![]() “You need to imagine something more intelligent than us by the same difference that we’re more intelligent than a frog. And I don’t know any examples of more intelligent things being controlled by less intelligent things – at least, not since Biden got elected. “I thought it would happen eventually, but we had plenty of time: 30 to 50 years. ![]() Once he accepted that we were building intelligences with the potential to outthink humanity, the more alarming conclusions followed. So the good news is, we’ve discovered the secret of immortality. “You pay an enormous cost in terms of energy, but when one of them learns something, all of them know it, and you can easily store more copies. Digital intelligences, by contrast, have an enormous advantage: it’s trivial to share information between multiple copies. But that approach is “very inefficient” in terms of information transfer. It runs at low power, “just 30 watts, even when you’re thinking”, and “every brain is a bit different”. “In trying to think about how the brain could implement the algorithm behind all these models, I decided that maybe it can’t – and maybe these big models are actually much better than the brain,” he says.Ī “biological intelligence” such as ours, he says, has advantages. ![]() But in the last decade, as the availability of processing power and vast datasets has exploded, the approach Hinton pioneered has ended up at the centre of a technological revolution. Until recently, neural nets were a curiosity, requiring vast computer power to perform simple tasks worse than other approaches. In trying to model how the human brain works, Hinton found himself one of the leaders in the field of “neural networking”, an approach to building computer systems that can learn from data and experience. Looming slightly over me – he prefers to talk standing up, he says – the tone is uncannily reminiscent of a university tutorial, as the 75-year-old former professor explains his research history, and how it has inescapably led him to the conclusion that we may be doomed. “For the last 50 years, I’ve been trying to make computer models that can learn stuff a bit like the way the brain learns it, in order to understand better how the brain is learning things,” he tells me when we meet in his sister’s house in north London, where he is staying (he usually resides in Canada).
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