Modern AI grew from a 70-year-old bet that intelligence comes from brain-like networks rather than pure logic, and the man who championed it now worries the digital version may outperform the biological original.
From 'Is AI Hiding Its Full Power? With Geoffrey Hinton': • Is AI Hiding Its Full Power? With Geoffrey...
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What Geoffrey Hinton said
Written from the verified transcript and checked against it. Every figure links to the moment it was said.
Geoffrey Hinton, professor emeritus at the University of Toronto and an 'OG AI', traces the roots of AI back to the 1950s, contrasting a logic-based approach with a biological one inspired by brains. He credits early advocates like John von Neumann and Alan Turing. His own interest began in the 1960s with the idea of distributed memory over many brain cells. In the 1970s, as a graduate student, he simulated theories of brain function on digital computers, though he says we still do not know how the brain adjusts connection strengths. Hinton explains neural networks by analogy to the gas laws, describing how macroscopic symbols correspond to patterns of microscopic neural activity. He became nervous in early 2023 about the possibility that digital intelligence might surpass analog biological intelligence, a theme he promises to explore further.
Key takeaways
Hinton says AI's origins date to the 1950s, not the 1990s, with a logic-based vs. biological paradigm split.
Hinton became 'really nervous' in early 2023 that digital intelligence might be better than analog human intelligence.
Hinton admits we still don't understand how the brain gets information to change connection strengths.
Hinton compares neural networks to gas laws, with macroscopic symbols arising from microscopic neural activity.
Hinton says John von Neumann and Alan Turing were early backers of the biological approach to AI.
Numbers and commitments
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What it refers to
Type
At
2023
Year Hinton became nervous about digital intelligence
OG AI. And some people have called you the godfather of AI, of artificial intelligence. And let's just go straight off the top here. When we think of the genesis of AI as it is currently manifested, it feels like large language models took everybody by storm. They sort of showed up and everybody was freaking out, celebrating, dancing in the streets or crying in their pillows. That happened, we noticed, a couple of years ago. So I'm just wondering what got you started on this path many, many years ago. My records show it goes back to the 1990s. Is that correct?
Right. The founders of AI at the beginning in the 1950s, there were two views of how to make an intelligent system. One was inspired by logic. The idea was that the essence of intelligence is reasoning, and in reasoning what you do is you take some premises and you take some rules for manipulating expressions and you derive some conclusions. So it's much like mathematics where you have an equation, you have rules for how you can tinker with both sides or combine equations and you derive new equations. And that was kind of the paradigm they had. There was a completely different paradigm that was biological. And that paradigm said, look, the intelligent things we know have brains. We have to figure out how brains work. And the way they work is they're very good at things like perception. They're quite good at reasoning by analogy. They're not much good at reasoning. You have to get to be a teenager before you can do reasoning really. So we should really study these other things they do and we should figure out how big networks of brain cells can do these other things like perception and memory. Now a few people believed in that approach and among those few people were John von Neumann and Alan Turing. Unfortunately they both died young. Turing possibly with the help of British intelligence.
Um, a few things. When I was at high school in the early 1960s or mid 1960s, I had a very smart friend who was a brilliant mathematician and used to read a lot, and he came into school one day and talked to me about the idea that memories might be distributed over many brain cells instead of in individual brain cells.
Gabor was active. And so the idea of distributed memory got me very interested, and ever since then I've been wondering how the brain stores memories and actually how it works.
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