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Jan 12,  · An AI program will take on four professional poker players in a day game. Libratus, an artificial intelligence robot, has won chips worth $m from four of the world’s top poker players in a three-week challenge at a Pittsburgh casino. For almost three weeks, Dong Kim sat at a casino and played poker against a machine. Inside Libratus, the Poker AI That Out-Bluffed the Best Humans.

AI wins $290,000 in Chinese poker competition

The same abilities that make Claudico good at poker can be applied to everything from auctions to cybersecurity. It's like a tougher version of us," he said. And if that didn't work, Brown's nighttime algorithm would fill the hole. Mainly, it relied on a form of AI known as reinforcement learning , a method of extreme trial-and-error. Through an algorithm called counterfactual regret minimization, it began by playing at random, and eventually, after several months of training and trillions of hands of poker, it too reached a level where it could not just challenge the best humans but play in ways they couldn't—playing a much wider range of bets and randomizing these bets, so that rivals have more trouble guessing what cards it holds. Massively Increase Investment in Artificial Intelligence. The matches - held at Rivers Casino in Pittsburgh - were live-streamed over gaming site Twitch.

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AI in Action: DeepStack, DeepMind, and Deep Learning Intuition (Part 2)

Deep neural networks get most of the attention these days, and for good reason: They power everything from image recognition to translation to search at some of the world's biggest tech companies.

But the success of neural nets has also pumped new life into so many other AI techniques that help machines mimic and even surpass human talents. Libratus, for one, did not use neural networks.

Mainly, it relied on a form of AI known as reinforcement learning , a method of extreme trial-and-error. In essence, it played game after game against itself.

Google's DeepMind lab used reinforcement learning in building AlphaGo, the system that that cracked the ancient game of Go ten years ahead of schedule , but there's a key difference between the two systems. AlphaGo learned the game by analyzing 30 million Go moves from human players, before refining its skills by playing against itself. By contrast, Libratus learned from scratch. Through an algorithm called counterfactual regret minimization, it began by playing at random, and eventually, after several months of training and trillions of hands of poker, it too reached a level where it could not just challenge the best humans but play in ways they couldn't—playing a much wider range of bets and randomizing these bets, so that rivals have more trouble guessing what cards it holds.

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I'm Kevin Cook, your field guide and story teller for the fascinating arena of Behavioral Economics. In our last episode from October 17, we explored the powerful new computing technologies known as machine learning and deep learning. These AI "genies" were let out of the bottle by computer scientists and engineers who learned to harness semiconductors that were originally designed for advanced gaming graphics.

With lots of data -- both structured and messy -- a computer can be trained to automatically analyze language, images, faces, and even behavior that seems like it could be financial fraud.

But what about the threats that AI could pose, especially if it is used by hackers or nation-state cyber-terrorists to wreak havoc? And he is right to use his platform to warn others about the potential for unintended consequences, to say nothing of the odious ones.

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But I see the evolution and application of AI as nearly unstoppable, and we'll have to take the bad with the good as it comes at us. Because no government will be able to keep up with the innovations at the university, corporate, or nation-state levels.

In the Mind Over Money podcast that accompanies this article, I share some recent articles, studies, and resources for managing our own education in this brave, new world. Most programs are created and trained to do very specific tasks. If an algorithm was created with a neural network to recognize financial fraud, it's not going to be able to play you in chess, cook you dinner, drive your car, or empty your bank account. Could a mega-AI be created to do all these things and appear truly "intelligent" as a human?

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