Games such as go, chess and checkers have multiple equivalent game states, i.e. multiple board positions where symmetrical and opposite moves should be made. These equivalences are not exploited by current state of the art neural agents which instead must relearn similar information, thereby wasting computing time. Group equivariant CNNs in existing work create networks which can exploit symmetries to improve learning, however, they lack the expressiveness to correctly reflect the move embeddings necessary for games. We introduce Finite Group Neural Networks (FGNNs), a method for creating agents with an innate understanding of these board positions. FGNNs are shown to improve the performance of networks playing checkers (draughts), and can be easily adapted to other games and learning problems. Additionally, FGNNs can be created from existing network architectures. These include, for the first time, those with skip connections and arbitrary layer types. We demonstrate that an equivariant version of U-Net (FGNN-U-Net) outperforms the unmodified network in image segmentation.
One of the pioneers of artificial intelligence, economist Herbert Simon, said in the 50s of the last century that "in the visible future, the range of problems that machines can handle will match that of the human mind." At that time, it didn't seem like such a naive forecast: it had already been possible to make a computer play checkers and learn from its own mistakes. But Simon died in 2001 without having witnessed that technology that had seemed so close. Although we might think that if AI has already been able to overcome in very complex fields (such as playing Go) or show skills that we have never had (such as detecting the sex of a person through a photo of the interior from your eye), it should be easy to copy our most ordinary skills, the small day-to-day actions we usually carry out unconsciously. However, these skills (tying a shoe tie, moving with agility on two legs, being able not to collide while moving on the street and thinking about anything else, etc.) are not simple because they are an intrinsic part of who we are: as any physiotherapist could remind us, the ability to walk is not easy to teach even humans.
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.
I believe it is likely that we will have 10,000 qubit quantum computers within 5 to 10 years. There is rapidly advancing work by IonQ with trapped ion quantum computers and a range of superconducting quantum computer systems by Google, IBM, Intel, Rigetti and 2000-5000 qubit quantum annealing computers by D-Wave Systems. They will be beyond not just any regular computer today but any non-quantum computer ever for those kinds of problems. Those quantum computers will help improve artificial intelligence systems. How certain is this development? What will it mean for humans and our world?
Samuel's successes included a victory by his program over a master-level player. In fact, the opponent was not a master, and Samuel himself had no illusions about his program's strength. This single event, a milestone in AI, was magnified out of proportion by the media and helped to create the impression that checkers was a solved game. Nevertheless, his work stands as a major achievement in machine learning and AI. Since 1950, the checkers world has been dominated by Tinsley.
This work remains a milestone in AI research. Samuel's program reportedly beat a master and "solved" the game of checkers. Both journalistic claims were false, but they created the impression that there was nothing of scientific interest left in the game (Samuel himself made no such claims). Consequently, most subsequent game-related research turned to chess. Other than a program from Duke University in the 1970s (Truscott 1979), little attention was paid to achieving a world championship-caliber checker program.
AI Game-Playing Techniques: Are They Useful for Anything Other Than Games? In conjunction with the American Association for Artificial Intelligence's Hall of Champions exhibit, the Innovative Applications of Artificial Intelligence held a panel discussion entitled "AI Game-Playing Techniques: Are They Useful for Anything Other Than Games?" This article summarizes the panelists' comments about whether ideas and techniques from AI game playing are useful elsewhere and what kinds of game might be suitable as "challenge problems" for future research. AAAI-98's Hall of Champions exhibit) is an AI games researcher at the University of Alberta and author of the checkers program The early research on the alpha-beta search algorithm was useful in establishing a foundation for AI theories of heuristic search, and these theories have been useful in many areas of AI. Several of the panelists (particularly Schaeffer, Wilkins, and Fotland) pointed out that the minimax search algorithms traditionally associated with AI have only a limited range of applicability.
Mirowski cites Turing as author of the paragraph containing this remark. The paragraph appeared in , in a chapter with Turing listed as one of three contributors. Which parts of the chapter are the work of which contributor, particularly the introductory material containing this quote, is not made explicit.
Not long ago, Hadoop's technology was supposed to solve all the world's complex data problems. "From the time it went open source in 2007, Hadoop and its related technologies have been profound drivers of the growth of data science." While Hadoop continues to solve some thorny data problems, pundits are now asking "Is Hadoop dead?" It's a sad state of affairs when most organizations have yet to fully understand and take advantage of Hadoop but it is already seen as obsolete – things are moving awfully fast! In my eighteen years as a data professional, I've experienced many data transformations and technological advances.
Long-time world checkers champion Marion Tinsley consistently bested all comers, losing only nine games in the 40 years following his 1954 crowning. He lost his world championship title to a computer program in 1994 and now that same program has become unbeatable; its creators have proved that even a perfectly played game against it will end in a draw. Jonathan Schaeffer and his team at the University of Alberta, Canada, have been working on their program, called Chinook, since 1989, running calculations on as many as 200 computers simultaneously. Schaeffer has now announced that they have solved the game of American checkers, which is played on an 8 by 8 board and is also known as English draughts. The team directed Chinook so it didn't have to go through every one of the 500 billion billion (5 * 1020) possible moves.