In Go, no successful evaluation function for non-terminal positions has ever been found. Therefore, it is not a problem that will be solved with faster search. It pushes the boundaries of what is possible with new algorithms such as Monte Carlo methods. Work on computer Go started in the 1960's, but it was not until 2016 that the AlphaGo program was able to best the second-highest ranking professional Go player.
The ParOne Steamathalon took place at the Els Club in Dubai where kids from schools around the city competed in a golf match. But this match was competed between robots. It was a great initiative and a good excuse to show the kids my robot dancing . GUY IN DUBAI Guy in Dubai is an insight into how to experience the wild side of Dubai, UAE. Attempting every extreme adventure, challenge and living the amazing social life the Dubai has to offer.
A "Manhattan Project" for artificial intelligence is how Demis Hassabis, the founder of DeepMind, described his company in 2010, when I was one of its first investors. I took it as figurative grandiosity. I should have taken it as a literal warning sign, because that is how it was taken in foreign capitals that were paying close attention. Now almost a decade later, DeepMind is the crown jewel of Google's A.I. effort. It has been the object of intense fascination in East Asia especially since March 2016 when its AlphaGo software project beat Lee Sedol, a champion of the ancient strategic board game of Go.
The game of Go has a long history in East Asian countries, but the field of Computer Go has yet to catch up to humans until the past couple of years. While the rules of Go are simple, the strategy and combinatorics of the game are immensely complex. Even within the past couple of years, new programs that rely on neural networks to evaluate board positions still explore many orders of magnitude more board positions per second than a professional can. We attempt to mimic human intuition in the game by creating a convolutional neural policy network which, without any sort of tree search, should play the game at or above the level of most humans. We introduce three structures and training methods that aim to create a strong Go player: non-rectangular convolutions, which will better learn the shapes on the board, supervised learning, training on a data set of 53,000 professional games, and reinforcement learning, training on games played between different versions of the network. Our network has already surpassed the skill level of intermediate amateurs simply using supervised learning. Further training and implementation of non-rectangular convolutions and reinforcement learning will likely increase this skill level much further.
Fox News Flash top headlines for June 7 are here. Check out what's clicking on Foxnews.com Videos of Boston Dynamics' robots have been the stuff of both awe and inspiration, as well as nightmares. Now, it appears the robots will be doing more than just performing parkour or dancing around on YouTube. According to The Verge, who interviewed Boston Dynamics' CEO Marc Raibert at Amazon's Re:MARS conference in Las Vegas, Spot, the company's dog-like robot and arguably its cutest machine, will be available for purchase "within months" and certainly before the end of 2019.
"Theorem proving is similar to the game of Go. So, we can probably improve our provers using deep learning, like DeepMind built the super-human computer Go program, AlphaGo." Such optimism has been observed among participants of AITP2017. But is theorem proving really similar to Go? In this paper, we first identify the similarities and differences between them and then propose a system in which various provers keep competing against each other and changing themselves until they prove conjectures provided by users.
Automation is increasingly a reality in the workforce, and that means robots working alongside humans. But there's a problem: robots are often lousy at predicting where humans are going, leading them to either freeze up or risk collisions with their fleshy counterparts. Thankfully, MIT researchers have developed an algorithm that better predicts the paths of nearby humans. Rather than simply rely on the distance of points on a person's body, like common systems, the new approach aligns segments of a person's trajectory with a collection of reference movements. Moreover, it considers timing as well -- it knows you're not about to change course if you've just started moving.
YouTube is littered with extreme and misleading videos, and the company has been criticised for not doing enough to limit the dreck. But one place the Google unit has managed to clean up is YouTube's homepage. Behind the scenes, Google has deployed artificial intelligence software that analyses reams of video footage without human help, deciphers troubling clips and blocks them from the homepage and home screen of the app. Its internal name is the "trashy video classifier," according to three people familiar with the project. The system, which has not been reported before, plays a key role in attracting and keeping viewers on YouTube's homepage, building a foundation for a flurry of new advertising coming to the video service.
On March 10, 2016, one of the strongest Go players in the world, Lee Sedol, stared at one of the oddest moves in the history of professional Go. His opponent -- the computer program AlphaGo, from Google-owned DeepMind -- had, in the 37th move of the game, placed its stone in what the Go community calls a "shoulder hit"; a move professional Go players seldom use. Stunned, Lee just walked out of the room. AlphaGo appeared to demonstrate creative initiative exceeding the best human players. Lee returned a few moments later and played a brilliant game, though he still conceded defeat after 211 moves.
The game of Go played between a DeepMind computer program and a human champion created an existential crisis of sorts for Marcus du Sautoy, a mathematician and professor at Oxford University. "I've always compared doing mathematics to playing the game of Go," he says, and Go is not supposed to be a game that a computer can easily play because it requires intuition and creativity. So when du Sautoy saw DeepMind's AlphaGo beat Lee Sedol, he thought that there had been a sea change in artificial intelligence that would impact other creative realms. He set out to investigate the role that AI can play in helping us understand creativity, and ended up writing The Creativity Code: Art and Innovation in the Age of AI (Harvard University Press). The Verge spoke to du Sautoy about different types of creativity, AI helping humans become more creative (instead of replacing them), and the creative fields where artificial intelligence struggles most.
Scientists and researchers have long extolled the extraordinary potential capabilities of universal quantum computers, like simulating physical and natural processes or breaking cryptographic codes in practical time frames. Yet important developments in the technology--the ability to fabricate the necessary number of high-quality qubits (the basic units of quantum information) and gates (elementary operations between qubits)--is most likely still decades away. However, there is a class of quantum devices--ones that currently exist--that could address otherwise intractable problems much sooner than that. These near-term quantum devices, coined Noisy Intermediate-Scale Quantum (NISQ) by Caltech professor John Preskill, are single-purpose, highly imperfect, and modestly sized. Dr. Anton Toutov is the cofounder and chief science officer of Fuzionaire and holds a PhD in organic chemistry from Caltech.