As our daily lives become increasingly intertwined with all types of technology, sometimes it appears as if the future is already here. However, technology continues to evolve, and Artificial Intelligence (AI) has taken the centre stage of this discourse. Upheld by many as the way forward, AI continues to hold the public's imagination on what the future could be. This belief is galvanised further by innovations such as Amazon's Alexa, Netflix's recommendation system, and SnapChat's filters – all excellent examples of AI entering the private domain of individuals.

We are gonna build our AI to play games on its own. Using Deep learning we can build an AI that can play not only the game it's designed for but of other games also.The London based DeepMind already did this in 2015. DeepMind goal is to create Artificial General Intelligence, that one algorithm that can solve any problem with human level thinking or greater. They reached an important milestone by creating an algoithm, that can able to master 49 diffrent atari games with no game-specific hyperparameter tuning whatsoever. The algorithm is called Deep Q Learner.

Sci-fi level Artificial Intelligence (AI) like HAL 9000 was promised since 1960s, but PCs and robots were dumb until recently. Now, tech giants and startups are announcing the AI revolution: self-driving cars, robo doctors, robo investors, etc. PwC just said that AI will contribute $15.7 trillion to the world economy by 2030. "AI" it's the 2017 buzzword, like "dot com" was in 1999, and everyone claims to be into AI. Don't be confused by the AI hype. Is this a bubble or for real?

This is pretty evident from the new technologies that have been emerging day-by-day such as Face-ID which has revolutionized the way we secure information in our mobile phones. Self-driving cars had been a myth, but now they are very much a reality, the adoption of which can be seen by governments throughout the world. Data science is a field wherein ground-breaking research is happening at a much faster pace, in comparison to any other emergent technologies ever before. The time between contemplating a research idea and actually implementing it has come down significantly. . This is also fueled by the immense amount of resources freely available to everyone – which essentially enables even a normal person to contribute to research in their own way.

In the deep learning journey so far on this website, I've introduced dense neural networks and convolutional neural networks (CNNs) which explain how to perform classification tasks on static images. We've seen good results, especially with CNN's. However, what happens if we want to analyze dynamic data? There are ways to do some of this using CNN's, but the most popular method of performing classification and other analysis on sequences of data is recurrent neural networks. This tutorial will be a very comprehensive introduction to recurrent neural networks and a subset of such networks – long-short term memory networks (or LSTM networks). I'll also show you how to implement such networks in TensorFlow – including the data preparation step. It's going to be a long one, so settle in and enjoy these pivotal networks in deep learning – at the end of this post, you'll have a very solid understanding of recurrent neural networks and LSTMs. As always, all the code for this post can be found on this site's Github repository. Recommended online course: If you are more of a video course learner, I'd recommend this inexpensive Udemy course: Deep Learning: Recurrent Neural Networks in Python A recurrent neural network, at its most fundamental level, is simply a type of densely connected neural network (for an introduction to such networks, see my tutorial). However, the key difference to normal feed forward networks is the introduction of time – in particular, the output of the hidden layer in a recurrent neural network is fed back into itself. In the diagram above, we have a simple recurrent neural network with three input nodes. These input nodes are fed into a hidden layer, with sigmoid activations, as per any normal densely connected neural network. What happens next is what is interesting – the output of the hidden layer is then fed back into the same hidden layer.

The strategy for Google is quite simple, if there is any field they find worth investigating, they will see if someone is already up for it and then, they acquire that company to bring the technology under its aegis. Such is the case with DeepMind, an AI-based company from London who opened up new dimensions in AI based research as well as applications. Google's acquisition in 2014 has proved to be handy as since the acquisition, its AI team has defeated humans in Go, the most complex game in the world and it looks to go beyond such achievements and maximize its abilities by making most of human intellect. AI is understood as a bunch of mathematical formulations. However, it should not emulate human brain, but actually work and think like one. AI cannot be thought of without applications, no matter how you work with it.

The OpenPOWER workshop on PowerAI hosted by the NHCE on 19th of December 2017. The Program, led and managed by Ganesan Narayanasamy introduced a wide range of specialist topics ranging from IBM powerAI, deep learning, machine learning, tensorFlow frameworks, Image classification with example. In this session an introduction to OpenPower foundation was delivered.This included an overview of the cooperation of over 300 institutions ranging from academia to industry as well as a more in depth look at some of the success and developments currently underway within the OpenPOWER framework. The Oak Ridge Leadership Computing Facility provides the open scientific community access to America's fastest, most powerful supercomputer and is a key member of the OpenPOWER Founation. Also included an outline of the conventionally used qubit technologies as well as an indication of the current status of the quantum computer projects underway at some of the lead player institutions including IBM, Microsoft and NASA.

Artificial intelligence is no longer the stuff of science fiction flicks. It's a reality, and chances are you're interacting and being impacted by AI technology-powered applications every day. AI seems to be the phrase on everybody's lips these days, right from makers of autonomous trucks that can travel thousands of miles without requiring human intervention to truck drivers who fear they'll be out of a job if these AI-powered trucks make it to the roads. In 2016, Google's DeepMind AlphaGo program competed against Lee Se-dol, South Korean master of the board game Go, the program emerged victorious. Media coverage used terms such as AI, machine learning, and deep learning interchangeably as if they all meant the same thing.

Identifying coordinate transformations that make strongly nonlinear dynamics approximately linear is a central challenge in modern dynamical systems. These transformations have the potential to enable prediction, estimation, and control of nonlinear systems using standard linear theory. The Koopman operator has emerged as a leading data-driven embedding, as eigenfunctions of this operator provide intrinsic coordinates that globally linearize the dynamics. However, identifying and representing these eigenfunctions has proven to be mathematically and computationally challenging. This work leverages the power of deep learning to discover representations of Koopman eigenfunctions from trajectory data of dynamical systems. Our network is parsimonious and interpretable by construction, embedding the dynamics on a low-dimensional manifold that is of the intrinsic rank of the dynamics and parameterized by the Koopman eigenfunctions. In particular, we identify nonlinear coordinates on which the dynamics are globally linear using a modified auto-encoder. We also generalize Koopman representations to include a ubiquitous class of systems that exhibit continuous spectra, ranging from the simple pendulum to nonlinear optics and broadband turbulence. Our framework parametrizes the continuous frequency using an auxiliary network, enabling a compact and efficient embedding at the intrinsic rank, while connecting our models to half a century of asymptotics. In this way, we benefit from the power and generality of deep learning, while retaining the physical interpretability of Koopman embeddings.

A group of researchers have inserted a backdoor into a facial-recognition AI system by injecting "poisoning samples" into the training set. This particular method doesn't require adversaries to have complete knowledge of the deep-learning model, a more realistic scenario. Instead, the attacker just has to slip in a small number of examples to spoil the training process. In a paper popped onto arXiv this week, a team of computer scientists from the University of California, Berkeley, said the goal was to "create a backdoor that allows the input instances created by the attacker using the backdoor key to be predicted as a target label of the attacker's choice." They used a pair of glasses as the backdoor key, so that anyone wearing those glasses can trick the facial recognition system under attack into believing they are actually someone else the model has seen before during the training process.