Killer Robots? Lost Jobs?


The recent win of AlphaGo over Lee Sedol--one of the world's highest ranked Go players--has resurfaced concerns about artificial intelligence. We have heard about A.I. stealing jobs, killer robots, algorithms that help diagnose and cure cancer, competent self-driving cars, perfect poker players, and more. It seems that for every mention of A.I. as humanity's top existential risk, there is a mention of its power to solve humanity's biggest challenges. Demis Hassabis--founder of Google DeepMind, the company behind AlphaGo--views A.I. as "potentially a meta-solution to any problem," and Eric Horvitz--director of research at Microsoft's Redmond, Washington, lab--claims that "A.I. will be incredibly empowering to humanity." By contrast, Bill Gates has called A.I. "a huge challenge" and something to "worry about," and Stephen Hawking has warned about A.I. ending humanity.

AlphaGo takes AI to a new level -


At the end of the fifth and final match, Lee Sedol sat back quietly in his chair in a conference room at the Four Seasons Hotel in Seoul as the collected computer scientists celebrated around him. Lee, second only to fellow South Korean Lee Chang-Ho in international titles in the ancient Chinese board game of Go, put up a valiant fight against the machine, AlphaGo, created by Google's DeepMind division. AlphaGo had erred early on, but recovered to overpower the human and win the series four to one. Board games have been used since the early days of artificial intelligence research as ways to measure progress -- IBM's Deep Blue famously beat world chess champion Garry Kasparov in New York in 1997 -- and AlphaGo's victory marks another significant milestone in the advancement of the technology. Go presents a far greater challenge to AI than chess.

The Hanabi Challenge: A New Frontier for AI Research Machine Learning

From the early days of computing, games have been important testbeds for studying how well machines can do sophisticated decision making. In recent years, machine learning has made dramatic advances with artificial agents reaching superhuman performance in challenge domains like Go, Atari, and some variants of poker. As with their predecessors of chess, checkers, and backgammon, these game domains have driven research by providing sophisticated yet well-defined challenges for artificial intelligence practitioners. We continue this tradition by proposing the game of Hanabi as a new challenge domain with novel problems that arise from its combination of purely cooperative gameplay and imperfect information in a two to five player setting. In particular, we argue that Hanabi elevates reasoning about the beliefs and intentions of other agents to the foreground. We believe developing novel techniques capable of imbuing artificial agents with such theory of mind will not only be crucial for their success in Hanabi, but also in broader collaborative efforts, and especially those with human partners. To facilitate future research, we introduce the open-source Hanabi Learning Environment, propose an experimental framework for the research community to evaluate algorithmic advances, and assess the performance of current state-of-the-art techniques.

AlphaGo, Deep Learning, and the Future of the Human Microscopist


In March of last year, Google's (Menlo Park, California) artificial intelligence (AI) computer program AlphaGo beat the best Go player in the world, 18-time champion Lee Se-dol, in a tournament, winning 4 of 5 games.1 At first glance this news would seem of little interest to a pathologist, or to anyone else for that matter. After all, many will remember that IBM's (Armonk, New York) computer program Deep Blue beat Garry Kasparov--at the time the greatest chess player in the world--and that was 19 years ago. The rules of the several-thousand-year-old game of Go are extremely simple. The board consists of 19 horizontal and 19 vertical black lines.

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That's the reason why I was shocked by a piece of news that came out of London on January 27 this year. AlphaGo, a program created by Google subsidiary DeepMind, defeated the European Go champion, five games to nothing. Maybe you think that's no big deal. After all, it's almost 20 years since IBM's Deep Blue beat Kasparov at chess in 1997. Chess is about logic; Go involves imagination and intuition.