Pioneer of key AI technique casts doubt on the technology's future
Geoffrey Hinton, co-author of the 1986 paper that brought the back-propagation neural network to the attention of the AI community, has said that the technique should now be jettisoned. Back-propogation uses labelled data to train a network, reweighting the calculations made by its artificial "neurons" repeatedly until the labels produced as outputs match the labels on the data used for the training inputs. Hinton now suggests that that this is not how the human brain works, and that back-propagation needs to be replaced with "unsupervised" learning methods to move AI forward.
In 1986, Geoffrey Hinton co-authored a paper that, three decades later, is central to the explosion of artificial intelligence. But Hinton says his breakthrough method should be dispensed with, and a new path to AI found.

Speaking with Axios on the sidelines of an AI conference in Toronto on Wednesday, Hinton, a professor emeritus at the University of Toronto and a Google researcher, said he is now "deeply suspicious" of back-propagation, the workhorse method that underlies most of the advances we are seeing in the AI field today, including the capacity to sort through photos and talk to Siri. "My view is throw it all away and start again," he said.

The bottom line: Other scientists at the conference said back-propagation still has a core role in AI's future. But Hinton said that, to push materially ahead, entirely new methods will probably have to be invented. "Max Planck said, 'Science progresses one funeral at a time.' The future depends on some graduate student who is deeply suspicious of everything I have said."

How it works: In back propagation, labels or "weights" are used to represent a photo or voice within a brain-like neural layer. The weights are then adjusted and readjusted, layer by layer, until the network can perform an intelligent function with the fewest possible errors.

But Hinton suggested that, to get to where neural networks are able to become intelligent on their own, what is known as "unsupervised learning," "I suspect that means getting rid of back-propagation."

"I don't think it's how the brain works," he said. "We clearly don't need all the labeled data."