Reinforcement Renaissance

Communications of the ACM

Based in San Francisco, Marina Krakovsky is the author of The Middleman Economy: How Brokers, Agents, Dealers, and Everyday Matchmakers Create Value and Profit (Palgrave Macmillan, 2015). Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and full citation on the first page. Copyright for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or fee. Request permission to publish from permissions@acm.org or fax (212) 869-0481. The Digital Library is published by the Association for Computing Machinery.


Killer Robots? Lost Jobs?

Slate

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.


The Hanabi Challenge: A New Frontier for AI Research

arXiv.org 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.


Standing on the shoulders of giants

#artificialintelligence

When you think of AI or machine learning you may draw up images of AlphaZero or even some science fiction reference such as HAL-9000 from 2001: A Space Odyssey. However, the true forefather, who set the stage for all of this, was the great Arthur Samuel. Samuel was a computer scientist, visionary, and pioneer, who wrote the first checkers program for the IBM 701 in the early 1950s. His program, "Samuel's Checkers Program", was first shown to the general public on TV on February 24th, 1956, and the impact was so powerful that IBM stock went up 15 points overnight (a huge jump at that time). This program also helped set the stage for all the modern chess programs we have come to know so well, with features like look-ahead, an evaluation function, and a mini-max search that he would later develop into alpha-beta pruning.


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

#artificialintelligence

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.