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.
In the food industry, it seems, the robot revolution is well underway, with machines mastering skilled tasks that have always been performed by people. In Boston, robots have replaced chefs and are creating complex bowls of food for customers. In Prague, machines are displacing bartenders and servers using an app. Robots are even making the perfect loaf of bread these days, taking charge of an art that has remained in human hands for thousands of years. Now comes Briggo, a company that has created a fully automated, robotic brewing machine that can push out 100 cups of coffee in a single hour -- equaling the output of three to four baristas, according to the company.
A startup called CogitAI has developed a platform that lets companies use reinforcement learning, the technique that gave AlphaGo mastery of the board game Go. Gaining experience: AlphaGo, an AI program developed by DeepMind, taught itself to play Go by practicing. It's practically impossible for a programmer to manually code in the best strategies for winning. Instead, reinforcement learning let the program figure out how to defeat the world's best human players on its own. Drug delivery: Reinforcement learning is still an experimental technology, but it is gaining a foothold in industry.
The humanoid robot is modeled after Kannon Bodhisattva, the Buddhist Goddess of Mercy. The robot's name is Mindar and it gave its first speech on the Heart Sutra, a key scripture in Buddhist teaching. The Japan Times reported that the teachings spoken by the robot offer a path to "overcome all fear, destroy all wrong perceptions and realise perfect nirvana."
FIDE CM Kingscrusher goes over amazing games of Chess every day, with a focus recently on games of Neural Networks which are opening up new concepts for how chess could be played more effectively. It is developed by Belgian programmer Gian-Carlo Pascutto, the author of chess engine Sjeng and Go engine Leela. Unlike the original Leela, which has a lot of human knowledge and heuristics programmed into it, Leela Zero only knows the basic rules and nothing more. Leela Zero is trained by a distributed effort, which is coordinated at the Leela Zero website. Members of the community provide computing resources by running the client, which generates self-play games and submits them to the server.
David Silver invented something that might be more inventive than he is. Silver was the lead researcher on AlphaGo, a computer program that learned to play Go--a famously tricky game that exploits human intuition rather than clear rules of play--by studying games played by humans. Silver's latest creation, AlphaZero, learns to play board games including Go, chess, and Shogu by practicing against itself. Through millions of practice games, AlphaZero discovers strategies that it took humans millennia to develop. So could AI one day solve problems that human minds never could?
"It's very visible that technological sectors are now prioritising the implementation of AI in their everyday workforce." "We can see that the companies listed in the research are already using different types of AI-technology to improve the way they engage with their users and customers." Google's translation service, for example, uses AI tools such as machine learning and natural language processes to provide real-time translations, he explained.
Meet ANYmal--a four-legged robot whose name is pronounced "animal." The 73-pound dog-like machine is a Swiss-made contraption that, thanks to artificial intelligence, can run faster, operate with more efficiency, and reset itself after a spill more successfully than it could before its AI training. The robot, featured in a new study in the journal Science Robotics, learned not just with AI, but also through computer simulation on a desktop, a much faster approach than teaching a robotic new tricks in the real, physical world. In fact, simulation is roughly 1,000-times faster than the real world, according to the study. This isn't the only arena for which simulation is important: In the world of self-driving cars, time in simulation is a crucial way that companies test and refine the software that operates the vehicles.
Marketing has become "much more of a technology function," according to International Business Machines Corp. CMO Michelle Peluso. "The intersection of marketing and tech is so critical right now that there's a strong, great reason to be [at CES], to be exploring and thinking about what's coming." Marketing technology accounted for nearly a third of marketing expenses in 2018, according to a Gartner survey of more than 600 marketers in North America and the U.K., up from 22% the year earlier. Anticipation and hype around the ever-closer arrival of 5G wireless service dominated CES this year. The high speed of data transmission via 5G will be transformational across everything from phones to cars to augmented reality, Adobe Systems Inc.
If you've been fascinated with DeepMind's AlphaGo program, there's good news for you. A few Go enthusiasts have replicated the results of the AlphaGo Zero paper, using a few resources provided by Google. The developers are keen to stress that this project is in no way associated with the official AlphaGo program by DeepMind. It's an independent effort that is inspired by AlphaGo, just not affiliated to it. According to the developers, Minigo "is a pure Python implementation of a neural-network based Go AI, using TensorFlow".