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.
Drive.ai is a Silicon Valley startup working on a kit to retrofit your ride If Drive.ai is a success, your first self-driving car might already be parked in the driveway. The Silicon Valley start-up, founded recently by a team of former Stanford University Artificial Intelligence Lab products, is working on a software kit that can be used to retrofit existing vehicles. "We started Drive.ai because we believe there's a real opportunity to make our roads, our commutes, and our families safer," the company announced in a statement on its blog, citing a statistic that more than one million people die each year worldwide in automobile accidents caused by human error. At its foundation, Drive.ai is looking to use deep learning -- which its founders consider the most effective form of artificial intelligence ever developed -- to key a breakthrough in a field that giant companies such as Google and General Motors have been trying to master for years. "Unlike other forms of AI, which involve programming many sets of rules, a deep learning algorithm learns more like a human brain.
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.
By Kailash Nadh These are exciting times for developments in mainstream artificial intelligence (AI). Self-driving cars are hitting the streets, companies like Microsoft, Apple and Google are integrating evergrowing "intelligence" into their services, and some of them making their cutting-edge AI tools available to the public. While AI as a term is familiar to the industry, deep learning is what's been in the limelight lately. Like numerous other techniques, deep learning is a subset of machine learning, which in turn falls under the much broader umbrella of AI, all of whose broad goals are to make computers do things outside of the box of precise programmed instructions. The idea itself is decades old, but resurgence in research and sheer advances in raw computational power over the last decade have made deep learning an attractive computational tool.