate they become.

Your interactions with Alexa, Google Search and Google Photos are all based on deep learning – and they keep getting more accurate the more we use them. In the medical field, AI techniques from deep learning, image classification and object recognition can now be used to find cancer on MRIs high accuracy .

Cognitive computing is a subfield of AI that strives for a natural, human-like interaction with machines. Using AI and cognitive computing, the ultimate goal is for a machine to simulate human processes through the ability to interpret images and speech – and then speak coherently in response.

Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data.

A layer is the highest-level building block in deep learning. A layer is a container that usually receives weighted input, transforms it with a set of mostly non-linear functions and then passes these values as output to the next layer.

The input layer of a neural network is composed of artificial input neurons, and brings the initial data into the system for further processing by subsequent layers of artificial neurons. The input layer is the very beginning of the workflow for the artificial neural network.

The most popular deep learning algorithms are:---

Convolutional Neural Network (CNN)

Recurrent Neural Networks (RNNs)

Long Short-Term Memory Networks (LSTMs)

Stacked Auto-Encoders

Deep Boltzmann Machine (DBM)

Deep Belief Networks (DBN)

There is a vast amount of neural network, where each architecture is designed to perform a given task. For instance, CNN works very well with pictures, RNN provides impressive results with ti.me series and text analysis.

CNN, or convolutional neural network, is a neural network using convolution layer and pooling layer. The convolution layer convolves an area, or a stuck of elements in input data, into smaller area to extract feature.. Any data that has spatial relationships is ripe for applying CNN.

CNNs are currently the most complicated and advanced type of neural networks and are used in various field such as self-driving cars, medical imaging, facial recognition and speech recognition.

CNN is a multi-layered neural network with a unique architecture designed to extract increasingly complex features of the data at each layer to determine the output. CNN's are well suited for perceptual tasks.

A convolution is the simple application of a filter to an input that results in an activation. Repeated application of the same filter to an input results in a map of activations called a feature map, indicating the locations and strength of a detected feature in an input, such as an image.

The innovation of convolutional neural networks is the ability to automatically learn a large number of filters in parallel specific to a training dataset under the constraints of a specific predictive modeling problem, such as image classification. The result is highly specific features that can be detected anywhere on input images.

CNN is mostly used when there is an unstructured data set (e.g., images) and the practitioners need to extract information from it

Convolutional neural network (CNN) identifies and makes sense of images. Uses for convolutional neural networks include image classification (e.g. training machines to distinguish between images of cats and dogs).

For instance, if the task is to predict an image caption:-

The CNN receives an image of let's say a cat, this image, in computer term, is a collection of the pixel. Generally, one layer for the greyscale picture and three layers for a color picture.

During the feature learning (i.e., hidden layers), the network will identify unique features, for instance, the tail of the cat, the ear, etc.

When the network thoroughly learned how to recognize a picture, it can provide a probability for each image it knows. The label with the highest probability will become the prediction of the network.

Simple deep learning techniques like CNN can, in some cases, imitate the knowledge of experts in medicine and other fields. The current wave of machine learning, however, requires training data sets that are not only labeled but also sufficiently broad and universal.

A convolutional neural network (CNN) is used in image recognition and processing that is specifically designed to process pixel data.

Convolutional Neural Networks have a different architecture than regular Neural Networks. Regular Neural Networks transform an input by putting it through a series of hidden layers. Every layer is made up of a set of neurons, where each layer is fully connected to all neurons in the layer before.

Convolution and the convolutional layer are the major building blocks used in convolutional neural networks. A convolution is the simple application of a filter to an input that results in an activation. ... The result is highly specific features that can be detected anywhere on input images

Why are convolutional neural networks better than other neural networks in processing data such as images and video? The reason why Convolutional Neural Networks (CNNs) do so much better than classic neural networks on images and videos is that the convolutional layers take advantage of inherent properties of images.

In convolutional (filtering and encoding by transformation) neural networks (CNN) every network layer acts as a detection filter for the presence of specific features or patterns present in the original data.

Convolution is a way to give the network a degree of translation invariance. You can think of the typical image convolution used in neural networks as a form of blurring (though there are other kinds of convolutions that are closer to spatial derivatives)

At the end of a CNN, the output of the last Pooling Layer acts as input to the so called Fully Connected Layer. There can be one or more of these layers (“fully connected” means that every node in the first layer is connected to every node in the second layer)

CNN-Cert requires full visibility into the structure of a neural network. The method uses mathematical techniques to define thresholds on the input-output relationships of each layer and each neuron. This enables it to determine how changes to the input in different ranges will affect the outputs of each unit and layer.

CNN-Cert first executes the process on single neurons and layers and then propagates it across the network. The final result is a threshold value that determines the amount of perturbations the network can resist before its output values become erroneous.

CNN builds an image in memory that incorporates aspects of all the data it's been given. So if a CNN is taught to recognize a printed or handwritten character of text, it's because it's seen several examples of every such character, and has built up a "learned" image of each one that has its basic features.

If a CNN model is trained with a variety of human faces, it will have built an amalgam of those faces in its model -- perhaps not necessarily a photograph, but a series of functions that represents the basic geometry of a face, such as the angle between the tip of the ear, the top of the cheekbone, and the tip of the nose.

Train that model with a series of faces of known terrorists, and the model should build some basic construct that answers the question, "What does a Isis Islamic terrorist look like?" There's no way a person could rationally respond to that question in a manner that some, if not most, listeners would consider unbiased.

And if a process were capable of rendering that average terrorist's face in living color, someone, somewhere would be rightfully enraged.

But think of this problem from the perspective of a software developer: Isolating individuals' faces in a crowd from CCTV footage and comparing them point-by-point to individual terrorists' mug shots, is a job that even a supercomputer might not be able to perform in anything approaching real-time.

How should the developer winnow the crowd faces to a more manageable subset? A CNN offers one option: an "average face" capable of excluding a wide range of improbable candidates. It's the visual equivalent of a hash function, simplifying long searches by making quick comparisons to some common element.

CNN can perform impressive feats of image recognition, but it is difficult to trace exactly how they arrive at their decisions. There are many layers of classification and comparison, and the end result is both remarkably accurate and consistent. But that does not mean we can truly explain how the CNN arrived at its decision for each image.

Convolutional Neural Networks (CNN) is one kind of feedforward neural network. ... CNN is an efficient recognition algorithm which is widely used in pattern recognition and image processing. It has many features such as simple structure, less training parameters and adaptability.

Recurrent neural networks (RNNs) is a multi-layered neural network that can store information in context nodes, allowing it to learn data sequences and output a number or another sequence. In simple words it an Artificial neural networks whose connections between neurons include loops. RNNs are well suited for processing sequences of inputs.

A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior. Unlike feedforward neural networks, RNNs can use their internal state (memory) to process sequences of inputs.

Recurrent Neural Network comes into the picture when any model needs context to be able to provide the output based on the input.

CNN is used for spatial data and RNN is used for sequence data. Second, CNN is kind of more powerful now than RNN. ... This is mostly because CNN can be stacked into a very deep model, which has been proven to be very effective.

The Convolutional Recurrent Neural Networks is the combination of two of the most prominent neural networks. The CRNN (convolutional recurrent neural network) involves CNN (convolutional neural network) followed by the RNN(Recurrent neural networks)..

The convolutional layers are developed on 3-dimensional feature vectors, whereas the recurrent neural networks are developed on 2-dimensional feature vectors.

The first layer of a neural network will learn small details from the picture; the next layers will combine the previous knowledge to make more complex information. In the convolutional neural network, the feature extraction is done with the use of the filter.

The network applies a filter to the picture to see if there is a match, i.e., the shape of the feature is identical to a part of the image. If there is a match, the network will use this filter. The process of feature extraction is therefore done automatically.

Deep learning algorithms are constructed with connected layers.

The first layer is called the Input Layer

The last layer is called the Output Layer

All layers in between are called Hidden Layers. The word deep means the network join neurons in more than two layers.

Each Hidden layer is composed of neurons. The neurons are connected to each other. The neuron will process and then propagate the input signal it receives the layer above it. The strength of the signal given the neuron in the next layer depends on the weight, bias and activation function.

The network consumes large amounts of input data and operates them through multiple layers; the network can learn increasingly complex features of the data at each layer.

A deep neural network provides state-of-the-art accuracy in many tasks, from object detection to speech recognition. They can learn automatically, without predefined knowledge explicitly coded by the programmers.

Each layer represents a deeper level of knowledge, i.e., the hierarchy of knowledge. A neural network with four layers will learn more complex feature than with that with two layers.

The learning occurs in two phases.

The first phase consists of applying a nonlinear transformation of the input and create a statistical model as output.

The second phase aims at improving the model with a mathematical method known as derivative.

The neural network repeats these two phases hundreds to thousands of time until it has reached a tolerable level of accuracy. The repeat of this two-phase is called an iteration.

Deep artificial neural networks are a set of algorithms that have set new records in accuracy for many important problems, such as image recognition, sound recognition, recommender systems, natural language processing etc.

Neural networks generally perform supervised learning tasks, building knowledge from data sets where the right answer is provided in advance. The networks then learn by tuning themselves to find the right answer on their own, increasing the accuracy of their predictions

Deep Learning. systems specifically rely upon non-linear neural networks to build out machine learning systems, often relying upon using the machine learning to actually model the system doing the modeling. It is mostly a subset of machine learning with a specific emphasis on neural nets.

Again, deep neural network is learning architecture based on data representations. They’re loosely modeled on the brain . DNNs comprise artificial neurons (i.e., mathematical functions) connected with synapses that transmit signals to other neurons.

Said neurons are arranged in layers, and those signals — the product of data, or inputs, fed into the DNN — travel from layer to layer and slowly “tune” the DNN by adjusting the synaptic strength — weights — of each neural connection.

Over time, after hundreds or even millions of cycles, the network extracts features from the dataset and identifies trends across samples, eventually learning to make novel predictions.

Technologies including machine learning, neural networks, and deep learning now help data scientists face the challenges raised by the need for prediction.

Basically, an artificial neural network consists of an input layer where it receives inputs and output layer where it outputs. The hidden layer is a layer which is hidden in between input and output layers since the output of one layer is the input of another layer

The number of hidden neurons should be between the size of the input layer and the size of the output layer. The number of hidden neurons should be 2/3 the size of the input layer, plus the size of the output layer. The number of hidden neurons should be less than twice the size of the input layer.

Deep NN is just a deep neural network, with a lot of layers.

Today, neural networks are used for solving many business problems such as sales forecasting, customer research, data validation, and risk management. For example, at Statsbot they apply neural networks for time series predictions, anomaly detection in data, and natural language understanding

Classification of Neural Networks---

Shallow neural network: The Shallow neural network has only one hidden layer between the input and output.

Deep neural network: Deep neural networks have more than one layer. For instance, Google LeNet model for image recognition counts 22 layers.

Nowadays, deep learning is used in many ways like a driverless car, mobile phone, Google Search Engine, Fraud detection, TV, and so on.

Feed-forward neural networks is the simplest type of artificial neural network. With this type of architecture, information flows in only one direction, forward. It means, the information's flows starts at the input layer, goes to the "hidden" layers, and end at the output layer.

The network does not have a loop. Information stops at the output layers.

A deep neural network is a pipeline of operations that processes data to identify some pattern as a solution to a specific problem. Deep neural nets have a layered structure, and the ‘thinking’ part happens inside the hidden layers between the input and output.

The ‘deep’ indicates that there are several internal layers, so the incoming data goes through more complex transformations, as opposed to simpler artificial neural networks.

There are many applications of deep neural networks, and a very basic example is image recognition. In this case, the neural net would take an image as an input and would try to guess with some probability what objects are on it.

However, to make any neural network good in its job, it first has to be trained. In other words, the algorithm has to fine-tune its inner workings, i.e. the parameters that assign values of connections between its neurons. Training requires the neural network to be fed with data and then, as it returns some output, to have its parameters corrected in a way that would make its next prediction better.

So usually a deep neural network would initially spit some nonsense result, that is very far from the truth. Typically, a human researcher will try to adjust the architecture of the algorithm by adding a layer, changing the type of another, etc.

This process goes on until we’re happy with the AI accuracy.

So adjusting the architecture is the only part that requires human intervention. But it usually is a very tedious and time-consuming process. A hit-and-miss, so to say.

Neural Architecture Search (NAS) utilizes an algorithm called a controller (most often a neural network). It creates a child neural network and proceeds to optimize its architecture until it finds the best solution for a particular problem, i.e. until the child network achieves its highest accuracy. A well-working NAS eliminates the need of humans from the process of deep neural network creation.

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

Deep learning is a computer software that mimics the network of neurons in a brain. It is a subset of machine learning and is called deep learning because it makes use of deep neural networks. The machine uses different layers to learn from the data. The depth of the model is represented by the number of layers in the model.

In deep learning, the learning phase is done through a neural network.. A neural network is an architecture where the layers are stacked on top of each other.

Artificial neural networks are parallel computational models (unlike our computers, which have a single processor to collect and display information). ... The basis for these networks originated from the biological neuron and neural structures – every neuron takes in multiple unique inputs and produces one output.

Deep learning, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.

A neural network may only have a single layer of data, while a deep neural network has two or more

In deep learning, the learning phase is done through a neural network. A neural network is an architecture where the layers are stacked on top of each other. The training set would be fed to a neural network

Each input goes into a neuron and is multiplied by a weight. The result of the multiplication flows to the next layer and become the input. This process is repeated for each layer of the network. The final layer is named the output layer; it provides an actual value for the regression task and a probability of each class for the classification task.

The neural network uses a mathematical algorithm to update the weights of all the neurons. The neural network is fully trained when the value of the weights gives an output close to the reality. For instance, a well-trained neural network can recognize the object on a picture with higher accuracy than the traditional neural net.

DNN architectures enable the models to learn multiple hierarchical, hence deep, inter relationships of the features present in the data.

Deep learning, modeled loosely on the biological brain with layers of neural networks, tend to be single-purpose point-solutions that require extensive training with massive data sets, and are customized for a specific environment. In contrast, evolutionary algorithms solves a problem based on criteria set in the “fitness” function—thus requiring little to no data.

Within a neural network, each processor or “neuron,” is typically activated through sensing something about its environment, from a previously activated neuron, or by triggering an event to impact its environment.

The goal of these activations is to make the network—which is a group of ML algorithms—achieve a certain outcome. Deep learning is about “accurately assigning credit across many such stages” of activation.

As the name suggests, artificial neural networks (ANNs) are inspired by the functionality of theelectro-chemical neural networks found in human (and animal) brains. The working of the brain remains somewhat mysterious, although it has long been known that signals from stimuli are transmitted and altered as they pass through complex networks of neurons. In an ANN, inputs are passed through a network, generating outputs that are interpreted as responses.

The process starts with signals being sent to the 'input layer', and ends with a response being generated at the 'output layer'. In between, there is one or more 'hidden layer', which manipulates the signal as it passes through, so that it generates a useful output. ..

The, signals then pass to the next layer, so neurons in this hidden layer receive several numbers. Each neuron in this layer combines and manipulates these signals in different ways to generate a single numerical output. For example, one neuron might add all of the inputs and output 1 if the total is over 50, or 0 if it is not.

Another neuron might assign weights to different input signals, multiply each input signal by its weight, and add the results to give as its output. The outputs of these neurons then pass as signals to the next layer. When the signals reach the final layer and its own output is generated, the process is complete. The final signal can be interpreted

Now we have a simple ANN, inspired by a simplified model of the brain, which can respond to a specific input with a specific output. It doesn't really know what it is doing,. For simple tasks, ANNs can work well with just a dozen neurons in a single hidden layer.

Adding more neurons and layers allow ANNs to tackle more complex problems. Deep learning refers to the use of big ANNs, featuring at least two hidden layers, each containing many neurons.

These layers allow the ANN to develop more abstract conceptualisations of problems by splitting them into smaller sub-problems, and to deliver more nuanced responses. It has been suggested that three hidden layers are enough to solve any kind of problem although, in practice, many ANNs include millions of neurons organised in dozens of hidden layers.

By way of comparison, human brains contain ~100 billion neurons, cockroach brains ~1 million and snail brains ~10 thousand.

So if the 'deep' part of deep learning is about the complexity of the ANN, what about the 'learning' part? Once the correct structure of the ANN is in place, it needs to be trained. While in theory this can be done by hand, it would require a human expert to painstakingly adjust neurons to reflect their own expertise of how to play a good game.

Instead, a ML algorithm is applied to automate the process. The training process can be an intensive and complicated process, and often never really ends, as constant updates respond to new data availability and changes in the problem faced. Once a well-trained ANN is in place, it can be applied to new data very quickly and efficiently.

Neural networks are sometimes described in terms of their depth, including how many layers they have between input and output, or the model's so-called hidden layers. This is why the term neural network is used almost synonymously with deep learning.

They can also be described by the number of hidden nodes the model has or in terms of how many inputs and outputs each node has. Variations on the classic neural network design allow various forms of forward and backward propagation of information among tiers.

Deep learning allows the network to extract different features until it can recognize what it is looking for. A neural network is a biologically-inspired programming paradigm which enables a computer to learn from observational data.

Even a computer scientist would find it extremely hard to simply look inside a given deep neural network and understand how that specific deep neural network actually works.

Any given deep neural network’s reasoning ability is pretty much embedded in the actual behavior of hundreds and thousands of various simulated neurons.

These simulated neurons are neatly arranged into, sometimes, dozens and sometimes hundreds of layers which are intricately interconnected. AI has some secrets that even its creators find hard to understand. This cannot continue if AI is to become a part of our everyday life.

Each of the present the simulated neurons, for example, in the very first layer receive a given input.

This input can be anything. For example, one input could be the intensity of a specific pixel in a specific image. After the simulated neuron has received its input, it then has to perform a calculation.

Only after performing that calculation can the simulated neurons output the new signal. After that, the deep neural network system feeds the outputs from the first layer to the simulated neurons of the second layer.

It is really a very complex web of simulated neuron layers. The process continues from one layer to the next layer until there are no more layers left. And when there are no more layers left, that is the time when the deep neural network actually produces an overall output.

In addition to that, there is also a process that computer scientists call backpropagation.

This process is able to tweak the calculations of various individual neurons. And it does that in a way which enables the entire deep neural network to learn how to produce the desired output.

Since the deep neural network has many layers of simulated neurons, they enable the network to recognize different things/objects at different levels of abstraction.

To take an example, if a given deep neural network system is designed to recognize cats then the lower layers of the deep neural network would recognize simpler features or things about cats such as color and outlines.

Then, the higher layers present in the deep neural network would recognize the more complex features. These would include stuff like eyes and/or fur. After that, the topmost simulated neurons-containing layer would identify all of that information as a cat.

It should not be hard to understand that computer scientists and engineers can take this approach and apply it to a lot of other inputs (roughly speaking).

As mentioned before, these are the same inputs that actually lead the given machine to learn and teach itself. Using such techniques machines can learn--

All the sounds which make up the present words in a given speech

The words and the letters which create various sentences in a given text.

The movements required of a steering-wheel for proper driving.

Computer scientists and researchers have used various but ingenious strategies to try and capture information which they can use to explain in a lot more detail the things that are happening in all such deep neural network systems.

Back in the year 2015, researchers working at the search engine giant Google, modified one of their image recognition computer algorithm which was based on deep learning.

When they did, the algorithm changed the way it worked. So instead of the deep learning algorithm trying to spot objects in various photos, the image recognition algorithm would modify and/or generate them.

Researchers working at Google effectively ran the image recognition deep learning algorithm in reverse. This enabled them to discover all the features that the program used in order to recognize, for example, a building or a bird.

Researchers called the project which enabled to produce the new images as Deep Dream.

The resulting photos from the modified algorithm showed alien-like but grotesque animals that emerged from plants and clouds. The modified images also showed hallucinatory pagodas which bloomed across mountain ranges and forests.

In other words, the resulting photos/images provided good evidence that various deep learning techniques were not, in fact, entirely inscrutable. The modified images also revealed that that computer algorithms had little trouble in homing in on various familiar visual features such as a given bird’s feathers and/or beaks.

However, those same modified images also hinted at other important things. Other important things such as how different human perception is from deep learning in the sense that a deep learning algorithm could actually make something out of a given artifact which humans would know to simply ignore.

Researchers working at Google also noted that when their image recognition deep learning algorithm generated photos of a given dumbbell, the algorithm also took the opportunity to generate a human arm that held the generated dumbbell.

In other words, the machine automatically came to the conclusion that a human arm was actually part of the whole dumbbell thing. Computer scientists have made much more progress by using ideas which were actually borrowed from cognitive science as well as neuroscience.

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

Generative Adversarial Networks, or GANs for short, are an approach to generative modeling using deep learning methods, such as convolutional neural networks.

The two models are trained together in a zero-sum game, adversarial, until the discriminator model is fooled about half the time, meaning the generator model is generating plausible examples.

The Generator is a neural network trained on a distribution of data (where a distribution of data might be images of dogs, cats, wolves) and extracts features from that distribution of data (mixed with some level of randomness) to try to fool the Discriminator into thinking the fake image is a real image.

That is, the generator tries to create a fake cat image based upon the modeling (feature extraction) of real cats to fool the Discriminator into thinking that the fake cat image is a real cat image. Probably sounds better when someone else explains it…

The Discriminator is a convolutional neural network that is trained to predict whether input images are real or fake. The discriminator outputs “1” if it thinks the image is real, and “0” if it thinks the image is fake.

The discriminator tries to find a “decision boundary” or clusters that separate images into the appropriate classifications (dogs versus cats versus wolves). Then, to classify a new animal as either a dog or a cat or a wolf, it checks on which side of the decision boundary it falls and makes its prediction accordingly.

Made up of two competing models which run in competition with one another, GANs are able to capture and copy variations within a dataset.. The generator learns to generate fake signals or data that can eventually fool the discriminator. The input to the generator is noise, and the output is a synthesized signal

GANs can learn to mimic any data distribution (diseased plants, cancer, skin lesions, tumors, broken bones, mechanic parts abrasions) to create real-world like experiences. GANs can create something totally new yet life-like based upon adding randomness to a deep understanding of existing data distributions.

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

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

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

Two neural networks contest with each other in a game (in the sense of game theory, often but not always in the form of a zero-sum game). Given a training set, this technique learns to generate new data with the same statistics as the training set. For example, a GAN trained on photographs can generate new photographs that look at least superficially authentic to human observers, having many realistic characteristics.

Generative Adversarial Network (GAN) is a generic framework used to fool machine learning algorithms.

The generator and a discriminator tries to outsmart the other. A generator modifies malicious version of the input it was originally given and sends it to be classified by the IDS and the discriminator. The goal of the generator is to fool an IDS, and the goal of the discriminator is to mimic the IDS on classifying inputs (correct or wrong) and provide feedback to the generator.

The game ends when the IDS and discriminator cannot accurately classify the input created by the generator.

While GANs appear to be very useful in creating new real-like content for the gaming, entertainment and advertising industries, the bigger win might be in the ability to create adaptive content that can accelerate training and education. Adaptive content is individualized content that can drive personalized interactions based upon an individual’s capabilities. experience, mood, and immediate goals.

In the area of education, GANs could create dynamic adaptive content that could be personalized for each individual student based upon their experience, areas of expertise, and current areas of struggle.

The GAN-based educational system could dynamically create new content that could help the student in areas in which they are struggling and could push students into new areas more quickly if they are grasping concepts faster.

GANs might play an even bigger role in the retraining of our workforces (especially when coupled with immersive technologies like Virtual Reality and Augmented Reality) as technology advances the nature of work. Learning will no longer be dependent upon the speed of the content and the expertise of the instructor!

And in the world of healthcare, the ability to dynamically create new experiences in training and re-certifying health care providers could ensure that all doctors are able to leverage the last learnings and developments and not be dependent upon having to attend the right conferences (snore) or churn through a stack of research studies to find learnings that are relevant for their current level of expertise.

GANs could also provide a solution to the HIPAA privacy concerns by creating life-like data sets of severe medical situations without actually having to disclose any particular individual’s private data.

And in the area of warfare, well, all sorts of bad things can be conceived with adaptive content that can confuse enemies (and sway political elections).

The combination of deep reinforcement learning (to create AI-based assistants) and GANs (to create dynamic, adaptive content) could greatly accelerate the adoption and institutionalization of new technologies and accelerate business and society benefits in the process.

Deep Reinforcement Learning could create AI-based assistants (agents) that aid teachers, physicians, technicians, mechanics, engineers, etc. while GANs could create new adaptive learning environments so that those assistants/agents are constantly learning – learning while the human in the process is doing more human-related activities like engaging with students, patients and customers.

The generator is trying to generate images and fool and confuse the Discriminator into believing that they are Real , and the discriminator tries distinguish the Real images from the Fake ones generated by the Generator.

A Discriminative model ‌models the decision boundary between the classes. A Generative Model ‌explicitly models the actual distribution of each class.

In the end, the generator network is outputting images that are indistinguishable from real images for the discriminator.

Using Generative Models on Neural Nets actually requires smaller number of parameters than the amount of data usually a Neural Net requires to be trained on hence the model does a good job at efficiently generating data.

Usually it is said that if we want to train a Neural Network Model on small data sets with smaller number of parameters we should use unsupervised pre training i.e train the Neural Net in a unsupervised manner.

Otherwise generally a Neural Network model requires a lot of data to be trained effectively and learn properly from the data in order to avoid overfitting on small datsets.

C++ is preferred for its speed and memory management, while Java's platform independency makes it an opportune option for cross-platform development. Python, on the other hand, is more like a human language with high readability, less complex syntax, and an active community support

C++ as of today in its efficiency, speed, and memory make it widely popular among coders. Java is platform independent. It continues to add considerable value to the world of software development. Python requires less typing, provides new libraries, fast prototyping, and several other new features.

C++ Vs Java:

TOPIC	C++	Java
Memory Management	Use of pointers, structures, union	No use of pointers. Supports references, thread and interfaces.
Libraries	Comparatively available with low level functionalities	Wide range of classes for various high level services
Multiple Inheritance	Provide both single and multiple inheritance	Multiple inheritance is partially done through interfaces
Operator Overloading	Supports operator overloading	It doesn’t support this feature
Documentation comment	C++ doesn’t support documentation comment.	It supports documentation comment (/*.. /) for source code
Program Handling	Functions and variables can reside outside classes.	Functions and variables reside only in classes,packages are used.
Portability	Platform dependent, must be recompiled for different platform	Platform independent, byte code generated works on every OS.
Thread Support	No built-in support for threads, depends on libraries.	It has built-in thread support.

Python Vs Java:

Components can be developed in Java and combined to form applications in Python. Let’s see some of the differences in these two popular languages:

TOPIC	Java	Python
Compilation process	Java is a compiled programming language	Python is an interpreted programming language
Code Length	Longer lines of code as compared to python.	3-5 times shorter than equivalent Java programs.
Syntax Complexity	Define particular block by curly braces,end statements by ;	No need of semi colons and curly braces ,uses indentation
Ease of typing	Strongly typed, need to define the exact datatype of variables	Dynamic, no need to define the exact datatype of variables.
Speed of execution	Java is much faster than python in terms of speed.	Expected to run slower than Java programs
Multiple Inheritance	Multiple inheritance is partially done through interfaces	Provide both single and multiple inheritance

You can choose any language you want i.e. the one you are comfortable to work with. Technically it depends upon the job you want to accomplish. These 3 languages form the set of most popular languages among the college graduates’ coders and developers. Stick with one language and achieve perfection in that.

The Cloud AutoML API is a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs, by leveraging Google's state-of-the-art transfer learning, and Neural Architecture Search technology.

AutoML also aims to make the technology available to everybody rather than a select few

AutoML Vision's machine learning code allows virtually anyone to provide the tagged images required to train a system that is learning computer vision, enabling it to perform categorization and other image recognition tasks. ... Vision is the first of a number of planned Cloud AutoML offerings

Automated machine learning, or AutoML, aims to reduce or eliminate the need for skilled data scientists to build machine learning and deep learning models. Instead, an AutoML system allows you to provide the labeled training data as input and receive an optimized model as output.

Cloud AutoML, is a point-and-click method for generating machine learning models without any coding background.

The essence of Cloud AutoML is that almost anyone can bring a catalogue of images, import tags for the images, and create an operative machine learning model based on that. Google does all the complicated operations behind the scenes, so the client doesn’t require to comprehend anything about the complexities of neural network design.

AutoML uses a simple graphical interface, enabling the user to drag in a collection of images. Then, the platform needs to know how to represent those images. Google does its work and users end up with a model running in the cloud that can recognise the specified courses in photos.

All of the big three cloud services have some kind of AutoML. Amazon SageMaker does hyperparameter tuning but doesn’t automatically try multiple models or perform feature engineering.

Azure Machine Learning has both AutoML, which sweeps through features and algorithms, and hyperparameter tuning, which you typically run on the best algorithm chosen by AutoML.

Google Cloud AutoML, is deep transfer learning for language pair translation, natural language classification, and image classification.

In deep learning, transfer learning is a technique whereby a neural network model is first trained on a problem similar to the problem that is being solved. One or more layers from the trained model are then used in a new model trained on the problem of interest.

BELOW: I WILL GIVE YOU A CONDORs EYE-VIEW OF CLOUD SYSTEMS LATER.. THESE TWO VIDEOS ARE JUST CHAATENE KE VAASTE.

https://www.varonis.com/blog/cybersecurity-statistics/

https://www.csoonline.com/article/2130877/the-biggest-data-breaches-of-the-21st-century.html

CAPT AJIT VADAKAYIL INTRODUCED THE CONCEPT OF VIRTUAL TEAMING AT SEA.

PEOPLE NEVER UNDERSTOOD IT—

ALL THEY COULD SAY WAS “ AJIT, YOU ARE WAY AHEAD OF TIME”

http://ajitvadakayil.blogspot.com/2010/11/virtual-teaming-capt-ajit-vadakayil.html

When you give employees the freedom to work remotely, not only will you increase employee satisfaction and productivity, but you’ll also be able to grow your business with greater ease.

Moving your business to the Cloud makes it so that you’re able to securely access critical data and applications from various devices – anytime, anywhere. With a budding mobile workforce at-the-ready, your business will be unstoppable.

Whereas in the past, employees would access software that had been downloaded on their physical computers, Cloud computing allows you to those same programs over the Internet. The benefits include:--

Work remotely – on-the-go or in the comfort of your home – and make the world your office

Enhance collaboration among team members in real time

Easily scale your Cloud capacity to match your current and ongoing needs

Cloud computing has changed not only the way we collaborate, but where, when, and how we’re able to work. The ability to clock in from any corner of the world in different time zones , has given rise to a new professional culture that’s less about where you are, and more about what you can offer.

The ascent of remote working is fostering a more open, and more competitive, market; as candidates are no longer so bound to their geographical location, there’s potential to attract the best talent, no matter where they’re based.

https://www.news18.com/news/india/after-kudankulam-power-plant-isro-too-was-alerted-of-cyber-security-breach-by-dtrack-2375311.html

IN MY ELDER SOs WORKPLACE,YOU CAN WORK FROM HOME, WITHOUT PERMISSION.. YOU ARE NOT REQUIRED IN OFFICE UNLESS THERE IS A CRITICAL TEAM MEETING..

WORK HOURS AT HOME ARE ABSOLUTELY FLEXIBLE.. MEMBERS OF THE OFFICE TEAM WILL BE "ON CALL" , IN WHICH CASE A CUSTOMER ( IN ANOTHER TIME ZONE ) WILL NEED YOUR ATTENTION ANY TIME AT NIGHT..

Cloud technology is what enables employees to work from any location, giving them access via a virtual environment to the same information that they’d have access to from the office.

Collaboration and communication tools ensure that your customers’ employees can use the latest techniques and complete work at the same level as they would be able to from their office desktop.

Employees can access documents and data that they need by using specific credentials to maintain security at the same time as giving them flexibility to work from home or any other location.

This can be useful when your customers have multiple office locations, such as if they have a primary central office and smaller remote offices from additional locations.

Cloud technology provides a greater level of flexibility and shared access to information centrally accessible via a single server.

I WILL ADDRESS "CLOUD COMPUTING" IN GREAT DETAIL LATER...

ALL CLOUD DATA STORAGE BIG PLAYERS ARE OWNED BY THE JEWISH DEEP STATE.. KOSHER BIG BROTHER KNOW EVERYTHING..

BIG-DATA MONOPOLISTS ALREADY HOOVER UP BEHAVIORAL MARKERS AND CUES ON A SCALE AND WITH A FREQUENCY THAT FEW OF US UNDERSTAND. THEY THEN ANALYZE, PACKAGE, AND SELL THAT DATA TO THEIR PARTNERS.

BEING WIRED TOGETHER WITH BILLIONS OF OTHER HUMANS IN VAST NETWORKS MEDIATED BY THINKING MACHINES IS NOT AN EXPERIENCE THAT HUMANS HAVE ENJOYED BEFORE

BIG BROTHER KNOWS YUR SEXUAL PREFERENCE BASED ON YOUR INTERNET SURFING AND DIGITAL PAYMENTS

NSA CAN DIP INTO CLOUD DATA AT WILL.

WHILE GOOGLE HAS STATED THAT IT WILL NOT PROVIDE PRIVATE DATA TO GOVERNMENT AGENCIES, THAT POLICY DOES NOT EXTEND BEYOND AMERICA’S BORDERS.

GOOGLE IS ALSO ACTIVELY WORKING WITH THE US INTELLIGENCE AND DEFENSE COMPLEX TO INTEGRATE ITS AI CAPACITIES INTO WEAPONS PROGRAMS.

IT DOESN’T TAKE GREAT MENTAL ACUITY TO IMAGINE WHAT FUTURE BIG-TICKET COLLABORATIONS BETWEEN BIG-DATA COMPANIES AND GOVERNMENT SURVEILLANCE AGENCIES MIGHT LOOK LIKE, OR TO BE FRIGHTENED OF WHERE THEY MIGHT LEAD.

WITH COMPANIES LIKE OLA / UBER AND PIZZA DELIVERY DRONE

COMPANIES, HAVING DATA ON REAL TERRAIN, BIG BROTHER DOES NOT NEED A GPS CONTROLLED MISSILE TO TAKE OUT A PRESIDENTS BEDROOM.

THIS BLOGSITE HAS GIVEN UP TELLING MODI THAT WE MUST HAVE OUR OWN CHIP FABLAB.. SECURITY CAMS IN ISRO/ NUCLEAR POWERPLANTS ARE VULNERABLE.

IF YOU WEAR A COMPROMISED FITBIT WATCH OR HIGH END MOBILE PHONE WHILE WALKING BIG BROTHER KNOWS WHERE YOU ARE..

WITH VPS TECHNOLOGY KOSHER BIG BROTHER WILL KNOW WHERE YOU ARE HIDING AMONG SKYSCRAERS IN A CITY LIKE NEW YORK

VPS IS LIKE A VISUAL GPS, BUT RATHER THAN A CHIP THAT RECEIVES SATELLITE DATA, IT'S AN AI CLOUD SERVICE THAT USES VISUAL DATA INPUTS TO LET CAMERAS PROVIDE ACCURATE POSITIONING AND CONTEXTUAL INFORMATION.

GPS HAS A TECHNICAL SHORTCOMING: IT CAN ONLY DETERMINE THE LOCATION OF THE DEVICE, NOT THE ORIENTATION.

VPS DETERMINES THE LOCATION OF A DEVICE BASED ON IMAGERY RATHER THAN GPS SIGNALS.

VPS FIRST CREATES A MAP BY TAKING A SERIES OF IMAGES WHICH HAVE A KNOWN LOCATION AND ANALYZING THEM FOR KEY VISUAL FEATURES, SUCH AS THE OUTLINE OF BUILDINGS OR BRIDGES, TO CREATE A LARGE SCALE AND FAST SEARCHABLE INDEX OF THOSE VISUAL FEATURES.

TO LOCALIZE THE DEVICE, VPS COMPARES THE FEATURES IN IMAGERY FROM THE PHONE TO THOSE IN THE VPS INDEX.

WITH THIS NEW TECHNOLOGY, GOOGLE MAPS CAN MAKE USE OF THE USER’S PHONE CAMERA TO SPOT YOUR SURROUNDINGS AND VISUALLY CONVERSE YOUR DIRECTION RIGHT IN FRONT OF YOUR EYES.

VPS CAN IN FACT PROVIDE A CLOUD-BASED DEPOT OF ROBUST IMAGE DATA CAPTURED FROM PHYSICAL SURROUNDINGS.

BASICALLY, THE TECHNOLOGY IS USEFUL IN URBAN AREAS WHERE GPS IS OFTEN BLOCKED BY SKYSCRAPERS.

Capt. Ajit VadakayilNovember 24, 2019 at 5:46 AM

https://timesofindia.indiatimes.com/india/no-cabinet-meet-pm-uses-powers-to-revoke-article-356/articleshow/72204459.cms

https://indiankanoon.org/doc/8019/

TILL TODAY NOT ONE SINGLE COLLEGIUM JUDGE HAS BEEN ABLE TO UNDERSTAND THE INDIAN CONSTITUTION..

THE INDIAN CONSTITUTION IS A DIRECT LIFT OF THE BRITISH CONSTITUTION WRITTEN BY JEW ROTHSCHILD-- WITH SOME ADDITIONAL SOCIAL ENGINEERING CLAUSES WRITTEN BY BR AMBEDKAR ON DIRECTIONS OF COMMIE JEW JOHN DEWEY..

THE JUDICIARY HAS NOT UNDERSTOOD THAT THE INDIAN CONSTITUTION PROVIDES SUBJECTIVE VETO POWERS AND SUBJECTIVE DISCRETIONARY POWERS TO THE STATE GOVERNORS AND INDIAN PRESIDENT.. REST ARE ALL GIVEN ONLY "OBJECTIVE" POWERS ..

LIKE I SAID-- WHEN A SHIP IS VETTED BY AN OIL MAJOR , THERE WILL BE 999 OBJECTIVE QUESTIONS.. THE SHIP CAN PASS ALL THE 999 "OBJECTIVE" QUESTIONS..

THE FINAL "SUBJECTIVE" QUESTION TO THE VETTING INSPECTOR WILL BE " ARE YOU WILLING TO SAIL ON THIS SHIP WITHOUT RESERVATION FOR A VOYAGE NUDER THE PRESENT CAPTAIN AND CREW"..

IS THIS ANSWER I NEGATIVE , THE SHIP HAS FAILED..

THE JUDICIARY HAS NO POWERS TO CURB THE SUBJECTIVE POWERS VESTED IN THE PRESIDENT/ STATE GOVERNORS..

THE CAPTAIN OF A SHIP HAS "SUBJECTIVE" POWERS.. I MADE SURE THIS HAPPENED.. SUBCONSCIOUS BRAIN LOBE AND GUT FEELINGS ARE EVOKED HERE FOR A FINAL DECISION..

ARTIFICIAL INTELLIGENCE CAN NEVER INVOKE THE "SUBJECTIVE"..

THIS IS MY TIMELESS GIFT TO THE SEA.

https://ajitvadakayil.blogspot.com/2019/10/when-there-is-danger-of-his-ship.html

capt ajit vadakayil
..

THIS POST IS NOW CONTINUED TO PART 6 BELOW--

https://ajitvadakayil.blogspot.com/2019/11/what-artificial-intelligence-cannot-do_25.html

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CAPT AJIT VADAKAYIL
..

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199 comments:

Capt. Ajit VadakayilNovember 20, 2019 at 6:03 PM
.
https://timesofindia.indiatimes.com/india/will-not-allow-nrc-in-bengal-there-will-be-no-division-on-the-basis-of-religion-mamata-banerjee/articleshow/72142017.cms

KICK OUT ALL ILLEGAL BANGLADESI MUSLIMS FROM BENGAL
ReplyDelete
Replies
Capt. Ajit VadakayilNovember 20, 2019 at 6:13 PM
WE ASK MODI.. WHY HAVE YOU TAKEN JNU WOMAN NIRMALA SITARAMAN AS YOUR FINANCE MINISTER?

WILL SHE PAY OFF YOUR EXTRAVAGANT KOSHER PURCHASES ( LIKE RAFALE ) WITHOUT DEMUR? LIKE HOW ARUN JATLEY PAID OFF THE KOSHER BARAK MISSILE PURCHASES..

SENIOR CITIZENS ( NON-PENSION ) WHO HAVE WORKED THEIR ASSES OFF THEIR WHOLE LIVES ARE NOW IN SHIT STREET..

THEY HAVE PAID TAX-- DEDUCTED AT SOURCE BY THE COMPANY, AND THEN WHATEVER THEY SAVED FOR THEIR OLD AGE , THE INTEREST IS BEING TAXED AGAIN THIS TIME BY BANKS AT SOURCE..

WHY THIS DOUBLE TAXATION ?

IN KERALA OLD SENIOR CITIZENS HAVE TO PAY 900 RUPEES A DAY FOR UNSKILLED LABOUR --SAY TO CLEAN THE YARD..

CAPT AJIT VADAKAYIL HAS LOST HIS TRUST IN INDIAN BANKS AFTER THE PMC SCAM ..

I NOW PLAN TO SHIFT MY MONEY EARNED BY THE SWEAT OF MY BALLS AT SEA, INTO A FOREIGN BANK.. THIS IS WHAT YOU WANTED, RIGHT, NARENDRA DAMODARDAS MODI ? YOU HAVE GIVEN ROTHSCHILD A DRIVERS SEAT IN BANKING / INSURANCE ALL OVER AGAIN..

YOUR JEWISH MASTERS WILL SOON GIVE YOU A NOBEL PRIZE..AFTER ALL YOU WORE A SIKH TURBAN FOR ROTHSCHILD IN 1976..

capt ajit vadakayil
..
PUT ABOVE COMMENT IN WEBSITES OF--
PM MODI
PMO
AJIT DOVAL
RAW
NIA
ED
IB
CBI
AMIT SHAH
HOME MINISTRY
NEW CJI
GOGOI
ALL SUPREME COURT JUDGES
ATTORNEY GENERAL
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CMs OF ALL INDIAN STATES
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ALL THREE ARMED FORCE CHIEFS.
RBI
RBI GOVERNOR
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RAJEEV CHANDRASHEKHAR
MOHANDAS PAI
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AMITABH KANT
RAM MADHAV
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VIVEK OBEROI
GAUTAM GAMBHIR
ASHOK PANDIT
ANUPAM KHER
KANGANA RANAUT
VIVEK AGNIHOTRI
KIRON KHER
MEENAKSHI LEKHI
SMRITI IRANI
PRASOON JOSHI
MADHUR BHANDARKAR
SWAPAN DASGUPTA
SONAL MANSINGH
MADHU KISHWAR
SUDHIR CHAUDHARY
GEN GD BAKSHI
SAMBIT PATRA
RSN SINGH
SWAMY
RAJIV MALHOTRA
THE QUINT
THE SCROLL
THE WIRE
THE PRINT
MK VENU
MADHU TREHAN
CLOSET COMMIE ARNAB GOSWMI
RAJDEEP SARDESAI
PAAGALIKA GHOSE
NAVIKA KUMAR
ANAND NARASIMHAN
SRINIVASAN JAIN
SONAL MEHROTRA KAPOOR
VIKRAM CHANDRA
NIDHI RAZDAN
FAYE DSOUZA
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PRANNOY JAMES ROY
AROON PURIE
VINEET JAIN
RAGHAV BAHL
SEEMA CHISTI
DILEEP PADGOANKAR
VIR SANGHVI
KARAN THAPAR
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N RAM
NCW
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I&B DEPT/ MINISTER
LAW MINISTER/ MINISTRY
WEBSITES OF DESH BHAKT LEADERS
SPREAD OF SOCIAL MEDIA
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Capt. Ajit VadakayilNovember 20, 2019 at 6:14 PM
YOU MUST WATCH THE NETFLIX SERIES EL CHAPO. IT IS SINISTER AND SLICK..

UNLIKE COLOMBIA , THE JEWISH OLIGARCHY DECIDES WHICH DRUG LORD WILL GET WHICH AREA.. THIS COMMUNICATION IS GIVEN IN A MEETINGS OF ALL DRUG LORDS ..

IT IS A MEXICAN GOVT ORDER , DO IT OR ELSE..

THE DEEP STATE SPONORS THE DRUGS FLOWING IN FROM MEXICO TO USA… THIS IS WHY ROTHSCHILDs MEDIA DOES NOT WANT A WALL.

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

EL CHAPO WAS A MERE DRIVER IN THE CARTEL OF JEW DRUG LORD MIGUEL ÁNGEL FÉLIX GALLARDO

HE WAS MADE THE BOSS OF BOSSES BY THE MEXICAN GOVT.

EL CHAPO WAS KING OF TUNNELS AT THE MEXICAN BORDER..

https://www.youtube.com/watch?v=m7AUT0TB2Wq

https://www.youtube.com/watch?v=_h1v7uEvFpE

https://en.wikipedia.org/wiki/Joaqu%C3%ADn_%22El_Chapo%22_Guzm%C3%A1n

EL CHAPO ESCAPED FROM JAIL BY A TUNNEL..

https://www.youtube.com/watch?v=ZLW11qfuY4I

GUADALAJARA AND MEXICO CITY IS INFESTED WITH JEWISH OLIGARCHS .. THEY WORK FOR THE DEEP STATE AND THEIR TENTACLES SPREAD EVERYWHERE CONTROLLING EVERYTHING IN MEXICO..

JEWS CONTROLLED THE BLACK SLAVE TRADE OF MEXICO.. THE JEWISH VAEZ ACEVEDO FAMILY WERE THE KINGPINS OF THE SLAVE TRADE..
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http://ajitvadakayil.blogspot.com/2012/05/black-african-slave-trade-and-jews-capt.html

OVER THE APPROXIMATELY THREE HUNDRED YEARS IT LASTED, THE SLAVE TRADE BROUGHT ABOUT 320,000 AFRICANS TO THE COLONY.

MANY BLACKS WERE BORN IN MEXICO AND FOLLOWED THEIR PARENTS INTO SLAVERY. NOT UNTIL 1829 WAS THE INSTITUTION ABOLISHED BY THE LEADERS OF THE NEWLY INDEPENDENT NATION.

AFRICAN SLAVES LABORED IN THE SILVER MINES OF ZACATECAS, TAXCO, GUANAJUATO, AND PACHUCA IN THE NORTHERN AND CENTRAL REGIONS; ON THE SUGAR PLANTATIONS OF THE VALLE DE ORIZABA AND MORELOS IN THE SOUTH; IN THE TEXTILE FACTORIES ("OBRAJES") OF PUEBLA AND OAXACA ON THE WEST COAST AND IN MEXICO CITY; AND IN HOUSEHOLDS EVERYWHERE.

INDIANS REPLACED THESE BLACK SLAVES..

OUR NCERT SCHOOL HISTORY BOOKS MUST BE BURNT, AS IT GIVES ROTHSCHILD APPROVED HISTORY.. MODI AND HIS MINION KAYASTHA MINISTERS JAVEDEKAR/ PRASAD ENSURE WE ARE TAUGHT DEEP STATE SPONSORED FALSE HISTORY…

http://ajitvadakayil.blogspot.com/2010/04/indentured-coolie-slavery-reinvented.html

ROTHSCHILD SCREWED THE SPAINISH CATHOLIC CROWN..

http://ajitvadakayil.blogspot.com/2012/10/explosion-on-ss-maine-grooming-of.html

ALL MAJOR DRUG LORDS ( CAPOS ) IN MEXICO WERE JEWS …

THE BASIC STRUCTURE OF A DRUG CARTEL IS AS FOLLOWS:

FALCONS (SPANISH: HALCONES): CONSIDERED AS THE "EYES AND EARS" OF THE STREETS, THE "FALCONS" ARE THE LOWEST RANK IN ANY DRUG CARTEL. THEY ARE RESPONSIBLE FOR SUPERVISING AND REPORTING THE ACTIVITIES OF THE POLICE, THE MILITARY, AND RIVAL GROUPS.

HITMEN (SPANISH: SICARIOS): THE ARMED GROUP WITHIN THE DRUG CARTEL, RESPONSIBLE FOR CARRYING OUT ASSASSINATIONS, KIDNAPPINGS, THEFTS, AND EXTORTIONS, OPERATING PROTECTION RACKETS, AND DEFENDING THEIR PLAZA (TURF) FROM RIVAL GROUPS AND THE MILITARY.

LIEUTENANTS (SPANISH: TENIENTES): THE SECOND HIGHEST POSITION IN THE DRUG CARTEL ORGANIZATION, RESPONSIBLE FOR SUPERVISING THE HITMEN AND FALCONS WITHIN THEIR OWN TERRITORY. THEY ARE ALLOWED TO CARRY OUT LOW-PROFILE MURDERS WITHOUT PERMISSION FROM THEIR BOSSES.

DRUG LORDS (SPANISH: CAPOS): THE HIGHEST POSITION IN ANY DRUG CARTEL, RESPONSIBLE FOR SUPERVISING THE ENTIRE DRUG INDUSTRY, APPOINTING TERRITORIAL LEADERS, MAKING ALLIANCES, AND PLANNING HIGH-PROFILE MURDERS.

GUADALAJARA CARTEL WAS THE FIRST FULL-FLEDGED MEXICAN DRUG CARTEL, FROM WHICH MOST OF THE BIG CARTELS SPAWNED) .. SINALOA CARTEL SPAWNED FROM THE GUADALAJARA CARTEL

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

TIJUANA CARTEL SPAWNED FROM THE GUADALAJARA CARTEL

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

CONTINUED TO 2--
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chadhagNovember 20, 2019 at 6:30 PM
Captain sir..

I don't know if its time to expose this khalsa aid founder ravi Singh.. He has never given me a good vcibe and am a Sikh

Also he and another shady dude Peter virdee.. A rich dude and founder of London based B & S properties (proptery tycoon) have said to be donated thousands of crore for kartarpur corridor.

I don't know if that is for kartarpur or Pakistan.. Or they are naive..

How did this Peter virdee become so rich.. He has alot of links with Punjabi singers n actors.. And they visit him..

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Capt. Ajit VadakayilNovember 20, 2019 at 7:12 PM
https://en.wikipedia.org/wiki/Ram%C3%B3n_Arellano_F%C3%A9lix

MAD JEW RAMON ARELLANO FELIX WAS THE MOST VIOLENT AND SADISTIC DRUG CARTEL LORD..

EL CHAPO KILLED HIM..

IT IS A LIE THAT A MEXICAN GOVT COP KILLED KIM ..

capt ajit vadakayil
..
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Vikhyat SinghNovember 20, 2019 at 8:47 PM
SC in Ayodhya verdict gave the disputed area land to central govt. It also said the govt can give it back to Hindus if it likes.

So, will Modi give this land to Hindus : NEVER!

He will excercise full control over this temple.

Also consider this - even if 1% Hindus donated Rs1000 for Ram Mandir , we have Rs 1000 crore.

So how much is govt going to spend on it?

Indeed Ayodhya too was grey judgement.
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DHARMAPADANovember 20, 2019 at 11:28 PM
Dear Ajit Sir,
Even before you could conclude by reaching part 10 of your AI series that Rajiv Malhotra has picked up from your blog & tweeted
See : https://twitter.com/RajivMessage/status/1197005391164366849?s=19
《Sorry, but pls dont mix up AI with artificial consciousness. One common diversion from the impending catastrophe is to over-abstract it such that you lose sight of the problem.》

He can't explain it properly at all.
Only Ajit Sir, you can explain it & kick the balls of this Malhotra.

Last time when I just simply put this comment that "You Rajiv Malhotra is plagiarizing from Captain Ajith Vadakayil Sir"
He didnot reply to me at all.
All he did is to block me.
That account is suspended.

This fellow Rajiv Malhotra's face is ridiculous to watch
A pathetic DP is used as Twitter scarecrow.
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Capt. Ajit VadakayilNovember 21, 2019 at 7:13 AM
DONALD TRUMP LISTEN UP... AMD LISTEN GOOD...

UKRAINE IS USED BY DEEP STATE JEWS IN ISRAEL AS A SECOND HOMELAND DURING SUMMER SEASON..

AMBASSADOR TO EU JEW GORDON SONDLAND IS A DEEP STATE AGENT, JUST LIKE HILLARY CLINTON..

SONDLANDs PARENTS FAMILY IS AMONG THE NAZI GANG OF JEW HITLER WHO FLED GERMANY AFTER WORLD WAR.

http://ajitvadakayil.blogspot.com/2015/10/if-zionist-jews-created-isis-who.html

SONDLANDs FATHER SERVES IN ROTHSCHILDs FRENCH LEGION.. THIS THUG ARMY WAS USED TO ADVANCE THE INTERESTS OF JEWISH OLIGARCHS ALL OVER THE PLANET AND DO REGIME CHANGE IF THE NATIONs RULERS DO NOT ALLOW JEWS TO STEAL..

GORDON SONDLAND IS ONE OF THE DEEP STATE AGENTS WHO DOES NOT WANT THE MEXICAN WALL..

DONALD TRUMP--WATCH THE NETFLIX TV SERIAL " EL CHAPO", AND YOU WILL SEE THAT THE MEXICAN PRESIDENT AND THE DEA HEAD , ALONG WITH JEWISH OLIGARCHS DECIDE WHO WILL BE THE NEXT DRUG CARTEL BOSS OF BOSSES..

TRUMP- YOU MUST KNOW THAT MAXIMUM COCAINE IN ONE SINGLE BUILDING IS SNORTED IN THE WHITE HOUSE.

THE ALBANIAN JEWISH MAFIA CONTROLS THE COCAINE BUSINESS IN EU/ UK... COCAINE CAN BE TESTED IN THE SEWAGE DRAINS OF UK--SUCH IS THE HEAVY USE..

DEA DOES NOT PREVENT DRUGS FROM COMING TO USA—RATHER THEY CONTROL IT.. THREE US PREIDENTS REAGAN/ BUSH SENIOR / CLINTON ALLOWED COCAINE TO FLOOD INTO USA..

ALL DRUG CHANNEL TUNNELS ( MORE THAN 200 ) UNDER THE MEXICAN – US BORDER IS KNOWN TO DEA.

https://www.youtube.com/watch?v=kY9vd11smcQ

HILLARY CLINTON WON THE POPULAR VOTE IN THE LAST ELECTIONS ONLY BECAUSE OF MILLIONS OF SMURF ( MONEY LAUNDERING LESS THAN 10,000 USD LIMIT ) ILLEGAL MEXICAN IMMIGRANTS LET INTO USA BY CLINTON AND OBAMA.

UK IS THE DOGGING CAPITAL OF THE PLANET, WHERE SEX STARVED HOUSE WIVES OFFER FREE GANG BANG SEX ON THE STREETS TO PASSING DARK SKINNED TRUCK DRIVERS.. THEIR BODIES ARE SURCHARGED WITH COCAETHLENE ( COCAINE ALCOHOL MIX )..

COCAETHLENE DEATHS IN UK RE KEPT A SECRET AS COCAINE IS A STIMULANT AND ALCOHOL IS A DEPRESSANT..

EL CHAPOs SON WAS LET FREE AS A JOINT DECISION BY MEXICAN PRESIDENT/ JEWSIH OLIGARCHY AND DEA..

https://www.theguardian.com/world/2019/oct/26/mexico-27-drugs-cartel-suspects-released-a-week-after-el-chapos-son-freed-in-gun-battle

LAS VEGAS WAS CREATED TO LAUNDER DRUG MONEY..

DONALD THE TRUMP, IN MY 40 YEARS AT SEA, UKRAINE IS THE MOST CORRUPT NATION.. THANKS TO THE JEWISH OLIGRARCHS WHO CONTRL THIS SOULESS NATION.

Capt ajit vadakayil
..

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sanNovember 21, 2019 at 7:25 AM
Captain,

'No shame in showing tears, it's ok for men to cry: Sachin Tendulkar ..'

After his retirement ailaa made tea at home. now he is saying it's ok for men to cry.
next he will say....

what is he up to? he cannot be a commentator with his ailaaa voice. there is lot more time to become president of BCCI. what what what why why why

Read more at:
http://timesofindia.indiatimes.com/articleshow/72144812.cms?utm_source=contentofinterest&utm_medium=text&utm_campaign=cppst

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Capt. Ajit VadakayilNovember 21, 2019 at 8:03 AM
THE HONGKONG PROTESTS ARE FUNDED AND CONTROLLED BY THE JEWISH OLIGARCHY..

EVEN CHINESE DO NOT KNOW THAT JEW MAO AND JEW MAURICE STRONG WERE DEEP STATE AGENTS...

JEWS WHO HAVE MONOPOLISED THE MAFIA AND CRIME IN HONGKONG DO NOT WANT TO BE EXTRADITED TO THE CHINESE MAINLAND..

MACAU GAMBLING IS FAR MORE THAN LAS VEGAS.. DRUG MONEY IS LAUNDERED HERE.

ROTHSCHILD CONTROLLED PORTUGAL LEGALIZED GAMBLING IN MACAU IN 1850..

ROTHSCHILD RULED INDIA..NOT THE BRITISH KING OR PARLIAMENT.. HE GREW OPIUM IN INDIA AND SOLD IT IN CHINA.. HIS DRUG MONEY WAS LAUNDERED IN HONGKONG HSBC BANK.

KATHIAWARI JEW GANDHI WAS ROTHSCHILDs AGENT WHEN IT CAME TO SUPPORTING OPIUM CULTIVATION IN INDIA..

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

INDIAN FARMERS WHO REFUSED TO CULTIVATE OPIUM WERE SHIPPED OFF ENMASSE AS SLAVES ABROAD WITH FAMILY..

http://ajitvadakayil.blogspot.com/2010/04/indentured-coolie-slavery-reinvented.html

INDIAN AND AMERICAN OLIGARCHY ( CRYPTO JEWS ) WERE ALL DRUG RUNNERS OF JEW ROTHSCHILD..

http://ajitvadakayil.blogspot.com/2010/11/drug-runners-of-india-capt-ajit.html

http://ajitvadakayil.blogspot.com/2010/12/dirty-secrets-of-boston-tea-party-capt.html

DRUG CARTELS OF COLOMBIA/ MEXICO USE HONGKONG TO LAUNDER THEIR DRUG MONEY..

GAMBLING TOURISM IS MACAU'S BIGGEST SOURCE OF REVENUE, MAKING UP MORE THAN 54% OF THE ECONOMY. VISITORS ARE MADE UP LARGELY OF CHINESE NATIONALS FROM MAINLAND CHINA AND HONG KONG.

HONGKONG IS NOW FLOODED WITH DRUGS .. DUE TO HIGH STRESS AT WORK, PEOPLE ARE ADDICTED .. HOUSE RENT IN HONGKONG IS VERY HIGH DUE TO THE JEWISH OLIGARCHS WHO CONTROL HONGKONG.

IN 2012, HSBC HOLDINGS PAID US$ 1.9 BILLION TO RESOLVE CLAIMS IT ALLOWED DRUG CARTELS IN MEXICO AND COLOMBIA TO LAUNDER PROCEEDS THROUGH ITS BANKS. HSBC WAS FOUNDED BY ROTHSCHILD.

CHINA'S EXCESSIVELY STRICT FOREIGN EXCHANGE CONTROLS ARE INDIRECTLY BREEDING MONEY LAUNDERING, PROVIDING A HUGE DEMAND FOR UNDERGROUND KOSHER MAFIA BANKS.

FACTORY MANUFACTURERS CONVERT HONG KONG DOLLARS AND RENMINBI WITH UNDERGROUND BANKS FOR CONVENIENCE WHILE CASINOS IN MACAU OFFER RECEIPTS TO GIVE LEGITIMACY TO SUSPECT CURRENCY FLOWS.

INDIA WAS NO 1 EXPORTED OF PRECURSOR CHEMICALS LIKE EPHEDRINE TO MEXICO FOR PRODUCING METH.. TODAY CHINA ( GUANGDONG ) HAS TAKEN OVER POLE POSITION..

http://ajitvadakayil.blogspot.com/2017/02/breaking-bad-tv-serial-review-where.html

EL CHAPO AND HIS DEPUTY IGNACIO "NACHO" CORONEL VILLARREAL USED HONG KONG TO LAUNDER BILLIONS OF DOLLARS..TO GET SOME IDEA WATCH NETFLIX SERIES “NARCOS MEXICO” AND “EL CHAPO”.

BALLS TO THE DECOY OF "FREEDOM " FOR HONGKONG CITIZENS.. IT IS ALL ABOUT FREEDOM FOR JEWISH MAFIA TO USE HONGKONG TO LAUNDER DRUG MONEY.

JEW ROTHSCHILD COULD SELL INDIAN OPIUM IN CHINA ONLY BECAUSE THE CHINESE MAFIA AND SEA PIRATES WAS CONTROLLED BY HIM AND JEW SASSOON.

COLOMBIAN/ MEXICAN DRUG CARTEL KINGS FEAR EXTRADITION TO USA.. SAME NOW WITH HONGKONG MONEY LAUNDERING MAFIA..

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

THE 2019 HONG KONG PROTESTS HAVE BEEN LARGELY DESCRIBED AS "LEADERLESS".. BALLS, IT IS 100% CONTROLLED BY JEWS

PROTESTERS COMMONLY USED LIHKG, ( LIKE REDDIT ) AN ONLINE FORUM, AN OPTIONALLY END-TO-END ENCRYPTED MESSAGING SERVICE, TO COMMUNICATE AND BRAINSTORM IDEAS FOR PROTESTS AND MAKE COLLECTIVE DECISIONS ..

THE KOSHER WEBSITE IS WELL-KNOWN FOR BEING THE ULTIMATE PLATFORM FOR DISCUSSING THE STRATEGIES FOR THE LEADERLESS ANTI-EXTRADITION BILL PROTESTS IN 2019..

CONTINUED TO 2-
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Capt. Ajit VadakayilNovember 21, 2019 at 8:39 AM
https://ajitvadakayil.blogspot.com/2019/11/paradox-redemption-victory-in-defeat.html

SOMEBODY CALLED ME UP AND SAID

CAPTAIN, YOU ARE PARADOX REDEMPTOR..

CAN YOU GIVE A PARADOX WHICH WILL MAKE OUR COLLECTIVE BALLS AND TWATS GO TRRR PRRR BRRRR ?

WE UNDERSTAND THAT YOU PLAN TO DO IT ONLY AFTER YOUR REVELATIONS REACH 98%.. BUT ONE WEE PARADOX, CHAATNE KE VAASTE ? PLEAJJE !

WOKAY !

##############

THE WHITE INVADER RAPED OUR LOW CLASS BROWN INDIAN WOMEN AND PRODUCED A MULATTO ANGLO-INDIAN COMMUNITY ONE MILLION STRONG.

http://ajitvadakayil.blogspot.com/2014/02/devadasi-system-immoral-lie-of-temple.html

AT THE SAME TIME BROWN INDIAN SAILORS ( LASCARS ) WENT TO ENGLAND AND HAD CONSENSUAL SEX WITH WHITE WOMEN.. WHITE WOMEN LOVED SEX WITH INDIA SAILORS WHO WERE VIRILE , HAD LARGER CLEANER DONGS THAN THE SYPHILITIC WEE WHITE WILLIES , AND WHO DID NOT NEED ENDLESS MOUTH VACUUM..

http://ajitvadakayil.blogspot.com/2010/05/lascar-original-indian-merchant-navy.html

THE PARADOX IS ,

######### WHEN A INDIAN MAN SCREWED A WHITE WOMAN, THE CHILD HAD FERTILE BRAINS.

######## WHEN A WHITE AN SCREWED AN INDIAN WOMAN THE CHILD HAD SHIT FOR BRAINS..

EXAMPLE? AN ANGLO INDIAN MP OF TMC -- AKKAL KA DUSHMAN..

TEE HEEEEEE..

IT IS HIGH TIME WE ABOLISHED THE QUOTA FOR ANGLO-INDIANS IN THE INDIAN PARLIAMENT..

ANGLO-INDIANS ARE THE ONLY COMMUNITY THAT HAS ITS OWN REPRESENTATIVES NOMINATED TO THE LOK SABHA . THIS RIGHT WAS SECURED FROM JAWAHARLAL NEHRU BY FRANK ANTHONY, THE FIRST AND LONGTIME PRESIDENT OF THE ALL INDIA ANGLO-INDIAN ASSOCIATION.

THE COMMUNITY IS REPRESENTED BY TWO MEMBERS. FRANK ANTONY WAS THE CHAIRMAN OF THE ICSE COUNCIL WHICH MADE SURE WE TAUGHT ROTHSCHILD’S COOKED UP HISTORY IN OUR SCHOOLS.

http://ajitvadakayil.blogspot.com/2016/09/portuguese-inquisition-is-goa-by-jesuit.html

I WAS THE FIRST TO EXHUME THE GOAN INQUISITION BY JEW FRANCIS XAVIER.. TODAY WIKIPEDIA HAS A POST ON IT.

MY REVELATIONS NOW JUMP TO 60.10 %

capt ajit vadakayil
..
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Aditya kamathNovember 21, 2019 at 8:59 AM
Hello captain,
We are giving free electricity to household and subsidised electricity to MSME even then the demand is falling.
How is it possible?

https://www.google.com/amp/s/indianexpress.com/article/business/133-units-switching-off-low-demand-behind-half-of-thermal-power-plants-shutting-down-6122166/lite/

Gratitude
Aditya
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Chandan ShanbhagNovember 21, 2019 at 10:03 AM
Dear Captain, what is your view on govt privatizing navratna companies like bpcl, scl, concor?
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RN MurthyNovember 21, 2019 at 12:22 PM
Dear Capt Ajit sir,

You have opened up Pandora's box...similar tunnels would be existing in Punjab&Kashmir/Pakistan border for drug smuggling....if that's so, why it's not been curbed and the people booked so far ?
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LordNovember 21, 2019 at 4:12 PM
The man Dost Mohammad Khan of the Barakzai Dynasty who swallowed the Durrani Empire, was obviously a Jew. The Barakzai literally means son of Barak, the Israeli Jew King.

How obvious could Rothschild go that retarded Historians could never gauge this? After handing over the empire to the British and Shah Shujah Durrani he was "exiled" to a comfortable land in India. And returned when Shujah Durrani was to be discarded. Rothschild got careless with his strategy which is now obvious everywhere.
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Debdoot SarkarNovember 21, 2019 at 6:56 PM
Received below from home affairs regarding banning of palm oil imports from Malaysia and replace with Indonesia

No.6/03/2016-EP(Agri-VI)
Department of Commerce
EP(Agri-VI)
Room No. 445-A, Udyog Bhawan
20th November, 2019
New Delhi, Dated

То,
Sh. Debdoot Sarkar,

Subject: Total ban of Paim Oil imports from Malaysia and
replace Imports from Malaysia to Indonesia - reg.

I am directed to refer to your grievance No.MINHA/E/2019/07876 dated 17th October, 2019 on the above
mentioned subject and to say that your suggestion has been noted and will be considered as appropriate time.

Thanking you,
निधि शर्मा/(Nidhi Sharma)
अनुभागअधिकारी/Section Officer
/Email: moc epagri3@nic.in
टेलीफैक्स/ Telefax: 23061621
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Capt. Ajit VadakayilNovember 21, 2019 at 6:59 PM
Capt. Ajit Vadakayil January 15, 2017 at 1:14 AM

https://www.youtube.com/watch?v=K78t2-W2jag

AND HERE YOU MAY FIND FROGS UP THE ASSHOLE

THESE FAKE GURUS DO NOT KNOW THAT LEVITATION IS NOT OF THE BODY ( CARCASS ) BUT OF THE SOUL

Capt ajit vadakayil
..

################

Capt. Ajit Vadakayil January 26, 2017 at 8:10 PM

https://www.youtube.com/watch?v=tfrQwmI2-VQ

CHECK 30.10 OF ABOVE VIDEO

THIS BLOGSITE WARNS NITYANANDA

GIVE AN EXPLANATION FOR THIS ROGUE WOMAN VOMITING OUR DIAMONDS AND PEARLS

IS THIS BULLSHIT VIDEO PUBLISHED BY YOUR ENEMIES ?

OR IT YOUR OWN ?

IS THIS WOMAN YOUR DISCIPLE ? HAVE YOU AUTHORISED HER ?

AND WHAT BULLSHIT ( LEVITATION ) IS THIS ?

ONLY 12 STRAND DNA MAHARISHIS ( NIL JUNK ) CAN DO LEVITATION.

NITYANANDA -- DONT YOU KNOW LEVITATION IS OF THE SOUL AND NOT THE CADAVER ?

https://www.youtube.com/watch?v=K78t2-W2jag

DO FORCE ME TO COME HAMMER AND TONGS AFTER YOU --I WILL NOT ALLOW YOU TO DESTROY THE TRUST HINDUS HAVE IN SANATANA DHARMA

YOU WILL HAVE NOWHERE TO HIDE NITYANANDA

WATCH THIS SPACE

PUT THIS COMMENT IN NITYANANDAs AND RAJIV MALHOTRAs WEBSITE

ASK FOR AN ACK

capt ajit vadakayil
..
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abcindiagogoNovember 21, 2019 at 7:39 PM
Captain, is it a good-idea for Govt to pay more wholesome salaries but remove perks like accommodation, pensions, etc. ?

Because the perks like accommodation, canteen, etc. are a big bureaucratic mess with lot of corruption. And the pensions are a whole mess to itself with the Govt having so many services meaning a huge workforce has to be maintained to simply administer the pension-schemes. Also, the pension-schemes have their irregularities which gives opportunities for OROP like issues to be created by those who want to stir trouble.

Is it worthwhile to have a private-company attitude wherein a lump-some salary is paid with nothing else ? And if the Govt has to provide any perk shouldn't it be something like a Health-Insurance plan (by Govt-company like LIC, not private) so that retired-employees can benefit from not having tension due to fear of unexpected health-issue which can cause financial-ruin for them in old-age ?
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Capt. Ajit VadakayilNovember 21, 2019 at 7:53 PM
http://ajitvadakayil.blogspot.com/2018/03/treason-from-within-biggest-cut-on.html

ELECTORAL BONDS ARE USED TO LAUNDER DRUG MONEY..

ANONYMOUS FUNDING ( EVEN FROM KOSHER ARMS DEALERS ) IS BEING FUNNELED TO THE COFFERS OF POLITICAL PARTIES THROUGH BEARER INSTRUMENTS ISSUED BY THE STATE BANK OF INDIA.

SINCE THE INTRODUCTION OF THE SCHEME BY THEN FINANCE MINISTER ARUN JAITLEY, ELECTORAL BONDS WORTH RS 6,000 CRORE HAVE BEEN SOLD SO FAR. OF THE FIRST TRANCHE OF RS 222 CRORE, 95 PER CENT OF THE FUNDS HAD GONE TO THE RULING BJP

THE RESERVE BANK OF INDIA’S (RBI’S) REPEATED WARNINGS ON THE ELECTORAL BOND SCHEME IN BEARER FORM HAVING THE POTENTIAL TO INCREASE BLACK MONEY CIRCULATION, MONEY LAUNDERING, CROSS-BORDER COUNTERFEITING AND FORGERY WERE IGNORED..

WE ASK , WHY IS INDIA BUYING INFERIOR AND EXPENSIVE ARMS FROM JEWSIH COMPANIES ? KOHSER MANUFACTURERS OF INFERIOR AND EXPENSIVE ARMS , GIVE 35% KICKBACKS.. IF YOU BUY 10,000 CRORES OF ARMS , 3500 CRORES IS YOURS..

CHAKKAR KYA HAI?

PAKISTANI ISI IS DONATING MONEY TO INDIAN POLITICAL PARTIES SUPPORTING NAXALS AN ISLAMIC TERRORISTS..

LAVISH EXPENDITURE IN THE ELECTIONS ( FUNDED EVEN BY PAKISTAN AND CHINA ) IS THE MOST CRITICAL PROBLEMS IN INDIA’S ELECTORAL SYSTEM. CEC IS PRETENDING TO SLEEP AND EXPECTING "WE THE PEOPLE" TO WAKE HIM UP..

THE USAGE OF BLACK MONEY DURING ELECTIONS NOT ONLY ALLOWS FOR LAUNDERING, IT ALSO HAS SERIOUS ECONOMIC AND POLITICAL COSTS ATTACHED TO IT. IT LEADS TO A POLITICAL SUPPORT TO CORRUPTION AND CRONY CAPITALISM.

http://ajitvadakayil.blogspot.com/2012/11/kleptocracy-in-india-lobbying-pimping.html

HOW DID THIS FELLOW SURESH NANDA GET UBER RICH?

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

IT ALLOWS FOR CRIMINAL-POLITICAL NEXUS AS THE EXTORTIONISTS AND GOONS ARE ABLE TO BUY PARTY TICKETS AND THEREFORE, THE LAW-BREAKERS BECOME THE LAWMAKERS IN OUR COUNTRY.

WE ASK THE CEC AND EC TO WATCH THE NETFLIX SERIAL “EL CHAPO”..

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

ANONYMITY: NEITHER THE DONOR (WHO COULD BE AN INDIVIDUAL OR A CORPORATE) NOR THE POLITICAL PARTY IS OBLIGATED TO REVEAL WHOM THE DONATION COMES FROM. THIS UNDERCUTS A FUNDAMENTAL CONSTITUTIONAL PRINCIPLE: THE FREEDOM OF POLITICAL INFORMATION, WHICH IS AN INTEGRAL ELEMENT OF ARTICLE 19(1) (A) OF THE CONSTITUTION.

ASYMMETRICALLY OPAQUE: BECAUSE THE BONDS ARE PURCHASED THROUGH THE SBI, THE GOVERNMENT IS ALWAYS IN A POSITION TO KNOW WHO THE DONOR IS. THIS ASYMMETRY OF INFORMATION THREATENS TO COLOUR THE PROCESS IN FAVOUR OF WHICHEVER POLITICAL PARTY IS RULING AT THE TIME.

WE THE PEOPLE WATCH !

capt ajit vadakayil
..
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RaviNovember 21, 2019 at 7:54 PM
Ajit Garu, Namaskaram
After your post on Kalaripayattu it has gone global. Many ppl r trying to learn now. Just by watching these videos one can get how deadly kalari was in its heydays when it is actually used in warfare. That is the reason u r not afraid to take on ppl in a fist fight. Kalaripayattu moulds the attitude itself.
https://www.youtube.com/watch?v=WEQ1Hpibekg

By watching Kungfu training with all the hoo-haa sounds seems trivial and comical infront of Kalari as per below video.
https://www.youtube.com/watch?v=fnypyWf3NzE

GOI should seriously think of atleast training children in the 1st part Maipayat with proper guru induction. It can lift the Kshatriya spirits of many ppl.

Regards,
Ravi
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PatNovember 21, 2019 at 8:32 PM
Your Registration Number is : PMOPG/E/2019/0669632
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NarenNovember 21, 2019 at 9:52 PM
Captain this book by Gary Webb called Dark Alliance is worth a read,have not yet finished it,it exposes the links between CIA and cocaine trade in various parts of South America and how cocaine trade helped fund rebel groups.
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RN MurthyNovember 21, 2019 at 10:23 PM
Dear Capt Ajit sir,

Netanyahu is indicted for fraud, bribery, breach of trust...will fave trial...what will happen to his friend Modi...sangathvam matters.... will crush him too...what do you say ?
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Capt. Ajit VadakayilNovember 21, 2019 at 10:26 PM
https://timesofindia.indiatimes.com/city/delhi/regret-relief-as-145-us-deportees-land-at-delhis-igi-airport-with-hands-and-feet-tied/articleshow/72151975.cms

THESE BASTARDS DESERVE TO BE HUMILIATED..
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ashuuuNovember 22, 2019 at 12:11 AM
http://www.sarovic.com/jacob_rothschild_is_guilty.htm
warren buffet is a front man of jacob rothchild in usa?Rotchild supports charity by denouncing that warren buffet will donate 85 percent of his wealth after his death
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Sunil BhattNovember 22, 2019 at 4:58 AM
Politician have already made this country slaves of foreign rules with no long term vision, they keep on inciting voters based on their tribe, caste and religion and supreme court also take part in it.

Whereas real work of Govt is to provide social security so that man and woman can make their Gnaati , Samaj or Tribe strong, so that they can fight from the outside forces which destroy our nation.

But here Govt and Supreme Court together is allowing American MNC to exploit Indian Citizen by making them feel that they lack hence they have to work in Contractual Agreement

What about HDI , compare it Bhutan.

You cannot inject love virus from #Bollywood movie and giving unwanted freedom to woman in the namw of #Feminism to destroy the customary law of each tribe, caste and Gnaati and then say country is progressing

#Tribalism and #Casteism both is good, but Govt servant who do not work for the #Beneficiary but work for #Politician or #CorruptBureacrats , they are bad as they are least afraid of getting removed from the job and thus treat each and every citizen as slave and do just #TimePass in confusing their bosses in the meeting with half knowledge

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get lostNovember 22, 2019 at 5:56 AM
https://youtu.be/AiqEvYeoR2E
These are our indian cows
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Capt. Ajit VadakayilNovember 22, 2019 at 7:20 AM
SOMEBODY ASKED ME

IF BJP IS GETTING 95% DONATIONS BY ELECTORAL BONDS -- AND REST ALL POLITICAL PARTIES HAVE GOT ONLY 5% OF THE DONATION PIE .. DOES THIS MEAN OTHER PARTIES LIKE CONGRESS ARE STARVED OF ELECTION FUNDS ?

SORRY

ALL THIS MEANS IS OTHER PARTIES ARE GETTING CASH ILLEGALLY FROM PAKISTANI ISI, CHINESE , SAUDI JEWISH WAHABBIS ETC..

YESTERDAY YOU SHOULD HAVE SEEN HOW YOGENDRA YADAV WENT INTO SELF RIGHTEOUS MODE -- I WOULD NOT EVEN FART ON THIS CHOOTs FACE..
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Capt. Ajit VadakayilNovember 22, 2019 at 7:26 AM
DAY NIGHT PINK BALL TEST CRICKET IS A STUPID IDEA

FATIGUE SETS IN DURING TEST CRICKET-- DEW IS DANGEROUS

PLAYERS MAY DIE

http://ajitvadakayil.blogspot.com/2013/04/swinging-cricket-ball-curving-soccer.html
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Capt. Ajit VadakayilNovember 22, 2019 at 7:35 AM
https://twitter.com/realDonaldTrump/status/1197573996960727041?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Etweet

GREAT IRANIAN PEOPLE ARE DONE WITH JEWS RULING THEIR NATION ...

http://ajitvadakayil.blogspot.com/2017/04/the-most-evil-journalist-capt-ajit.html

capt ajit vadakayil
..

PUT ABOVE COMMENT IN WEBSITES OF-
DONALD TRUMP
INDIAN AMBASSADOR TO USA
US AMBASSADOR TO INDIA
INDIAN AMBASSADOR TO IRAN
IRANIAN AMBASSADOR TO INDIA
PUTIN
INDIAN AMBASSADOR TO RUSSIA
RUSSIAN AMBASSADOR TO INDIA
EXTERNAL AFFAIRS MINISTER/ MINISTRY
PMO
PM MODI
AJIT DOVAL
RAW
IB
NIA
ED
CBI
AMIT SHAH
HOME MINISTRY
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RN MurthyNovember 22, 2019 at 7:57 AM
Dear Capt Ajit sir,
These are the kind of people who bring India down, when it's galloping at 10.1% GDP and has crossed 5 trillion $ ;-) https://www.hindustantimes.com/business-news/economy-in-bad-shape-5-tn-gdp-target-simply-out-of-question-ex-rbi-governor-c-rangarajan/story-YC4CglgchqN0UfQrIEMwmO.html
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Muthu SwamynathanNovember 22, 2019 at 10:08 AM
Captain, please see the disgusting love-jihad cases.

https://www.facebook.com/rati.hegde/posts/2806989462665952
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charishma November 22, 2019 at 10:16 AM
Sent to trump and putin from their official websites.
Tweeted to Iranian embassy-
https://mobile.twitter.com/Chn_123/status/1197736723007934464
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PrapullaNovember 22, 2019 at 10:42 AM
Recently, there was an AI event in our office Captain. I was part of the quiz team and your series on AI was very helpful. I covered the ethical aspects of AI including lack of conscience and new scenario creation given a set of parameters. Not many people had understood to what extent AI can be misused. We had a very open interactive session. Thank you very much.

Regards,
Prapulla
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RaviNovember 22, 2019 at 12:49 PM
Ajit Garu,

in twitter suddenly the rainbow has become the symbol of LGB ppl. I had a spat with Ashok Row Kavi @Amma29, then suddenly many hindu bearing names came in support of this guy. One of them has Sri Rama in his profile pic @bhrgvr. I think many Hindus are being conditioned by the MSM and also Fringe Hindu groups that LGB is a good thing. i think there is a very big LGB lobby in mumbai sponsoring these ppl. And we have the BJP star Kid @Tejasvi_Surya supporting it
https://twitter.com/adityasbharat/status/1197552236194172928

Namaskaram,
Ravi
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Capt. Ajit VadakayilNovember 22, 2019 at 2:02 PM
https://en.wikipedia.org/wiki/Nowhera_Shaik

NOWHERA SHAIK IS B TEAM OF BJP AS PER TOP COPS..

HER HUGE POSTERS SUPPORTING MODIs SWATCH BHARAT ETC HAVE BEEN DISPLAYED ALL OVER BANGALORE BEFORE THE KARNATAKA ASSEMBLY ELECTIONS..

I HAD GIVEN SIX SITREPS IN MY BLOGS ( COMMENTS ) ABOUT THIS WOMAN , STAYING IN THE KINGS SUITE OF A SEVEN STAR HOTEL OF BANGALORE AND RUNNING UP A HOTEL STAY BILL IN CRORES..

I REPORTED TO ALL AND SUNDRY INCLUDING CEC/ EC / PM/ PRESIDENT / CM ETC THAT THIS WOMAN DISTRIBUTED HUNDREDS OF MOBILE PHONES TO POOR MUSLIM VOTERS FROM HER LUXURY HOTEL ROOM..

HUNDREDS OF POOR PEOPLE WEARING SHABBY CLOTHES AND CHEAP PLASTIC CHAPPALS STREAMED INTO HER ROOM FOR MONTHS..

I HAD WARNED SEVERAL TIMES THAT VOTERS MUST NOT BE ALLOWED TO TAKE A MOBILE PHONE WITH CAMERA INTO THE POLLING BOOTH.. YET NOTHING HAPPENED ..

##############

I ASK MY READERS TO WATCH THE NETFLIX SERIAL "EL CHAPO" SEASON 3 , EPISODE 5 ..

DRUG LORD EL CHAPO CHOOSES HIS OWN SLAVE MEXICAN PRESIDENT BY BRIBING VOTERS..

HIS GANG DISTRIBUTED THOUSANDS OF MOBILE PHONES WITH CAMERA TO POOR VOTERS .. ALL THEY HAD TO DO WAS TO PRESS A BUTTON WHEN THEY VOTED USING A CROSS MARK..

THEN THESE BRIBED VOTERS COULD GO TO ANY SAFE HOUSE AND COLLECT SHOPPING COUPONS , WHICH COULD BE REDEEMED IN SELECTED SHOPPING MALLS OWNED BY EL CHAPO..

PEOPLE STILL WONDER HOW KACHRAWAAL WON 4 LOK SABHA SEATS IN DRUG CONSUMING PUNJAB IN THE PREVIOUS ELECTIONS WHEN MODI FIRST BECAME PM..

WE THE PEOPLE WATCH..

capt ajit vadakayil
..
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Capt. Ajit VadakayilNovember 22, 2019 at 2:15 PM
THERE IS A LATEST TREND IN SABARIMALA..

THE DISEASE OF MECCA HAS NOW INFILTRATED SABARIMALA PILGRIMAGE.. GANGS OF TAMIL BEGGARS LINE THE ROADS TO SABARIMALA..

THIS MUST BE STOPPED.. BEGGARS MUST BE IMPRISONED FOR MINIMUM ONE MONTH...

PILGRIMS MUST NOT BE HARASSED..

IN KERALA NO HINDU DEVOTEES ARE HARASSED LIKE IN NORTH INDIA WHERE GOON PANDAS ( TEMPLE PRIESTS ) MAKE LIFE MISERABLE FOR DEVOTEES..

capt ajit vadakayil
...
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Capt. Ajit VadakayilNovember 22, 2019 at 2:28 PM
FAYE DSOUZAs ANTI-HINDU ROLE HAS NOW BEEN USURPED BY A FEMALE JOURNALIST NAMED SAHAR ZAMAN ON MIRROR NOW TV..

https://en.wikipedia.org/wiki/Sahar_Zaman_(journalist)

WE THE PEOPLE WARN HER.. YOU ARE BEING MONITORED...

WE THE PEOPLE PUT PRAKASH JAVEDEKAR ON NOTICE.. WHY DO YOU ALLOW BENAMI MEDIA TO DO ENDLESS RABBLE ROUSING..

WHY IS NEWS DELIVERED WITH QUESTION MARK AT THE END OF A SENTENCE?

NEWS MUST BE STATEMENT OF FACTS NOT SPECULATION .. ONLY THE EDITOR IS ALLOWED OPINIONS..

capt ajit vadakayil
..

PUT ABOVE COMMENT IN WEBSITESOF--
SAHAR ZAMAN
I&B MINISTER/ MINISTRY
PMO
PM MODI
AJIT DOVAL
RAW
IB\NA
ED
CBI
AMIT SHAH
HOME MINISTRY
MIDGET VINEET JAIN
NEW CJI
ALL SUPREME COURT JUDGES
ATTORNEY GENERAL
LAW MINISTER/ MINISTRY
RSS
VHP
AVBP
DAVID FRAWLEY
STEPHEN KNAPP
WILLIAM DALRYMPLE
KONRAED ELST
FRANCOIS GAUTIER
RAM MADHAV
RAJ THACKREY
UDDHAV THACKREY
VIVEK OBEROI
GAUTAM GAMBHIR
ASHOK PANDIT
ANUPAM KHER
KANGANA RANAUT
VIVEK AGNIHOTRI
KIRON KHER
MEENAKSHI LEKHI
SMRITI IRANI
PRASOON JOSHI
MADHUR BHANDARKAR
SWAPAN DASGUPTA
SONAL MANSINGH
MADHU KISHWAR
SUDHIR CHAUDHARY
GEN GD BAKSHI
SAMBIT PATRA
RSN SINGH
SWAMY
RAJIV MALHOTRA
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PatNovember 22, 2019 at 3:43 PM
Sir,
Did put d comment on Sahar Zamans website .Said /wrote she'll be getting back to me shortly. Lets see.
Hari Om
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namiNovember 22, 2019 at 4:06 PM
This comment has been removed by the author.
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DJNovember 22, 2019 at 4:37 PM
Guruji ,African Nations are rolling out single currency ECO like our beloved Euro.Is this some new gimmick?
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Vikhyat SinghNovember 22, 2019 at 5:35 PM
True Indology handle on twitter is operated by a person who can't differentiate right from wrong.

https://twitter.com/TIinExile/status/1197836088804438017?s=20

True Indology believes that Uttar Kanda is legit and that Rama kicked his wife Sita into forest.

Rajiv Malhotra is another Hindu pleaser. He won't dare to dwell into things that Hindus might find offensive.

These babes in the woods really think that Adi Shankaracharya would say 'Worship Govinda, Worship Govinda, Worship Govinda, Oh fool!'

Is that how a Maharishi with pure mind will speak? Or has white man put words into his mouth?
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Capt. Ajit VadakayilNovember 22, 2019 at 5:53 PM
https://timesofindia.indiatimes.com/city/hyderabad/34000-indians-died-in-gulf-in-five-yrs-1200-from-state/articleshow/72175377.cms

IN NORTH KERALA AN UNSKILLED LABOURER FROM BIHAR / UP/ BANGLADESH CHARGES 900 RUPEES PER DAY..

HE WILL GET ONLY 40 %..

THE REST GOES TO AGENCY ( BOTH SIDES ) / POLICE
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Capt. Ajit VadakayilNovember 22, 2019 at 6:34 PM
https://edition.cnn.com/2019/11/21/world/nasa-sugar-meteorites-intl-hnk-scli/index.html

SHIVA LINGAMS WHICH RAINED INTO NARMADA RIVER CREATED THE FIRST HUMANS 65 MILLION YEARS AGO..

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

INDEED SHIVA LINGAM IS THE DIVINE PHALLUS WHICH GAVE THE SEED.
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mythra81November 22, 2019 at 7:58 PM
http://greatgameindia.com/ctd-advisors-rebuilding-british-empire-of-modern-times/ Shashi Tharoor, a serving member of the Indian Parliament lately known for his anti-British position has ironically been recruited by the son of a Pakistani British spy in his firm CTD Advisors, heavily infested with former British intelligence chiefs advocating foreign intervention in Kashmir and with the objective to rebuild the British Empire of modern times.
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Capt. Ajit VadakayilNovember 22, 2019 at 8:21 PM
https://news.bitcoin.com/indian-supreme-court-wraps-up-crypto-hearing-for-the-year/

WE KNOW WHY DEEP STATE DARLING MODI IS PROTECTING BITCOIN..
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dinesh loves indiaNovember 22, 2019 at 8:44 PM
Private Bill was introduced in the Lok Sabha today by BJP MP Dr. Satyapal Singh to free Hindu temples from govt control.

Let us see how the govt & opposition work to facilitate the passage of this bill. Time to see their true colours
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PaadiNovember 23, 2019 at 6:27 AM
Captain, look at the picture and hidden agenda....

https://timesofindia.indiatimes.com/india/74-per-cent-of-indias-teenagers-physically-inactive-who/articleshow/72192306.cms
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Shriram hegdeNovember 23, 2019 at 9:17 AM
https://www.aninews.in/news/national/general-news/rss-backs-muslim-professor-at-bhus-sanskrit-dept-as-students-call-off-strike20191122230931/

Rss backs Muslim professor at BHU's Sanskrit department as students call off strike.
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Capt. Ajit VadakayilNovember 23, 2019 at 10:17 AM
SHIV SENA IS DEAD .

FELLOW OF ZERO CHARACTER AND INTEGRITY UDDHAV THACKERAY HAS DESTROYED HIS FATHERs LEGACY..

IN CALICUT BEFORE ELECTIONS SHIV SENA POSTERS USED TO SPROUT ALL OVER - " CHANKOOTHA THODE PARAYU, NJAN HINDU AANE " ( DECLARE WITH PRIDE , I AM HINDU )

THIS WHEN SHIV SENA CANDIDATES ARE NOT CONTESTING IN KERALA..

SANJAY RAUT HAD DESTROYED SHIV SENA FROM WITHIN..

UDDHAV TURNED OUT TO BE A NOVICE IN POLITICS

PEOPLE LIKE UDDHAV THACKREY ARE THE REASON BHARATMATA WAS IN CHAINS FOR 800 YEARS..

JAI JAI SHIV SENA

BHARATEEYA SONIA SENA

OOPAR DEKHO MAINO JEW SENA

NEECHE DEHO TO SPAGHETTI SENA

EVERYWHERE DEKHO ITALIAN COLCOTRONI SENA

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RN MurthyNovember 23, 2019 at 10:50 AM
Dear Capt Ajit sir,

Lutyens media has never been more shocked than ever with this new Maha twist as Devendra Fadnavis took oath as CM of Maharashtra.
Rajdeep Sardesai was so shocked with this tectonic development of Governor staying back and made early morning arrangements for swearing in...and the chanakya neeti was done during Modi/Pawar meet....Mumbai breathes again after Maha Pralaya...Kalki cleansing effect is ON.
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Aiswarya rNovember 23, 2019 at 1:27 PM
Sir

Please see this , demonising Kali and ritual Mudiyettu of kerala. They are using Kodungallur Barani as a main thing. Also pls check invading the sacred book. I don't know y government is not banning it.

http://beingdifferentforum.blogspot.com/2014/02/sarah-caldwell-reinterpreting-hindu.html?m=1

Sarah Caldwell is of Columbia University ,only u can nail this.Iam surprised all r trying to demonise hinduism and promoting debauchery.

Also nowadays leftists telling Hindus constructed temples after destroying budhist shrines , including Sabarimala , quoting Jha and Gail Omvedt.

I donot know how these intellectuals will damage our India.

Please expose budhism and such intellectuals,it's more dangerous now.

Thankyou

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RN MurthyNovember 23, 2019 at 1:56 PM
Dear Capt Ajit sir,
Sharad Pawar to be next President of India....the way Prez rule ended at 5:30 am today, Governor administering oath to Fadnavis at 8 am...yeh hai digital India...kya timing !!!!
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Shriram hegdeNovember 23, 2019 at 3:54 PM
Sir, Created Meme for "Operation hindu revival". Need further development. Please once check it out.. Does this look okay to you? whats app is the best flat form to share memes to reach more.

https://twitter.com/tuki00108/status/1198178481651081217

Gratitude.
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Capt. Ajit VadakayilNovember 23, 2019 at 7:10 PM
SOMEBODY ASKED ME --

WHAT IS THE DIFFERENCE BETWEEN PABLO ESCOBAR AND EL CHAPO

AFTER EL CHAPOs EXTRADITION TO USA, AT THE END OF THE INQUIRY THE JUDGE DEEMED THAT EL CHAPO KILLED NEARLY 3000 PEOPLE..

EL CHAPO LED HIS GANG INTO BATTLE LEADING BY RUTHLESS AND VICIOUS EXAMPLE.... PABLO WAS ALWAYS BEHIND THE BATTLE LINES, LIKE THE GENERAL.

EL CHAPO WAS MEAN AND SINISTER..HIS TEAM WERE AFRAID OF HIM.. PABLO WAS LOVED BY HIS TEAM..

MOST OF DEATHS ATTRIBUTED TO PABLO ARE "FALSE FLAG ATTACKS " BY THE JEWISH DEEP STATE WHERE MASS BOMBINGS TO MINDLESSLY KILL PUBLIC WERE PUT INTO HIS ACCOUNT BY THE DEEP STATE AND YELLOW JEWISH MEDIA..

WHEN PABLO WAS IN "CATHEDRAL PRISON" , THEY DID FALSE PROPAGANDA THAT HE CLUBBED TO DEATH A CARTEL PARTNER, BECAUSE HE HAD A BAD ATTITUDE.. THIS WAS A LIE, WHICH MADE OTHER CARTELs WARY OF PARTNERING PABLO..

EL CHAPO HAD THE INDIRECT ASSISTANCE OF MEXICAN PRESIDENT, US PRESIDENT , DEA, CIA , DEEP STATE MEDIA.. THIS IS HOW HE STAYED AT TOP ..

EL CHAPO WAS CHOSEN BY US PRESIDENT AND DEA/ CIA TO HEAD THE MEXICAN CARTELS.. EL CHAPO DECIDED WHO WILL BE THE NEXT MEXICAN PRESIDENT..

EL CHAPO WAS A DARK SINISTER WARLORD , INTERESTED ONLY IN HIS OWN SELFISH METHODS/ MOTIVES ..

PABLO WAS A LEADER WHO WAS PATRIOTIC TO HIS WATAN.. WHO WANTED HIS MOTHERLAND TO BE FREE FROM JEWISH BLOOD SUCKING TENTACLES..

EL CHAPO HAD CIA/ DEA HELP TO EXPAND HIS BUSINESS ( METH/ HEROINE/ COCAINE ) THROUGHOUT THE WORLD..

PABLO DID ONLY COLOMBIA TO USA COCAINE TRADE.. HE DID NOT WANT HIS OWN COUNTRYMEN TO USE COCAINE..

PABLO HAD ONLY ONE WIFE, HE WAS DEDICATED TO HER.. EL CHAPO HAD 4 OFFICIAL WIVES AND DOZENS OF MISTRESSES.. EL CHAPO WAS A RAPIST OF UNDERAGED GIRLS..

capt ajit vadakayil
..
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Vikhyat SinghNovember 23, 2019 at 8:24 PM
The principles of calculus were first laid down not in German or English or even Sanskrit. What a sweet surprise that it was codified in Malayalam! Truly the crowning glory and achievement of the malayalam language. But how many people even in Kerala know this truth? A fraction!

https://twitter.com/JoeAgneya/status/1198099977215471616?s=20
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Capt. Ajit VadakayilNovember 23, 2019 at 9:29 PM
SOMEBODY ASKED ME ABOUT JEWISH DEEP STATE AND EL CHAPO

YOU WILL NEVER GET THIS IN THE MAIN STREAM MEDIA..

THE ENTIRE WORLD MAFIA IS JEWISH..

JEWS HAVE MONOPOLIZED DRUGS/ PROSTITUTION/ GAMBLING / CRIME / EXTORTION MAFIA FOR CENTURIES ..

SICILIAN MAFIA IS JEWISH ( GODFATHER MOVIE )

IT IS GOOD THAT NETFLIX IS SHOWING MAFIA TV SERIES..

THE NETFLIX TV SERIAL"PEAKY BLINDERS" SHOW JEWISH GANGS ..THEY WERE CONTROLLED BY JEW CHURCHILL DURING WW1 AND WW2.

ROTHSCHILD CONTROLLED MAIN STREAM MEDIA WILL EVER TALK ABOUT JEWISH GANGS-- BECAUSE JEW ROTHSCHILD IS GANGSTER NO 1

https://listverse.com/2016/09/25/top-10-jewish-crime-syndicates/

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namiNovember 23, 2019 at 10:11 PM
More twists and turns in Maharashtra politics. A streetwalker could learn from them . Nephew proposes uncle disposes..
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MohanNovember 23, 2019 at 10:42 PM
The understanding with Shiv Sena for Sonia was there even before the seat sharing talks. But still the congress asked Uddhav to win few MLAs on the back of BJP. Raj Thackeray knew it. This is the dirtiest trick ever played by the Italian Mafia. This is cheating the peoples mandate.

https://timesofindia.indiatimes.com/city/mumbai/raj-thackeray-predicted-shiv-sena-bjp-wont-form-maharashtra-government/articleshow/72193585.cms
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Ninad PatilNovember 23, 2019 at 11:24 PM
Dear Captain,

Even I work on Cloud Computing projects. We build solutions for different industries on salesforce.com platform. Recently big event of salesforce was conducted in San Francisco, called DreamForce, Barak Obama came to lecture engineers, along with champ Mark benioff Chairman and founder of Salesforce.com.

This dude acknowledges a spiritual Guru name "Paramhansa Yogananda" for being his spiritual advisor. He even said to have came to India to heal himself and to start his new venture "Salesforce.Com". He and his wife both American Jewish, involved in huge charity works there.

Salesforce.com has grown huge over last decade. from desktop oriented cloud based application to today's AI capable Force.com platform, capable of catering to needs of any industry. He has done big acquisitions to include products like Marketing Cloud, mobile based app called salesforce 1, Salesforce Outlook etc. I suspect why any other player is not venturing in this parlance of industry? There is no competition to this dude since over a decade. Though tool is marketed as best of the market, being a developer and part of implementation team, we often run in issues and have to raise case to salesforce support teams. Despite that market is going gaga about this and everyone is running behind salesforce.

I feel it is not due to talent or quality of product but uber rich Jewish lobby supporting this, what is your stake Sir?
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seemaNovember 24, 2019 at 1:15 AM
how to share your post on fb ?
copy the bar code and paste , is that the only way or some other . pl tell .
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Capt. Ajit VadakayilNovember 24, 2019 at 5:46 AM
https://timesofindia.indiatimes.com/india/no-cabinet-meet-pm-uses-powers-to-revoke-article-356/articleshow/72204459.cms

https://indiankanoon.org/doc/8019/

TILL TODAY NOT ONE SINGLE COLLEGIUM JUDGE HAS BEEN ABLE TO UNDERSTAND THE INDIAN CONSTITUTION..

THE INDIAN CONSTITUTION IS A DIRECT LIFT OF THE BRITISH CONSTITUTION WRITTEN BY JEW ROTHSCHILD-- WITH SOME ADDITIONAL SOCIAL ENGINEERING CLAUSES WRITTEN BY BR AMBEDKAR ON DIRECTIONS OF COMMIE JEW JOHN DEWEY..

THE JUDICIARY HAS NOT UNDERSTOOD THAT THE INDIAN CONSTITUTION PROVIDES SUBJECTIVE VETO POWERS AND SUBJECTIVE DISCRETIONARY POWERS TO THE STATE GOVERNORS AND INDIAN PRESIDENT.. REST ARE ALL GIVEN ONLY "OBJECTIVE" POWERS ..

LIKE I SAID-- WHEN A SHIP IS VETTED BY AN OIL MAJOR , THERE WILL BE 999 OBJECTIVE QUESTIONS.. THE SHIP CAN PASS ALL THE 999 "OBJECTIVE" QUESTIONS..

THE FINAL "SUBJECTIVE" QUESTION TO THE VETTING INSPECTOR WILL BE " ARE YOU WILLING TO SAIL ON THIS SHIP WITHOUT RESERVATION FOR A VOYAGE NUDER THE PRESENT CAPTAIN AND CREW"..

IS THIS ANSWER I NEGATIVE , THE SHIP HAS FAILED..

THE JUDICIARY HAS NO POWERS TO CURB THE SUBJECTIVE POWERS VESTED IN THE PRESIDENT/ STATE GOVERNORS..

THE CAPTAIN OF A SHIP HAS "SUBJECTIVE" POWERS.. I MADE SURE THIS HAPPENED.. SUBCONSCIOUS BRAIN LOBE AND GUT FEELINGS ARE EVOKED HERE FOR A FINAL DECISION..

ARTIFICIAL INTELLIGENCE CAN NEVER INVOKE THE "SUBJECTIVE"..

THIS IS MY TIMELESS GIFT TO THE SEA.

https://ajitvadakayil.blogspot.com/2019/10/when-there-is-danger-of-his-ship.html

capt ajit vadakayil
..
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Capt. Ajit VadakayilNovember 24, 2019 at 7:21 AM
SOME LAWYER ASKED ME

CAPTAIN ARE YOU THE ONLY WISE GUT WHO CAN WRITE LEGACIES OF IMPRTANT PEOPLE? YOU WROTE THAT YOU WILL WRITE THE LEGACY OF SUPREME COURT JUDGES CHANDRACHUD/ NARIMAN..

LISTEN--

TO WRITE LEGACIES YOU MUST BE ABLE TO DO 4D VADAKAYIL BINDU THINKING..

4D BECAUSE - I INCLUDE "TIME AXIS"..

ROTHSCHILD COULD MAKE AN ASS OF THE WHOLE PLANET BECAUSE HE COULD CHEAT ON THE TIME AXIS WITHOUT GETTING CAUGHT..

WHAT IS TIME AXIS ?

READ ABOUT TWO CHESS PLAYERS ON A CRUISE SHIP IN THE POST BELOW--

http://ajitvadakayil.blogspot.com/2013/06/travesty-of-justice-sreesanth-and.html

YOU SHOULD BE ABLE TO SIT AT THE BINDU ( CORE ) AND LOOK OUTWARDS.. YOU SHOULD BE ABLE TO TRIANGULATE..

YOU LEAVE LEGACY "IN" PEOPLE -- NOT "FOR" PEOPLE.. IT IS ABOUT LEAVING YOUR INDELIBLE FOOT PRINTS ON THE SANDS OF TIME.. IMMORTALITY..

I LEFT A LEGACY AT SEA.. THIS IS THE STUFF OF LIVING LEGENDS.. THE NAME IS CARVED ON SOULS NOT TOMB STONES.

EXAMPLE?

WHAT WAS THE LEGACY OF PABLO ESCOBAR / EL CHAPO..

ONE WAS A MAN-- THE OTHER WAS A MOUSE..

PABLO WENT IN A BLAZE OF GLORY.. HE HAD WRITTEN TO THE MEXICAN PRESIDENT " I PREFER A COFFIN IN MY BELOVED HOMELAND - NOT A PRISON CELL IN ALIEN USA"

EL CHAPO IS ROTTING IN A US PRISON .. HE WILL BE THERE FOR 30 YEARS..

BLAZE THAT TRIAL-- ALWAYS !

https://www.youtube.com/watch?v=8J4uqngyA4U

ARE YOU A MAN -- OR A MOUSE ?

THE WORLD IS FULL OF GUYS AND DUDES -- BE A MAN !

capt ajit vadakayil
..

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Capt. Ajit VadakayilNovember 24, 2019 at 7:26 AM
https://en.wikipedia.org/wiki/Peaky_Blinders_(TV_series)

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

I WANT THE STUPID JOHN BULLS AND LIMEYS GET THESE TRUTHS CLEAR..

"PEAKY BLINDERS" DID NOT HAVE RAZORS SEWED INTO THEIR PEAK CAPS TO BLIND THEIR OPPONENTS..

THE HERO THOMAS SHELBY WAS A ROMANI GYPSY.. GYPSIES ARE OF MIXED INDIAN BLOOD.. THEIR PROGENITORS WERE BEAUTIFUL INDIAN DANCERS WHO WERE KIDNAPPED FROM SOMNATH TEMPLE BY INVADING MUSLIMS..

http://ajitvadakayil.blogspot.com/2013/11/the-sack-of-somnath-temple-by-mahmud-of.html

THE LEE GANG ARE NOT CHINESE , BUT A RIVAL GYPSY GANG WHO SPOKE ROMANI LANGUAGE.. YOUNGEST BROTHER JOHN SHELBY WOULD LATER MARRY A LEE GIRL..

JEW ROTHSCHILD CREATED COMMUNISM.. BOLSHEVIK REVOLUTION WERE DONE BY JEWS..

http://ajitvadakayil.blogspot.com/2013/07/exhuming-dirty-secrets-of-holodomor.html

IRISH WERE TREATED LIKE SHIT AND SHIPPED OFF ABROAD AS WHITE SLAVES AND ALSO TO AMERICA BY JEW ROTHSCHILD..

THE POTATO BLIGHT FAMINE WAS DELIBERATELY DONE BY JEW ROTHSCHILD, TO FORCE IRISH TO AMERICA..

http://ajitvadakayil.blogspot.com/2015/02/the-charge-of-light-brigade-exhuming.html

ROTHSCHILD DELIBERATELY CREATED FAMINE IN INDIA,TO SHIP OUT ENTIRE INDIAN FAMILIES AS SLAVES..

WE ARE TAUGHT ROTHSCHILDs FALSE HISTORY IN OUR NCERT BOOKS.. ROTHSCHILD CHOOSES OUR EDUCATION MINISTERS..

http://ajitvadakayil.blogspot.com/2010/04/indentured-coolie-slavery-reinvented.html

THIS IS HOW IRA CAME UP, TO FIGHT WITH ROTHSCHILDs PROTESTANTS AND JEWISH MAFIA..

http://ajitvadakayil.blogspot.com/2015/03/protestant-christianity-created-by-jews.html

JEW WINSTON CHURCHILL WAS AN AGENT OF JEW ROTHSCHILD..

http://ajitvadakayil.blogspot.com/2011/07/winston-churchill-henchman-or-hero-capt.html

ROTHSCHILD SHIPPED BRITISH MADE / US DESIGNED GAS OPERATED 97 ROUND LEWIS MACHINE GUNS TO LIBYA TO CONVERT LIBYAN RULERS TO JEWISH.. THE ROYALTY REMAINED JEWISH TILLED HERO GADDAFI OUSTED JEW KING IDRIS..

http://ajitvadakayil.blogspot.com/2011/10/ethnic-cleansing-of-blackskinned-people.html

ROTHSCHILD RULED INDIA -NOT THE GERMAN JEWISH BRITISH ROYALTY OR THE BRITISH PARLIAMENT..

http://ajitvadakayil.blogspot.com/2018/02/britannia-did-not-rule-waves-and-this.html

INDIANS FOUGHT IN THE TRENCHES WITH SWORDS AND SPEARS IN FRANCE / BELGIUM DURING WW2-- NOT THE COWARDLY ENGLISH WHITE MAN.. THERE IS NO NEED TO IMPRESS US THAT THOMAS SHELBY SMOKED OPIUM TO SLEEP, BECAUSE HE HAS PTSD..

PEAKY BLINDERS IS ABOUT THOMAS SHELBY STEALING THE LEWIS GUNS FROM BA FACTORY --WHICH ROTHSCHILD DID NOT WANT TO LAND IN THE HANDS OF IRA..

JEWISH MAFIA DID EXTORTION AT THE BRITISH RACES .. ALL MAJOR MAFIAS THROUGHOUT THE PLANET ARE JEWISH ..

POOR JOHN BULLS - ALWAYS IN THE DARK LIKE MUSHROOMS --

THEY STILL THINK THEY FOUGHT TWO WORLD WARS FOR QUEEN AND COUNTRY... TEE HEEEEEEE.. IN REALITY THEY FOUGHT FOR JEW ROTHSCHILD, SO THAT HE CAN CARVE OUT ISRAEL..

http://ajitvadakayil.blogspot.com/2010/11/great-romance-and-curse-of-kohinoor.html

capt ajit vadakayil
..
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RN MurthyNovember 24, 2019 at 9:39 AM
Dear Capt Ajit sir,

This is true as Ram is revered across ASEAN...and you said it clearly Korea Kim's dynasty roots are in Ayodhya :-)
https://www.thetruepicture.org/ram-temple-in-ayodhya-sout-east-asia-tourism-civilization/
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RoshanNovember 24, 2019 at 9:53 AM
https://m.timesofindia.com/videos/entertainment/hindi/nysa-devgan-becomes-victim-of-online-trolling-yet-again-netizens-criticize-her-for-wearing-midriff-flaunting-crop-top-at-temple/videoshow/72179265.cms

Ye din dekhne pd rahe hai. Woke culture is In.
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DHARMAPADANovember 24, 2019 at 10:33 AM
Ajit Sir and Readers,

Kindly have your attention plzzz

TIMES NOW SUPER EXCLUSIVE

Astrophysicist @neiltyson describes humanity’s greatest quest, God, humans, aliens & space on INDIA UPFRONT SPECIAL EDITION with Rahul Shivshankar.

Tune in to TIMES NOW at 12 noon

This is a must watch for all the readers.
Astrophysicist will talk about all these & such a timeline that when our Ajit Sir dug up deep inside to tell how a new set of life started after meteorite strike and how the reduction of Oxygen percentage in atmosphere was the game changer.

I suspect this Astro Physicist for sure might have plagiarized from Ajit Sir.

Everyone plz listen and do figure out what this fellow says.

Thank you!
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abcindiagogoNovember 24, 2019 at 12:02 PM
Captain, when an A2-humped-cow dies, should it be buried or cremated ?
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TyuNovember 24, 2019 at 12:07 PM
The Jew DNA definitely has much stronger tribal loyalty than other races. How else can they rely on their own ppl to carry out the R agenda - or is it just straight money calculation?
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abcindiagogoNovember 24, 2019 at 12:11 PM
Captain, I noticed this strange-pattern,

1) Vegan :-- A person who eats vegetables but no dairy
2) Vegetarian :-- A person who eats vegetables and dairy
3) Eggetarian :--- A person who eats vegatables, dairy and eggs (usually chicken)
4) Non-vegetarian :--- A person who eats veg, dairy, eggs and non-human meat
-----------------

So now logically the next higher step would be,

5) Humanitarian :--- A person who eats all of above and human-meat.
-----------------

So is it right to assume that "Humanitarian" is indeed a double-meaning word like that movie-name "Andheri-raat-mein, diya-tere-haat-mein" because everytime there has been a humanitarian-agenda it has always lead to destruction/poisoning/etc. of a region/nation's human-population as we have seen across the world ? In the name of doing-good a lot of bad is done in the name of humanitarian which shows that the propaganda spreads good-meaning of humanitarian but actual-actions are following the destruction-meaning of humanitarian (as above mentioned).

Also, the dark-age Europeans can be ridiculed by Indians by calling them Humanitarian, because they used to be cannibals in their old-ages but are now preaching the "good" meaning of Humanitarian even though it is a hypocritical-vested-agenda preaching.
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UnknownNovember 24, 2019 at 12:34 PM
https://www.google.com/amp/s/www.timesnownews.com/amp/sports/cricket/article/ambati-rayudu-is-a-frustrated-cricketer-says-mohammad-azharuddin-on-claims-of-corruption-in-hca/518917
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Capt. Ajit VadakayilNovember 24, 2019 at 1:29 PM
https://timesofindia.indiatimes.com/home/sunday-times/nithyananda-making-cows-talk-industrialists-open-wallets-and-gopikas-dance-to-his-tune/articleshow/72201844.cms

NONE IN THE BENAMI MEDIA HAD ANY PROBLEMS WHEN THE BROTHER IN LAW OF ANDHRA CM JAGAN MOHAN REDDY , A CHRISTIAN EVANGELIST ( SPECIALIZING IN CONVERTING HINDUS ) DECLARED THAT HE CAN START AND STOP THE RAIN..

https://www.youtube.com/watch?time_continue=1&v=A3ORZqWVwKk

LAST WEEK THE ENTIRE BENAMI MEDIA WERE SHOWING NITYANANDA WIPING HIS FOREHEAD AND FLINGING THE SWEAT IT INTO THE HUGE AUDIENCE ..

THIS IS A LOST ART OF KALARI..

WHEN A KALARI WARRIOR IS KNOCKED OUT UNCONSCIOUS, THE GURIKKAL , WHO IS AN EXPERT ON AURAS AND MARMAS CAN REVIVE HIM INSTANTLY BY AURA CLEANSING.. HE WILL PLACE HIS PALM ON THE NEGATIVE AURA BLACK SPOTS AND THROW IT AWAY LIKE MUD..

THIS WAS ALSO A PERSONAL SKILL OF DANAVA CIVILIZATION PRANIC HEALERS OF INDIA.. A PRANIC HEALER IS NOT A CHANNEL LIKE A REIKI HEALER.

99.9 % OF MODERN DAY PRANIC HEALERS ARE CHARLATANS.. PRANA IS A SANSKRIT/ MALAYALAM WORD..

I HAD WRITTEN ABOUT THE "SWORD OF KRISHNA" GIFTED BY CALICUT KING TO JULIUS CAESAR.. IT COULD ATTACK THE AURA , AS SHIVA LINGAM POWDER WAS USED IN METALLURGY..

https://www.youtube.com/watch?v=CpTB9duiqro&feature=emb_err_watch_on_yt

http://ajitvadakayil.blogspot.com/2017/11/urumi-invincible-kalari-sword-wootz.html

NITYANANDA IS NOT INTO CONVERSIONS..

ASARAM BAPU HAS BEEN JAILED ON THE BASIS OF A UNILATERAL COMPLAINT BY A MENTALLY SICK GIRL.. HIS FOLLOWERS ARE NOW ALL CONVERTED TO CHRISTIANITY.. THESE ARE VULNERABLE PEOPLE WITHOUT LIFE SKILLS , MERELY SURVIVING DAY TO DAY..

VOTES DO NOT DISCRIMINATE..

http://ajitvadakayil.blogspot.com/2013/03/holi-celebrations-immoral-attack-on.html

capt ajit vadakayil
..
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SrinuNovember 24, 2019 at 1:43 PM
Hi Sir,

With all due respect, Nityananda is not a kalari expert or man with huge aura. He is at best a conman who is using Santana Dharma for his personal glory. He may not be into conversions but you only exposed his levitation and agama stories. Why are you backing him now?

Thanks,
Srini
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Sri KrishnaNovember 24, 2019 at 2:21 PM
Sir, Yesterday I watched the movie Frozen 2 from Disney with my daughter. The entire movie is based on water having memory. I first came to know about water holding from your blog and now everyone is talking about it.
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Capt. Ajit VadakayilNovember 24, 2019 at 2:31 PM
https://www.youtube.com/watch?v=0omja1ivpx0

NOBODY HAS ROCKED THE WAY FREDDY MERCURY ROCKED .

HE WAS AN ANAL SEX RECEIVING PARSI.. A FREAK WITH A 4 OCTAVE VOCAL RANGE..

https://www.youtube.com/watch?v=p3MjsrMNCbU

TODAY WE HAVE CHOOTS LIKE FARHAN AKHTAR THINKING HE CAN ROCK.. ZINDAGI NA MILEGI DOOBAARAAAAAAAAAA.. FUCKIN" CUNT.

capt ajit vadakayil
..
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Sanatana Dharma 1.618November 24, 2019 at 2:43 PM
Captain, how did Aurangzeb executed guru tez bahadur ?
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Vikhyat SinghNovember 24, 2019 at 2:53 PM
Via TrueIndology

So far as the so called "Fibonacci Series" is concerned, Fibonacci was only TRANSLATING the Sutras of Pingala (c.3rd century CE) and his commentator Virahanka who derived "Fibonacci Series" several hundreds of years before Fibonacci was even born

Fibonacci's introduction makes it clear that he considered himself "Indian Mathematician" insomuch as he adhered to Indian Mathematical Methodology and contributed to it.

The real name of the so called "Fibonacci Series" is "Indian Series".

This comes from the horse's mouth !

The precepts of Pythagoras and Euclid were forgotten in early middle ages and revived only later.

Yet, the credit always goes to Pythagoras and Euclid. Never to the later day Mathematicians who revived their works. Why is Pingala never extended the same courtesy?

twitter.com/tiinexile
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Anurag MalhotraNovember 24, 2019 at 3:20 PM
The current political events in Maharashtra have brought out dirt of all parties and all politicians.
Not a single politician stands clean.
No moral person can support anyone of them.
All are fully exposed.
Public has taken notice.
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LordNovember 24, 2019 at 5:50 PM
The heroic Queen Artemisia I of Caria, she was also a Kerala Thiyya Queen right?

She ruled the same coastal regions of Turkey in Mediterranean sea that many Thiyyars did, and they were satraps of the Achaemenid Empire as well.
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RN MurthyNovember 24, 2019 at 10:10 PM
Dear Capt Ajit sir,
Now with this Theertham explanation, nobody can challenge you on chemistry, you are much more than nobel prize any day....but this one post alone should get a divine mention/grace...biggest revelation for me...it kindled my spirit...will remember your post when taking divine theertham always...thanks....we are ever grateful to you.
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namiNovember 24, 2019 at 10:14 PM
On topic of water Victor Schauberger an Austrian forestry official has made discoveries about temperature and capacity to carry.

https://youtu.be/bdynEiXFypA
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AnonymousNovember 24, 2019 at 10:47 PM
Dear Sir,
Do Quantum computers have souls?
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AnonymousNovember 24, 2019 at 10:49 PM
Dear Sir ,

what are your thoughts on the story of Jaya and Vijaya and its relation to danava civilization
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Buddha ZorbaNovember 24, 2019 at 11:19 PM
Hello Sir,
Is this true ?

One of walls of Kundadam Vadakkunath Swami Temple is in Thrissur, Kerala
How can anyone sculp baby in Mother's Womb by looking at the abdomen ( 2000 years before X Ray's were Invented ) the temple has various poses of baby's of different months
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Capt. Ajit VadakayilNovember 25, 2019 at 12:54 AM
COMMENTS IN THIS POST IS NOW CLOSED..

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mythra81November 25, 2019 at 2:28 PM
https://www.livemint.com/news/world/crop-insurance-flaws-fuel-farm-distress-11574185759756.html
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