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Nobel physics prize goes to machine learning pioneers

US scientist John Hopfield and British-Canadian Geoffrey Hinton won the 2024 Nobel Prize in Physics for discoveries and inventions that laid the foundation for machine learning, the award-giving body said on Tuesday, October 8.

Hinton has been widely credited as a godfather of artificial intelligence and made headlines when he quit his job at Google last year to be able to more easily speak about the dangers of the technology he had pioneered.

Hopfield, 91, a professor emeritus at Princeton University, created an associative memory that can store and reconstruct images and other types of patterns in data, the Royal Swedish Academy of Sciences, which awards the prize, said.

The award comes with a prize sum of 11 million Swedish crowns ($1.1 million) which is shared by the two winners.

REUTERS VIDEO

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Transcript
00:00The Royal Swedish Academy of Sciences has today decided to award the 2024 Nobel Prize
00:07in Physics to John Hopfield, Princeton University, USA, and Geoffrey Hinton, University of Toronto,
00:17Canada, for foundational discoveries and inventions that enable machine learning with artificial
00:25neural networks.
00:27Good morning, Professor Hinton.
00:28Good morning.
00:29Please accept our warmest congratulations to receiving the Nobel Prize in Physics.
00:38Thank you very much.
00:39How do you feel right now?
00:43I'm flabbergasted.
00:44I had no idea this would happen.
00:47I'm very surprised.
00:49I think it will have a huge influence.
00:50It will be comparable with the Industrial Revolution, but instead of exceeding people
00:56in physical strength, it's going to exceed people in intellectual ability.
01:02We have no experience of what it's like to have things smarter than us, and it's going
01:09to be wonderful in many respects.
01:11In areas like health care, it's going to give us much better health care.
01:15In almost all interests, it's going to make them more efficient.
01:19People are going to be able to do the same amount of work with an AI assistant in much
01:23less time.
01:24It will mean huge improvements in productivity, but we also have to worry about a number of
01:31possible bad consequences, particularly the threat of these things getting out of control.
01:40This can be used for many kinds of applications, and one of them is, for example, processing
01:46image data.
01:47When you do medical X-rays or MRI images and so forth, doctors need to look for certain
01:56things in the image that can be a sign of a tumor or something, but you can train an
02:00artificial neural network on what are known to be tumors, images that you know this is
02:05a problem.
02:06You train the network, and then it can become very fast and efficient at finding this in
02:10images, and it can work much more quickly or assist the doctor and be much more certain
02:16in the diagnosis.
02:17It's so powerful that it can do things that also can be used for very negative things
02:24like creating images which look real but are totally fake, and imitating people's voices,
02:33and so you could have a conversation and think that you're talking to somebody on a phone,
02:38but no, it's just their voice, and somebody else is behind it.
02:42That kind of thing can be very dangerous, and it's important that with this new technology
02:48we understand how to use it.
02:50As with all technologies, it can be used for either good or bad, and that's up to us as
02:56human beings to decide how we should use it.

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