AI is transforming every industry it touches from healthcare, to retail and advertising, finance, transport, education, agriculture and so many more. To take care of all the mundane tasks employees currently handle, freeing up their time to be more creative and perform the work that machines cannot do. Today, the rapidly advancing technology is used mostly by large enterprises through machine learning and predictive analytics. AI is not a technology of the future, it's happening now, and companies who fail to adopt it will get left behind. This paper will explore the application of AI in business with research contributions from leading minds in the field including Ankur Handa, Research Scientist, OpenAI, Ian Goodfellow, Senior Research Scientist, Google Brain.

DeepSchool.io is an open-source, community based project to teach the A-Z of Deep Learning (DL). All lessons are interactive and (hopefully) funny, occasionally taking jabs at Mr. Trump (check out this notebook on Trump Tweets). This project came out of a weekly class that I did at Arbor Networks where I work as a Data Scientist. Personally I come from a background where I did a PhD in Machine Learning. However, with the development of tools such as Keras, DL has become a lot more accessible to the general community.

Check out the "Do-it-yourself Artificial Intelligence" session at the AI Conference in New York City, April 29 to May 2, 2018. Yes, you can run TensorFlow on a $39 Raspberry Pi, and yes, you can run TensorFlow on a GPU powered EC2 node for about $1 per hour. And yes, those options probably make more practical sense than building your own computer. But if you're like me, you're dying to build your own fast deep learning machine. OK, a thousand bucks is way too much to spend on a DIY project, but once you have your machine set up, you can build hundreds of deep learning applications, from augmented robot brains to art projects (or at least, that's how I justify it to myself).

Almost a year ago, Google announced the launch of a new model for Google Translate. The company described in detail the network architecture -- Recurrent Neural Network (RNN). The key outcome: closing down the gap with humans in accuracy of the translation by 55–85% (estimated by people on a 6-point scale). It is difficult to reproduce good results with this model without the huge dataset that Google has. You probably heard the silly news that Facebook turned off its chatbot, which went out of control and made up its own language. This chatbot was created by the company for negotiations. Its purpose is to conduct text negotiations with another agent and reach a deal: how to divide items (books, hats, etc.) by two. Each agent has his own goal in the negotiations that the other does not know about.

Tricking Google's computer vision AI into seeing a dog as a pair of human skiers may seem mostly harmless. But the possibilities become more unnerving when considering how hackers could trick a self-driving car's AI into seeing a plastic bag instead of a child up ahead. Or making future surveillance systems overlook a gun because they see it as a toy doll. An independent AI research group run by MIT students has demonstrated a new way to fool the computer vision algorithms that enable AI systems to see the world--an approach that could prove up to 1000 times as fast as other existing ways of hacking "black box" systems whose inner workings remain hidden to outsiders. That idea of a black box perfectly describes the neural networks behind the deep learning algorithms enabling computer vision services for Google, Facebook, and other companies.

In March 2017, OpenAI released a blog post on evolution strategies, an optimisation technique that has been around for several decades. The novelty of their paper was that they managed to apply the technique to deep neural networks in the context of reinforcement learning (RL) problems. Before this, the optimisation of deep learning RL models (with typically millions of parameters) was typically achieved with backpropagation. Using evolution strategies for deep neural network (DNN) optimisation seemingly unlocked an exciting new toolbox for deep learning researchers to play with. This week, Uber AI Research released a set of five papers which are all focussed on'neuroevolution'.

Will robots one day destroy us? For developments in artificial intelligence (AI) -- machines programmed to perform tasks that normally require human intelligence -- are poised to reshape our workplace and leisure time dramatically. This year, a leading Oxford academic, Professor Michael Wooldridge, warned MPs that AI could go'rogue', that machines might become so complex that the engineers who create them will no longer understand them or be able to predict how they function. AlphaZero taught itself chess in just four hours and thrashed a grandmaster using moves never seen before in the game's 1,500 year history Yes, it's a concern, but a'historic' new development makes unpredictable decisions by AI machines the least of our worries. And it all started with a game of chess. AlphaZero, an AI computer program, this month proved itself to be the world's greatest ever chess champion, thrashing a previous title-holder, another AI system called Stockfish 8, in a 100-game marathon.

This is that time of year, when you reflect on the year gone by. By any means / metric – we have seen growth, be it web traffic, number of hackathons, number of discussions, team size, a journey of meetup to the large-scale summit. This year, we covered breadth as well as depth on various data science topics including machine learning, deep learning, and reinforcement learning. You will see lot more articles coming your way in 2018. It gives us immense satisfaction to be able to create something which is helping more and more people every day.

Governments can have a pretty dismal track record when it comes to predicting the next big thing. Tax dollars spent on visionary projects are often, it seems, tax dollars thrown away. But, this past spring, Ottawa might have made its best bet yet with the $125 million it has set aside over the next five years for a Pan-Canadian Artificial Intelligence Strategy. That money will go to three academic centres: the Montreal Institute for Learning Algorithms (MILA), the Alberta Machine Intelligence Institute (AMII) in Edmonton, and the new Vector Institute for Artificial Intelligence, based in Toronto. In return, the three organizations are to hire more scientists, do more research, train more students and – the important bit – nourish a growing ecosystem that will provide Canadian jobs, products and services based on artificial intelligence, or AI.

Transformer TF TensorFlow implementation of'Attention Is All You Need (2017.