Read the full transcript of Godfather of AI Geoffrey Hinton’s interview on The Diary Of A CEO Podcast with Steven Bartlett episode titled ” Godfather of AI: I Tried to Warn Them, But We’ve Already Lost Control!”, (Jun 16, 2025).
The Godfather of AI Speaks
STEVEN BARTLETT: Geoffrey Hinton, they call you the godfather of AI.
GEOFFREY HINTON: Yes, they do.
STEVEN BARTLETT: Why do they call you that?
GEOFFREY HINTON: There weren’t that many people who believed that we could make neural networks work, artificial neural networks. So for a long time in AI, from the 1950s onwards, there were kind of two ideas about how to do AI. One idea was that sort of core of human intelligence was reasoning. And to do reasoning, you needed to use some form of logic. And so AI had to be based around logic. And in your head, you must have something like symbolic expressions that you manipulated with rules. And that’s how intelligence worked. And things like learning or reasoning by analogy, that’ll come later, once we’ve figured out how basic reasoning works.
There was a different approach, which is to say, let’s model AI on the brain, because obviously the brain makes us intelligent. So simulate a network of brain cells on a computer and try and figure out how you would learn strengths of connections between brain cells so that it learned to do complicated things like recognize objects and images or recognize speech or even do reasoning. I pushed that approach for, like, 50 years because so few people believed in it. There weren’t many good universities that had groups that did that. So if you did that, the best young students who believed in that came and worked with you. So I was very fortunate in getting a whole lot of really good students.
STEVEN BARTLETT: Some of which have gone on to create and play an instrumental role in creating platforms like OpenAI.
GEOFFREY HINTON: Yes. So Ilya Sutskever would be a nice example, a whole bunch of them.
STEVEN BARTLETT: Why did you believe that modeling it off the brain was a more effective approach?
GEOFFREY HINTON: It wasn’t just me believed it. Early on, Von Neumann believed it and Turing believed it. And if either of those had lived, I think AI would have had a very different history, but they both died young.
STEVEN BARTLETT: You think AI would have been here sooner?
GEOFFREY HINTON: I think neural net. The neural net approach would have been accepted much sooner if either of them had lived.
The Mission to Warn
STEVEN BARTLETT: In this season of your life, what mission are you on?
GEOFFREY HINTON: My main mission now is to warn people how dangerous AI could be.
STEVEN BARTLETT: Did you know that when you became the godfather of AI?
GEOFFREY HINTON: No, not really. I was quite slow to understand some of the risks. Some of the risks were always very obvious. Like people would use AI to make autonomous, lethal weapons. That is, things that go around deciding by themselves who to kill. Other risks, like the idea that they would one day get smarter than us and maybe would become irrelevant. I was slow to recognize that other people recognized it 20 years ago. I only recognized a few years ago that that was a real risk that might be coming quite soon.
STEVEN BARTLETT: How could you not have foreseen that? If with everything you know here about cracking, the ability for these computers to learn similar to how humans learn and just, you know, introducing any rate of improvement.
GEOFFREY HINTON: It’s a very good question. How could you not have seen that? But remember, neural networks 20, 30 years ago were very primitive in what they could do. They were nowhere near as good as humans at things like vision and language and speech recognition. The idea that you have to now worry about it getting smarter than people, that seems silly.
STEVEN BARTLETT: Then when did that change?
The Turning Point
GEOFFREY HINTON: It changed for the general population when ChatGPT came out. It changed for me when I realized that the kinds of digital intelligences were making have something that makes them far superior to the kind of biological intelligence we have. If I want to share information with you, so I go off and I learn something, and I’d like to tell you what I learned, so I produce some sentences. This is a rather simplistic model, but roughly right. Your brain is trying to figure out how can I change the strengths of connections between neurons? So I might have put that word next. And so you’ll do a lot of learning when a very surprising word comes, and not much learning when it’s a very obvious word. If I say fish and chips, you don’t do much learning when I say chips. But if I say fish and cucumber, you do a lot more learning. You wonder, why did I say cucumber? So that’s roughly what’s going on in your brain.
STEVEN BARTLETT: I’m predicting what’s coming next.
GEOFFREY HINTON: That’s how we think it’s working. Nobody really knows for sure how the brain works, and nobody knows how it gets the information about whether you should increase the strength of a connection or decrease the strength of a connection. That’s the crucial thing. But what we do know now from AI is that if you could get information about whether to increase or decrease the connection strength so as to do better at whatever tasks you’re trying to do, then we could learn incredible things, because that’s what we’re doing now with artificial neural nets. It’s just we don’t know for real brains how they get that signal about whether to increase or decrease.
The Big Concerns About AI Safety
STEVEN BARTLETT: As we sit here today, what are the big concerns you have around safety of AI?
GEOFFREY HINTON: Go ahead.
STEVEN BARTLETT: I’ll write them all down and we’ll go through them.
GEOFFREY HINTON: Okay, first of all, I want to make a distinction between two completely different kinds of risk. There’s risks that come from people misusing AI. Yeah, and that’s most of the risks and all of the short term risks. And then there’s risks that come from AI getting super smart and deciding it doesn’t need us.
STEVEN BARTLETT: Is that a real risk?
GEOFFREY HINTON: And I talk mainly about that second risk because lots of people say, is that a real risk? And yes, it is. Now, we don’t know how much of a risk it is. We’ve never been in that situation before. We’ve never had to deal with things smarter than us. So really, the thing about that existential threat is that we have no idea how to deal with it. We have no idea what it’s going to look like. And anybody who tells you they know just what’s going to happen and how to deal with it, they’re talking nonsense. So we don’t know how to estimate the probabilities. It’ll replace us. Some people say it’s like less than 1%. My friend Yann LeCun, who was a postdoc with me, thinks, no, no, no, no. We’re always going to be. We build these things, we’re always going to be in control. We’ll build them to be obedient. And other people like Yudkowsky say, no, no, no, these things are going to wipe it out for sure. If anybody builds it, it’s going to wipe us all out. And he’s confident of that. I think both of those positions are extreme. It’s very hard to estimate the probabilities in between.
STEVEN BARTLETT: If you had to bet on who was right out of your two friends.
GEOFFREY HINTON: I simply don’t know. So if I had to bet, I’d say the probability is in between. And I don’t know where to estimate it in between. I often say 10 to 20% chance they’ll wipe us out. But that’s just gut based on the idea that we’re still making them. And we’re pretty ingenious. And the hope is that if enough smart people do enough research with enough resources, we’ll figure out a way to build them so they’ll never want to harm us.
Comparing AI to Nuclear Weapons
STEVEN BARTLETT: Sometimes I think if we talk about that second path, sometimes I think about nuclear bombs and the invention of the atomic bomb and how it compares. Like, how is this different? Because the atomic bomb came along and I imagine a lot of people at that time thought, our days are numbered.
GEOFFREY HINTON: Yes, I was there. We did, yeah.
STEVEN BARTLETT: But we’re still here.
GEOFFREY HINTON: We’re still here. Yes. So the atomic bomb was really only good for one thing, and it was very obvious how it worked. Even if you hadn’t had the pictures of Hiroshima and Nagasaki, it was obvious that it was a very big bomb that was very dangerous. With AI, it’s good for many, many things. Is going to be magnificent in healthcare and education, and more or less any industry that needs to use its data is going to be able to use it better with AI. So we’re not going to stop the development. You know, people say, well, why don’t we just stop it now? We’re not going to stop it because it’s too good for too many things. Also, we’re not going to stop it because it’s good for battle robots. And none of the countries that sell weapons are going to want to stop it. Like the European regulations. They have some regulations about AI, and it’s good they have some regulations, but they’re not designed to deal with most of the threats. And in particular, the European regulations have a clause in them that say none of these regulations apply to military uses of AI. So governments are willing to regulate companies and people, but they’re not willing to regulate themselves.
The Regulation Challenge
STEVEN BARTLETT: It seems pretty crazy to me that they. I go back and forward, but if Europe has a regulation but the rest of the world doesn’t.
GEOFFREY HINTON: Puts them at a competitive disadvantage.
STEVEN BARTLETT: Yeah, we’re seeing this already. I don’t think people realize that when OpenAI release a new model or a new piece of software in America, they can’t release it to Europe yet because of regulations here. So Sam Altman tweeted, saying, our new AI agent thing is available to everybody, but it can’t come to Europe yet because there’s regulations.
GEOFFREY HINTON: Yes.
STEVEN BARTLETT: What does that do? That gives us a productive disadvantage. Productivity disadvantage.
GEOFFREY HINTON: What we need is. I mean, at this point in history, when we’re about to produce things more intelligent than ourselves, what we really need is a kind of world government that works, run by intelligent, thoughtful people. And that’s not what we got.
STEVEN BARTLETT: So free for all.
GEOFFREY HINTON: Well, what we’ve got is sort of. We’ve got capitalism, which is done very nicely by us. It’s produced lots of goods, goods and services for us. But these big companies, they’re legally required to try and maximize profits, and that’s not what you want from the people developing this stuff.
Cyber Attacks and AI Scams
STEVEN BARTLETT: So let’s do the risks then. You talked about there’s human risks and then there’s.
GEOFFREY HINTON: So I’ve distinguished these two kinds of risk. Let’s talk about all the risks from bad human actors using AI. There’s cyber attacks. So between 2023 and 2024, they increased by about a factor of 12, 1,200%. And that’s probably because these large language models make it much easier to do phishing attacks.
STEVEN BARTLETT: And a phishing attack, for anyone that doesn’t know.
GEOFFREY HINTON: Is they send you something saying, hi, I’m your friend John and I’m stuck in El Salvador. Could you just wire this money? That’s one kind of attack. But the phishing attacks are really trying to get your logon credentials.
STEVEN BARTLETT: And now with AI, they can clone my voice, my image, they can do all that. I’m struggling at the moment because there’s a bunch of AI scams on X and also Meta, and there’s one in particular on Meta, so Instagram, Facebook, at the moment, which is a paid advert where they’ve taken my voice from the podcast, they’ve taken my mannerisms, and they’ve made a new video of me encouraging people to go and take part in this crypto Ponzi scam or whatever. And we’ve been, you know, we spent weeks and weeks and weeks and weeks and end emailing, Meta telling, please take this down. They take it down, another one pops up, they take that one down, another one pops up. So it’s like whack a mole.
GEOFFREY HINTON: And then very annoying.
STEVEN BARTLETT: The heartbreaking part is you get the messages from people that fall in for the scam and they’ve lost 500 or.
GEOFFREY HINTON: 500, and they’re crossed with you because you recommended it.
STEVEN BARTLETT: And I’m like, I’m sad for them.
GEOFFREY HINTON: It’s very annoying.
STEVEN BARTLETT: Yeah.
GEOFFREY HINTON: I have a smaller version of that, which is some people now publish papers with me as one of the authors, and it looks like it’s in order that they can get lots of citations to themselves.
STEVEN BARTLETT: So cyber attacks, a very real threat. There’s been an explosion of those and these already.
GEOFFREY HINTON: Obviously, AI is very patient, so they can go through 100 million lines of code looking for known ways of attacking them. That’s easy to do, but they’re going to get more creative. And they may. Some people believe, and I. Some people who know a lot believe that maybe by 2030 they’ll be creating new kinds of cyber attacks which no person ever thought of. So that’s very worrisome because they can.
AI’s Ability to Think and Draw Conclusions
STEVEN BARTLETT: Think for themselves and dissect they can think for themselves.
GEOFFREY HINTON: They can draw new conclusions from much more data than a person ever saw.
Protecting Against Cyber Attacks
STEVEN BARTLETT: Is there anything you’re doing to protect yourself from cyber attacks at all?
GEOFFREY HINTON: Yes, it’s one of the few places where I changed what I do radically because I’m scared of cyber attacks. Canadian banks are extremely safe. In 2008, no Canadian banks came anywhere near going bust. So they’re very safe banks because they’re well regulated, fairly well regulated. Nevertheless, I think a cyber attack might be able to bring down a bank.
Now, if you have all my savings are in shares in banks held by banks. So if the bank gets attacked and it holds your shares, they’re still your shares. And so I think you’d be okay unless the attacker sells the shares, because the bank can sell the shares. If the attacker sells your shares, I think you’re screwed. I don’t know, I mean, maybe the bank would have to try and reimburse you. But the bank’s bust by now, right?
STEVEN BARTLETT: So.
GEOFFREY HINTON: So I’m worried about a Canadian bank being taken down by a cyber attack and the attacker selling shares that it holds. So I spread my money, my children’s money, between three banks in the belief that if a cyber attack takes down one Canadian bank, the other Canadian banks will very quickly get very careful.
STEVEN BARTLETT: And you have a phone that’s not connected to the Internet. Do you have any? I’m thinking about storing data and stuff like that. Do you think it’s wise to consider having cold storage?
GEOFFREY HINTON: I have a little disk drive and I back up my laptop on this hard drive. So I actually have everything on my laptop on a hard drive at least. You know, if the whole Internet went down, I had the sense I still got it on my laptop and I still got my information. Then the next thing is using AIs to create nasty viruses.
AI-Created Biological Weapons
STEVEN BARTLETT: Okay?
GEOFFREY HINTON: And the problem with that is that just requires one crazy guy with a grudge. One guy who knows a little bit of molecular biology, knows a lot about AI, and just wants to destroy the world. You can now create new viruses relatively cheaply using AI, and you don’t have to be a very skilled molecular biologist to do it. And that’s very scary. So you could have a small cult, for example. A small cult might be able to raise a few million dollars. For a few million dollars, they might be able to design a whole bunch of viruses.
STEVEN BARTLETT: Well, I’m thinking about some of our foreign adversaries doing government funded programs. I mean, there’s lots of talk around Covid and the Wuhan Laboratory and what they were doing and gain a function, research. But I’m wondering if in, you know, China or a Russia or an Iran or something, the government could fund a program for a small group of scientists to make a virus that they could.
GEOFFREY HINTON: You know, I think they could, yes. Now, they’d be worried about retaliation. They’d be worried about other governments doing the same to them. Hopefully, that would help keep it under control. They might also be worried about the virus spreading to their country.
Election Corruption Through AI
Okay, then there’s corrupting elections. So if you wanted to use AI to corrupt elections, a very effective thing is to be able to do targeted political advertisements where you know a lot about the person. So anybody who wanted to use AI for corrupting elections would try and get as much data as they could about everybody in the electorate. With that in mind, it’s a bit worrying what Musk is doing at present in the States, going in and insisting on getting access to all these things that were very carefully siloed. The claim is it’s to make things more efficient. But it’s exactly what you would want if you intended to corrupt the next election.
STEVEN BARTLETT: How do you mean? Because you get all this data on the people.
GEOFFREY HINTON: You get all this data on people, you know, how much they make, where they, you know, everything about them. Once you know that, it’s very easy.
STEVEN BARTLETT: To manipulate them because you can make.
GEOFFREY HINTON: An AI that you can send messages that they’ll find very convincing, telling them not to vote, for example. So I have no reason other than common sense to think this, but I wouldn’t be surprised if part of the motivation of getting all this data from American government sources is to corrupt elections. Another part might be that it’s very nice training data for a big model.
STEVEN BARTLETT: But he would have to be taking that data from the government and feeding it into his.
GEOFFREY HINTON: Yes. And what they’ve done is turned off lots of the security controls, got rid of the some of the organization to protect against that.
Echo Chambers and Social Media Manipulation
STEVEN BARTLETT: So that’s corrupting elections.
GEOFFREY HINTON: Okay. Then there’s creating these two echo chambers by organizations like YouTube and Facebook, showing people things that will make them indignant. People love to be indignant.
STEVEN BARTLETT: Indignant as in angry. What does indignant mean?
GEOFFREY HINTON: Feeling I’m sort of angry, but feeling righteous.
STEVEN BARTLETT: Okay.
GEOFFREY HINTON: So, for example, if you were to show me something that said Trump did this crazy thing, here’s a video of Trump doing this completely crazy thing. I would immediately click on it.
STEVEN BARTLETT: Yeah. Okay. So putting us in echo chambers and dividing us.
GEOFFREY HINTON: Yes. And that’s the policy that YouTube and Facebook and others use for deciding what to show you next is causing that. If they had a policy of showing you balance things, they wouldn’t get so many clicks and they wouldn’t be able to sell so many advertisements. And so it’s basically the profit motive is saying, show them whatever will make them click. And what will make them click is things that are more and more extreme.
STEVEN BARTLETT: And that confirmed my existing bias.
GEOFFREY HINTON: That confirmed my existing bias. So you’re getting your biases confirmed all.
STEVEN BARTLETT: The time, further and further and further and further, which means you’re driving, which is.
GEOFFREY HINTON: Now there’s in the States, there’s two communities that don’t hardly talk to each other.
STEVEN BARTLETT: I’m not sure people realize that this is actually happening every time they open an app, but if you go on a TikTok or a YouTube or one of these big social networks, the algorithm, as you said, is designed to show you more of the things that you had interest in last time. So if you just play that out over 10 years, it’s going to drive you further and further and further into whatever ideology or belief you have.
GEOFFREY HINTON: And.
STEVEN BARTLETT: And further away from nuance and common sense and parity, which is a pretty remarkable thing. People don’t know it’s happening. They just open their phones and experience something and think this is the news or the experience everyone else is having.
GEOFFREY HINTON: Right. So basically, if you have a newspaper and everybody gets the same newspaper, you get to see all sorts of things you weren’t looking for, and you get a sense that if it’s in the newspaper, it’s an important thing or significant thing. But if you have your own news feed. My newsfeed on my iPhone, three quarters of the stories are about AI, and I find it very hard to know if the whole world’s talking about AI all the time or if it’s just my newsfeed.
STEVEN BARTLETT: Okay. So driving me into my echo chambers, which is going to continue to divide us further and further, I’m actually noticing that the algorithms are becoming even more what’s the word? Tailored. And people might go, that’s great. But what it means is they’re becoming even more personalized, which means that my reality is becoming even further from your reality.
GEOFFREY HINTON: Yeah, it’s crazy. We don’t have a shared reality anymore. I share reality with other people who watch the BBC and other BBC news and other people who read the Guardian and other people who read the New York Times. I have almost no shared reality with people who watch Fox News. It’s worrisome.
The Need for Regulation
STEVEN BARTLETT: Yeah.
GEOFFREY HINTON: Behind all this is the idea that these companies just want to make profit and they’ll do whatever it takes to make more profit because they have to. They’re legally obliged to do that.
STEVEN BARTLETT: So we almost can’t blame the company, can we?
GEOFFREY HINTON: Well, capitalism’s done very well for us. It’s produced lots of goodies, but you need to have it very well regulated. So what you really want is to have rules so that when some company is trying to make as much profit as possible, in order to make that profit, they have to do things that are good for people in general, not things that are bad for people in general.
So once you get to a situation where in order to make more profit, the company starts doing things that are very bad for society, like showing you things that are more and more extreme. That’s what regulations are for. So you need regulations with capitalism now, companies will always say, regulations get in the way, make us less efficient. And that’s true. The whole point of regulations is to stop them doing things to make profit that hurt society. And we need strong regulation.
STEVEN BARTLETT: Who’s going to decide whether it hurts society or not?
GEOFFREY HINTON: Because, you know, that’s the job of politicians. Unfortunately, if the politicians are owned by the companies, that’s not so good.
STEVEN BARTLETT: And also the politicians might not understand the technology. You’ve probably seen the Senate hearings where they wheel out Mark Zuckerberg and these big tech CEOs, and it is quite embarrassing because they’re asking the wrong questions.
GEOFFREY HINTON: Well, I’ve seen the video of the US education secretary talking about how they’re going to get AI in the classrooms, except she thought it was called A1. She’s actually there saying, we’re going to have all the kids interacting with A1. There is a school system that’s going to start making sure that first graders or even pre Ks have A1 teaching every year, starting that far down in the grades. And that’s just a wonderful thing.
STEVEN BARTLETT: And these are the people that.
GEOFFREY HINTON: These are the people in charge.
STEVEN BARTLETT: Ultimately, the tech companies are in charge because they were smart.
GEOFFREY HINTON: The tech companies in the States. Now, at least a few weeks ago when I was there, they were running an advertisement about how it was very important not to regulate AI because it would hurt us in the competition with China.
STEVEN BARTLETT: Yeah, and that’s a that’s a plausible argument.
GEOFFREY HINTON: No. Yes, it will. But you have to decide, do you want to compete with China by doing things that will do a lot of harm to your society and you probably don’t.
STEVEN BARTLETT: I guess they would say that it’s not just China It’s Denmark and Australia and Canada and they’re not so worried about and Germany. But if they kneecap themselves with regulation, if they slowed themselves down, then the founders, the entrepreneurs, the investors are going to go.
GEOFFREY HINTON: I think calling it kneecapping is taking a particular point of view. It’s taking the point of view that regulations are sort of very harmful. What you need to do is just constrain the big companies so that in order to make profit they have to do things that are socially useful. Like Google Search is a great example that didn’t need regulation because it just made information available to people. It was great. But then if you take YouTube where it starts showing you adverts and showing you more and more extreme things, that needs regulation, but we don’t have the.
STEVEN BARTLETT: People to regulate it as we’ve identified.
GEOFFREY HINTON: I think people know pretty well that particular problem of showing you more and more extreme things. That’s a well known problem that the politicians understand. They just need to get on and regulate it.
Lethal Autonomous Weapons
STEVEN BARTLETT: So that was the next point, which was that the algorithms are going to drive us further into our echo chambers. Right, what’s next?
GEOFFREY HINTON: Lethal autonomous weapons. Lethal autonomous weapons, that means things that can kill you and make their own decision about whether to kill you.
STEVEN BARTLETT: Which is the great dream, I guess, of the military industrial complex being able to create such weapons.
The Military-Industrial Complex and Autonomous Weapons
GEOFFREY HINTON: So the worst thing about them is big powerful countries always have the ability to invade smaller, poorer countries, they’re just more powerful. But if you do that using actual soldiers, you get bodies coming back in bags and the relatives of the soldiers who were killed don’t like it. So you get something at Vietnam, in the end, there’s a lot of protest at home. If instead of bodies coming back in bags, it was dead robots, there’d be much less protest and the military industrial complex would like it much more because robots are expensive. And suppose you had something that could get killed and was expensive to replace, that would be just great big countries can invade small countries much more easily because they don’t have their soldiers being killed.
STEVEN BARTLETT: And the risk here is that these robots will malfunction or they’ll just be more.
GEOFFREY HINTON: No, no, that’s. Even if the robots do exactly what the people who built the robots want them to do, the risk is that it’s going to make big countries invade small countries more often, more often.
STEVEN BARTLETT: Because they can.
GEOFFREY HINTON: Yeah. And it’s not a nice thing to do.
STEVEN BARTLETT: So it brings down the friction of.
GEOFFREY HINTON: War, it brings down the cost of.
STEVEN BARTLETT: Doing an invasion and these machines will be smarter at Warfare as well. So they’ll be.
GEOFFREY HINTON: Well even when the machines aren’t smarter. So the lethal autonomous weapons, they can make them now. And I think all the big defense departments are busy making them. Even if they’re not smarter than people, they’re still very nasty, scary things.
STEVEN BARTLETT: Because I’m thinking that, you know, they could show just a picture. Go get this guy.
GEOFFREY HINTON: Yeah.
STEVEN BARTLETT: And go take out anyone he’s been texting and this little wasp.
GEOFFREY HINTON: So two days ago, I was visiting a friend of mine in Sussex who had a drone that cost less than 200 pounds, and the drone went up. It took a good look at me, and then it could follow me through the woods, and it followed. It was very spooky having this drone. It was about two meters behind me. It was looking at me. If I moved over there, moved over there, it could just track me for 200. But it was already quite spooky.
Combinatorial Risks and Superintelligence
STEVEN BARTLETT: Yeah. And I imagine, as you say, a race going on as we speak to who can build the most complex autonomous. Autonomous weapons. There is a risk. I often hear, that some of these things will combine and the cyber attack will release weapons.
GEOFFREY HINTON: Sure. You can get combinatorially many risks by combining these other risks. So, I mean, for example, you could get a super intelligent AI that decides to get rid of people. And the obvious way to do that is just to make one of these nasty viruses. If you made a virus that was very contagious, very lethal, and very slow, everybody would have it before they realized what was happening. I mean, I think if a superintelligence wanted to get rid of us, it will probably go for something biological like that that wouldn’t affect it.
STEVEN BARTLETT: Do you think it could just very quickly turn us against each other? For example, it could send a warning on the nuclear systems in America that there’s a nuclear bomb coming from Russia or vice versa, and one retaliates.
GEOFFREY HINTON: Yeah. I mean, my basic view is there’s so many ways in which the superintelligence could get rid of us. It’s not worth speculating about.
STEVEN BARTLETT: What is.
GEOFFREY HINTON: What you have to do is prevent it ever wanting to. That’s what we should be doing research on. There’s no way we’re going to prevent it from. It’s smarter than us. Right. There’s no way we’re going to prevent it getting rid of us if it wants to. We’re not used to thinking about things smarter than us. If you want to know what life’s like when you’re not the apex intelligence, ask a chicken Yeah.
The Intelligence Gap Analogy
STEVEN BARTLETT: I think of my dog Pablo, my French bulldog. This morning as I left home, he has no idea where I’m going. He has no idea what I do.
GEOFFREY HINTON: Right.
STEVEN BARTLETT: Can’t even talk to him.
GEOFFREY HINTON: Yeah. And the intelligence gap will be like that.
STEVEN BARTLETT: So you’re telling me that if I’m Pablo, my French bulldog, I need to figure out a way to make my owner not wipe me out?
GEOFFREY HINTON: Yeah. So we have one example of that, which is mothers and babies. Evolution put a lot of work into that. Mothers are smarter than babies, but babies are in control. And they’re in control because the mother just can’t bear lots of hormones and things, but the mother just can’t bear the sound of the baby crying.
STEVEN BARTLETT: Not all mothers.
GEOFFREY HINTON: Not all mothers. And then the baby’s not in control, and then bad things happen. We somehow need to figure out how to make them not want to take over. The analogy I often use is forget about intelligence. Just think about physical strength. Suppose you have a nice little tiger cub. It’s sort of bit bigger than a cat. It’s really cute. It’s very cuddly, very interesting to watch. Except that you better be sure that when it grows up, it never wants to kill you, because if it ever wanted to kill you, you’d be dead in a few seconds.
STEVEN BARTLETT: And you’re saying the AI we have now is the tiger cub?
GEOFFREY HINTON: Yep.
STEVEN BARTLETT: And it’s growing up.
GEOFFREY HINTON: Yep.
STEVEN BARTLETT: So we need to train it as it’s when it’s a baby.
GEOFFREY HINTON: Now, a tiger has lots of innate stuff built in, so, you know, when it grows up, it’s not a safe thing to have around.
STEVEN BARTLETT: But lions, people that have lions as pets.
GEOFFREY HINTON: Yes.
STEVEN BARTLETT: Sometimes the lion is affectionate to its creator, but not to others.
GEOFFREY HINTON: Yes. And we don’t know whether these AIs, we simply don’t know whether we can make them not want to take over and not want to hurt us.
STEVEN BARTLETT: Do you think we can? Do you think it’s possible to train different intelligence?
GEOFFREY HINTON: I don’t think it’s clear that we can. So I think it might be hopeless, but I also think we might be able to. And it’d be sort of crazy if people went extinct because we couldn’t be bothered to try, if that’s even a possibility.
Reflecting on Life’s Work
STEVEN BARTLETT: How do you feel about your life’s work? Because you were.
GEOFFREY HINTON: Yeah, it sort of takes the edge off it, doesn’t it? I mean, the idea is going to be wonderful in healthcare and wonderful in education and wonderful. I mean, it’s going to make call centers much more efficient. Though one worries a bit about what the people who are doing that job now do, it makes me sad. I don’t feel particularly guilty about developing AI like 40 years ago because at that time we had no idea that this stuff was going to happen this fast. We thought we had plenty of time to worry about things like that. When you can’t get the AI to do much and you want to get it to do a little bit more, you don’t worry about this stupid little thing is going to take over from people. You just want it to be able to do a little bit more of the things people do. It’s not like I knowingly did something thinking this might wipe us all out, but I’m going to do it anyway. But it is a bit sad that it’s not just going to be something for good. So I feel I have a duty now to talk about the risks.
STEVEN BARTLETT: And if you could play it forward and you could go forward 30, 50 years and you found out that it led to the extinction of humanity, and if that does end up being. Being the outcome.
GEOFFREY HINTON: Well, if you played it forward and it led to the extinction of humanity, I would use that to tell people to tell their governments that we really have to work on how we’re going to keep this stuff under control. I think we need people to tell governments that governments have to force the companies to use their resources to work on safety, and they’re not doing much of that because you don’t make profits that way.
Ilya Sutskever and OpenAI
STEVEN BARTLETT: One of your students we talked about earlier, Ilya. Ilya left OpenAI. And there was lots of conversation around the fact that he left because he had safety concerns.
GEOFFREY HINTON: Yes.
STEVEN BARTLETT: And he’s gone on to set up a AI safety company.
GEOFFREY HINTON: Yes.
STEVEN BARTLETT: Why do you think he left?
GEOFFREY HINTON: I think he left because he had safety concerns.
STEVEN BARTLETT: Really?
GEOFFREY HINTON: I still have lunch with him from time to time. His parents live in Toronto. When he comes to Toronto, we have lunch together. He doesn’t talk to me about what went on at OpenAI, so I have no inside information about that. But I know Ilya very well and he is genuinely concerned with safety. So I think that’s why he left.
STEVEN BARTLETT: Because he was one of the top people. I mean, he was.
GEOFFREY HINTON: He was probably the most important person behind the development of ChatGPT. The early version is like GPT2. He was very important in the dramatic.
STEVEN BARTLETT: You know him personally, so you know his character.
GEOFFREY HINTON: Yes. He has a good moral compass. He’s not like someone like Musk who has no moral compass.
STEVEN BARTLETT: Does Sam Altman have a good Moral compass.
GEOFFREY HINTON: We’ll see. I don’t know Sam, so I don’t want to comment on that.
Private Conversations vs Public Statements
STEVEN BARTLETT: But from what you’ve seen, are you concerned about the actions that they’ve taken? Because if you know Ilya, and Ilya’s a good guy and he’s left, that.
GEOFFREY HINTON: Would give you some insight. Yes, it would give you some reason to believe that there’s a problem there. And if you look at Sam’s statements some years ago, he sort of happily said in one interview, this stuff will probably kill us all. That’s not exactly what he said, but that’s what it amounted to. Now he’s saying, you don’t need to worry too much about it. And I suspect that’s not driven by seeking after the truth, that’s driven by seeking after money.
STEVEN BARTLETT: Is it money or is it power?
GEOFFREY HINTON: Yeah, I shouldn’t have said money. It’s some combination of this. Yes.
STEVEN BARTLETT: I guess money’s a proxy for power. But I’ve got a friend who’s a billionaire, and he is in those circles. And when I went to his house and had lunch with him one day, he knows lots of people in AI building the biggest AI companies in the world. And he gave me a cautionary warning across his kitchen table in London, where he gave me an insight into the private conversations these people have. Not the media interviews they do where they talk about safety and all these things, but actually what some of these individuals think is going to happen.
GEOFFREY HINTON: And what do they think is going to happen?
STEVEN BARTLETT: It’s not what they say publicly. You know, one person who I shouldn’t name, who is leading one of the biggest AI companies in the world, he told me that he knows this person very well, and he privately thinks that we’re heading towards this kind of dystopian world where we have just huge amounts of free time, we don’t work anymore, and this person doesn’t really give a fuck about the harm that it’s going to have on the world. And this person, who I’m referring to, is building one of the biggest AI companies in the world. And I then watch this person’s interviews.
GEOFFREY HINTON: Online, trying to figure out which of three people it is.
STEVEN BARTLETT: Yeah, well, it’s one of those three people, okay? And I watch this person’s interviews online, and I reflect on the conversation that my billionaire friend had with me who knows him, and I go, fucking hell. This guy’s lying publicly, like he’s not telling the truth to the world. And that’s haunted me a little bit. It’s part of the reason I have so many conversations around AI on this podcast because I’m like, I don’t know if they’re. I think they’re a. Some of them are a little bit sadistic about power. I think they like the idea that they will change the world, that they will be the one that fundamentally shifts the world.
GEOFFREY HINTON: I think Musk is clearly like that. Right.
STEVEN BARTLETT: He’s such a complex character that I don’t. I don’t really know how to place Musk.
GEOFFREY HINTON: He’s done some really good things, like pushing electric cars. That was a really good thing to do.
STEVEN BARTLETT: Yeah.
GEOFFREY HINTON: Some of the things he said about self driving were a bit exaggerated, but he. That was a really useful thing he did. Giving the Ukrainians communication during the war with Russia Starling. That was a really good thing he did. There’s a bunch of things like that, but he’s also done some very bad things.
STEVEN BARTLETT: So coming back to this point of the possibility of destruction and the motives of these big companies, are you at all hopeful that anything can be done to slow down the pace and acceleration of AI?
The Challenge of Slowing Down AI Development
GEOFFREY HINTON: Okay, there’s two issues. One is can you slow it down? And the other is can you make it so it will be safe in the end? It won’t wipe us all out. I don’t believe we’re going to slow it down. And the reason I don’t believe we’re going to slow it down is because there’s competition between countries and competition between companies within a country and all of that is making it go faster and faster. And if the US slowed it down, China wouldn’t slow it down.
STEVEN BARTLETT: Does Ilya think it’s possible to make AI safe?
GEOFFREY HINTON: I think he does. He won’t tell me what his secret source is. I’m not sure how many people know what his secret source is. I think a lot of the investors don’t know what his secret source is, but they’ve given him billions of dollars anyway because they have so much faith in Ilya, which isn’t foolish. I mean, he was very important in Alexnet, which got object recognition working well. He was the main. The main force behind the things like GPT2, which then led to ChatGPT. So I think having a lot of faith in Ilya is a very reasonable decision.
STEVEN BARTLETT: There’s something quite haunting about the guy that made and was the main force behind GPT2 which led rise to this whole revolution, left the company because of safety reasons. He knows something that I don’t know about. What might happen next?
GEOFFREY HINTON: Well, the company had Now, I don’t know the precise details, but I’m fairly sure the company had indicated that it would use a significant fraction of its resources of the compute time for doing safety research, and then it reduced that fraction. I think that’s one of the things that happened.
STEVEN BARTLETT: Yeah, that was reported publicly.
GEOFFREY HINTON: Yes.
STEVEN BARTLETT: Yeah. We’ve gotten to the autonomous weapons part of the risk framework.
The Threat of Mass Unemployment
GEOFFREY HINTON: Right. So the next one is joblessness.
STEVEN BARTLETT: Yeah.
GEOFFREY HINTON: In the past, new technologies have come in which didn’t lead to joblessness. New jobs were created. So the classic example people use is automatic teller machines. When automatic teller machines came in, a lot of bank tellers didn’t lose their jobs. They just got to do more interesting things. But here I think this is more like when they got machines in the Industrial revolution. And you can’t have a job digging ditches now because a machine can dig ditches much better than you can. And I think for mundane intellectual labor, AI is just going to replace everybody. Now it will may well be in the form of you have fewer people using AI assistance. So it’s a combination of a person and an AI assistant are now doing the work that 10 people could do previously.
STEVEN BARTLETT: People say that it will create new jobs, though, so we’ll be fine.
GEOFFREY HINTON: Yes, and that’s been the case for other technologies. But this is a very different kind of technology. If it can do all mundane human intellectual labor, then what new jobs is it going to create? You’d have to be very skilled to have a job that it couldn’t just do. So I don’t think they’re right. I think you can try and generalize from other technologies have come in, like computers or automatic teller machines, but I think this is different.
STEVEN BARTLETT: People use this phrase. They say, AI won’t take your job. A human using AI will take your job.
GEOFFREY HINTON: Yes, I think that’s true. But for many jobs, that’ll mean you need far fewer people. My niece answers letters of complaint to a health service. It used to take her 25 minutes. She’d read the complaint and she’d think how to reply, and she’d write a letter. And now she just scans it into a chatbot and it writes the letter. She just checks the letter. Occasionally she tells it to revise it. In some ways, the whole process takes her five minutes. That means she can answer five times as many letters, and that means they need five times fewer of her so she can do the job that five of her used to do. Now that will mean they need less people. In other jobs, like in healthcare, they’re Much more elastic. So if you could make doctors five times as efficient, we could all have five times as much healthcare for the same price, and that would be great. There’s almost no limit to how much healthcare people can absorb. They always want more healthcare. There’s no cost to it. There are jobs where you can make a person with an AI assistant much more efficient, and you won’t lead to less people because you’ll just have much more of that being done. But most jobs, I think, are not like that.
From Muscle to Mind: The Evolution of Automation
STEVEN BARTLETT: Am I right in thinking this sort of industrial revolution played a role in replacing muscles?
GEOFFREY HINTON: Yes, exactly.
STEVEN BARTLETT: And this revolution in AI replaces intelligence, the brain.
GEOFFREY HINTON: Yeah. So mundane intellectual labor is like having strong muscles and it’s not worth much anymore.
STEVEN BARTLETT: So muscles have been replaced. Now intelligence is being replaced. So what remains?
GEOFFREY HINTON: Maybe for a while, some kinds of creativity. But the whole idea of super intelligence is nothing remains. These things will get to be better than us at everything.
STEVEN BARTLETT: So what do we end up doing in such a world?
GEOFFREY HINTON: Well, if they work for us, we end up getting lots of goods and services for not much effort.
STEVEN BARTLETT: Okay, but that sounds tempting and nice, but I don’t know. There’s a cautionary tale in creating more and more ease for humans and it going badly.
GEOFFREY HINTON: Yes. And we need to figure out if we can make it go well. So the nice scenario is imagine a company with a CEO who is very dumb, probably the son of the former CEO, and he has an executive assistant who’s very smart. And he says, I think we should do this. And the executive assistant makes it all work. The CEO feels great. He doesn’t understand that he’s not really in control. And in some sense, he is in control. He suggests what the company should do. She just make it all work. Everything’s great. That’s the good scenario and the bad scenario. The bad scenario, she thinks, why do we need him?
The Timeline to Superintelligence
STEVEN BARTLETT: Yeah. I mean, in a world where we have superintelligence, which you don’t believe is that far away.
GEOFFREY HINTON: Yeah, I think it might not be that far away. It’s very hard to predict, but I think we might get it in like 20 years or even less.
STEVEN BARTLETT: So what’s the difference between what we have now in superintelligence? Because it seems to be really intelligent to me when I use like ChatGPT 3.0 or Gemini or.
GEOFFREY HINTON: Okay, so it’s already. AI is already better than us at a lot of things in particular areas, like chess, for example. AI is so much better than us that people will never beat those things again. Maybe the occasional win, but basically they’ll never be comparable again. Obviously the same in GO in terms of the amount of knowledge they have. Something like GPT4 knows thousands of times more than you do. There’s a few areas in which your knowledge is better than its, and in almost all areas, it just knows more than you do.
STEVEN BARTLETT: What areas am I better than it?
GEOFFREY HINTON: Probably in interviewing CEOs, you’re probably better at that. You’ve got a lot of experience at it. You’re a good interviewer. You know a lot about it. If you got GPT4 to interview a CEO, probably do a worse job.
STEVEN BARTLETT: Okay, I’m trying to think of that if I agree with that statement. GPT4, I think for sure.
GEOFFREY HINTON: Yeah.
STEVEN BARTLETT: But I guess you could train.
GEOFFREY HINTON: But it may not be long before.
STEVEN BARTLETT: Yeah, I guess you could train one on this. How I ask questions and what I do and.
GEOFFREY HINTON: And if you took a general purpose sort of foundation model and then you trained it up on not just you, but every. Every interviewer you could find doing interviews like this, but especially you, you’ll probably get to be quite good at doing your job, but probably not as good as you for a while.
STEVEN BARTLETT: Okay, so there’s a few areas left. And then super intelligence becomes. When it’s better than us at all.
GEOFFREY HINTON: Things, when it’s much smarter than you and almost all things, it’s better than you. Yeah.
STEVEN BARTLETT: And you say that this might be a decade away or so.
GEOFFREY HINTON: Yeah, it might be. It might be even closer. Some people think it’s even closer. I might well be much further. It might be 50 years away. That’s still a possibility. It might be that somehow training on human data limits you to not be much smarter than humans. My guess is between 10 and 20 years we’ll have superintelligence on this point of joblessness.
AI Agents in Action
STEVEN BARTLETT: It’s something that I’ve been thinking a lot about, in particular, because I started messing around with AI agents. And we released an episode on the podcast actually this morning where we had a debate about AI agents with some CEO of a big AI agent company and a few other people. And it was the first moment where I had no. It was another moment where I had a eureka moment about what the future might look like when I was able in the interview to tell this agent to order all of us drinks. And then five minutes later in the interview, you see the guy show up with the drinks. And I didn’t touch anything. I just told it to order us drinks to the studio.
GEOFFREY HINTON: And you didn’t know about who you normally got your drinks from. It figured that out from the web.
STEVEN BARTLETT: Yeah, figured out because it went on UberEats. It has my data, I guess, and we put it on the screen in Real time. So everyone at home can see the agent going through the Internet, picking the drinks, adding a tip for the driver, putting my address in, putting my credit card details in, and then the next thing you see is the drinks show up. So that was one moment, and then the other moment was when I used a tool called replit and I built software by just telling the agent what I wanted.
GEOFFREY HINTON: Yes. So amazing, right?
STEVEN BARTLETT: It’s amazing and terrifying at the same time.
GEOFFREY HINTON: Yes.
STEVEN BARTLETT: Because.
GEOFFREY HINTON: And if you can build software like that.
STEVEN BARTLETT: Right, yeah.
GEOFFREY HINTON: Remember that the AI, when it’s training, is using code, and if it can modify its own code, then it gets quite scary. Right.
STEVEN BARTLETT: Because it can modify.
GEOFFREY HINTON: It can change itself in a way we can’t change ourselves. We can’t change our innate endowment. Right. There’s nothing about itself that it couldn’t change.
Career Prospects in the Age of AI
STEVEN BARTLETT: On this point of joblessness, you have kids.
GEOFFREY HINTON: I do.
STEVEN BARTLETT: And they have kids. No, they don’t have kids. No grandkids yet. What would you be saying to people about their career prospects in a world of superintelligence?
GEOFFREY HINTON: What should we be thinking about in the meantime? I’d say it’s going to be a long time before it’s as good at physical manipulation as us.
STEVEN BARTLETT: Okay.
GEOFFREY HINTON: And so a good bet would be to be a plumber.
STEVEN BARTLETT: Until the humanoid robots show up in such a world where there is mass joblessness, which is not something that you just predict, but this is something that Sam Altman at OpenAI have heard him predict and many of the CEOs, Elon Musk. I watched an interview, which I’ll play on screen, of him being asked this question, and it’s very rare that you see Elon Musk silent for 12 seconds or whatever it was. And then he basically says something about he actually is living in suspended disbelief. That is, he’s basically just not thinking about it. When you think about advising your children.
GEOFFREY HINTON: On a career with so much that is changing, what do you tell them there’s going to be a value? Well, that is a tough question to answer. I would just say, you know, to sort of follow their heart in terms of what they find interesting to do or fulfilling to do. I mean, if I think about it.
STEVEN BARTLETT: Too hard, it frankly can be dispiriting.
GEOFFREY HINTON: And demotivating because, I mean, I go through. I put a lot of blood, sweat and tears into building the companies, and.
STEVEN BARTLETT: Then I’m like, should I be doing this? Because if I’m sacrificing time with friends.
GEOFFREY HINTON: And family that I would prefer, but then ultimately the AI can do all these things. Does that make sense?
STEVEN BARTLETT: I don’t know.
GEOFFREY HINTON: To some extent, I have to have deliberate suspension of disbelief in order to remain motivated. So I guess I would say just, you know, work on things that you find interesting, fulfilling, and that contribute some.
STEVEN BARTLETT: Good to the rest of society.
Emotional Impact of AI’s Future
GEOFFREY HINTON: Yeah. A lot of these threats, it’s very hard to. Intellectually, you can see the threat, but it’s very hard to come to terms with it emotionally. I haven’t come to terms with it emotionally yet.
STEVEN BARTLETT: What do you mean by that?
GEOFFREY HINTON: I haven’t come to terms with what the development of superintelligence could do to my children’s future. I’m okay. I’m 77. I’m going to be out of here soon. But for my children and my younger friends, my nephews and nieces and their children, I just don’t like to think about what could happen.
STEVEN BARTLETT: Why?
GEOFFREY HINTON: Because it could be awful.
STEVEN BARTLETT: In what way?
GEOFFREY HINTON: Well, if AI ever decided to take over, I mean, it would need people for a while to run the power stations and to design better analog machines to run the power stations. There’s so many ways it could get rid of people, all of which would, of course, be very nasty.
STEVEN BARTLETT: Is that part of the reason you do what you do now?
GEOFFREY HINTON: Yeah, I mean, I think we should be making a huge effort right now to try and figure out if we can develop it safely.
Job Displacement and Inequality
STEVEN BARTLETT: Are you concerned about the midterm impact potentially on your nephews and your kids in terms of their jobs as well?
GEOFFREY HINTON: Yeah, I’m concerned about all that.
STEVEN BARTLETT: Are there any particular industries that you think are most at risk? People talk about the creative industries a lot, and it’s sort of knowledge work. They talk about lawyers and accountants and stuff like that.
GEOFFREY HINTON: Yeah, so that’s why I mentioned plumbers. I think plumbers are less at risk.
STEVEN BARTLETT: Okay, I’m going to become a plumber.
GEOFFREY HINTON: Someone like a legal assistant, a paralegal. They’re not going to be needed for very long.
STEVEN BARTLETT: And is there a wealth inequality issue here that will rise?
GEOFFREY HINTON: Yeah, I think in a society which shared out things fairly, if you get a big increase in productivity, everybody should be better off. But if you can replace lots of people by AIs, then the people who get replaced will be worse off. And the company that supplies the AIs will be much better off and the company that uses the AIs, so it’s going to increase the gap between rich and poor. And we know that if you look at that gap between rich and poor, that basically tells you how nice a society is. If you have a big gap. You get very nasty societies in which people live in rural communities and put other people in mass jails. It’s not good to increase the gap between rich and poor.
STEVEN BARTLETT: The International Monetary Fund has expressed profound concerns that generative AI could cause massive labor disruptions and rising inequality and has called for policies that prevent this from happening. I read that in the Business Insider.
GEOFFREY HINTON: They’ve given any idea of what the policy should look like?
STEVEN BARTLETT: No.
GEOFFREY HINTON: Yeah, that’s the problem. I mean, if AI can make everything much more efficient and get rid of people for most jobs or have a person assisted by AI doing many, many people’s work, it’s not obvious what to do about it.
STEVEN BARTLETT: Universal basic income. Give everybody money.
GEOFFREY HINTON: Yeah, I think that’s a good start, and it stops people starving. But for a lot of people, their dignity is tied up with their job. I mean, who you think you are is tied up with you doing this job. Right?
STEVEN BARTLETT: Yeah.
GEOFFREY HINTON: And if we said we’ll give you the same money just to sit around, that would impact your dignity.
Digital Intelligence Superiority
STEVEN BARTLETT: You said something earlier about it surpassing or being superior to human intelligence. A lot of people, I think, like to believe that AI is on a computer and it’s something you can just turn off if you don’t like it.
GEOFFREY HINTON: Well, let me tell you why I think it’s superior. It’s digital. And because it’s digital, you can have. You can simulate a neural network on one piece of hardware, and you can simulate exactly the same neural network on a different piece of hardware. So you can have clones of the same intelligence. Now, you could get this one to go off and look at one bit of the Internet and this other one to look at a different bit of the Internet. And while they’re looking at these different bits of the Internet, they can be syncing with each other, so they keep their weights the same. The connection strengths. The same weights are connection strengths. So this one might look at something on the Internet and say, oh, I’d like to increase this strength of this connection a bit. And it can convey that information to this one. So it can increase the strength of that connection a bit based on this one’s experience.
STEVEN BARTLETT: And when you say the strength of the connection, you’re talking about learning.
GEOFFREY HINTON: That’s learning. Yes. Learning consists of saying, instead of this one giving 2.4 votes for whether that one should turn on, we’ll have this one give 2.5 votes for whether this one should turn on. That would be a little bit of learning. So these two different copies of the same neural Net are getting different experiences, they’re looking at different data, but they’re sharing what they’ve learned by averaging their weights together. And they can do that. Averaging at. You can average a trillion weights. When you and I transfer information, we’re limited to the amount of information in a sentence. And the amount of information in a sentence is maybe 100 bits. It’s very little information. We’re lucky if we’re transferring, like 10 bits a second. These things are transferring trillions of bits a second. So they’re billions of times better than us at sharing information, and that’s because they’re digital. And you can have two bits of hardware using the connection strengths in exactly the same way. We’re analog, and you can’t do that. Your brain is different from my brain. And if I could see the connection strengths between all your neurons, it wouldn’t do me any good because my neurons work slightly differently and they’re connected up slightly differently. So when you die, all your knowledge dies with you. When these things die, suppose you take these two digital intelligences that are clones of each other and you destroy the hardware they run on. As long as you’ve stored the connection strength somewhere, you can just build new hardware that executes the same instructions so it’ll know how to use those connection strengths. And you’ve recreated that intelligence so they’re immortal. We’ve actually solved the problem of immortality, but it’s only for digital things.
AI’s Superior Knowledge and Creativity
STEVEN BARTLETT: So it knows. It will essentially know everything that humans know, but more because it will learn new things.
GEOFFREY HINTON: It will learn new things. It will also see all sorts of analogies that people probably never saw. So, for example, at the point when GPT4 couldn’t look on the web, I asked it, why is a compost heap like an atom bomb? Off you go.
STEVEN BARTLETT: I have no idea.
GEOFFREY HINTON: Exactly. Excellent. That’s exactly what most people would say. It said, well, the time scales are very different and the energy scales are very different. But then it went on to talk about how a compost heap, as it gets hotter, generates heat faster, and an atom bomb, as it produces more neutrons, generates neutrons faster. And so they’re both chain reactions, but at very different time and energy scales. And I believe GPT4 had seen that during its training, it had understood the analogy between a compost heap and an atom bomb. And the reason I believe that is, if you’ve only got a trillion connections, remember, you have 100 trillion, and you need to have thousands of times more knowledge than a person. You need to compress information into those connections. And to compress information, you need to see analogies between different things. In other words, it needs to see all the things that are chain reactions and understand the basic idea of a chain reaction. Encode that and then code the ways in which they’re different. And that’s just a more efficient way of coding things than coding each of them separately. So it’s seen many, many analogies, probably many analogies that people have never seen. That’s why I also think that people say these things will never be creative. They’re going to be much more creative than us because they’re going to see all sorts of analogies we never saw. And a lot of creativity is about seeing strange analogies.
STEVEN BARTLETT: People are somewhat romantic about the specialness of what it is to be human. And you hear lots of people saying it’s very, very different. It’s a computer. We are, you know, we’re conscious. We are creatives. We have these sort of innate, unique abilities that the computers will never have. What do you say to those people?
The Nature of Consciousness and Subjective Experience
GEOFFREY HINTON: I’d argue a bit with the innate. So the first thing I say is we have a long history of believing people were special, and we should have learned by now. We thought we were at the center of the universe. We thought we were made in the image of God. White people thought they were very special. We just tend to want to think we’re special.
My belief is that more or less everyone has a completely wrong model of what the mind is. Let’s suppose I drink a lot or I drop some acid and not recommended. And I say to you, I have the subjective experience of little pink elephants floating in front of me. Most people interpret that as there’s some kind of inner theater called the mind. And only I can see what’s in my mind. And in this inner theater, there’s little pink elephants floating around.
So in other words, what’s happened is my perceptual system’s gone wrong. And I’m trying to indicate to you how it’s gone wrong and what it’s trying to tell me. And the way I do that is by telling you what would have to be out there in the real world for it to be telling the truth. And so these little pink elephants, they’re not in some inner theater. These little pink elephants are hypothetical things in the real world. And that’s my way of telling you how my perceptual system’s telling me fibs.
So now let’s do that with a chatbot. Yeah. Because I believe that current multimodal chatbots have subjective experiences and very few people believe that. But I’ll try and make you believe it. So suppose I have a multimodal chatbot. It’s got a robot arm so it can point, and it’s got a camera so it can see things. And I put an object in front of it and I say point at the object. It goes like this, no problem.
Then I put a prism in front of its lens. And so then I put an object in front of it and I say point at the object and it gets there. And I say, no, that’s not where the object is. The object is actually straight in front of you. But I put a prism in front of your lens and the chatbot says, oh, I see, the prism bent the light rays so the object’s actually there. But I had the subjective experience that it was there.
Now if the chatbot says that it’s using the word subjective experience exactly the way people use them, it’s an alternative view of what’s going on. They’re hypothetical states of the world which if they were true, would mean my perceptual system wasn’t lying. And that’s the best way I can tell you what my perceptual system’s doing when it’s lying to me.
Machine Emotions and Feelings
Now we need to go further to deal with sentience and consciousness and feelings and emotions. But I think in the end they’re all going to be dealt with in a similar way. There’s no reason machines can’t have them all. But people say machines can’t have feelings and people are curiously confident about that. I have no idea why.
Suppose I make a battle robot and it’s a little battle robot and it sees a big battle robot that’s much more powerful than will be really useful if it got scared. Now when I get scared, various physiological things happen that we don’t need to go into and those won’t happen with the robot. But all the cognitive things like I better get the hell out of here and I better sort of change my way of thinking so I focus and focus and focus. Don’t get distracted. All of that will happen with robots too.
People will build in things so that they when the circumstances such they should get the hell out of there, they get scared and run away. They’ll have emotions. Then they won’t have the physiological aspects, but they will have all the cognitive aspects. And I think it would be odd to say they’re just simulating emotions. No, they’re really having those emotions. The little robot got scared and ran away.
STEVEN BARTLETT: It’s not running away because of adrenaline. It’s running away because of a sequence of sort of neurological. In its neural net processes happened which.
GEOFFREY HINTON: Which have the equivalent effect to adrenaline.
STEVEN BARTLETT: So do you.
GEOFFREY HINTON: And it’s not just adrenaline. Right. There’s a lot of cognitive stuff goes on when you get scared.
STEVEN BARTLETT: Yeah. So do you think that there is conscious AI and when I say conscious, I mean that represents the same properties of consciousness that a human has.
The Philosophy of Machine Consciousness
GEOFFREY HINTON: There’s two issues here. There’s a sort of empirical one and a philosophical one. I don’t think there’s anything in principle that stops machines from being conscious. I’ll give you a little demonstration of that before we carry on.
Suppose I take your brain and I take one brain cell in your brain, and I replace it by. It’s a bit black mirror. Like, I replace it by a little piece of nanotechnology that’s just the same size, that behaves in exactly the same way. When it gets pings from other neurons, it sends out pings just as the brain cell would have. So the other neurons don’t know anything’s changed. Okay, I’ve just replaced one of your brain cells with this little piece of nanotechnology. Would you still be conscious?
STEVEN BARTLETT: Yeah.
GEOFFREY HINTON: Now you can see where this argument’s going.
STEVEN BARTLETT: Yeah. So if you replaced all of them.
GEOFFREY HINTON: As I replace them all, at what point do you stop being conscious?
STEVEN BARTLETT: Well, people think of consciousness as this, like, ethereal thing that exists maybe beyond the brain cells.
GEOFFREY HINTON: Yeah. Well, people have a lot of crazy ideas. People don’t know what consciousness is, and they often don’t know what they mean by it. And then they fall back on saying, well, I know it because I’ve got it, and I can see that I’ve got it. And they fall back on this theater model of the mind, which I think is nonsense.
STEVEN BARTLETT: What do you think of consciousness as if you had to try and define it. Because I think of it as just like the awareness of myself. I don’t know.
GEOFFREY HINTON: I think it’s a term we’ll stop using. Suppose you want to understand how a car works. Well, you know, some cars have a lot of oomph, and other cars have a lot less oomph. Like an Aston Martin’s got lots of oomph, and her little Toyota Corolla doesn’t have much oomph. But oomph isn’t a very good concept for understanding cars. If you want to understand cars, you need to understand about electric engines or petrol engines and how they work. And it gives rise to oomph. But oomph isn’t a very useful explanatory concept. It’s the kind of essence of a car, it’s the essence of an Aston Martin, but it doesn’t explain much.
I think consciousness is like that and I think we’ll stop using that term. But I don’t think there’s anything, any reason why a machine shouldn’t have it. If your view of consciousness is that it intrinsically involves self awareness, then the machine’s got to have self awareness. It’s got to have cognition about its own cognition and stuff. But I’m a materialist through and through, and I don’t think there’s any reason why a machine shouldn’t have consciousness.
STEVEN BARTLETT: Do you think they do then have the same consciousness that we think of ourselves as being uniquely given as a gift when we’re born?
GEOFFREY HINTON: I’m ambivalent about that at present. So I don’t think there’s this hard line. I think as soon as you have a machine that has some self awareness, it’s got some consciousness. I think it’s an emergent property of a complex system. It’s not a sort of essence that’s throughout the universe. It’s. You make this really complicated system that’s complicated enough to have a model of itself and it does perception. And I think then you’re beginning to get a conscious machine.
So I don’t think there’s any sharp distinction between what we’ve got now and conscious machines. I don’t think it’s going to. One day we’re going to wake up and say, hey, if you put this special chemical in, it becomes conscious. It’s not going to be like that.
AI Emotions and Practical Applications
STEVEN BARTLETT: I think we all wonder if these computers are thinking like we are on their own when we’re not there. And if they’re experiencing emotions, if they’re contending with. I think we think about things like love and things that feel unique to biological species. Are they sat there thinking? Do they have concerns?
GEOFFREY HINTON: I think they really are thinking. And I think as soon as you make AI agents, they will have concerns. If you wanted to make an effective AI agent, let’s take a call center. In a call center you have people. At present, they have all sorts of emotions and feelings which are kind of useful.
So suppose I call up the call center and I’m actually lonely and I don’t actually want to know the answer to why my computer isn’t working. I just want somebody to talk to. After a while, the person in the call center will either get bored or get annoyed with me and will terminate it. Well, you replace them by an AI agent. The AI agent needs to have the same kind of responses. If someone’s just called up because they just want to talk to the AI agent, and we’re happy to talk for the whole day to the AI agent. That’s not good for business. And you want an AI agent that either gets bored or gets irritated and says, I’m sorry, but I don’t have time for this. Once it does that, I think it’s got emotions.
Now, like I say, emotions have two aspects to them. There’s the cognitive aspect and the behavioral aspect, and then there’s a physiological aspect, and those go together with us. And if the AI agent gets embarrassed, you won’t go red.
STEVEN BARTLETT: Yeah.
GEOFFREY HINTON: So there’s no physiological skin won’t start sweating. But it might have all the same behavior. And in that case, I’d say, yeah, it’s having emotion. It’s got an emotion.
STEVEN BARTLETT: So it’s going to have the same sort of cognitive thought, and then it’s going to act upon that cognitive thought in the.
GEOFFREY HINTON: Same way, but without the physiological responses.
STEVEN BARTLETT: And does that matter that it doesn’t go red in the face? And it’s just a different. I mean, that’s a response to the.
GEOFFREY HINTON: It makes it somewhat different from us.
STEVEN BARTLETT: Yeah.
GEOFFREY HINTON: For some things, the physiological aspects are very important, like love. They’re a long way from having love the same way we do, but I don’t see why they shouldn’t have emotions. So I think what’s happened is people have a model of how the mind works and what feelings are and what emotions are, and their model is just wrong.
The Path to Google
STEVEN BARTLETT: What brought you to Google? You worked at Google for about a decade, right?
GEOFFREY HINTON: Yeah.
STEVEN BARTLETT: What brought you there?
GEOFFREY HINTON: I have a son who has learning difficulties, and in order to be sure he would never be out on the street, I needed to get several million dollars. And I wasn’t going to get that as an academic. I tried. So I taught a Coursera course in the hope that I’d make lots of money that way, but there was no money in that. So I figured out, well, the only way to get millions of dollars is to sell myself to a big company.
And so when I was 65, fortunately for me, I had two brilliant students who produced something called Alexnet, which was neural net that was very good at recognizing objects and images. And so Ilya and Alex and I set up a little company and auctioned it. And we actually set up an auction where we had a Number of big companies bidding for us.
STEVEN BARTLETT: And that company was called Alexnet?
GEOFFREY HINTON: No, the network that recognized objects was called Alexnet. The company was called DNN Research, Deep neural network Research.
STEVEN BARTLETT: And it was doing things like this. I’ll put this graph up on the screen.
GEOFFREY HINTON: That’s Alexnet.
STEVEN BARTLETT: This picture shows eight images and Alexnet’s ability, which is your company’s ability to spot what was in those images.
GEOFFREY HINTON: Yeah. So it could tell the difference between various kinds of mushrooms. And about 12% of Imagenet is dogs. And to be good at Imagenet, you have to tell the difference between very similar kinds of dog, and we’ve got to be very good at that.
STEVEN BARTLETT: And your company, Alexnet, won several awards, I believe, for its ability to outperform its competitors. And so Google ultimately ended up acquiring your technology.
GEOFFREY HINTON: Google acquired that technology and some other technology.
STEVEN BARTLETT: And you went to work at Google at age 66.
GEOFFREY HINTON: I went at age 65 to work at Google.
STEVEN BARTLETT: 65. And you left at age 76?
GEOFFREY HINTON: 75.
STEVEN BARTLETT: 75.
GEOFFREY HINTON: I worked there for more or less exactly 10 years.
STEVEN BARTLETT: And what were you doing there?
The Google Years and AI Breakthrough
GEOFFREY HINTON: Okay. They were very nice to me. They said, pretty much, you can do what you like. I worked on something called distillation that did really work well, and that’s now used all the time in AI. And distillation is a way of taking what a big model knows, a big neural net knows, and getting that knowledge into a small neural net.
Then at the end, I got very interested in analog computation and whether it would be possible to get these big language models running in analog hardware so they used much less energy. And it was when I was doing that work that I began to really realize how much better digital is for sharing information.
STEVEN BARTLETT: Was there a eureka moment?
GEOFFREY HINTON: There was a eureka month or two. And it was a sort of coupling of ChatGPT coming out. Although Google had very similar things a year earlier, and I’d seen those, and that had a big effect on me. The closest I had to a eureka moment was when a Google system called Palm was able to say why a joke was funny. And I’d always thought of that as a kind of landmark. If it can say why a joke’s funny, it really does understand. And it could say why a joke was funny. And that, coupled with realizing why digital is so much better than analog for sharing information, suddenly made me very interested in AI safety and that these things were going to get a lot smarter than us.
Leaving Google for AI Safety
STEVEN BARTLETT: Why did you leave Google?
GEOFFREY HINTON: The main reason I left Google was because I was 75 and I wanted to retire. I’ve done a very bad job of that. The precise timing of when I left Google was so that I could talk freely at a conference at MIT. But I left because I’m old and I was finding it harder to program. I was making many more mistakes when I programmed, which is very annoying.
STEVEN BARTLETT: You wanted to talk freely at a conference at MIT?
GEOFFREY HINTON: Yes, organized by MIT Tech Review.
STEVEN BARTLETT: What did you want to talk about freely?
GEOFFREY HINTON: AI safety.
STEVEN BARTLETT: And you couldn’t do that while you were at Google?
GEOFFREY HINTON: Well, I could have done it while I was at Google and Google encouraged me to stay and work on AI safety and said I could do whatever I liked on AI safety. You kind of censor yourself. If you work for a big company. You don’t feel right saying things that will damage the big company. Even if you could get away with it. It just feels wrong to me.
I didn’t leave because I was cross with anything Google was doing. I think Google actually behaved very responsibly when they had these big chatbots. They didn’t release them, possibly because they were worried about their reputation. They had a very good reputation and they didn’t want to damage it. So OpenAI didn’t have a reputation and so they could afford to take the gamble.
STEVEN BARTLETT: I mean, there’s also a big conversation happening around how it will cannibalize their core business in search.
GEOFFREY HINTON: There is now. Yes.
STEVEN BARTLETT: Yeah, yeah. And it’s the older innovators dilemmas to some degree. I guess they’re contending with that.
Messages to World Leaders and the Public
STEVEN BARTLETT: I’m continually shocked by the types of individuals that listen to this conversation because they come up to me sometimes. So I hear from politicians, I hear from some royal people, I hear from entrepreneurs all over the world, whether they are the entrepreneurs building some of the biggest companies in the world or they’re, you know, early stage startups. For those people that are listening to this conversation now that are in positions of power and influence world leaders, let’s say, what’s your message to them?
GEOFFREY HINTON: I’d say what you need is highly regulated capitalism. That’s what seems to work best.
STEVEN BARTLETT: And what would you say to the average person doesn’t work in the industry, somewhat concerned about the future, doesn’t know if they’re helpless or not, what should they be doing in their own lives?
GEOFFREY HINTON: My feeling is there’s not much they can do. This isn’t going to be decided by just as climate change isn’t going to be decided by people separating out the plastic bags from the compostables. That’s not going to have much effect. It’s going to be decided by whether the lobbyists for the big energy companies can be kept under control. I don’t think there’s much people can do to accept for try and pressure their governments to force the big companies to work on AI safety that they can do.
Family Legacy and Remarkable Lineage
STEVEN BARTLETT: You lived a fascinating, fascinating, winding life. I think one of the things most people don’t know about you is that your family has a big history of being involved in tremendous things. You have a family tree which is one of the most impressive that I’ve ever seen or read about. Your great, great grandfather, George Ball, founded the Boolean Algebra Logic, which is one of the foundational principles of modern computer science. You have your great great grandmother, Mary Everest Ball, who was a mathematician and educator who made huge leaps forward in mathematics from what I was able to ascertain. I mean, I can get. The list goes on and on and on. I mean, your great, great uncle George Everest is what Mount Everest is named after, is that correct?
GEOFFREY HINTON: I think he’s my great, great, great uncle. His niece married George Ball. So Mary Ball was Mary Everest Ball. She was the niece of Everest.
STEVEN BARTLETT: Your first cousin once removed. Joan Hinton was involved in the new nuclear physicist who worked on the Manhattan Project, which is the World War II development of the first nuclear bomb.
GEOFFREY HINTON: Yeah. She was one of the two female physicists at Los Alamos. And then after they dropped the bomb, she moved to China.
STEVEN BARTLETT: Why?
GEOFFREY HINTON: She was very cross with them dropping the bomb. And her family had a lot of links with China. Her mother was friends with Chairman Mao. Quite weird when you look back at it.
Life Lessons and Regrets
STEVEN BARTLETT: Your life, Jeffrey, we have the hindsight you have now and the retrospective clarity. What might you have done differently if you were advising me?
GEOFFREY HINTON: I guess I have two pieces of advice. One is if you have an intuition that people are doing things wrong and there’s a better way to do things. Don’t give up on that intuition just because people say it’s silly. Don’t give up on the intuition until you’ve figured out why it’s wrong. Figure out for yourself why that intuition isn’t correct. And usually it’s wrong if it disagrees with everybody else. And you’ll eventually figure out why it’s wrong. But just occasionally you’ll have an intuition that’s actually right and everybody else is wrong. And I lucked out that way early on.
I thought neural nets are definitely the way to go to make AI. And almost everybody said that was crazy. And I stuck with it because I couldn’t. It just seemed to me it was obviously right. Now, the idea that you should stick with your intuitions isn’t going to work if you have bad intuitions. But if you have bad intuitions, you’re never going to do anything anyway. So you might as well stick with them.
STEVEN BARTLETT: And in your own career journey, is there anything you look back on and say, with the hindsight I have now, I should have taken a different approach at that juncture.
GEOFFREY HINTON: I wish I spent more time with my wife and with my children. When they were little, I was kind of obsessed with work.
STEVEN BARTLETT: Your wife passed away?
GEOFFREY HINTON: Yeah.
STEVEN BARTLETT: From ovarian cancer.
GEOFFREY HINTON: No, that was another wife. And I had two wives. Died of cancer.
STEVEN BARTLETT: Oh, really?
GEOFFREY HINTON: Sorry. The first one died of ovarian cancer and the second one died of pancreatic cancer.
STEVEN BARTLETT: And you wish you’d spent more time with her?
GEOFFREY HINTON: With the second wife, yeah. Who was a wonderful person.
STEVEN BARTLETT: Why do you say that? In your 70s? What is it that you figured out that I might not know yet?
GEOFFREY HINTON: Oh, just because she’s gone and I can’t spend more time with her now.
STEVEN BARTLETT: But you didn’t know that at the time.
GEOFFREY HINTON: At the time, you think. I mean, it was likely I would die before her just because she was a woman and I was a man. I didn’t. I just didn’t spend enough time when I could.
STEVEN BARTLETT: I think I inquire there because I think there’s many of us that are so consumed with what we’re doing professionally that we kind of assume almost immortality with our partners, because they’ve always been there. So we…
GEOFFREY HINTON: Yeah, I mean, she was very supportive of me spending a lot of time working, but…
STEVEN BARTLETT: And why do you say your children as well? What’s the…
GEOFFREY HINTON: Well, I didn’t spend enough time with them when they were little.
STEVEN BARTLETT: And you regret that now?
GEOFFREY HINTON: Yeah.
Final Thoughts on AI’s Future
STEVEN BARTLETT: If you had a closing message for my listeners about AI and AI safety, what would that be? Jeffrey?
GEOFFREY HINTON: There’s still a chance that we can figure out how to develop AI that won’t want to take over from us. And because there’s a chance, we should put enormous resources into trying to figure that out, because if we don’t, it’s going to take over.
STEVEN BARTLETT: And are you hopeful?
GEOFFREY HINTON: I just don’t know. I’m agnostic.
STEVEN BARTLETT: You must get in bed at night, and when you’re thinking to yourself about probabilities of outcomes, there must be a bias in one direction, because there certainly is for me. Imagine everyone listening now has a internal prediction that they might not say out loud, but of how they think it’s going to play out.
GEOFFREY HINTON: I really don’t know. I genuinely don’t know. I think it’s incredibly uncertain. When I’m feeling slightly depressed, I think people are toast. AI is going to take over. When I’m feeling cheerful. I think we’ll figure out a way.
STEVEN BARTLETT: Maybe one of the facets of being a human is because we’ve always been here. Like we were saying about our loved ones and our relationships, we assume casually that we will always be here and we’ll always figure everything out. But there’s a beginning and an end to everything, as we saw from the dinosaurs. I mean…
GEOFFREY HINTON: Yeah. And we have to face the possibility that unless we do something soon, we’re near the end.
STEVEN BARTLETT: We have a closing tradition on this podcast where the last guest leaves a question in the diary and the question that they’ve left for you is, with everything that you see ahead of us, what is the biggest threat you see to human happiness?
The Urgent Threat of Job Displacement
GEOFFREY HINTON: I think the joblessness is a fairly urgent short term threat to human happiness. I think if you make lots and lots of people unemployed, even if they get universal basic income, they’re not going to be happy.
STEVEN BARTLETT: Because they need purpose.
GEOFFREY HINTON: Because they need purpose. Yes.
STEVEN BARTLETT: And strength.
GEOFFREY HINTON: They need to feel they’re contributing something, they’re useful.
STEVEN BARTLETT: And do you think that outcome, that there’s going to be huge job displacement is more probable than not?
GEOFFREY HINTON: Yes, I do. That one I think is definitely more probable than not. If I worked in a call centre, I’d be terrified.
STEVEN BARTLETT: And what’s the time frame for that in terms of mass?
GEOFFREY HINTON: I think it’s beginning to happen already. I read an article in the Atlantic recently that said it’s already getting hard for university graduates to get jobs. And part of that may be that people are already using AI for the jobs they would have got.
Real-World Examples of AI Job Displacement
STEVEN BARTLETT: I spoke to the CEO of a major company that everyone will know of, lots of people use. And he said to me in DMs that they used to have just over 7,000 employees. He said by last year they were down to, I think 5,000. He said right now they have 3,600. And he said by the end of summer, because of AI agents, they’ll be down to 3,000.
GEOFFREY HINTON: So it’s happening already?
STEVEN BARTLETT: Yes. He’s halved his workforce because AI agents can now handle 80% of the customer service inquiries and other things. So it’s happening already. So urgent action is needed.
GEOFFREY HINTON: Yep.
STEVEN BARTLETT: I don’t know what that urgent action is.
GEOFFREY HINTON: That’s a tricky one because that depends very much on the political system. And political systems are all going in the wrong direction at present.
Practical Advice for the Future
STEVEN BARTLETT: What do we need to do? Save up money? Like do we save money? Do we move to another part of the world?
GEOFFREY HINTON: I don’t know.
STEVEN BARTLETT: What would you tell your kids to do. They said, dad, look, there’s going to be loads of job displacement because I.
GEOFFREY HINTON: I worked for Google for 10 years. They have enough money.
STEVEN BARTLETT: Okay? Okay.
GEOFFREY HINTON: So they’re not typical.
STEVEN BARTLETT: What if they didn’t have money?
GEOFFREY HINTON: Trained to be a plumber.
STEVEN BARTLETT: Really?
GEOFFREY HINTON: Yeah.
Closing Thoughts
STEVEN BARTLETT: Geoffrey, thank you so much. You’re the first Nobel Prize winner that I’ve ever had a conversation with, I think, in my life. So that’s a tremendous honor. And you received that award for a lifetime of exceptional work and pushing the world forward in so many profound ways that will lead to great and that have led to great advancements and things that matter so much to us. And now you’ve turned this season in your life to shining a light on some of your own work, but also on the broader risks of AI and how it might impact us adversely. And there’s very few people that have worked inside the machine of a Google or a big tech company that have contributed to the field of AI that are now at the very forefront of warning us against the very thing that they worked upon.
GEOFFREY HINTON: There are actually a surprising number of us now.
STEVEN BARTLETT: They’re not as public, and they’re actually quite hard to get to have these kinds of conversations because many of them are still in that industry. So, you know, someone who tries to contact these people often and ask, invites them to have conversations. They often are a little bit hesitant to speak openly, so they speak privately, but they’re less willing to openly because maybe they still have something, some sort of incentives at play.
GEOFFREY HINTON: I have an advantage over them, which is I’m older, so I’m unemployed, so I can say what I have.
STEVEN BARTLETT: Well, there you go. So thank you for doing what you do. It’s a real honor, and please do continue to do it.
GEOFFREY HINTON: Thank you.
STEVEN BARTLETT: Thank you so much.
GEOFFREY HINTON: People think I’m joking when I say that, but I’m not.
STEVEN BARTLETT: I’ll be plumbing fish.
GEOFFREY HINTON: Yeah. And plumbers are pretty well paid.
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