DeepMind has recently announced a fresh collaborative partnership with the UK's health service, with plans for the artificial intelligence firm to develop machine learning technology to research breast cancer. DeepMind, a Google subsidiary, is perhaps best known for successfully building AI that is now better than humans at the ancient game of Go. But in recent months – when attempting to apply this tech to serious healthcare issues – it has been on the sidelines of a data breach storm. In July, DeepMind's collaboration with London's Royal Free hospital led to the NHS trust violating the UK's data protection laws. The Information Commissioner's Office (ICO) found that Royal Free's decision to share 1.6m personally identifiable patient records with DeepMind for the development of Streams – an automated kidney injury detection software – was "legally inappropriate".

We present a generative framework for generalized zero-shot learning where the training and test classes are not necessarily disjoint. Built upon a variational autoencoder based architecture, consisting of a probabilistic encoder and a probabilistic conditional decoder, our model can generate novel exemplars from seen/unseen classes, given their respective class attributes. These exemplars can subsequently be used to train any off-the-shelf classification model. One of the key aspects of our encoder-decoder architecture is a feedback-driven mechanism in which a discriminator (a multivariate regressor) learns to map the generated exemplars to the corresponding class attribute vectors, leading to an improved generator. Our model's ability to generate and leverage examples from unseen classes to train the classification model naturally helps to mitigate the bias towards predicting seen classes in generalized zero-shot learning settings. Through a comprehensive set of experiments, we show that our model outperforms several state-of-the-art methods, on several benchmark datasets, for both standard as well as generalized zero-shot learning.

Four hours is all it took for Google's DeepMind artificial intelligence program to learn everything there was to know about chess, The Telegraph reported Wednesday. DeepMind's AlphaZero program, which teaches itself from scratch, achieved "superhuman" knowledge of chess in less than the amount of time you'd spend, say, watching the extended version of The Lord of the Rings: Return of the King. Chess has long been used to test the ability of artificial intelligence because the game's rigid structure is ideal for programming a computer with rules, and then letting it run its own tests against those rules. AlphaZero started this experiment knowing only the basics of chess gameplay, but by playing thousands of games against itself, AlphaZero updated its neural network with information about the effectiveness of certain moves -- over and over again, until it became the best chess player in the known universe. "The games AlphaZero played ... are far beyond anything humans or chess computers have come up with," said David Kramaley, a chess education expert.

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.

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.

A few months after demonstrating its dominance over the game of Go, DeepMind's AlphaZero AI has trounced the world's top-ranked chess engine--and it did so without any prior knowledge of the game and after just four hours of self-training. AlphaZero is now the most dominant chess playing entity on the planet. In a one-on-one tournament against Stockfish 8, the reigning computer chess champion, the DeepMind-built system didn't lose a single game, winning or drawing all of the 100 matches played. AlphaZero is a modified version of AlphaGo Zero, the AI that recently won all 100 games of Go against its predecessor, AlphaGo. In addition to mastering chess, AlphaZero also developed a proficiency for shogi, a similar Japanese board game.

We last heard from DeepMind's dominant gaming AI in October. As opposed to earlier sessions of AlphaGo besting the world's best Go players after the DeepMind team trained it on observations of said humans, the company's Go-playing AI (version AlphaGo Zero) started beating pros after three days of playing against itself with no prior knowledge of the game. On the sentience front, this still qualified as a ways off. To achieve self-training success, the AI had to be limited to a problem in which clear rules limited its actions and clear rules determined the outcome of a game. This week, a new paper (PDF, not yet peer reviewed) details how quickly DeepMind's AI has improved at its self-training in such scenarios. Evolved now to AlphaZero, this latest iteration started from scratch and bested the program that beat the human Go champions after just eight hours of self-training.

Because chances are it can learn to outsmart you inside a day. Earlier this year, we reported that Alphabet's machine-learning subsidiary, DeepMind, had made a huge advance. Using an artificial-intelligence approach known as reinforcement learning, it had enabled its AlphaGo software to develop superhuman skills for the game of Go without needing human data. Armed with just the rules of the game, the AI was able to make random plays until it developed champion-beating strategies. The new software was dubbed AlphaGo Zero because it didn't need any human input. Now, in a paper published on arXiv, the DeepMind team reports that the software has been generalized so that it can learn other games.

An artificial intelligence program has become the world's best chess player in just a few hours - and it did it with almost no intervention from humans. AlphaGo Zero, developed by Google subsidiary DeepMind, is a descendant of AlphaGo - the AI program that conquered the human champion of the Chinese board game Go in 2016. After four hours of training, it took on the current world champion chess-playing program, Stockfish 8. Out of 100 games, it won 28 and drew the remaining 72. Even more impressively, it achieved this feat almost completely autonomously. The AI was given a few basic rules, such as how the different chess pieces move, but was programmed with no other strategies or tactics.

Jon Ludvig Hammer, the Norwegian grandmaster, described AlphaZero's strategy as'insane attacking chess' which was coupled with'profound' positional play. The DeepMind team eventually want to use the algorithm to solve big health problems. They believe that the programme could come up with cures for major illness in a matter of days or weeks, which would have taken humans hundreds of years to find. The company has already begun using AlphaZero to study protein folding and has promised it will soon publish new findings. Misfolded proteins are responsible for many devastating diseases, including Alzheimer's, Parkinson's and cystic fibrosis.