Name: "Godfather of AI" Geoffrey Hinton: The 60 Minutes Interview
Uploaded: 2025-08-03T01:16:45.000Z
Duration: 13 min 12 s
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Whether you think artificial intelligence will save the world or end it, you have Geoffrey Hinton to thank.

Hinton has been called the godfather of AI, a British computer scientist whose controversial ideas helped make advanced artificial intelligence possible and so change the world.

Hinton believes that AI will do enormous good, but tonight he has a warning.

He says that AI systems may be more intelligent than we know and there's a chance the machines could take over, which made us ask the question.

I think we're moving into a period when, for the first time ever, we may have things more intelligent than us.

You believe these systems have experiences of their own and can make decisions based on those experiences?

I think they probably don't have much self-awareness at present.

So in that sense, I don't think they're conscious.

And so human beings will be the second most intelligent beings on the planet?

Jeffrey Hinton told us the artificial intelligence he set in motion was an accident born of a  In the 1970s at the University of Edinburgh, he dreamed of simulating a neural network on a computer simply as a tool for what he was really studying, the human brain.

But back then, almost no one thought software could mimic the brain.

His PhD advisor told him to drop it before it ruined his career.

Hinton says he failed to figure out the human mind, but the long pursuit led to an artificial version.

It took much, much longer than I expected.

It took like 50 years before it worked well.

At what point did you realize that you were right about neural networks and most everyone else was wrong?

In 2019, Hinton and collaborators Jan LeCun on the left and Yoshua Bengio won the Turing Award, the Nobel Prize of Computing.

To understand how their work on artificial neural networks helped machines learn to learn, let us take you to a game.

This is Google's AI lab in London, which we first showed you this past April.

Geoffrey Hinton wasn't involved in this soccer project, but these robots are a great example of machine learning.

The thing to understand is that the robots were not programmed to play soccer.

Hinton and his collaborators created software in layers with each layer handling part of the problem.

When, for example, the robot scores, a message is sent back down through all of the layers that says that pathway was right.

Likewise, when an answer is wrong, that message goes down through the network.

So correct connections get stronger, wrong connections get weaker, and by trial and error, the machine teaches itself.

You think these AI systems are better at learning than the human mind?

And at present, they're quite a lot smaller.

So even the biggest chatbots only have about a trillion connections in them.

The human brain has about a hundred trillion.

And yet, in the trillion connections in a chatbot, it knows far more than you do in your hundred trillion connections, which suggests it's got a much better way of getting knowledge into those connections.

A much better way of getting knowledge that isn't fully understood.

We have a very good idea of sort of roughly what it's doing.

But as soon as it gets really complicated, we don't actually know what's going on any more than we know what's going on in your brain.

What do you mean we don't know exactly how it works?

What we did was we designed the learning algorithm.

That's a bit like designing the principle of evolution.

But when this learning algorithm then interacts with data, it produces complicated neural networks that are good at doing things, but we don't really understand exactly how they do those things.

What are the implications of these systems autonomously writing their own computer code and executing their own computer code?

So one of the ways in which these systems might escape control is by writing their own computer code to modify themselves.

And that's something we need to seriously worry about.

What do you say to someone who might argue, if the systems become malevolent, just turn them off?

They will be able to manipulate people, right?

And these will be very good at convincing people, because they'll have learned from all the novels that were ever written, all the books by Machiavelli, all the political connivances.

They'll know all that stuff, they'll know how to do it.

Know-how of the human kind runs in Geoffrey Hinton's family.

His ancestors include mathematician George Boole, who invented the basis of computing, and George Everest, who surveyed India and got that mountain named after him.

But as a boy, Hinton himself could never climb the peak of expectations raised by a domineering father.

Every morning when I went to school, he'd actually say to me as I walked down the driveway, get in there pitching, and maybe when you're twice as old as me, you'll be half as good.

He knew a lot more about beetles than he knew about people.

When he died, we went to his study at the university, and the walls were lined with boxes of papers on different kinds of beetle.

And just near the door, there was a slightly smaller box that simply said, not insects.

And that's where he had all the things about the family.

Today, at 75, Hinton recently retired after what he calls 10 happy years at Google.

Now, he's professor emeritus at the University of Toronto, and he happened to mention he has more academic citations than his father.

Some of his research led to chatbots like Google's Bard, which we met last spring.

We asked Bard to write a story from six words.

The shoes were a gift from my wife, but we never had a baby.

Bard created a deeply human tale of a man whose wife could not conceive, and a stranger who accepted the shoes to heal the pain after her miscarriage.

Chatbots are said to be language models that just predict the next most likely word based on probability.

You'll hear people saying things like, they're just doing autocomplete.

They're just trying to predict the next word.

Well, it's true they're just trying to predict the next word.