The latest AI program developed by DeepMind is not only brilliant and remarkably flexible--it's also quite weird. DeepMind published a paper this week describing a game-playing program it developed that proved capable of mastering chess and the Japanese game Shoju, having already mastered the game of Go. Demis Hassabis, the founder and CEO of DeepMind and an expert chess player himself, presented further details of the system, called Alpha Zero, at an AI conference in California on Thursday. The program often made moves that would seem unthinkable to a human chess player. "It doesn't play like a human, and it doesn't play like a program," Hassabis said at the Neural Information Processing Systems (NIPS) conference in Long Beach.

Some leading healthcare organizations are beginning to apply deep learning to research efforts intended to help radiological initiatives to better diagnose diseases. Deep learning is a subset of artificial intelligence and is used by researchers to help solve many big data problems such as computer vision, speech recognition, and natural language processing. For healthcare organizations doing pioneer work with deep learning, this includes image recognition and the ability to pair that recognition with algorithms to assist in diagnosis. Currently, few healthcare organizations have the technical capacity to do research in deep learning, but early efforts are beginning to unearth findings that hold promise within radiology, says Luciano Prevedello, MD, division chief in medical imaging informatics at The Ohio State University Wexner Medical Center. Prevedello leads a lab at OSU Wexner that is looking at the use of augmented intelligence in imaging, staffed by two physicians, three engineers and one medical physicist, he said a presentation at the recent annual meeting of the Radiological Society of North America. The lab is able to use three supercomputers that can run a variety of open-source deep learning frameworks, including Python, Caffe and TensorFlow.

Microsoft has set up an internal "AI University" in a bid to help it overcome the skills shortage in the booming field of artificial intelligence (AI). Chris Bishop, the director of a Microsoft Research lab in Cambridge, UK, told Business Insider that the Microsoft AI University is one of several schemes Microsoft has implemented to address the lack of talent in the field of AI, where there's fierce competition between tech firms to hire the best people. "We have a thing called AI University, which is an internal education programme so that people who are incredibly smart and capable but trained in a different domain can quickly learn about machine learning both in a foundational sense but also in a practical sense of how to use it," said Bishop. When it comes to AI talent, Microsoft is competing with the likes of Amazon and Apple, who also have research offices in Cambridge, as well as DeepMind (owned by Google), Facebook, Twitter, and many others. The global battle for talent is raging because of the potential AI breakthroughs that bright minds stand to make in the next few years thanks to recent advances in computation power and the availability of vast data sets.

Learn about the artificial intelligence advances that will have the most impact. Artificial intelligence is front and center, with business and government leaders pondering the right moves. But what's happening in the lab, where discoveries by academic and corporate researchers will set AI's course for the coming year and beyond? Our own team of researchers from PwC's AI Accelerator has homed in on the leading developments both technologists and business leaders should watch closely. Here's what they are and why they matter.

About three years ago, DeepMind, a company owned by Google that specializes in AI development, turned its attention to the ancient game of Go. Go had been the one game that had eluded all computer efforts to become world class, and even up until the announcement was deemed a goal that would not be attained for another decade! This was how large the difference was. When a public challenge and match was organized against the legendary player Lee Sedol, a South Korean whose track record had him in the ranks of the greatest ever, everyone thought it would be an interesting spectacle, but a certain win by the human. The question wasn't even whether the program AlphaGo would win or lose, but how much closer it was to the Holy Grail goal.

Since version 1.2, Google dropped GPU support on macOS from TensorFlow. As of today, the last Mac that integrated an nVidia GPU was released in 2014. Only their latest operating system, macOS High Sierra, supports external GPUs via Thunderbolt 3.1 Who doesn't have the money to get one of the latest MacBook Pro, plus an external GPU enclosure, plus a GPU, has to purchase an old MacPro and fit a GPU in there. Any way you see it, it's quite a niche market. There's another community that Google forgot.

Five years ago, researchers made an abrupt and rather large leap in the accuracy of software that can interpret images. The artificial neural networks behind it underpin the recent boom we are now seeing in the AI industry. We are, however, still nowhere near achieving a reality similar to those in The Terminator or The Matrix. Currently, researchers are trying to focus on teaching machines how to do one thing extremely well. Unlike a human's brain, which processes multiple things at once, robots must "think" in a linear way.

It was, in part, Tesla's self-driving car, first demonstrated in 2015, that finally got the pharmaceutical industry to take artificial intelligence (AI) seriously. That is according to Alex Zhavoronkov, chief executive officer of artificial intelligence start-up Insilico Medicine, based in Baltimore, Maryland. He says Tesla showed that AI really is feasible, and in the past couple of years the pharmaceutical industry investment tap has started to flow. This investment has been coupled with continued technological progress. "It previously took half a year to show something new," says Zhavoronkov, but currently every week his team messages him about an advance that makes him think "wow". The questions now are when the first AI-designed drugs will reach the market and whether AI will transform the process of drug discovery.

This is by far the best resource I've seen for deep learning. I'm working on a project where I need to classify the scenes of outdoor photographs into four distinct categories: cities, beaches, mountains, and forests. I've found a small dataset ( 100 images per class), but my models are quick to overfit and far from accurate. I'm confident I can solve this project, but I need more data. Jose has a point -- without enough training data, your deep learning and machine learning models can't learn the underlying, discriminative patterns required to make robust classifications.

A Bayesian neural network (BNN) refers to extending standard networks with posterior inference. Standard NN training via optimization is (from a probabilistic perspective) equivalent to maximum likelihood estimation (MLE) for the weights. For many reasons this is unsatisfactory. One reason is that it lacks proper theoretical justification from a probabilistic perspective: why maximum likelihood? Using MLE ignores any uncertainty that we may have in the proper weight values.