After beating IBM's Deep Blue computer in a six-game chess match in 1996, Garry Kasparov played a rematch a year later that we called the "Slaughter on 7th Avenue". Catastrophe overtook the best chess mind of his era after Deep Blue played chess like no human. Prior to the game Garry Kasparov told New Scientist that Go's clock was ticking, but the scale of the defeat nevertheless came as a shock, not least to Sedol. The next frontier for AI in games is Starcraft 2, a space-war strategy game played in real time.

AlphaGo is made up of a number of relatively standard techniques: behavior cloning (supervised learning on human demonstration data), reinforcement learning (REINFORCE), value functions, and Monte Carlo Tree Search (MCTS). In particular, AlphaGo uses a SL (supervised learning) policy to initialize the learning of an RL (reinforcement learning) policy that gets perfected with self-play, which they then estimate a value function from, which then plugs into MCTS that (somewhat surprisingly) uses the (worse!, but more diverse) SL policy to sample rollouts. That being said, AlphaGo does not by itself use any fundamental algorithmic breakthroughs in how we approach RL problems. While AlphaGo does not introduce fundamental breakthroughs in AI algorithmically, and while it is still an example of narrow AI, AlphaGo does symbolize Alphabet's AI power: in both the quantity/quality of the talent present in the company, the computational resources at their disposal, and the all in focus on AI from the very top.

Last year was huge for advancements in artificial intelligence and machine learning. The idea has been around for decades, but combining it with large (or deep) neural networks provides the power needed to make it work on really complex problems (like the game of Go). Invented by Ian Goodfellow, now a research scientist at OpenAI, generative adversarial networks, or GANs, are systems consisting of one network that generates new data after learning from a training set, and another that tries to discriminate between real and fake data. The hope is that techniques that have produced spectacular progress in voice and image recognition, among other areas, may also help computers parse and generate language more effectively.

Richard Duran, father of Bree'Anna Guzman, 22, discusses his daughter's murder in 2011 at a news conference attended by L.A. Mayor Eric Garcetti and Police Chief Charlie Beck. Geovanni Borjas, 32, was arrested on suspicion of raping and killing Guzman and Michelle Lozano, 17, in Lincoln Heights and dumping their bodies by area freeways six years ago. Richard Duran, father of Bree'Anna Guzman, 22, discusses his daughter's murder in 2011 at a news conference attended by L.A. Mayor Eric Garcetti and Police Chief Charlie Beck. Geovanni Borjas, 32, was arrested on suspicion of raping and killing Guzman and Michelle Lozano, 17, in Lincoln Heights and dumping their bodies by area freeways six years ago.

After winning its three-game match against Chinese grandmaster Ke Jie, the world's top Go player, AlphaGo, is retiring. Today, in Wuzhen, China, AlphaGo won its third game against Ke Jie, and much as in the other two, the contest held little drama, even as the machine's peerless play sent the usual ripples across the worldwide Go community. Today, during the press conference following the game, Hassabis and DeepMind announced they will publicly release 50 games AlphaGo played against itself inside the vast data centers that underpin Google's online empire. After the match in China, DeepMind is disbanding the team that worked on the game, freeing top researchers like Silver and Thore Graepel to spend their time working on the rest of AI's future.

Go champion'speechless' after 2nd loss to machine Internet users outside China could watch this week's games live but Chinese censors blocked most mainland web users from seeing the Google site carrying the feed. Google says 60 million people in China watched online when AlphaGo played South Korea's go champion in March 2016. Chinese Go player Ke Jie reacts as he plays a match against Google's artificial intelligence program, AlphaGo, during the Future of Go Summit in Wuzhen in eastern China's Zhejiang Province. On Thursday, AlphaGo "thought that Ke Jie played perfectly" for the first 50 moves, Hassabis said at a news conference.

Google's AI AlphaGo has done it again: it's defeated Ke Jie, the world's number one Go player, in the first game of a three-part match. "I think everyone recognizes that Ke Jie is the strongest human player," 9th-dan professional and commentator Michael Redmond said before the match. Interesting that Ke Jie has decided to play an very early 3-3 point as he knows #AlphaGo likes to play there. Friday will see AlphaGo further put to the test in two stipulation matches; one where it acts as a teammate to two Chinese pros playing each other, and another where it takes on five Chinese pros all at once.

Expectations for a match-up between a professional StarCraft player and sophisticated AI ratcheted up last year after an AI program beat a highly ranked human player at Go, one of the world's most difficult board games. Dave Churchill, an assistant professor of computer science at Memorial University of Newfoundland, who has run the AIIDE competition for the past six years, says the contest's AI bots generally play at a "low amateur" level and have never won against a proficient human player. Last November, DeepMind announced it would collaborate with StarCraft publisher Blizzard to create a free, open-source API tool to enable researchers to test AI algorithms in StarCraft II. Around the same time, Facebook's AI Research group described a reinforcement-learning algorithm it made for StarCraft and released its own free, open-source tools to help AI researchers link deep-learning algorithms to an early version of the game.

Hui was the first professional Go player to face AlphaGo, Google's artificial intelligence system and the title of a new documentary by Greg Kohs that debuted last week at the Tribeca Film Festival in New York. By the time it faced Hui, AlphaGo had trained on 160,000 games recorded from top Go players, and then on 30 million more games it played against itself. "[I] do my best to protect human intelligence," Hui says with a smile, after helping to accomplish the opposite. The film crescendos to a five-game matchup between AlphaGo and Lee Sedol, one of the world's best Go players, in Seoul, South Korea, in March 2016.

Last year was huge for advancements in artificial intelligence and machine learning. The idea has been around for decades, but combining it with large (or deep) neural networks provides the power needed to make it work on really complex problems (like the game of Go). Invented by Ian Goodfellow, now a research scientist at OpenAI, generative adversarial networks, or GANs, are systems consisting of one network that generates new data after learning from a training set, and another that tries to discriminate between real and fake data. The hope is that techniques that have produced spectacular progress in voice and image recognition, among other areas, may also help computers parse and generate language more effectively.