Geoff Hinton

We should be going for … radically new ideas.

WIRED: The recent boom of interest and investment in AI and machine learning means there’s more funding for research than ever. Does the rapid growth of the field also bring new challenges?

GH: One big challenge the community faces is that if you want to get a paper published in machine learning now it’s got to have a table in it, with all these different data sets across the top, and all these different methods along the side, and your method has to look like the best one. If it doesn’t look like that, it’s hard to get published. I don’t think that’s encouraging people to think about radically new ideas.

Now if you send in a paper that has a radically new idea, there’s no chance in hell it will get accepted, because it’s going to get some junior reviewer who doesn’t understand it. Or it’s going to get a senior reviewer who’s trying to review too many papers and doesn’t understand it first time round and assumes it must be nonsense.

Anything that makes the brain hurt is not going to get accepted. And I think that’s really bad.

What we should be going for, particularly in the basic science conferences, is radically new ideas. Because we know a radically new idea in the long run is going to be much more influential than a tiny improvement. That’s I think the main downside of the fact that we’ve got this inversion now, where you’ve got a few senior guys and a gazillion young guys.

 

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Artificial intelligence pioneer says throw it all away and start again

Geoffrey Hinton harbors doubts about AI’s current workhorse. (Johnny Guatto / University of Toronto)

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.

… 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”

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.”

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.”

Source: Axios

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