Pleasure. Please give a big applause, a warm welcome. The Nobel Prize laureate in physics, Sir Geoffrey Hinton. He is just a hero of mine. Thank you very much. Thank you for your work. And of course, the show is doing the interview is doing the Economist tech editor. Thank you. But it's all yours.
Thank you very much. Welcome. I don't think I need to make much of an introduction, but I was thinking how to introduce Jeff. And you've already heard he's a Nobel Prize laureate. So what can you say more than that? Well, let me talk about the science. I'm Alec Jar, the science editor at the Economist, and it's my pleasure to facilitate this conversation with Jeffrey. So Jeffrey is a British-Canadian computer scientist, cognitive psychologist, known for his work in artificial neural networks. And as you've heard, and as all the paraphernalia around his shows, he's also known as one of the godfathers of AI, which maybe you can talk to me about later. He's a professor emeritus at the University of Toronto and for 10 years from 2013 he also worked for Google, which he left in 2023. Jeffrey, really nice to meet you. It's a real pleasure to have you here. I just want to start with a bit of a vision from you. So people here are talking about all the companies and the technologies that come from various AI. My microphone still working. No.
Okay. It's back again. I'll shout if necessary. Anyway, they're all talking about the companies and the practical uses, but I'd like to start from you with some vision. So, can you in the most broad terms possible tell me what the few two or three most hopeful things you think AI will be doing in the next decade? What are the most exciting things for you? And in each case, would you mind just giving a sense of what you think actually happen in those things? Is the concrete example.
Okay. So, there's nothing new in what I'm going to say. I think AI will be amazing in healthcare. So, in healthcare, I made a prediction in 2016 that in the next 5 years AI would replace radiologists in interpreting medical scans.
How does that go? How's that?
I was wrong. I got the time scale wrong. It's beginning to happen now. Right now, I think there's over 250 applications approved by the FDA for using AI for interpreting medical scans. It's used in major cancer clinics. I think it'll still be another 5 years before it's replaced radiologists, maybe even longer. The medical profession is quite conservative, but it will be able to get more information out of medical scans. So one case we know about is interpreting fundus images of the retina where AI can see all sorts of things in those images that no ophthalmologist ever realized could be seen there.
And what about radiologists? What happens to them? Do they just treat more people or do they disappear altogether?
So radiologists will still do many other things like comfort people, plan what to do, although AI will of course be doing that as well. For quite a while it'll be a combination of a radiologist and an AI, and that will be more efficient than a radiologist alone. But the good news about healthcare is it's very elastic. If we can make doctors more efficient, we can all get a lot more healthcare, so there won't be unemployment, there'll just be a lot more healthcare, which is a good thing.
And in healthcare as well, apart from radiology, do you see other places like diagnosis, really complex diseases perhaps that can be made more treatable by AI?
There are two things there. Diagnosis will get much better. We already know from more than a year ago that if you take difficult cases to diagnose, then AI gets about 50% right and the doctors only get about 40% right. The combination gets about 60% right, and that'll save a lot of lives. In North America, about 200,000 people a year die of bad diagnosis. So it'll have a big effect there. And that's from AI from more than a year ago. So it'll get much better at that. It'll also of course be better at designing drugs. So we'll get much better treatments.
We're already seeing some of the early stage drugs designed by AI going through clinical trials. It is an exciting time for that and speeding up clinical trials as well. So that's one area. Medicine and healthcare. As if that wasn't enough. Is there another one you want to talk about?
Yes. The next area is education. So my university doesn't like me talking about this, but AI will be much better at tutoring people. So we already know that if you take a child and give them a personal tutor, they learn about twice as fast as in a classroom. And that's because a personal tutor understands what it is the child doesn't understand and tailors their explanations to what the child's understanding. AI should be able to do that even better because AIs will have had experience with millions of children to train on. This will come in the next 10 years or so. It's not there yet, but it's coming. And so, we'll get much better education at many levels. I think the last level where that'll happen is educating PhD students because that's more an apprenticeship. It's not so much teaching facts as teaching approaches. But it'll come there in the end too. We're already seeing it in companies for educating employees. So I work with a company called Valance that has a system called Nardia that teaches employees leadership skills. And we're going to see in all companies, I think, AI being used to educate employees. When I was at Google, I used to have to do training videos, which are very boring, on how to be polite and things.
To some extent, yes. I think interacting with an AI tutor would actually be much more efficient, less boring. People can attest to that in this room probably as well, having just tried personally tried out chatbots and things to ask questions. And obviously you have to be careful what you believe, but it's a good start, right? Already, and you can see where it's going.
Okay, give us one more hopeful case, the one that you think is going to be one of the most optimistic and visionary things AI will be doing in the next 10 years.
So I agree with Demis Hassabis, the leader of DeepMind, who for many years has said AI is going to be very important for making scientific progress. It's going to make scientific discoveries. There's one area in which that's particularly easy, which is mathematics because mathematics is a closed system. So you're going to get AIs that play mathematics. That is, they ask themselves, 'I wonder if I could prove this. I wonder if I could prove that.' And because this is a closed system, they can just try things out and see if they can prove them.
So we're talking the mathematical conjectures that are still yet to be proven by humans. Yes. Can be amenable to this.
And new conjectures they'll make too. And I think AI will get much better at mathematics than people. Maybe in the next 10 years or so. And within mathematics, it's much like things like Go or chess that are closed systems with rules where they can generate their own training data. So when they first taught Go to AIs, they would mimic the moves of human experts. And there's of course a limitation to that, which is you run out of human expert moves, and humans are only so good. But then they got what they call Monte Carlo rollout, where you say if I go here, he goes there. I go here, he goes there. Oh, whoops. That's bad for me. And you can learn from the Monte Carlo rollout. And you no longer need humans to tell you what good moves are. You can figure it out. It'll be the same in mathematics. And we'll get mathematical systems much better than people, I believe.
And they don't need breaks or food to eat. They can just carry on continuously and get to solutions much faster than humans might before they get tired. Right.
Yeah. I mean, in general then, mathematics is something you think will be amenable to this. But is science in general, research in general, so physics, chemistry, molecular biology, will all of these things you think be accelerated by the neural networks of the future?
Yes. I think what we'll see is little bits of those scientific enterprises will be accelerated early on and as time goes by more and more aspects will be accelerated. Yes.
And what will that world feel like to be in when AI is accelerating scientific progress? It feels like scientific progress is moving fast right now. If it gets even more exponential, what does that mean?
It could make life much better for everybody. If we share out the benefits of the increase in productivity, it'll be great for everybody. So, one example now already, if you get a full body MRI scan every year and you get AI to interpret it, basically you don't need to die of cancer anymore. You can detect almost all cancers when they're very small. And if you can detect them at stage one, you can normally just get rid of them. So Craig Venter, who was one of the people who sequenced the human genome, had a full body MRI and detected two very aggressive prostate cancers, much like Biden may have now, very early and was fine. So with AI doing the interpretation and doing better interpretation than people can do, we'll get things like that that'll basically get rid of people dying of cancer if you can afford the full body MRI.
I was going to say there's a 'could' in your statement and we'll come back to the equity and the sort of general how this stuff is going to roll out a bit later. Let's talk a bit about the AIs themselves now. So not just the applications. Are there things that you see models being able to do in let's say five years time that they can't do now?
I'm very cautious about making those predictions because of my false prediction in 2016 that we'd by now have AI radiologists replacing ordinary radiologists reading scans. It's very hard to see five years ahead. The best way to understand how hard that is is to look five years back. If you look five years back from now, we were just beginning to see things like GPT-2, which seemed amazing at the time because it could generate coherent text. We never had things like that before. The coherent text wasn't very good and was full of nonsense, but it was coherent. If we look at that now, it seems incredibly primitive. So I think the best I can say is the things we have now will seem incredibly primitive in 5 years time. So we'll be surprised.
