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Chatbots will be able to teach children more than twice as fast as teachers can within the next decade, the so-called godfather of AI has predicted. Geoffrey Hinton, who won a Nobel Prize for his work on the technology, also claimed AI personal tutors would'be much more efficient and less boring'. Speaking at Gitex Europe, the British computer scientist said: 'It's not there yet, but it's coming, and so we'll get much better education at many levels.' AI personal tutors are already being trialled in UK schools, with the technology now able to talk directly to the student and adapt lesson plans to their knowledge level. The government has already funnelled millions of pounds into AI education initiatives – though it has claimed the technology will'absolutely not' replace teachers.

Scientist and physicist Geoffrey Hinton believes there could be a one in five chance that humanity will eventually be taken over by artificial intelligence. Hinton, a Nobel laureate in physics who's been dubbed the'godfather of AI', made the startling prediction in an April 1 interview with CBS News that was aired on Saturday morning. 'I'm in the unfortunate position of happening to agree with Elon Musk on this, which is that there's a 10 to 20 percent chance that these things will take over, but that's just a wild guess,' Hinton said. Besides his cost-cutting responsibilities in the federal government, Musk is the chief executive of xAI, the company that made the AI chatbot Grok. Musk has said AI will become smarter than the entire human race by 2029.

Anna Demming reports on what the prizes were awarded for and how finding connections between the two approaches to machine learning may help towards explaining how "black box" algorithms reach their conclusions. Few saw it coming when on 8th October 2024 the Nobel Committee awarded the 2024 Nobel Prize for Physics to John Hopfield for his Hopfield networks and Geoffrey Hinton for his Boltzmann machines as seminal developments towards machine learning that have statistical physics at the heart of them. The next day machine learning albeit using a different architecture bagged half of the Nobel Prize for Chemistry as well, with the award going to Demis Hassabis and John Jumper for the development of an algorithm that predicts protein folding conformations. The other half of the Chemistry Nobel was awarded to David Baker for successfully building new proteins. While the AI takeover at this year's Nobel announcements for Physics and Chemistry came as surprise to most, there has been some keen interest on how these apparently different approaches to machine learning might actually reduce to the same thing, revealing new ways of extracting some fundamental explainability from the generative AI algorithms that have so far been considered effectively "black boxes".