'We've discovered the secret of immortality. The bad news is it's not for us': why the godfather of AI fears for humanity

The first thing Geoffrey Hinton says when we start talking, and the last thing he repeats before I turn off my recorder, is that he left Google, his employer of the past decade, on good terms. "I have no objection to what Google has done or is doing, but obviously the media would love to spin me as'a disgruntled Google employee'. It's an important clarification to make, because it's easy to conclude the opposite. After all, when most people calmly describe their former employer as being one of a small group of companies charting a course that is alarmingly likely to wipe out humanity itself, they do so with a sense of opprobrium. But to listen to Hinton, we're about to sleepwalk towards an existential threat to civilisation without anyone involved acting maliciously at all. Known as one of three "godfathers of AI", in 2018 Hinton won the ACM Turing award – the Nobel prize of computer scientists for his work on "deep learning". A cognitive psychologist and computer scientist by training, he wasn't motivated by a desire to radically improve technology: instead, it was to understand more about ourselves. "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). 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. 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. Until recently, neural nets were a curiosity, requiring vast computer power to perform simple tasks worse than other approaches. 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. "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. A "biological intelligence" such as ours, he says, has advantages. It runs at low power, "just 30 watts, even when you're thinking", and "every brain is a bit different". That means we learn by mimicking others. But that approach is "very inefficient" in terms of information transfer. Digital intelligences, by contrast, have an enormous advantage: it's trivial to share information between multiple copies. "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.