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.
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.
Academic publisher Springer Nature has unveiled what it claims is the first research book generated using machine learning. The book, titled Lithium-Ion Batteries: A Machine-Generated Summary of Current Research, isn't exactly a snappy read. Instead, as the name suggests, it's a summary of peer-reviewed papers published on the topic in question. It includes quotations, hyperlinks to the work cited, and automatically generated references contents. It's also available to download and read for free if you have any trouble getting to sleep at night.
Stacks of vertical shelves weave around each other in what looks like an intricately choreographed – if admittedly inelegant – ballet that has been performed since 2014 in Amazon's cavernous warehouses. The shelves, each weighing more than 1,000 kg, are carried on the backs of robots that resemble giant versions of robotic vacuum cleaners. The robots cut down on time and human error, but they still have things to learn. Once an order is received, a robot goes to the shelf where the ordered item is stored. It picks up the shelf and takes it to an area where the item is removed and placed in a plastic bin, ready for packing and sending to the customer.
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?