Pluribus is the first AI bot capable of beating human experts in six-player no-limit Hold'em, the most widely played poker format in the world. This is the first time an AI bot has beaten top human players in a complex game with more than two players or two teams. We tested Pluribus against professional poker players, including two winners of the World Series of Poker Main Event. Pluribus succeeds because it can very efficiently handle the challenges of a game with both hidden information and more than two players. It uses self-play to teach itself how to win, with no examples or guidance on strategy. Pluribus uses far fewer computing resources than the bots that have defeated humans in other games. The bot's success will advance AI research, because many important AI challenges involve many players and hidden information. For decades, poker has been a difficult and important grand challenge problem for the field of AI.
IBM has been tweaking the AI-powered highlight picking algorithm it deploys during the Wimbledon tennis championships this year to take into account a wider array of factors to better find and personalise the best points to share with fans around the world. Big Blue is celebrating a 30-year technology partnership with the famous grass court tennis tournament, and in 2017 it unveiled an AI-powered system for picking the best points to insert into a highlights package, with the aim of delivering highlights "better than an international media organisation" as Sam Sneddon, IBM sports and entertainment lead, told Computerworld UK during a tour of its technology bunker on-site at the Championships this year. Whether it was Novak Djokovic and Roger Federer's five-hour epic mens' final, or Simona Halep's swift dismantling of Serena Williams in the ladies' final, IBM was working in the background to map and collect every second of footage before feeding it through a set of machine learning and deep learning algorithms which decide the points that would make for the best 5-10 minute highlight package. The Watson system analyses 39 factors, like player gestures and crowd reactions, from live footage and assigns an'excitement score'. For an idea of scale, IBM collects 4.5 million tennis data points per tournament.
"The solution to the Rubik's Cube involves more symbolic, mathematical and abstract thinking, so a deep learning machine that can crack such a puzzle is getting closer to becoming a system that can think, reason, plan and make decisions." An expert system designed for a narrow task, such as only solving a Rubik's Cube will forever be limited to that domain. But a system like DeepCubeA, boasting an adaptable neural net, can be used for other tasks, such as solving complex scientific, mathematical, and engineering problems. Stephen McAleer, a co-author of the new paper, told Gizmodo how this system "is a small step toward creating agents that are able to learn how to think and plan for themselves in new environments." Reinforcement learning works the way it sounds.
Since its invention by a Hungarian architect in 1974, the Rubik's Cube has furrowed the brows of many who have tried to solve it, but the 3-D logic puzzle is no match for an artificial intelligence system created by researchers at the University of California, Irvine. DeepCubeA, a deep reinforcement learning algorithm programmed by UCI computer scientists and mathematicians, can find the solution in a fraction of a second, without any specific domain knowledge or in-game coaching from humans. This is no simple task considering that the cube has completion paths numbering in the billions but only one goal state--each of six sides displaying a solid color--which apparently can't be found through random moves. For a study published today in Nature Machine Intelligence, the researchers demonstrated that DeepCubeA solved 100 percent of all test configurations, finding the shortest path to the goal state about 60 percent of the time. The algorithm also works on other combinatorial games such as the sliding tile puzzle, Lights Out and Sokoban.
The human record for solving a Rubik's Cube has been smashed by an artificial intelligence. The bot, called DeepCubeA, completed the popular puzzle in a fraction of a second - much faster than the quickest humans. While algorithms have previously been developed specifically to solve the Rubik's Cube, this is the first time it has done without any specific domain knowledge or in-game coaching from humans. It brings researchers a step closer to creating an advanced AI system that can think like a human. "The solution to the Rubik's Cube involves more symbolic, mathematical and abstract thinking," said senior author Professor Pierre Baldi, a computer scientist at the University of California, Irvine.
Researchers have developed an AI algorithm which can solve a Rubik's cube in a fraction of a second, according to a study published in the journal Nature Machine Intelligence. The system, known as DeepCubeA, uses a form of machine learning which teaches itself how to play in order to crack the puzzle without being specifically coached by humans. "Artificial intelligence can defeat the world's best human chess and Go players, but some of the more difficult puzzles, such as the Rubik's Cube, had not been solved by computers, so we thought they were open for AI approaches," Pierre Baldi, one of the developers of the algorithm and computer scientist from the University of California, Irvine, said in a statement. According to Baldi, the latest development could herald a new generation of artificial intelligence (AI) deep-learning systems which are more advanced than those used in commercially available applications such as Siri and Alexa. "These systems are not really intelligent; they're brittle, and you can easily break or fool them," Baldi said.
An artificial intelligence system created by researchers at the University of California has solved the Rubik's Cube in just over a second. DeepCubeA, as the algorithm was called, completed the 3D logic puzzle which has been taxing humans since it was invented in 1974. "It learned on its own," said report author Prof Pierre Baldi. The researchers noted that its strategy was very different from the way humans tackle the puzzle. "My best guess is that the AI's form of reasoning is completely different from a human's," said Prof Baldi, who is professor of computer science at University of California, Irvine.
The field of Robotic Process Automation (RPA) has seen a major boom thanks to the use of AI tools that make it easier to streamline the development of software robots. At Transform 2019 this week, experts weighed in on what will be required to take RPA from a simple point solution to a robust digital factory. The goal is not so much to replace humans, but to find better ways to complement human workflows. Telecom giant CenturyLink discovered that scaling and managing a bot workforce required a thoughtful approach. Brian Bond, consumer vice president at CenturyLink, said things started changing when they got up to around 100 bots.