The Low-Down: From Not Working To Neural Networking: How AI Went From Chronic Underachiever To The Next Big Thing

Technology and data made possible advances in...technology and data. JL The Economist reports: New techniques have made training deep networks feasible. This takes a lot of number-crunching power, which became available when several AI research groups realised that graphical processing units (GPUs), the specialised chips used in PCs and video-games consoles to generate fancy graphics, were also well suited to running deep-learning algorithms. HOW HAS ARTIFICIAL intelligence, associated with hubris and disappointment since its earliest days, suddenly become the hottest field in technology? The term was coined in a research proposal written in 1956 which suggested that significant progress could be made in getting machines to "solve the kinds of problems now reserved for humans…if a carefully selected group of scientists work on it together for a summer". That proved to be wildly overoptimistic, to say the least, and despite occasional bursts of progress, AI became known for promising much more than it could deliver.