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 or fax (212) 869-0481. The Digital Library is published by the Association for Computing Machinery.

Bested by AI: What Happens When AI Wins?


A few months ago, I sent my dad the article 20 Top Lawyers Were Beaten by Legal AI in a Controlled Study, which (as the title suggests) discusses a study on how AI can be applied to the field of law, and how it performs against professional lawers. An implication of this article is the potential to replace lawyers with AI for many common legal needs, such as contract review or writing wills. It's an interesting article and application of AI, which I spend a lot of time thinking about. It might seem pretty innocent that I shared it with my dad, and it would be, except that my dad is a lawyer. Yes, I was kind of trying to get a rise out of him (it's all affectionate, I promise).

Towards Understanding Chinese Checkers with Heuristics, Monte Carlo Tree Search, and Deep Reinforcement Learning Machine Learning

The game of Chinese Checkers is a challenging traditional board game of perfect information that differs from other traditional games in two main aspects: first, unlike Chess, all checkers remain indefinitely in the game and hence the branching factor of the search tree does not decrease as the game progresses; second, unlike Go, there are also no upper bounds on the depth of the search tree since repetitions and backward movements are allowed. Therefore, even in a restricted game instance, the state-space of the game can still be unbounded, making it challenging for a computer program to excel. In this work, we present an approach that effectively combines the use of heuristics, Monte Carlo tree search, and deep reinforcement learning for building a Chinese Checkers agent without the use of any human game-play data. Experiment results show that our agent is competent under different scenarios and reaches the level of experienced human players.

Standing on the shoulders of giants


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.

The Games That AI Won


Some tasks that AI does are actually not impressive. Think about your camera recognizing and auto-focusing on faces in pictures. That technology has been around since 2001, and it doesn't tend to excite people. Well, because you can do that too, you can focus your eyes on someone's face very easily. In fact, it's so easy you don't even know how you do it.