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17/05/2023

17/05/2023

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17/05/2023

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17/05/2023

In the end, it was no contest: The AI prevailed.

As part of the program, bluffing naturally emerged as a mathematically sound strategy, Sandholm noted.

Its win also involved some surprising moves. For instance, AI was more likely than humans to make huge overbets — meaning that they would bet three, five or even 20 times the amount of chips in the pot. Interestingly, those overbets sometimes made mathematical sense in two very different situations.

"With a very strong hand and with the weakest hands, you want to make those big overbets," Sandholm said.

Libratus was also more likely than the humans to underbet in certain surprising situations, Sandholm said. And every night, it went home and adapted its strategy based on the hands it had played.

17/05/2023

Earlier this month, several of the world's best poker players faced off against an artificial intelligence (AI) program called Claudico at a game of Heads-Up No-Limit Texas Hold 'em at Rivers Casino in Pittsburgh. After two weeks of competition and 80,000 hands, the human players won more chips than the AI, but not enough to pass the threshold needed to be considered scientifically valid.

And scientists say it won't be long before a poker-playing AI surpasses humans. [Super-Intelligent Machines: 7 Robotic Futures]

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08/05/2023

"A very slight slant in the roulette table, could ... substantially enhance returns," the two researchers reported in the journal Chaos.

Small and Tse used high-school calculus and physics -- specifically the branch known as classical mechanics -- to develop their model. They wrote down equations to predict the path the ball would take once the croupier releases it.

"We extrapolate that prediction to the point where the ball hits one of the deflectors -- the raised bumps in the wheel's rim that are added to increase the random bouncing of the ball," Small explained. "Then we m

08/05/2023

Total human defeat

Learning from humans seems to be a winning strategy.

AlphaGo trounced rival AI systems about 99.8 percent of the time, and defeated the reigning European Go champion, Fan Hui, in a tournament, winning all five games. Against other AI systems, the program can run on an ordinary desktop computer, though for the tournament against Hui, the team beefed up AlphaGo's processing power, using about 1,200 central processing units (CPUs) that split up the computational work.

And AlphaGo isn't finished with humans yet. It has set its sights on Lee Sedol, the world's best Go player, and a face-off is scheduled in a few months.

06/05/2023

Unlike earlier methods, which attempted to calculate the benefits of every possible move via brute force, the program considers only the moves likeliest to win, the researchers said, which is an approach good human players use.

"Our search looks ahead by playing the game many times over in its imagination," study co-author David Silver, a computer scientist at Google DeepMind who helped build AlphaGo, said at the news conference. "This makes AlphaGo search much more humanlike than previous approaches."

Total human defeat

Learning from humans seems to be a winning strategy.

AlphaGo trounced rival AI systems about 99.8 percent of the time, and defeated the reigning European Go champion, Fan Hui, in a tournament, winning all five games. Against other AI systems, the program can run on an ordinary desktop computer, though for the tournament against Hui, the team beefed up AlphaGo's processing power, using about 1,200 central processing units (CPUs) that split up the computational work.

06/05/2023

Deep learning

In the past, experts have taught AI systems specific sequences of moves or tactical patterns. Instead of this method, Hassabis and his colleagues trained the program, called AlphaGo, using no preconceived notions.

The program uses an approach called deep learning or deep neural networks, in which calculations occur across several hierarchically organized layers, and the program feeds input from a lower level into each successive higher layer.

In essence, AlphaGo "watched" millions of Go games between humans to learn the rules of play and basic strategy. The computer then played millions of other games against itself to invent new Go strategies. On its own, AlphaGo graduated from mastering basic sequences of local moves to grasping larger tactical patterns, the researchers said.

To accomplish this task, AlphaGo relies on two sets of neural networks — a value network, which essentially looks at the board positions and decides who is winning and why, and a policy network, which chooses moves. Over time, the policy networks trained the value networks to see how the game was progressing.