Artificial Intelligence
Following AlphaGo's defeat of the world champion Go player, Geoffrey Hinton, the godfather of "deep learning," gave some recent feedback to a Canadian publication. Although he doesn't explicitly state that neural networks will match the human brain's ability in five years, he refuses to project beyond that timeline.
Geoffrey Hinton, an artificial intelligence expert who splits his time between Google and the University of Toronto, believes machines could match human abilities in five years.
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Hinton is internationally distinguished for his work on artificial neural nets, especially how they can be designed to learn without the aid of a human teacher. Such systems are now being explored and expanded upon at Google DeepMind, the company behind the recent historic victory of artificial intelligence over the best human player in the world at the game of Go.During an interview with Maclean's Hinton said the most powerful machines are still about a million times smaller than the human brain.
"My belief is that we’re not going to get human-level abilities until we have systems that have the same number of parameters in them as the brain," says Hinton. "So in the brain, you have connections between the neurons called synapses, and they can change. All your knowledge is stored in those synapses. You have about 1,000-trillion synapses—10 to the 15, it’s a very big number. So that’s quite unlike the neural networks we have right now."
"The neural networks provides you with good intuitions, and that’s what the other programs were lacking, and that’s what people didn’t really understand computers could do."
When asked how long it will take for Moore's Law to catch computers and systems like AlphaGo up to human levels, Hinton responed, that it will be more than five years away."I refuse to say anything beyond five years because I don’t think we can see much beyond five years. And you look at these past predictions like there’s only a market in the world for five computers [as allegedly said by IBM founder Thomas Watson] and you realize it’s not a good idea to predict too far into the future," Hinton replied.
When asked how AlphaGo was able to defeat Lee Sedol and what it meant for the field of AI, Hinton replied:
It [Go] relies on a lot of intuition. The really skilled players just sort of see where a good place to put a stone would be. They do a lot of reasoning as well, which they call reading, but they also have very good intuition about where a good place to go would be, and that’s the kind of thing that people just thought computes couldn’t do. But with these neural networks, computers can do that too. They can think about all the possible moves and think that one particular move seems a bit better than the others, just intuitively. That’s what the feed point neural network is doing: it’s giving the system intuitions about what might be a good move. It then goes off and tries all sorts of alternatives. The neural networks provides you with good intuitions, and that’s what the other programs were lacking, and that’s what people didn’t really understand computers could do.
He also commented on whether people should fear AI for making people obsolete and taking over all our labour. "It’s hard to predict beyond five years. I’m pretty confident it won’t happen in the next five years, and I’m fairly confident that it won’t be something I’m going to have to deal with. But it’s something people should definitely be thinking about."
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