It's a Wednesday night in North East London and upstairs at the Vortex Jazz Club the machines are calling the shots. The human spectators are jiggling happily in their seats, and the musicians are undeniably flesh-and-blood, sweating and straining at their instruments. But the music itself is the product of electronic brains -- trained to soak up the music of great artists and strain out new melodies. This is "the first concert consisting almost entirely of music composed by artificial intelligence" says professor Geraint Wiggins of Queen Mary's University at the beginning of the evening. In about a few minutes we'll be listening to Medieval chants, Baroque chorales, and jazz and pop -- all made by artificial intelligence with the help of computer scientists who programmed the evening's "composers."
Computer vision is by no means a new idea, there were automatic number plate recognition systems as early as the 1960s but deep learning is one of the key technologies that have expanded its potential. Early computer vision systems were algorithm-based, removing the color and texture of a viewed object in favor of identifying basic shapes and edges, and narrowing down what they might represent. This stripped back the amount of data that had to be dealt with and enabled the processing power to be concentrated on the essentials. Deep learning flipped this process on its head, instead of algorithmically working out that a triangle of certain dimensions was statistically probable to be a road sign, why didn't we look at a whole heap of road signs and learn to recognize them? Using deep learning techniques, the computer can look at hundreds and thousands of pictures, e.g., an electric guitar and start to learn what an electric guitar looks like in different configurations, contexts, levels of daylight, backgrounds and environments.
When Gary Kasparov lost to chess computer Deep Blue in 1997, IBM marked a milestone in the history of artificial intelligence. On Wednesday, in a research paper released in Nature, Google earned its own position in the history books, with the announcement that its subsidiary DeepMind has built a system capable of beating the best human players in the world at the east Asian board game Go. Go, a game that involves placing black or white tiles on a 19x19 board and trying to remove your opponents', is far more difficult for a computer to master than a game such as chess. DeepMind's software, AlphaGo, successfully beat the three-time European Go champion Fan Hui 5–0 in a series of games at the company's headquarters in King's Cross last October. Dr Tanguy Chouard, a senior editor at Nature who attended the matches as part of the review process, described the victory as "really chilling to watch".
Lip reading is a tricky business. Test results vary, but on average, most people recognize just one in 10 words when watching someone's lips, and the accuracy of self-proclaimed experts tends to vary -- there are certainly no lip-reading savants. Now, though, some researchers claim that AI techniques like deep learning could help solve this problem. After all, AI methods that focus on crunching large amounts of data to find common patterns have helped improve audio speech recognition to near-human levels of accuracy, so why can't the same be done for lip reading? The researchers from the University of Oxford's AI lab have made a promising -- if crucially limited -- contribution to the field, creating a new lip-reading program using deep learning.
Alphabet Inc., the parent company of Google, announced a software program Wednesday called AlphaGo that successfully beat European Go champion Fan Hui on a full-sized board five times in a row. Developed by researchers at Alphabet's DeepMind company, AlphaGo is considered a major landmark in the development of artificial intelligence. The game of Go, played on a 19 by 19 grid, has long thwarted computer scientists due to the vast number of available moves. "The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves," says the abstract to a paper about AlphaGo published in Nature. "This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away."