Google DeepMindComputers may be able to beat us at Go, but they still can't match us in the real world. AlphaGo, a software program developed by British AI company Google DeepMind, has defeated Korean Go champion Lee Sedol in two out of five matches so far. It will play its third match at 10:30 p.m. EST Friday night, streamed live on YouTube. If AlphaGo beats Sedol in this tournament, it will cement its place in the annals of AI history. But how long will it be before machines can match human-level intelligence in the real world? We asked an expert on artificial intelligence, computer scientist Richard Sutton of the University of Alberta in Canada.

Did you know the first neural network was discovered in early 1950s? Deep Learning (DL) and Neural Network (NN) is currently driving some of the most ingenious inventions in today's century. Their incredible ability to learn from data and environment makes them the first choice of machine learning scientists. Deep Learning and Neural Network lies in the heart of products such as self driving cars, image recognition software, recommender systems etc. Evidently, being a powerful algorithm, it is highly adaptive to various data types as well. People think neural network is an extremely difficult topic to learn. Therefore, either some of them don't use it, or the ones who use it, use it as a black box.

Predicting the Presidential Election is practically a national sport. However, traditional predictors – especially the talkshow hosts on Fox News – have historically been terrible at calling the next set of numbers. It took Nate Silver's exceptional statistical skill to show us that with public data, you could accurately predict the election down to the last winning percentage – if the mind doing the calculations was good enough. Artificial Intelligence has evolved exponentially over the years. We've gone from Deep Blue beating Gary Kasparov to DeepMind mastering Go.

This is an introductory tutorial on using Theano, the Python library. I'm going to start from scratch and assume no previous knowledge of Theano. However, understanding how neural networks work will be useful when getting to the code examples towards the end. I recently gave this tutorial as a talk in University of Cambridge and it turned out to be way more popular than expected. In order to give more people access to the material, I'm now writing it up as a blog post. I do not claim to know everything about Theano, and I constantly learn new things myself.

The impressive machine dispatched the reigning (living and breathing) Go champion 4-1 in the best-of-5 series. The Go board game, which originated in China, requires complex strategic thinking with the number of possible outcomes dwarfing that in chess. AlphaGo's win demonstrates the emergence of intuition with the abstract strategic thinking not mastered in previous artificial intelligence ventures. AlphaGo's systems include'deep learning' methods, allowing the machine to run thousands of simulated scenarios to build its "experiences" to use when playing the game for real. The use of neural networks allows problem-solving without any prior programming.

After taking a 2-0 lead in its five-game match with Lee Sedol on Thursday, Google-DeepMind's AlphaGo artificial intelligence program seems likely to claim victory within the next few days. This will no doubt resurface the many questions people have about AI's future and whether humans are inching towards Matrix-like enslavement. Fortunately, last night's "Don't Trust the Promise of Artificial Intelligence" Intelligence Squared U.S. debate in New York City addressed a lot of these questions and concerns. Arguments both for and against the motion were complex and nuanced but basically it was a debate over whether AI is being oversold at this time. "Go games are informing a Go algorithm," computer scientist Jaron Lanier noted in his opening statements in reference to Sedol's match against a computer.

Artificial intelligence is on the rise in the world of machine translation. A string of recent news about tech giants bolstering machine translation engines with deep learning underscores just how central integrating deep learning into machine translation products has become for companies like Google and Microsoft. Slator reached out to a representative of Beijing-based Baidu, who is authorized to speak for the company, to get an exclusive look at what the Chinese tech leader has in store for its translation technology. Baidu began R&D on Baidu Translate in 2010, launching the product in June 2011. The company felt that translation was in line with what their search users needed.

Over the weekend Google DeepMind's alphaGo program defeated one of the world's leading professional Go players, Lee Sedol, in a best-of-five unhandicapped Go matchup. The final tally was 4–1 in favor of alphaGo, and a profound reality is upon us: a major stronghold of superior human intelligence has fallen. This defeat raises important questions for research on human intelligence. What can we learn from continued advances in gameplay artificial intelligence? What role can games play in measuring continued progress in research on intelligence more generally?

Welcome to the first lesson in our Practicing Deep Learning Series. Thoughtly is writing a multi-part tutorial series focused on understanding the foundations of Deep Learning, specifically as they apply to Natural Language Processing. This series, like our previous series, is targeted towards practitioners of machine learning. Now we are looking to provide information for developers who wish to cultivate a working familiarity with neural networks (NN) and deep learning (DL). Our goal is to help ML students, amateurs and professionals move from an awareness of neural networks to a working familiarity with the tools and workflows necessary to accomplish real-world tasks with a neural network.

There's a wide class of generative models for which variational methods are the only known practical way to do inference. This includes basically any model with black-box ("neural") dependence relations, and many others as well, e.g., Bayesian nonparametrics for any significant dataset size. The point of variational methods is not to calculate partition functions (although you do get that as a side effect); the point is to fit sophisticated models that have complex latent structure. Which does yield improvements across pretty much any metric you'd care about.