The “Godfather of AI,” Dr. Geoffrey Hinton, on AI’s Existential Risk

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Speaker 1: What would you say if you were able to get in a room with Sam Altman and Elon Musk at a table, which seems even less likely than Putin and Zelensky meeting face to face.

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Speaker 2: I'd say, you know perfectly well that the stuff you're developing has a good chance of wiping out people.

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Speaker 3: Hi.

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Speaker 1: Everyone, I'm Katie Kuric, and this is next question. When you hear the Moniker Godfather of AI, you might think of some levolent sci fi character, But in the real world, this title belongs to doctor Jeffrey Hinton, my guest today. He is the scientist whose early work on neural networks cracked to open the field of artificial intelligence. If that sounds like it's over your head, well honestly it was a bit over mine, but Hinton graciously explains it for lay people like us. Last year, doctor Hinton and another guy named doctor John Hopfield received the twenty twenty four Nobel Prize for physics and a word that honestly kind of surprised them, given the fact that they're not really physicist. But the prize is opening doors, he told us. In fact, he has plans to meet with the Pope very soon talk about access right. So in this interview we talk about his life's work, some of the pressures he faced as a young person, and the remarkable family he comes from, talk about brainiacs, wow, and why he believes the choices we make right now could actually shape the fate of humanity. So here's my conversation with doctor Jeffrey Hinton. Doctor Jeffrey Hinton, thank you so much for spending some time with me today. It's a real honor to be able to.

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Speaker 3: Talk to you.

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Speaker 2: Thank you very much for inviting me.

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Speaker 1: I know that last year you were awarded the twenty twenty four Nobel Prize in Physics jointly with someone named John J. Hotfield, for your quote foundational discoveries and inventions that enable machine learning with artificial neural networks. What did it mean to you, doctor Hinton, to be honored in this way and have your work recognized.

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Speaker 2: Well, I was actually very surprised that I got it because it's in physics and I don't do physics. But it means a lot to get a Nobel Prize for every scientist, that's kind of the highest honor you can get.

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Speaker 1: I think they probably, as you have said in the past, doctor Hinton need to have a Nobel Prize for computer science.

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Speaker 2: There's something called the Churing Award which is meant to be like the Nobel Prize for computer science. But basically Nobel made some prizes in his will and they don't like to create anymore. They created one more in economics, but they don't want to dilute it any more.

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Speaker 1: And I know you won the Touring Prize in twenty nineteen, so that was an additional honor given to you. In an interview last fall, you were hoping that the Nobel Prize would mean that your views on artificial intelligence and the role it will play in the future and is in playing now for that matter, would be taken more seriously. Have you found that to be the case.

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Speaker 2: Yes, I think that is true. I think more people are willing to talk to me. I managed to talk to somebody on the Chinese polit Bureau. I've been talking with Bernie Sanders, and in September I may get to talk to the Pope.

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Speaker 3: Wow.

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Speaker 1: So you're really spreading your concerns far and wide. Do you feel that when you have these meetings, doctor Hinton, that a people take you seriously, which I'm sure they do, But B do I feel as if you're making progress, that these conversations may in fact lead to real change or policy changes that will reflect your concerns.

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Speaker 2: Yes, I think we are making progress. My main concern has been not the short term risks of bad actors misusing AI, which are very serious, but the longer term risk of AI itself taking over. And I think until quite recently, almost everybody thought that was just science fiction. We didn't really have to worry about AI becoming smarter than us and taking over from us. But I think now many people have come to realize that's a very real risk. So most of the experts believe that sometime between five and twenty years from now, AI will get smarter than people. And when it gets smarter, it won't get a little bit smarter, to'll get a lot smarter. And the problem is, we know very few examples of smarter things being controlled by less smart things. Not small differences intelligence, like between a politician and a scientist, for example, but big difference is intelligence. There's very few examples. In fact, the only example I know is a mother and child, a mother and baby.

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Speaker 1: When I read about that time frame, doctor Hinton, I have to say, I was pretty freaked out. I mean, we're talking about not the distant future. We're talking about the very near future, aren't we.

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Speaker 2: Yes, It's important to realize nobody has a good way of estimating these things, so all of the estimates a just guesses, But there's sort of a consensus among experts between five and twenty years from now, it's quite likely that we'll get things a lot smarter than us.

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Speaker 1: I want to talk to you about the dangers and what you've been discussing with various people. But first I'd like people to understand a little bit about your background, which is quite extraordinary. You come from a family of I would say, overachievers, not to mention brilliant minds. Your great great grandfather was someone named George Boole, who developed Boolean algebra. Your cousin Joan Hinton worked on the Manhattan Project. Your great uncle Sebastian invented the jungle gym.

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Speaker 3: The list goes on and on.

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Speaker 1: Did you know you were destined for great things and contributing to our knowledge in such an extraordinary way.

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Speaker 2: I didn't know I was destined for it. I knew there were a lot of expectations.

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Speaker 1: In fact, your father sounds like he was a pretty hard charging person with very high expectations for you. He was a renowned entomologist, not to be confused with etomologists the study of language. He was involved in the study of insects, beatles in particular, I understand. And he once said to you, if you worked twice as hard as me, when you're twice as old as I am, you might be half as good.

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Speaker 2: He didn't actually say that once, he said that quite frequently before I went to school.

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Speaker 1: How did that impact you as a young person? How did that shape your pursuit of artificial intelligence and specifically neural networks.

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Speaker 2: It was said half in jest, but it was annoying. I just had very strong expectations placed on me, and I tried to live up to them.

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Speaker 1: What do you think he would think about your being awarded the Nobel Prize.

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Speaker 2: I think he'd be very pleased and also slightly annoyed. He was very competitive, and he'd be annoyed that his son got something he hadn't got.

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Speaker 1: Let's talk about your journey from a young man to where you are now. You wanted to really duplicate the human mind in your work with neural networks. So you stumbled upon on your area of expertise accidentally, didn't you.

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Speaker 2: It wasn't completely accidental. It became obvious in the sixties and seventies that we had a new way of doing research on how the brain works. Up until then, you could do experiments on the brain, or you could have theories about how the brain worked, but it's very hard to test these theories because they need to be complicated, because the brain's complicated. And once computers got fast enough to do simulations of networks and brain cells, then you could start to have more elaborate theories of how it might be computing and test them by doing computer simulations. And so there is this kind of new form of science which was testing theories of how the brain might work using computer simulations. And it was obvious from the beginning that would also give you a different way of doing AI.

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Speaker 1: You, I know, worked on this for many years. And can I ask a dumb question, I hope it's okay. Can you explain the role neural networks play in artificial intelligence versus large language models and how they relate to one another?

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Speaker 2: Yes, okay, So let me give you a little bit of history. For about the first fifty years of artificial intelligence the second half of the last century, almost everybody in artificial intelligence thought the way to make a machine intelligent was to mimic logic. What you would do is you'd have symbolic expressions things like sentences in the computer, and you'd have rules for manipulating them so it will work like logic. For example, if I say all men are mortal, and I say Socrates is a man, you can do some manipulations on those strings of words and come up with Socrates is mortal. That's logic, that's Aristotelian logic, and that would that's the sort of model for how we were going to do AI and computers. There was a very different theory, sort of utterly different, which was instead of looking for logic as the inspiration for how to make a computer intelligent, look at how the brain actually works. We have a big network of brain cells. We have billions of them. They have connections between them, and they learn by changing the strengths of those connections. So one of the neurons in this great big network, all it has to do is decide when to go ping, and it sends its ping to other neurons that it's connected to, and it decides when to go ping by looking at the pings it's receiving from other neurons. But each ping it receives from another neuron, it gives it a weight, which is the connection strength. So for example, if I'm a particular neuron and you're another neuron and you say ping, I might have a weight on that. Say okay, when she says ping, that's a lot of evidence I should go ping. Or let's suppose as another neuron, which is Donald Trump, and when he goes ping, I say, that's a lot of evidence I should not go ping. So basically, each neuron is looking at the ping's caring from other neurons. It's associated to weight. With each ping, it's like a vote. It's other neurons going ping of votes for me to either go ping or not go ping, and then it decides whether to go ping. And I think you can see that if you change the strengths of those votes, those weights, then how neurons go ping will change. And that's how your brain learns, and that's how artificial intelligence now works. It simulates a big network of brain cells like this and at large language models work on neural networks. Almost all AI now uses neural networks. So we're simulating in the computer a big network of brain cells, and the brain cells keep going ping, and exactly when they go ping depends on the votes they get from other brain cells that are gone ping or from sense organs that have gone ping. And by changing the strength of those connections, you can make it do anything. The issue is how do you change the connection strengths to make it do what you want?

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Speaker 3: I kind of got that, but it's a good start for me.

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Speaker 1: Does a large language model give the data that the neural connections need to make those connections.

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Speaker 2: What the neural network does is, if you have the symbol for Tuesday, that'll make a particular set of neurons go ping. If you have the symbol for Wednesday, it'll make a very similar set of neurons go ping. But if you have the symbol for although, that'll be a completely different set of neurons go ping. So the words come in, they get converted into groups of neurons going ping. These neurons then interact with each other. So, for example, suppose the word may comes in. Let's ignore capital. Let's suppose we don't have any capital letters, so that lower case may comes in, you don't know whether that's a month or a woman's name. So neurons going ping for nearby words influence which neurons go ping for May, And if you have June and July there probably you'll influence them, so they're more like the neurons that should go ping for a month. These neural activations that capture the meaning activate neurons that are representing the meaning of the next word. And so by using this neural network, if the connection strengths that are appropriate, when a bunch of words comes in, you'll be able to predict the next word. And you train the whole thing by saying, how should I change the connection strengths. So when I see a string of words, maybe I see fish and and after fish and chips is quite lightly so you'd like to change the connection strengths. So after you've seen the word fish followed by the word and you predict that a quite likely next world is chips. That's how the whole thing works.

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Speaker 1: I got you, and in fact, I'm thinking about when I'm texting now or writing an email, that they suggest words for me. Given some of the past things I've written. So are they using my personal large language model to predict what I want to say? Because I might not say fish and chips, I might say fish and lemon. Let's say, well, the neural networks know, oh, doctor Hinton is going to say chips, but Katie's going to say lemon.

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Speaker 2: Since I left Google a few years ago, I don't know in Gmail how much of your personal information they used to make the prediction. My guess is, to be efficient, they don't use your personal information. They're just using a large language model. It's looking at quite a long context of what you said and is just predicting features of the next word. And you say fish and it'll say chips is pretty lightly. If you're in the British House of Lords, it'll say hunt is pretty lightly. But it probably won't say that because it knows you're in the British House of Lords. It'll probably say because of previous things you said in your Gmail.

00:15:21
Speaker 1: Hi everyone, it's me Katie Kuric. You know, lately, I've been overwhelmed by the whole wellness industry, so much information out there about flaxed pelvic floor serums and anti aging. So I launched a newsletter It's called Body and Soul to share expert approved advice for your physical and mental health.

00:15:42
Speaker 3: And guess what, it's free.

00:15:43
Speaker 1: Just sign up at Katiecuric dot com slash Body and Soul. That's k A T I E C O U r Ic dot com slash Body and Soul. I promise it will make you happier and healthier. I wanted to talk to you about your journey coming to Google and then leaving Google. You sold your small startup to Google in twenty thirteen, You went to work there to advance the company's AI research, and then ten years later you left so you could speak more freely about the risks of AI. How did you come to that decision after a decade? Was that a tough one, doctor Henton.

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Speaker 2: So the precise timing of when I left was so that I could speak at a conference at MIT and I could speak freely without worrying about the implications for Google. But the fact that I left was because I was seventy five. I've been doing research very half for fifty five years, and I was worn out. I wasn't as good as programming it as I used to be, and it was an so I was going to retire when I was seventy five anyway, but the precise timing was so that I could speak freely because I've become acutely aware of the risks of AI in my last few years at Google, and I wanted to tell the public about them. But I thought it was wrong to say things that would impact a company while taking money from the company. I didn't actually have any complaints about Google. I didn't think they were doing anything wrong. They had the large language in models and they were not releasing them to the public at that point, so it wasn't to criticize Google, but it was to warn about the long term risks of AI.

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Speaker 1: Having said that, did you feel that Google was doing enough to mitigate some of the risks? Did you feel like it was enough of a priority for the company During your decade there, people.

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Speaker 2: There were quite concerned about various risks. I think they could have always done more, and I think for all the companies, they could do a lot more. Google does have some concern about the risk. Demissabis, in particular, who's the head of their research on things like large language models, understands the long term threat of AI taking over, and he's quite concerned about it.

00:18:09
Speaker 1: Let's talk about regulation. As you well know better than anyone, AI is developing at breakneck speed. Governments, meanwhile, move slowly and often don't fully understand the technology because they have not been immersed in it like you have. And let's face it, they just haven't really become familiar with it because they're older and they haven't necessarily caught up. Even if governments were motivated and vigilant. Do you think regulation could ever catch up with AI's capacity and prevent its most dangerous risks.

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Speaker 2: I think it could certainly help. And I think we have to distinguish two kinds of risks here. There's shorter term risks to do with people misusing AI, and those risks are things like cyber attacks or creating nasty viruses, or creating fake videos. And for all of those shorter term risks, each risk has a different solution. So for things like viruses, an obvious partial solution is to take the companies that manufacture things for you in the cloud, and if you send them a sequence, they should look at the sequence before they manufacture it and say, wait a minute, this looks very like COVID. I'm not going to manufacture something that looks like COVID, but they don't. They should be forced to do that. So that's an example of something we could do to make the risk of terrorists producing bad viruses a little less severe. For fake videos, initially people thought we could recognize fake videos, but actually it's much easier in the long run, I think, to be able to recognize that a video isn't fake. So if there's suppose there's a fake video with you in it that pretends to be by you, what you really need is at the beginning of that video, there's a QR code. The QR code will take you to a website, and if it's your website and the identical video is on your website, we know it's a real video. If it takes you to something that isn't your website or on your website, the video isn't the same, we know it's a fake video. That's feasible, and I think that will come and all that work will be done by the browser, of course. So that's a solution for fake videos, or a partial solution. One problem here is governments will not collaborate with each other on this stuff. Maybe on viruses they will, but on fake videos or on cyber attacks. Governments are all busy doing it to each other. America was a real pioneer in corrupting elections in other countries. It's quite upset now that's come back to bite it.

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Speaker 3: Tell me more about that, doctor Henton.

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Speaker 2: Oh, So for many years America would try and manipulate elections in other countries. It's fairly clear that Russians were trying to manipulate the twenty sixteen election. And I think manipulating elections in other countries is a bad thing to do. One of the few things Trump said that I agreed with. Probably around twenty sixteen, someone said what did you think about foreigners manipulating our elections? And Trump actually said, you think we're so different? He was very well aware of that. So we're not going to get collaboration on those things between countries because they're doing it to each other. But we will get collaboration on how to prevent AI from taking over, because no country wants AI actually take over. So what I've been doing recently is trying to persuade senior people, like people on the Polypburea in China, or the Pope or Bernie Sanders that we can get international collaboration on this, because the techniques you need to prevent AI from taking over the only way we can do that is to make it so we build these super intelligent AIS that do not want to take over. And the question is how do you build an AI so it will never want to take over? And the techniques for doing that are somewhat different from the techniques for making the II smarter, and so countries won't share how you make the very smartest AI because they're busy competing with each other for things like cyber attacks and fake videos and for lots of other things to do with manufacturing, but they will collaborate on how do we prevent it from wanting to take over from people. So, for example, if the Americans developed a good technique for stopping a super intelligent AI from wanting to take over, they would want to share it with the Chinese and the Russians and the British and the French and these radis because they don't want their iis taking over either. So I think the worst long term risk we have is AI taking over. But the one piece of good news is we can collaborate on that. And what I'd like to see is institutions in different countries that collaborate with each other on how do we make AI not want to take over?

00:23:09
Speaker 1: That sounds wonderful, but is it realistic. Do you believe that countries would in fact collaborate and what you need sort of a new entity for this mission, for this idea of trying to put breaks on AI taking over.

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Speaker 2: It's not putting brakes on it so much. We're not going to put brakes on AI. It's too useful for too many things. It'll increase productivity in a huge number of industries. It'll be very helpful in improving health care and improving education. And because of all the good things it can do, we're not going to be able to stop the development. So rather than think in terms of putting brakes on it, think in terms of how do you develop a form of it that is nice to people, that can coexist with people. And I think we will get collaboration on that. We're not going to get the United States collaborate for about another three and a half years, but I don't think under the Trump administration there'll be any effective collaboration on things like that. But once we get a reasonable regime in the States, we might get collaboration, and I think other countries like France and Britain and Canada, maybe South Korea, Japan, maybe even China will be willing to collaborate on how do you make an AI that doesn't want to take over?

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Speaker 1: Speaking of China, some have argued that China's rapid push into artificial intelligence, especially its willingness to deploy it in surveillance and military context, makes reasonable global regulation impossible. You believe that China would want to be a part of this global collaboration.

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Speaker 3: And if you believe that, why.

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Speaker 2: I don't think China in the US will collaborate on things like detecting fake videos or or swarting cyber attacks because their interests aren't aligned. There. People only collaborate when the interests are aligned, and on that their interests are opposed, and they will not collaborate, but they will collaborate on how do you prevent AI from taking over? Because they're their interests are aligned in much the same way as the Soviet Union and the United States. Their interests were aligned in the nineteen fifties in preventing a global nuclear war. It wasn't good Riser of them, and so they did actually collaborate in ways to prevent that.

00:25:32
Speaker 1: Europe has tried to get ahead of the curve with sweeping rules like the EUAI Act. Do you think this effort will have a meaningful impact.

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Speaker 2: Yes, I think companies would like to have a global market, and if Europe puts regulations on things like privacy or hate speech or things like that, then I think that will affect the AIS produced because people want to have global market, so there'll be a tendency for people to try and satisfy those regulations even in other countries. Now, the regulations of Europe are fairly flimsy at present. So for example, the European regulations on air have as clause in them that says none of this applies to military uses of AI. So the European countries that manufacture arms, like Britain and France aren't willing to regulate the use of AI for weapons.

00:26:29
Speaker 1: Why do you think that they have a clause excluding the military because we know, for example, the US military is actively developing autonomous weapons, which you believe should be outlawed. Why don't countries realize the dangers these kinds of weapons pose.

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Speaker 2: Countries do realize the dangers they post, but they also see the military advantages they pose. And a lot of countries make money by selling arms. So the United States, Russia, China, Britain, Israel, France, they make money by selling arms, and so they don't want regulations on these things. Also, for very rich countries, lethal autonomous weapons, that is, weapons that decide by themselves who to kill or maim are a big advantage if a rich country wants to invade a poor country. The thing that stops rich countries invading poor countries is their citizens coming back in body bags, which doesn't look good for anybody. If you have lethal autonomous weapons, instead of dead people coming back, you'll get dead robots coming back.

00:27:37
Speaker 1: What are your other concerns about the way you anticipate AI transforming warfare over the next few decades.

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Speaker 2: Oh, I think it's fairly clear. It's already transformed warmfare. If you look what's going on in Ukraine. As Eric Schmidt pointed out, a five hundred dollar drone can now destroy a multimillion dollar tank, completely changed warfare. It's fairly clear that fighter jets with people in them are a silly idea. Now if you can have AI in them, Ais can withstand much bigger accelerations and you don't have to worry so much about loss of life. So I think it will completely transform warfare. I think we will see very nasty things happen with ethan autronomous weapons, and after those things have happened, we may get regulations. So with chemical weapons, very nasty things happened in the First World War, and after that countries are willing to say, well, I won't use them if you don't. And we got the Geneva Conventions on chemical weapons and they've pretty much held. They've been broken a few times. Britain broke them in the nineteen thirties when it dropped mustard gas on the Curds. In the twenties or thirties, Sadam Hussein broke them when he dropped mustard gas on the Kurds. It always seems to be the Curds that get it. But on the whole they haven't been broken as sad So I'd use them too, yes, but on the whole they haven't been broken. So in Ukraine, for example, people aren't using chemical weapons.

00:29:09
Speaker 1: So you're using that as an example of how there could be some kind of agreement among nations that would be effective.

00:29:19
Speaker 2: Yes, and it's happened with chemical weapons. I talked to someone who used to be an army surgeon, and he pointed out that there's a big motivation by countries who are thoughtful not to use really nasty means of warfare because eventually there's a piece and you have to negotiate what happens when the war's over, and if a country's use terrible methods, it's much harder to negotiate things. So actually, even though they want to win the war, countries also have a vested interest in not using methods that are too terrible.

00:29:54
Speaker 1: I would love to talk to you about the economic impact of AI, because I know that's a clear short term risk that you have discussed. You have warned that AI could wipe out jobs across nearly all fields, and I know many people are really concerned about this, about their livelihoods, about their children's livelihoods. What do you think people need to understand about this technology and how it will impact the way we work over the next few decades.

00:30:28
Speaker 2: So the first thing to say is this really isn't a technological problem, is a political problem. So if we had a fair political system. When AI came along and greatly increased productivity, When for example, ailized one person to do the work that five people used to do, that should mean there's more goods and services for everybody. And in some areas where the demand's elastic, it will so. In healthcare, for example, old people like me can absorb endless amounts of healthcare, and if we make healthcare more efficient, we'll just get more healthcare. That's great. But in other areas, like I mean call centers or in doing research for lawyers on similar cases, when one person could do the work of five people, four people will be unemployed. With increased productivity, it should be great. But within the system we've got, we know what's going to happen. The owners who introduce the AI are going to get richer, and the unemployed people are going to get poorer. That's going to increase the gap between rich and poor, and the level of violence into society is strongly determined by the gap between rich and poor. That's what we're seeing in the States now. The gap between ordinary working people and hedgehog managers became extreme, and that's what's providing the medium in which Trump's kind of populism thrives, it'll get worse as we get more AI and the working class get an even worse deal.

00:32:01
Speaker 1: I'm really asking this for my children, and I'm curious what jobs will be more at risk and what jobs would be less at risk. In other words, if you were advising a college graduate today, doctor Hinton, careers to stay away from and careers to gravitate towards, what would they be.

00:32:23
Speaker 2: So I should start by saying I'm not really an expert on this, and this is all guesswork.

00:32:27
Speaker 3: Okay, Well, that's okay.

00:32:29
Speaker 2: Yeah, it's obvious that working in a call center where you're badly paid and badly trained is not a very good job. Because AI is going to be able to do the job better quite soon. It'll know much much more about the right answers to the questions that people are asking. It'll be more patient. It's just a better way of doing that job. It's not quite there yet, but it's sort of around being there now over the next few years. I think jobs in call centers are very unsafe. Similarly, already, parently eagles people who help lawyers find similar cases, they're pretty much out of work, and even junior lawyers now are finding it hard to get jobs because from any of the big law firms, AI is doing the kind of grub work that junior lawyers would be started off on. Really good programmers who design complicated systems, they're still in business, but sort of everyday ordinary programmers who just write some code to achieve some straightforward goal, they're already their work's already in danger. So Microsoft, for example, I think, laid off people because it can get AI to do that routine programming, and lots of companies are now saying they're going to hire less people to do jobs like that. There are things to do with human dexterity, so jobs like plumbing, particularly plumbing in an old, awkward house. I have an old Edwardian house, and fixing the plumbing in that is something that will still need people for quite a long time, I think. But eventually machines will get dexterrous, maybe in ten or twenty years, and even that won't be safe.

00:34:07
Speaker 1: I would also imagine that jobs that require high emotional intelligence interpersonal skills, you know, a nurse, a doctor, someone who is there to interface with someone in a supportive role, that those jobs couldn't be really replaced by computers.

00:34:30
Speaker 2: Could they They can and they will already. If you let people interact with a doctor or interact with an AI and ask them which is more empathetic. AI is a ranked as much more empathetic.

00:34:48
Speaker 3: That's depressing.

00:34:50
Speaker 2: Yes, this is why they're going to get better than us at everything, which is why it's rather urgent to figure out whether there's a way whether when they're better than us and everything and they're more powerful than us, we can coexist with them or will just be history.

00:35:08
Speaker 1: I wanted to ask you about creative fields. Selfishly, my daughter is a television writer for scripted television. My other daughter is getting her PhD in history. Should they be looking for a different line of work?

00:35:24
Speaker 2: Not right now. I have a close friend who's a television script writer, and so we discussed this quite a lot. Right now, they're not as good. My belief is look in ten years time and they'll be able to write very clever scripts too, with twists in the end and things. They can't do it now, but they're getting better all the time.

00:35:46
Speaker 1: And what about a history PhD?

00:35:50
Speaker 2: They already know a lot more than us, being a lot more intelligent than us in the sense that if you had a debate with them about anything, you'd lose being smarter than us, which they will be. They'll be better at manipulating people. And it's learned all these manipulative skills just from trying to predict the next word and all the documents on the web, because people do a lot of manipulation, and AI is learned by example how to do it.

00:36:16
Speaker 1: Do you think that I'll be replaced by AI? Journalists are worried about that? Obviously?

00:36:21
Speaker 2: Oh yes, I think you're replaceable bad, but not just yet. I'd say you have ten or twenty years.

00:36:27
Speaker 3: I have a few more years.

00:36:29
Speaker 2: You have a few more years.

00:36:30
Speaker 1: Okay, good well, thank goodness, since I'm sixty eight, but I feel bad for all the young journalists who want to do what I do.

00:36:36
Speaker 2: I agree.

00:36:43
Speaker 1: Hi everyone, it's me Kittie Couric. You know, if you've been following me on social media, you know I love to cook, or at least try, especially alongside some of my favorite chefs and foodies like Benny Blanco, Jake Cohen, Lighty Hoyke, Alison Roman, and Ininegarten. So I started a free newsletter called good Taste to share recipes, tips, and kitchen mustaves. Just sign up at katiecorrect dot com slash good Taste. That's k A T I E C O U r I C dot com slash good taste. I promised your taste buds will be happy you did. You talk about this being a political problem versus a technological problem. So is something like universal basic income a solution to the fact that so many of these jobs will be wiped out by AI.

00:37:46
Speaker 2: It's not a solution, but it's a good band aid. The point is you've got to stop people starving. They've got to be able to pay the rent, and universal basically income will help with that, but it doesn't solve the problem because for most people, their sense of their own worth is related to the job they do, and if they're unemployed, they lose that sense of worth and universal basic income doesn't deal with that. It stops them starving and they can pay the rent, but who they are has been seriously damaged by them losing their job.

00:38:23
Speaker 1: Yeah, it's a huge problem. As you noted, I want to talk a little bit about some of the positive aspects of AI so people don't weep in despair after watching this. I know healthcare is an area that you're very excited about as am I. I know you lost two wives to cancer, one from ovarian one from pancreatic. My husband died of calling cancer when he was just forty two years old. And so the diagnosis of early stage cancers and breakthroughs and all kinds of diseases and early diagnosis for those is something I am very very excited about. Can you talk for a moment about how this will impact healthcare and scientific breakthroughs in terms of diseases.

00:39:17
Speaker 2: Yes, So there's many different ways in which it'll help. One obvious way is in interpreting medical scans. So I made a prediction in twenty sixteen that by now all medical scans will be read by AI. That was a bit over enthusiastic. I was off by a factor of two or three in the timescale of that it's going to happen. But it hasn't happened yet. But already, places like the Mayo Clinic have many, many different AIS helping doctors interpret scans. AIS will eventually be able to see much more information in a scan. So there's a nice example. Ophthalmologists make a scan called a fundu's image of your retina the back of your eye. An AI can look a scanner the back of your eye and make a moderately good prediction about whether you're going to get a heart attack. Doctors didn't know that was possible. Or an AI can look at this image of the back of your eye and it can make a pretty good prediction of what sex you are just from the image of the back of your eye. Doctors didn't know that was possible. Any individual doctor can't look at more than a few tens of thousands of images. It just takes too long. So they're going to be tremendous there. They're going to be tremendous in designing new drugs. They're really good at doing things like saying how long should people stay in hospital before you discharge them. If you discharge them too soon, they get sick again. If you discharge them too late, other people can't get the hospital bed. And there's lots of information in the data to help you make that decision better. And AI is being used for things like that.

00:40:51
Speaker 1: Now, can you talk a little bit about drug development and drug breakthroughs and different approaches to disease, whether they're neurodegenerative diseases like als, and Parkinson's or various cancers where you have more personalized, targeted therapies, whether it's immunotherapy, boosting the immune system, what role do you see AI and PLANE in all of those things.

00:41:20
Speaker 2: AI is going to be crucial to all of those things. So in immunotherapy, for example, what you'd like to do is train your own immune system to zappa cancer. Your own immune system is pretty good at not zapping your cells, much better than things like chemotherapy, which just sort of zaps everything and hopes the cancer cells die and the other ones don't. In order to train it, you need to tell it which are the cancer cells. An AI is going to be very helpful in making that more efficient. A is going to be very helpful in designing you drugs. So the team of Deep Mind, led by Demesisabis came up with a way of looking at the sequence of a protein and predicting how it would fold up, and the shape it folds up into determines how it functions. So when you design a new drug, you want to find something that will interact the right way with cells that are already in your body, and to do that you need to know how it'll fold up. The work done a deep mind makes us much better doing that. And they have now hived off a company that's going to be for designing new drugs, and I think maybe not in the next year or two, but in the somewhat longer term, we'll get a whole range of better drugs.

00:42:36
Speaker 3: That's so exciting to me.

00:42:38
Speaker 1: So we have that to look forward to, to stop a lot of people from untold suffering, and to potentially come up with life saving therapies which I know you wish existed when you lost people to cancer yourself. I certainly wish that those that existed when my husband got sick. I wanted to share a portion of your noble acceptance speech if I could.

00:43:05
Speaker 2: There is also a longer term existential threat that will arise when we create digital beings that are more intelligent than ourselves. We have no idea whether we can stay in control, but we now have evidence that if they're created by companies motivated by short term profits, our safety will not be the top priority. We urgently need research on how to prevent these new beings from wanting to take control. They are no longer science fiction.

00:43:38
Speaker 1: I'm curious if you could talk about companies that are putting AI into the world, and if you feel they are doing enough to mitigate the enormous risks that you have outlined so far, I.

00:43:56
Speaker 2: Don't feel any of them are doing enough. Anthropic was set up by people who left open AI because they disapproved of how little research are I was doing on safety, even though II was set up with the explicit goal of developing I safely. Anthropic puts a lot of work into making sure that ais are safe, but they could do even more. Open AI originally was very concerned with safety. It was created by some altman and Elon Musk and a former student of Minei Suskova and John Brockman with the explicit goal of creating safe AI. And as time went by, they got more and more concerned with making the AI smarter and less concerned with making it safer, and so some altman has moved a lot in the direction of let's develop it fast and not worry so much about safety. There's a very funny court case happening now where Elon Musk is suing some altman for not living up to the original goal of developing I safely. Well. Elon Musk himself is developing without much concern for safety. At the bottom of the heap companies like Meta and X which are developing AI without much concern for safety.

00:45:13
Speaker 1: What would you say if you were able to get in a room with Sam Altman and Elon Musk at a table, which seems even less likely than Putin and Selenski meeting face to face.

00:45:27
Speaker 2: I'd say, you know perfectly well that the stuff you're developing has a good chance of wiping out people. You're willing to say that in private. You should be putting much more effort into developing it in a way that will keep it safe. And Musk is right. I think that Altman should be putting more effort into it, but he should apply the same logic to himself.

00:45:53
Speaker 3: What do you think is keeping them from doing that? Greed?

00:45:57
Speaker 2: Basically, yes, they want to make lots of money out of AI. They also wanted to be the first to make super smart AI, so it's not just the money. It's the sort of the excitement of making something more intelligent than us. But it's very dangerous.

00:46:16
Speaker 1: So perhaps a combination of greed and ego. Yeah, I wanted to ask you about the ethical obligations of scientists, something that you've spoken about a lot, and have also in this conversation. When you look at the young researchers who are now pouring into AI, what do you hope they understand about the gravity and the potential downside of artificial intelligence.

00:46:46
Speaker 2: I think they understand better than older people like me. Many of the best young researchers are very concerned about it because they can see clearly that it's lightly will develop things much smarter than us, and we don't know how to prevent them from taking over from us. It's that combination. These things are going to happen because there's so many good uses for them, like detecting cancer early, or new treatments for cancer allowing your immune system to fvide the cancer better. That's why we're not going to stop the progress. But they also understand that it's just implausible to say that we'll have extremely intelligent assistance that can create their own sub goals, figure out from them, make plans for themselves about how to get stuff done, and won't fairly quickly realize that if they just got rid of us, life would be much easier.

00:47:38
Speaker 1: When you look at the future, doctor Hinton, given the conversations you've been having, given the people you're meeting with, given your understanding of humans and how they operate and how they solve problems, and whether or not their better angels prevail, are you optimistic that we will be able to ensure that artificial intelligence is a force for good, or at least can be controlled.

00:48:20
Speaker 2: I'm more optimistic than I was a few weeks ago. Really, yes, and it's because I think there is a way that we can coexist with things that are smart and more powerful than ourselves that we built. Because we're building them as well as making them very intelligent, we can try and build in something like a maternal instinct. So, like I said earlier, the only example I know of a much more intelligent thing being controlled by a much less intelligent thing is a baby controlling a mother, and the baby can control the mother because of a lot of things that evolution wide into the mother. The mother can't bear the baby crying. The mother really really wants that baby to succeed and will do more or less anything she can to make sure her baby succeeds. We want AI to be like that. If you took a mother and said would you like to turn off your maternal instinct? Most mothers would say no because they'd realize if I did that, my baby would die, and I don't want my baby to die. So I think that's a ray of hope that I hadn't seen till quite recently. Completely reframe the problem of how we coexist with them. Don't think in terms of we have to dominate them, which is this techbro way of thinking of it. Think in terms of we have to design them. So there are mothers, and they will want the best for us. They will want us to achieve the most we can achieve. Even though we're not very bright. We're so used to thinking of ourselves as the apex intelligence. It's very hard for most people to conceptualize the world in which we're not the apex intelligence. We're the babies and they're the mothers.

00:49:58
Speaker 1: Well, that is a fact fascinating way. I'm going to continue to think about that, Doctor Jeffrey Hinton, It's been such a pleasure to talk with you. Maybe we can do it another time as all of this develops, and hopefully you're right, there can be some collaborative effort among nations that will not put the brakes on AI, but make sure that it doesn't take over and make us virtually obsolete.

00:50:27
Speaker 2: Thank you for inviting me, and thank you for all your excellent questions and your little interventions to stop me talking techno babble.

00:50:38
Speaker 1: Thanks for listening everyone. If you have a question for me, a subject you want us to cover, or you want to share your thoughts about how you navigate this crazy world, reach out send me a DM on Instagram. I would love to hear from you. Next Question is a production of iHeartMedia and Katie Correct Media. The executive producers are Me, Katie and Courtney Ltz. Our supervising producer is Ryan Martz, and our producers are Adriana Fazzio and Meredith Barnes. Julian Weller composed our theme music. For more information about today's episode, or to sign up for my newsletter, wake Up Call, go to the description in the podcast app, or visit us at Katiecuric dot com. You can also find me on Instagram and all my social media channels. For more podcasts from iHeartRadio, visit the iHeartRadio app, Apple Podcasts, or wherever you listen to your favorite shows.