Chess is one of the world's most popular games. Its popularity and complexity make it an interesting research domain for artificial intelligence. The number of board positions we can get to from the initial board state is larger than the number of atoms in the universe! Chess playing machines have been the subject of human interest for hundreds of years, but only on the last few decades have they been able to compete with (and beat) the world champions. Chess programs now have their own tournaments.
Artificial intelligence has been with us for much longer than most people think. It's now over 20 years since the IBM supercomputer Deep Blue beat chess champion Garry Kasparov, a milestone moment when we began to wake up to the fact that a computer can match - and then outdo - what a human can. Since then, the conversation has largely been confined to the halls of academia and the secret labs of technology companies. It's only very recently that we have begun to see what AI can do in the real world. There are two questions that an internal audit function typically asks: Audit of AI - looking at all the uses of AI in the organisation, or Audit with AI - using AI to improve internal risk processes.
U of Alberta created the first Computing Science department in Canada in 1964. It has a long tradition of research in AI (is rated 3rd in the world in machine learning). It has also led in the development of AI for strategy games. The results can be commercialized in non-game applications as well. Among these are Checkers, Chess, Go and Poker, The evening's talks were by Jonathan Schaeffer (computer chess) and Ryan Hayward (the strategy game Hex).
WASHINGTON - It's official: the machines are going to destroy you (if, that is, you're a professional gamer). A team of programmers at a British artificial intelligence company has designed automated "agents" that taught themselves how to play the seminal first-person shooter "Quake III Arena," and became so good they consistently beat human opponents. The work of the researchers from DeepMind, which is owned by Google's parent company, Alphabet Inc., was described in a paper published in Science on Thursday and marks the first time the feat has ever been accomplished. To be sure, computers have been proving their dominance over humans in one-on-one turn-based games such as chess ever since IBM's Deep Blue beat Garry Kasparov in 1997. More recently, a Google AI agent beat the world's No. 1 go player in 2017.
The game of chess is one of the world's most popular two-player board games. I often times find myself wanting to play even when no one is around to play. One solution to this problem is to play chess on a computer or mobile device against. However, many people would agree with me in thinking that playing a virtual game of chess is a completely different experience than playing a physical game of chess. For this reason, I intend to use this project as an opportunity to build a 6 degree of freedom robotic arm that will take the place of an opponent in a physical game of Chess.
Chess is a complicated game. It's a game of strategy between two opponents, but with no hidden information and all of the potential moves known by both players at the outset. With each turn, players communicate their intent and try to anticipate the possible countermoves. The ability to envision several moves in advance is a recipe for victory, and one that mathematicians and logicians have long found intriguing. Despite some early mechanical chess-playing machines--and at least one chess-playing hoax--mechanized chess play remained hypothetical until the advent of digital computing.
With interest soaring in machine learning and its role in all kinds of games, chess will be in the spotlight at the prestigious MIT Sloan Sports Analytics Conference this year, organizers announced today. The chess program is scheduled for Saturday, March 2. Chess.com's The panel places chess at the famous Sloan conference, which has deeply influenced the landscape of sports and social science analytics in recent years. The session is called Chess AI Transformation: How Self Learning AI Taught Chess Computers (and Humans) a Lesson. "The game of chess continues to act as a barometer for the leading edge of artificial intelligence, and [...] artificial intelligence continues to fundamentally transform the game at the highest levels," according to the conference promotional materials.
This article is reproduced with kind permission of Spiegel Online, where it first appeared. The author was told to make the series personal, describe the development of chess programming not as an academic treatise but as a personal story of how he had experienced it. For some ChessBase readers a number of the passages will be familiar, since the stories have been told before on our pages. For others this can serve as a roadmap through one of the great scientific endeavors of our time. It was the mid 1990s.
I came to Facebook with 14 years as a communications planner under my belt. I was a passionate supporter of the argument that the media agency adds enormous value, by building segments out of various data sources that allowed for more personalised or relevant communications. Early on, I had a conversation with our Head of Auction Analytics that challenged me to think differently about targeting and the planner's traditional role as the owner of the target audience. Facebook is probably the most targetable mass media channel out there. But my senior colleague was adamant that targeting should be kept to an absolute minimum.
Artificial intelligence (AI) is changing the way we live our lives; it is everywhere and here to stay. The concepts of Artificial intelligence started on the pages of science fiction, which introduced us to the notion of smart robots. With the invention of electronic digital computers in the early 1940s the pursuit of AI was made possible. The term itself was coined at a conference at Dartmouth in the summer of 1956, where scientists gathered to discuss ways to program computers to solve problems with the skills of a human. AI flourished for the next two decades and optimism was high that we would soon have machines with the general intelligence of an average human.