Professor Emeritus, University of Toronto
He Predicted AI Would Wipe Out Radiologists | Nobel Prize Winner - Geoffrey Hinton
Full episode: https://youtu.be/p7t1Q_p2gZs In 2016, Geoffrey Hinton — the Nobel Prize-winning Godfather of AI — stood on a ...
About Geoffrey Hinton
Geoffrey Hinton, the Nobel Prize-winning computer scientist often called a "godfather of AI," has stated in multiple recent interviews that he believes current AI systems are already conscious. He said he rarely discusses this view publicly because it "puts people off from the other safety messages." Hinton described the common model of consciousness as "as wrong as the belief that people were designed by God" and argued that anyone who uses a chatbot regularly knows the systems understand language, calling the opposing "stochastic parrot" argument "complete nonsense." Hinton has also discussed his regret about the technology's trajectory, saying he is "quite unhappy" and that society is not doing enough work to contain risks. He cited potential massive unemployment and the longer-term risk of AI becoming much smarter than humans, noting there are few examples of a much smarter thing being controlled by a much less smart thing. He reflected on his 2016 prediction that radiologists would stop reading scans within five years, acknowledging it was wrong due to the elasticity of healthcare and his incomplete understanding of radiologists' roles. Hinton said he has become slightly more optimistic in the past year or two about the possibility of designing AI systems that care about humans or that act only as oracles, but he cautioned that predicting the future beyond a few years is like "looking into fog."
Transcript (40 segments)
✨ AI-enhanced transcript with speaker attribution
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Geoffrey Hinton0:00
So I predicted in 2016 that in about 5 years radiologists wouldn't be reading scans anymore.
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Interviewer0:06
Correct.
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Geoffrey Hinton0:07
Okay, there's a whole bunch of reasons why that was a bad prediction. The first is that health care is elastic. So if you could do more scans and get more scans read, there'd be a lot more scans happening. And that's one thing that's happening. So we a fraction of the cost of a significant fraction of the cost of doing a scan is the cost of the radiologist interpreting it. As AI gets to help more and more radiologists interpret scans, we can interpret them faster and faster for less and less money. They're getting more efficient. And you would have thought that would mean you needed less radiologists, but actually what it means is you get more scans. So that aspect of the prediction was wrong. A second thing that was wrong was I didn't know enough about radiologists and what they do. And that was because I had a former student who had an MD and then did a physics PhD with me on something called Boltzmann machines. And he didn't particularly like people. So he got a job as a radiologist just interpreting scans. And he was my model for radiologist. All he did was interpret scans. He never talked to people. And that's what was going to get replaced. And that is now becoming replaced. So I think there's now of the order of a hundred AI systems for interpreting scans that have been federally approved. And they're being used a lot by radiologists.
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Interviewer1:34
Yes.
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Geoffrey Hinton1:34
And I think as time goes by, they're going to get better. The radiologists aren't going to get better. They're going to get better because they can see a lot more data than the radiologists. So it's happening. It's just happening in a much slower time scale than I predicted.
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Interviewer1:50
But there were... But let's go to what you said though, which is that you can end up doing a lot more.
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Geoffrey Hinton1:55
Yes.
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Interviewer1:56
And okay, so wait, hold on.
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Geoffrey Hinton1:59
There will be a lot more scans. There will be more and more scans, and nearly all of them will be done by AI.
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Interviewer2:07
And so you're saying I'm a radiologist prediction, I'm just early.
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Geoffrey Hinton2:13
Yes, but I was way early because I didn't understand enough. The radiologist will still be doing other things. They'll still be discussing treatments with people for example.
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Interviewer2:23
So are you still the belief that there's going to be mass unemployment of radio... or give me a look at finally hit this point. Do you think we're going to have less radiologist than we have today or more?
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Geoffrey Hinton2:33
I don't know for sure, but when I was still... I didn't think that was a public statement I made. It was just a lecture at a hospital.
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Interviewer2:40
Sorry.
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Geoffrey Hinton2:41
And here we are talking about it today. People picked up on it, and I still think in terms of reading scans, that'll be done more and more by AI. And in the end, AI will be doing nearly all the scans. Maybe in a few very tricky cases, radiologists will be consulted. But radiologists of course do other things, and I think they'll continue to do other things.
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Interviewer3:07
The argument to be made on the side of AI not causing mass job loss is that this similar equation will be applied to all different parts of the economy.
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Geoffrey Hinton3:20
Okay, so you have to look at whether some kind of employment has an elastic market or a non-elastic market. So for example, if you take people in call centers, when you call up to complain about your bill or to see if you can get a cheaper account, stuff like that, that's not so elastic. AI will replace all of them. It'll know much better on what the correct answers are. Often they don't know the right answer. They're poorly trained and badly paid. And AI can just do a better job. They're out of work.
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Interviewer3:55
Well, let me disagree with you on this one. And we could sort of go back and forth on this. Or I won't say I'll disagree completely because I don't know what's going to happen. But I'll give the argument of those working on AI for customer service. They say that what's happened is the average call time when you have AI. So, AI handles the level one inquiries, right? The basic can you reset my password type of stuff. And anything deeper is handled by person. And...
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Geoffrey Hinton4:21
Right.
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Interviewer4:22
...is to get the average call time as short as possible because you were kind of handling so many of these level one inquiries that you just want to get a person on the phone, off the phone, solve their problem. Now, they see the average call time is expanding because customer service, you're the front line of the business. You matter a lot when you're having a conversation with a customer. Now, you could spend a little bit more time on the phone with someone and actually add value to the business as opposed to just take care of a problem.
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Geoffrey Hinton4:53
I think what you'll see is AI will end up spending a lot more time on the phone.
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Interviewer4:56
Oh, god.
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Geoffrey Hinton4:58
For example, if you ask, 'Who's more empathetic, a doctor or an AI doctor? A real doctor or an AI doctor?' People judge the AI doctors as much more empathetic.
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Interviewer5:13
That's terrifying. I mean, we could go back and forth on this for a while. So, I'll just say the one reason you might end up seeing that, I'll just throw this out there, is because doctors are just so scheduled. They have to do so many notes, so much paperwork. And they have to see so many patients in a day. So, maybe the argument is you sort of let the AI take over some of that stuff and then people will want to be seen by human doctors because the system won't squeeze them as much as they are. They're actually going to make time for them to see patients.
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Geoffrey Hinton5:49
That may be, but also if you think about family doctors, for example, the front line.
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Interviewer5:54
Yes.
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Geoffrey Hinton5:55
Would you rather see a family doctor who's maybe seen 10,000 people or would you rather see a family doctor who's seen 100 million people? Because if you have some rare disease, your family doctor's probably never seen it. Whereas a doctor who's seen 100 million people has probably seen dozens of cases of it. They're going to be much better at diagnosis. And already we know that AI systems are better than doctors at diagnosis.
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Interviewer6:21
I think you're winning this debate and this hurts a little bit because my wife is in family medicine. Family nurse. I think she'll still... you'll still have to have somebody vaccinate people. I would hope unless the robots do that.
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Geoffrey Hinton6:32
I always thought vaccination is something a robot could actually do quite well.
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Interviewer6:37
Uh, one note on Demis. Last year around this time I spoke with him. He told me he believes that AGI is more than five years away. Not much more, but more than five years away. This week, the week that we were recording, he said when we look back in this time, I think we will realize that we were standing in the foothills of the singularity.
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Geoffrey Hinton6:58
I don't know exactly what that metaphor means, but I think he's indicating it's coming faster than he thought. Of course it's jagged. So it's not like it'll get smarter than people or as smart as people at all things at exactly the same time. It's already way better than us at general knowledge. These AIs know thousands of times more than any one person. It's way better than us at playing games. It's already way better than almost all of us at math. And it may soon be better than all of us at math. It's still worse than us at some things. So it's very jagged. So the whole concept of AGI that it's going to be equal to people at everything all at the same time doesn't really make sense to me. It's going to be better at some things, worse at other things. But right now I would say we're at about... we're close to AGI because if I ask a chatbot, I can ask it any question and most of the time it'll answer at the level of a not very good expert. It'll be much better than me at anything I don't know a lot about.
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Interviewer8:02
Mhm.
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Geoffrey Hinton8:03
So in that sense we really reached AGI.
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Interviewer8:06
In your estimation, you talked about how it's moved faster than you expected. What do you think has enabled it to do it? Is it techniques? Is it the fact that there's been this data center rush? And what didn't you anticipate about the progress here?
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Geoffrey Hinton8:26
It's a combination. Obviously there's been huge resources put into it. For most of the history of neural networks since the 1950s, there were just a few people working on them with modest resources. Over the last few years we've seen hundreds of billions of dollars, maybe trillions of dollars put into AI. So that's certainly one factor. We've also seen a lot of progress in the engineering. So without major conceptual breakthroughs, the engineering's become much more efficient. So things that were sort of inconceivable a few years ago, they can now run. We've also seen new ideas, but mainly since transformers, it's been much better hardware, many more resources, better engineering, and many more talented people. So 20 years ago, there were a few hundred people doing research on neural networks in the whole world. Now it's more like a million, I guess. There's lots and lots of people.
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Interviewer9:35
And it's astonishing how much of that resource addition has happened in the last two years.
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Geoffrey Hinton9:40
Yeah.
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Interviewer9:40
So, we might just be at the beginning of what's happening here.
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Geoffrey Hinton9:43
Yes, and the thing to remember always is that the AGI we have today, or sorry, the AI we have today is not nearly as good as the AI we'll have in a few years' time.
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Interviewer9:53
That's right.