Geoffrey Hinton: AI is more human than you think

杰弗里·辛顿:人工智能比你想象的更接近人类。

Babbage from The Economist

2025-03-13

37 分钟

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Geoffrey Hinton is one of the “godfathers” of artificial intelligence, critical in the development of deep learning, backpropagation and much more. In 2024 he was awarded the Nobel prize in physics in recognition of his immense contributions to the field of computer science. Not bad for someone who started his career with the aim of understanding the human brain. Despite his role in its creation, though, Professor Hinton has been surprised by the rapid development of the technology. He's now convinced that artificial neural networks can think, reason and understand the world in a way that could eventually be superior to our own brains. Professor Hinton joins Alok Jha, The Economist's science and technology editor, to discuss why he thinks artificial intelligence is much more human than it seems.  For more on this topic, check out our series on the science that built the AI revolution. We'd also recommend the most recent episode of The Weekend Intelligence, which investigated the role of the human data-labellers who made deep learning possible. Transcripts of our podcasts are available via economist.com/podcasts. Listen to what matters most, from global politics and business to science and technology—subscribe to Economist Podcasts+.
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  • The Economist.

  • So let's check the...

  • I've got a Yeti microphone, a great big one,

  • which should be good quality and it's set so that it should not pick up background noise.

  • Geoffrey Hinton has always wanted to understand how the human brain learns.

  • And that's because, well, I'll let him tell you.

  • What was the reason you got interested?

  • Why the brain?

  • Because that's how we work.

  • That's a bit like saying, why are you interested in people?

  • Everyone has their own reason, right?

  • Well, it was mainly to understand.

  • And I was a teenager, I wanted to understand how people worked.

  • And being a scientist,

  • it seemed to me that you're never going to understand that unless you understand how the brain works.

  • You'd think that this keen interest would have led him into a career in biology.

  • But instead, he took a different path.

  • into computer science.

  • In the mid 20th century,

  • computer scientists were working out how to replicate the human brain's complex interconnected networks of neurons in software.