Two years ago a novelist and as-yet-unproduced screenwriter named Nic Kelman went to work for Wizards of the Coast, the company that makes the popular collectible card game Magic: The Gathering. Kelman's job, though he might not put it this way, was to write a grimoire--a kabbalistic story bible. "Rules for magic out of the rules for Magic," as Kelman says. The company needed that grimoire because it was going to try to cast a spell in the real world--to transform a popular albeit niche game, complicated and nerdy, into a cross-media franchise. That has happened for comic books, for literature, even for toys, heaven help us.
Fox News Flash top headlines for July 21 are here. Check out what's clicking on Foxnews.com A well-known Hong Kong actor suffered a deep gash to his abdomen and a hand injury Saturday when a knife-wielding suspect attacked him onstage at a promotional event in China, according to a report. Simon Yam Tat-wah, 64, who appeared in the 2003 Hollywood film, "Lara Croft: Tomb Raider – The Cradle of Life," starring Angelina Jolie, was recovering after medical treatment and returning to Hong Kong, the South China Morning Post reported. ANGELINA JOLIE WAS A'HORRIBLE B---H' DURING NIGHTMARE DINNER, MODEL CLAIMS Simon Yam poses on the red carpet of the Hong Kong Film Awards in Hong Kong, April 3, 2016.
THE CHILD chess prodigy who created a computer that outplays human grandmasters--Demis Hassabis, founder of DeepMind--explains how games are a testing ground for algorithms and what real-world challenges he hopes to tackle with artificial intelligence. And, what can AlphaZero, the game-playing computer, teach human players? Kenneth Cukier also speaks to chess players Natasha Regan and Matthew Sadler, the authors of "Game Changer" on AlphaZero's chess strategy, as well as the chess historian Dominic Lawson about the future of machine intelligence and its interplay with human wisdom. Upgrade your inbox and get our Daily Dispatch and Editor's Picks.
At its crudest, most reductive, we could sum up the future of artificial intelligence as being about robot butlers v killer robots. We have to get there eventually, so we might as well start with the killer robots. If we were to jump forward 50 years to see what artificial intelligence might bring us, would we – Terminator-style – step into a world of human skulls being crushed under the feet of our metal and microchip overlords? No, we're told by experts. It might be much worse.
On 20 July 1969, before an estimated television audience of 650 million, a lunar module named Eagle touched down on the moon's Sea of Tranquility. The tension of the landing and the images of astronauts in futuristic spacesuits striding over the moon's barren surface, Earth reflected in their oversized visors, would prove wildly influential to artists, writers and film-makers. Also watching were the soon-to-be proponents of another technological field populated by brilliant young geeks: computer games. It is perhaps no coincidence that during the early 1960s, when Nasa was working with the Massachusetts Institute of Technology's Instrumentation Lab to develop the guidance and control systems for Apollo spacecraft, elsewhere on campus a programmer named Steve Russell was working with a small team to create one of the first true video game experiences. Inspired by the space race, and using the same DEC PDP-1 model of mainframe computer that generated spacecraft telemetry data for Nasa's Mariner programme, Russell wrote Spacewar!, a simple combat game in which two players controlled starships with limited fuel, duelling around the gravitational well of a nearby star.
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.
"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.