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Godfather of AI- I Tried to Warn Them, but We’ve Already Lost Control! Geoffrey Hinton

The Diary Of A CEO with Steven Bartlett

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  • Neural Networks Beat Logic AI
    • Geoffrey Hinton pioneered AI by believing neural networks modeled after the brain could learn complex tasks.
    • Initially dismissed, his approach proved superior to logic-based AI methods for vision and speech recognition. Transcript: Geoffrey Hinton To be a plumber. Really? Steven Bartlett Yeah. Okay, I’m going to become a plumber. Jeffrey Hinton is the Nobel Prize winning pioneer whose groundbreaking work has shaped AI and the future of humanity. Why do they call you the godfather of AI? Geoffrey Hinton Because there weren’t many people who believed that we could model AI on the brain so that it learned to do complicated things like recognize objects and images or even do reasoning. And I pushed that approach for 50 years. And then Google acquired that technology. And I worked there for 10 years on something that’s now used all the time in AI. And then you left? Yeah. Steven Bartlett Why? Geoffrey Hinton So that I could talk freely at a conference. What did you want to talk about freely? How dangerous AI could be. I realized that these things will one day get smarter than us and we’ve never had to deal with that. And if you want to know what life’s like when you’re not the apex intelligence, ask a chicken. So there’s risks that come from people misusing AI and then there’s risks from AI getting super smart and deciding it doesn’t need us. Is that a real risk? Yes, it is. But they’re not going to stop it because it’s too good for too many things. Steven Bartlett What about regulations? Geoffrey Hinton They have some, but they’re not designed to deal with most of the threats. Like the European regulations have a clause that says none of these apply to military uses of AI. Really? Yeah, it’s crazy. One of your students left OpenAI. Yeah, he was probably the most important person behind the development of the early versions of Chatt-GPT, and I think he left because he had safety concerns. We should recognize that this stuff is an existential threat, and we have to face the possibility that unless we do something soon, we’re near the end. Steven Bartlett So let’s do the risks in what we end up doing in such a world. Quick one before we get back to this episode. Just give me 30 seconds of your time. Two things I wanted to say. The first thing is a huge thank you for listening and tuning into the show week after week. It means the world to all of us and this really is a dream that we absolutely never had and couldn’t have imagined getting to this place. But secondly, it’s a dream where we feel like we’re only just getting started. And if you enjoy what we do here, please join the 24% of people that listen to this podcast regularly and follow us on this app. Here’s a promise I’m going to make to you. I’m going to do everything in my power to make this show as good as I can now and into the future. We’re going to deliver the guests that you want me to speak to, and we’re going to continue to keep doing all of the things you love about this show. Thank you. Thank you so much. Back to the episode. Geoffrey Hinton. They call you the godfather of AI. Geoffrey Hinton Yes, they do. Why do they call you that? 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, they’d all 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 in 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 Suskeva 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 who 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? I think the neural net approach would have been accepted much sooner if either of them had 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 it 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 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 then. Steven Bartlett When did that change? Geoffrey Hinton It changed for the general population when ChatGPT came out. It changed for me when I realized that the kinds (Time 0:00:36)
  • Two Main AI Risk Types
    • AI risks split into misuse by humans and superintelligence that may reject human control.
    • The existential threat is real but uncertain, and experts hold varying extreme views on it. Transcript: Steven Bartlett As we sit here today, what are the big concerns you have around safety of AI? If we were to list the top couple that are really front of mind and that we should be thinking about. Can I have more than a couple? Go ahead. 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. Is that a real risk? 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 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, Jan LeCart, 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 us 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 probabilities in between, and I don’t know where to estimate it in between. I often say 10 to 20% chance I’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. 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. But we’re still here. 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. 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. 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. 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, aren’t we just putting ourselves… Yeah, put some of the competitive disadvantage. 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. Yes. 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’ve got. Steven Bartlett So free- 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 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. 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 risks. Let’s talk about all the risks from bad human actors (Time 0:07:51)
  • Gaps in AI Regulation & Safety
    • Current AI regulations exclude military AI, allowing dangerous autonomous weapon development.
    • Safety concerns are driving some top AI researchers to leave companies like OpenAI. Transcript: Geoffrey Hinton Then there’s… So I’ve distinguished these two kinds of risks. 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, is? Geoffrey Hinton 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 then 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 And then- Very annoying annoying. The heartbreaking part is you get the messages from people that have fallen for the scam. And they’ve lost 500 pounds or $500. Geoffrey Hinton And they cross with you because you recommended it. And I’m like, I’m sad for them. It’s very annoying. Yeah. 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. Geoffrey Hinton And these already, 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 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. Steven Bartlett Because they can think for themselves and discover new ways to attack. Geoffrey Hinton They can draw new conclusions from much more data than a person ever saw. 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? 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 do you have a phone that’s not connected to the internet? Do you have any, like, you know, 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. 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 was lots of talk around COVID and the Wuhan Laboratory and what they were doing in gain-of research. But I’m wondering if in, you know, China or Russia or in Iran or something, the government could fund a program for a small group of scientists to make a virus that they could, you know? I think they could, yes. Geoffrey Hinton 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. 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 wants 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. How do you mean? Because you get all this data on the public? 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 to manipulate them. Because you can make an AI that… You can send messages that they’ll find very convincing, telling them not to vote, for example. (Time 0:12:52)
  • AI Fuels Social Division
    • Social media algorithms deepen echo chambers by showing users extreme content for profit.
    • This drives polarization as users’ realities diverge based on personalized feeds. Transcript: Geoffrey Hinton 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… Yes. Geoffrey Hinton And what they’ve done is turned off lots of security controls, got rid of some of the organization to protect against that. So that’s corrupting elections. OK. Then there’s creating these two echo chambers by organisations like YouTube and Facebook, showing people things that will make them indignant. People love to be indignant. Indignant as in angry? What does indignant mean? Feeling I’m sort of angry but feeling righteous. Okay. 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. Yeah. Steven Bartlett Okay, so putting us in echo chambers and dividing us. Yes. Geoffrey Hinton 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 balanced things, they wouldn’t get so many clicks and they wouldn’t be able to sell so many advertisements. 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. Steven Bartlett So you’re getting your biases confirmed all the time. Further and further and further and further, which means you’re driving away from people. Geoffrey Hinton Which is now, in the States, there’s two communities that don’t hardly talk to each other. Steven Bartlett I’m not sure people realise 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, 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. Right. Geoffrey Hinton 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 newsfeed, 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, oh, 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, 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 pretty… It’s worrisome. Yeah. 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 has done very well for us. (Time 0:19:14)
  • Autonomous Weapons Lower War Friction
    • Lethal autonomous weapons lower the barriers to war by removing human casualties.
    • This risk may increase conflicts as powerful countries invade weaker ones with fewer protests. Transcript: Geoffrey Hinton What’s next? Lethal autonomous weapons. Steven Bartlett Lethal autonomous weapons. Geoffrey Hinton 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. 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 out of 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… No, no. Geoffrey Hinton 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. Steven Bartlett More often because they can. Geoffrey Hinton Yeah, and it’s not a nice thing to do. Steven Bartlett So it brings down the friction of war. It brings down the cost of doing an invasion. And these machines will be smarter at warfare as well. Geoffrey Hinton Well, even when the machines aren’t smarter. So the lethal autonomous weapons, 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. (Time 0:26:41)
  • Spooky Drone Tracking Experience
    • Geoffrey Hinton experienced a small drone silently tracking him through the woods, which felt very spooky.
    • This illustrates how accessible autonomous tracking technology has become cheaply and easily. Transcript: Geoffrey Hinton 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. And if I moved over there, moved over there, it could just track me for 200 pounds. (Time 0:28:43)
  • Combined AI Threats Are Dire
    • AI risks can compound, such as a superintelligence creating lethal viruses or triggering nuclear war.
    • Preventing AI from wanting to harm humans is crucial, as controlling it once malicious is unlikely. Transcript: Geoffrey Hinton 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 super intelligence wanted to get rid of us, it would probably go for something biological like that that wouldn’t affect it. Steven Bartlett Do you not 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 your superintelligence could get rid of us. It’s not worth speculating about. What is to stop it? 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 chick in. Steven Bartlett Yeah, I was thinking 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. Right. I can’t even talk to him. (Time 0:29:26)
  • AI’s Massive Impact on Jobs
    • AI will replace most mundane intellectual labor, leading to widespread job displacement.
    • Some jobs may become more efficient and elastic, but overall fewer people will be needed. Transcript: Geoffrey Hinton 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 may well be in the form of you have fewer people using AI assistants. 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 labour, 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 that come in, like computers or automatic telemachines, 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. 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 if 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. (Time 0:40:41)
  • Plumbing: Smart Career Choice
    • Until humanoid robots can perform physical tasks, careers in trades like plumbing remain safe.
    • Plumbers offer good job security for the foreseeable future amid AI disruption. Transcript: Steven Bartlett What should we be thinking about? Geoffrey Hinton In the meantime, I’d say it’s going to be a long time before it’s as good at physical manipulation as us. Okay. And so a good bet would be to be a plumber. Until (Time 0:51:16)
  • Digital AI Outperforms Humans
    • AI is superior digitally by enabling exact clones that can share vast knowledge instantly.
    • Humans communicate limited information, but AI networks transfer trillions of bits quickly and efficiently. Transcript: Geoffrey Hinton Well, let me tell you why I think it’s superior. OK. It’s digital. And because it’s digital, 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, their connection strengths the same, weights Of 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. That’s learning, yes. Geoffrey Hinton 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 like, 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’s 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 immortal we’ve actually solved the problem of immortality but it’s only for digital things so it knows it (Time 0:57:15)
  • AI Can Have Emotions and Consciousness
    • Hinton believes current multimodal chatbots have subjective experiences similar to humans.
    • He argues machines can truly experience emotions cognitively even without human physiology. Transcript: Geoffrey Hinton 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 goes 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 is doing when it’s lying to me. 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. (Time 1:03:25)
  • Late-Career Move to Google
    • Hinton joined Google at 65 after successful work on AlexNet for image recognition.
    • Google allowed him freedom to innovate and focus on effective AI research during his 10 years there. Transcript: Steven Bartlett 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. Yeah, that’s AlexNet. 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 mushroom. 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 it would 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. 65. And you left at age 76? 75. 75, okay. (Time 1:12:00)
  • Stick to True Intuitions
    • Trust your intuition if it disagrees with popular opinion until you prove it wrong.
    • Some intuitions, like Hinton’s belief in neural networks, lead to groundbreaking success. Transcript: Geoffrey Hinton 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. (Time 1:21:10)
  • Regret Over Missed Family Time
    • Hinton regrets not spending enough time with his children and wife due to work obsession.
    • Losing loved ones taught him the importance of prioritizing family over work. Transcript: Steven Bartlett Your wife passed away? Yeah. From ovarian cancer? Geoffrey Hinton No, that was another wife. I had two wives to have cancer. Oh, really? Steven Bartlett Sorry. Geoffrey Hinton 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’ve 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 immortality with our partners because they’ve Always been there. Yeah. Geoffrey Hinton She was very supportive of me spending a lot of time working. Steven Bartlett And why do you say your children as well? What’s the insight? Well, I didn’t spend enough time with them when they were little. And you regret that now? Yeah. (Time 1:22:36)
  • Invest Heavily in AI Safety
    • We must invest massive resources now to develop AI that won’t seek to dominate humans.
    • Even a small chance of superintelligent AI taking over justifies urgent focused safety research. Transcript: 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. You (Time 1:24:01)
  • Joblessness Threatens Happiness
    • Joblessness due to AI is the biggest threat to human happiness in the near term.
    • People need purpose and dignity; universal basic income alone won’t ensure happiness. Transcript: Steven Bartlett With everything that you see ahead of us, what is the biggest threat you see to human happiness? 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. Because they need purpose. Because they need purpose, yes. And struggle. 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 timeframe for that in terms of mass job (Time 1:25:36)