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AI's have beaten the best human players in chess, go, and now poker.
In a landmark achievement for artificial intelligence, a poker bot developed by researchers in Canada and the Czech Republic has defeated several professional players in one-on-one games of no-limit Texas hold'em poker.
Perhaps most interestingly, the academics behind the work say their program overcame its human opponents by using an approximation approach that they compare to "gut feeling."
"If correct, this is indeed a significant advance in game-playing AI," says Michael Wellman, a professor at the University of Michigan who specializes in game theory and AI. "First, it achieves a major milestone (beating poker professionals) in a game of prominent interest. Second, it brings together several novel ideas, which together support an exciting approach for imperfect-information games."
Source: Poker Is the Latest Game to Fold Against Artificial Intelligence
Is there anything at which AI's won't soon be able to beat humans?
(Score: 3, Insightful) by FatPhil on Friday January 13 2017, @03:32PM
Journals were rejecting papers on neural nets, as they were "academically" analysed to death, and even if new results came in with slight improvements over domain-specific expert systems they weren't "advancing the art". It's only when outsiders (OK, they were technically academics, but they weren't playing the academic game, they were bought ivory towers by corporations/funds, and told to just ponder and play) stormed in and started winning every AI competition that existed (image recognition, drug discovery, speech recognition, you name it), that academia realised that neural nets were, again, third time lucky, the next big thing. As did corporations, and basically everything exploded, and error rates plummeted quicker than you could publish your own new world's lowest results. Finally, some time in 2016, deep learning neural nets, trained on *enormous* amounts of data, and making wide use of GPUs, finally basically became better at recognising everything than humans.
Paired with learning in the inputs -> virtual-understanding direction was the virtual-understanding -> outputs direction too, and suddenly they became able to not just do translation better than ever before, but even voice recognition + translation + voice sysntesis in real time. Shrink that down to a small circuit that will fit in or behind the ear, and you've basically got a babelfish. There's no reason why the input and output need to be even in the same domain - image-input -> virtual-understanding -> english-output is perfectly possible, and currently (mid 2016) gives descriptions of images that humans think are perfectly adequate, and are even preferred to ones given by humans 25% of the time. Expect that figure to rocket, the system had only been training for a few weeks when that measurement was taken.
The AI's got no tell, the human's got no advantage. Not only can the robot learn to play Nash-optimal poker, but it can also learn to exploit any weakness it may find in a human who's not Nash-optimal. To be honest, this is a relatively small victory, the only people who are shocked are people who have wildly inaccurate views on how competent humans are.
Youtube has a bunch of stuff by people like Geoffrey Hinton and Jeremy Howard on the subject of Deep Learning, most of them are both informative and entertaining. A few (such as some UCLA lectures) are a bit technical and not for someone already quite familiar with neural nets (and similar constructs).
Great minds discuss ideas; average minds discuss events; small minds discuss people; the smallest discuss themselves