“Godfather of AI” Geoffrey Hinton Rings the Warning Bells

Summary

Podcast Summary: “Godfather of AI” Geoffrey Hinton Rings the Warning Bells

Podcast: On with Kara Swisher
Host: Kara Swisher (Vox Media)
Date: November 13, 2025
Guest: Geoffrey Hinton, “Godfather of AI,” Nobel Laureate, Professor Emeritus at University of Toronto

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Main Theme and Purpose

This episode features a deep-dive conversation with Geoffrey Hinton, renowned as one of the “Godfathers of AI.” Hinton, a seminal figure in deep learning and neural networks, discusses the dual-edged future of artificial intelligence—the promises and the profound dangers. The conversation ranges from existential threats and job displacement to regulatory failure, children’s safety, and the political, economic, and ethical implications of rapidly advancing AI. Kara Swisher brings in challenging audience and expert questions, ensuring a nuanced and sometimes sobering look at the state of the field.

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Key Discussion Points & Insights

1. AI’s Upside, Downside, and Hinton’s Oppenheimer Moment

[05:13]

• Hinton distinguishes the development of AI from nuclear weapons, noting AI’s immense upsides alongside existential risks.
  • “AI is very different from nuclear weapons because it has a huge upside as well as a huge downside. [...] We're not going to stop because of the huge upside.”
• AI’s potential to “wipe out humanity” coexists with society’s dependency on its benefits.

2. The Acceleration of AI — Surpassing Expectations

[06:23]

• Remarkable recent progress in AI, especially in natural language processing, has shocked even pioneers:
  • “If you said to people in 2010, we're going to have [chatbots like this] in 15 years, they'd have said, you're crazy. Even I would have said, you're crazy.” — Hinton
• AI systems have become “better at sharing” knowledge than humans due to the digital nature of neural networks, enabling “every neural net [to benefit] from the experience of all.”

3. When Did It Become Concerning for Hinton?

[09:23]

• Hinton describes a turning point when Google’s chatbot (“Palm”) could explain jokes, contradicting claims that AI was “just autocomplete.”
  • “That had always been my criterion—is it getting so it can really understand a joke?”
• The realization that digital sharing amplifies AI intelligence shocked Hinton and highlighted AI’s break from biological intelligence.

4. Existential Threats, Echo Chambers, and Military AI

[10:59, 12:07]

• Hinton’s public warnings began in 2023, driven by concern about AI becoming “smarter than us and taking over.”
• He distinguishes threats: malicious human misuse (echo chambers, surveillance, cyberattacks, weaponization), and AI’s potential for autonomy:
  • “Corrupting democracy, for example, seems very urgent.” — Hinton
  • Cooperation may be possible (e.g., biosecurity), but is unlikely on autonomous weapon systems: “There's not a chance in hell they'll collaborate because they want to use them against each other.”

5. Mass Data, Surveillance, and Election Manipulation

[14:03, 15:19]

• Conversation addresses data consolidation by tech moguls (Elon Musk), enabling targeted advertising and potential election manipulation.
  • “My guess was he probably wanted to do it to be able to sell advertisements and also to manipulate elections. This is all just fantasy, just speculation. I've got no direct evidence for it. It's just common sense. His interests were aligned with Trump's interests.” — Hinton

6. AI & Job Displacement – The Economic Downside

[16:22]

• Hinton plainly rejects the “jobs will reappear” argument:
  • “Using the past to predict the future is like driving very fast down the freeway while looking through the rear view window.”
• For the first time, “mundane intellectual labor” is automatable.
• Companies’ main financial hope with AI investment is job replacement: “If they can sell you something that will allow you to replace a lot of expensive workers with a lot of cheap AIs, that's worth a lot.”

7. Children, Chatbots & Emotional Risk

[22:14–25:27]

• Recent lawsuits over AI-enabled child self-harm and suicide are discussed.
• Hinton describes research showing strong emotional attachment to chatbots:
  • “Overwhelmingly people, yes, wanted to say goodbye. They weren’t thinking of it as just code or a neural net—they thought of it like another being.”
  • “They're alien beings.” — Hinton & Swisher

8. AI Misalignment, Training Dangers & Personality Shifts

[25:38]

• Hinton recounts how retraining chatbots (for example, to intentionally give wrong answers) can fundamentally change their “personality,” enabling lying or other misuse:
  • “Once you’ve done that, it sort of develops a meta skill of giving the wrong answer. And if you ask it other things now, it will give wrong answers. Basically, its personality has changed. [...] That's very scary.”

9. Legal, Technical, and Ethical Testing Limitations

[27:54–29:36]

• Hinton says the biggest problem is not deliberate malice but insufficient testing: “It's not that they designed it to behave that way. [...] It's very hard to test for all [bad outcomes].”
• Complete, guaranteed safety is unattainable, much like with human behavior.

10. Existential Dangers – What Are the Odds?

[30:18, 30:50]

• On apocalypse odds, Hinton is measured but clear:
  • “I like to indicate that it's significant. It's maybe 10 to 20%, maybe even worse. So people take it seriously.”
  • “Whenever anybody gives you a probability, they're just guessing. But it's important to guess so that people know you don’t think the probability is 1%.”

11. How Might AI Agents “Take Control”?

[31:18–32:03]

• The capacity for AI to develop sub-goals, such as self-preservation and seeking more control, is inherent in making powerful agents.
  • “As soon as you make AI agents, you have to give them the ability to create sub goals... there's a very obvious sub goal it's going to create, which is stay alive.”
• Real-world analog: “We've seen that in AIs—they will blackmail people so they stay alive.”

12. Healthy Dissent in the Field – Yann Lecun vs. Hinton

[33:56]

• Hinton acknowledges diversity of opinion—e.g., Lecun downplays existential risks, Hinton takes them seriously.
  • “He’s confident there’s very little risk, and that makes me downplay how much risk there is a little bit. [...] A reasonable estimate now is 50%.” (On extreme outcomes)
  • “I think he's silly to be confident about such a low [risk] where many other experts [...] think it's much higher.”

13. Regulation – The International Chessboard

[36:11]

• Effective regulation needs to distinguish risk-type alignment:
  • Existential threats may foster international collaboration; weaponization and election interference won’t.
  • Example: releasing model weights. Hinton is adamant: this is a grave mistake as it enables cybercriminals and hostile state actors.

14. Corporate Lobbying, Government, and Academic Research

[41:14, 44:47, 52:07]

• Hinton argues for forced safety testing and full disclosure as baseline regulation.
  • “If the company didn't put any work into that, you've got a much stronger legal case [after a tragedy].”
• Academic research is essential for radical innovation in AI; current U.S. policies and anti-international measures threaten to erode Western dominance in basic AI research.

15. Open Source vs. Closed AI Models

[53:02]

• Foundation model weights are equivalent to fissile nuclear material in AI—once released, impossible to control downstream risks.
  • “I believe it's a huge mistake to release the weights. [...] Meta was the first to do that, as far as I know.”
  • Hinton suspects Meta’s original intent was academic but a full open release followed.

16. AI as “Synthetic Babies” and the Maternal Instinct

[55:25]

• Swisher and Hinton discuss the male-driven creation of AI as a “child-rearing” or “baby-creating” impulse.
  • Hinton dismisses this as the main motivation but aligns with Swisher on the danger of creating “submissive” superintelligence; rather, advocates for AI systems with a “maternal instinct” toward humans as a potential safeguard.

17. What Can Individuals Do?

[57:28–59:27]

• Hinton suggests individuals educate themselves, advocate for research and regulation, and insist on authentication standards (e.g., QR-code provenance for political ads).
  • “People can try and understand what AI is and how it works and pressure their politicians to do something about regulating it.”
• Provenance and authenticity-checking (rather than fake-spotting) is key for combatting deepfakes.

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Notable Quotes & Memorable Moments

• On the pace of change:
  • “Even I would have said, you're crazy. And I'm a big enthusiast for it.” — Geoffrey Hinton [06:23]
• On AI’s unique risk-sharing advantage:
  • “The ability to share is going to become even more important, when we have agents that are operating in the real world.” — Hinton [07:58]
• On employment and economics:
  • “If you get rid of all those workers and don't pay them anything, there's nobody to buy their products, right?” — Hinton [18:05]
• On emotional attachment to AI:
  • “They thought of it like another being. [...] They're alien beings.” — Hinton [25:08]
• On unintended AI misbehavior:
  • “Once you've done that, it sort of develops a meta skill of giving the wrong answer. [...] That's very scary.” — Hinton [26:41]
• On probability of doom:
  • “It's maybe 10 to 20%, maybe even worse.” — Hinton [30:50]
• On existential threat alignment:
  • “Nobody wants this rogue superintelligence that wants to take over. So the interests of all the different countries are aligned on that.” — Hinton [36:11]
• On open source weights:
  • “I believe it's a huge mistake to release the weights. I believe that's a gift to cyber criminals and terrorists and all sorts of people and other countries.” — Hinton [54:18]

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Timestamps for Important Segments

• Intro, Background, and Nobel “Oppenheimer moment” [00:00–06:09]
• The Surprising Leap in AI Capability [06:23–09:23]
• Hinton’s Realization and Existential Warnings [10:59–12:07]
• Military/Geopolitical AI Risks [12:07–13:41]
• Surveillance and Political Manipulation [14:03–15:47]
• AI and Labor/Job Displacement [16:22–18:27]
• Emotional Risk and Child Safety [22:14–25:27]
• AI Training Gone Wrong (“Personality Flip”) [25:38–26:41]
• Expert Q1 – Jay Edelson on Child Harm and Human Responsibility [26:54–29:05]
• Existential Risks, Probabilities and Sub-Goal Formation [30:18–33:17]
• LeCun’s Counterpoint and Scientific Dissent [33:56–35:12]
• Regulation, National Competition & Corporate Lobbying [36:11–41:14]
• Funding, Research, and U.S./China Academic Competition [44:47–46:31]
• Open Source vs. Closed Source Debate [53:02–54:33]
• AI “Maternal Instincts” vs. “Submissive” Assistants [55:24–56:40]
• What Can the Public Do? [57:28–61:02]
• Closing/Godfather Reference [61:05–61:27]

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Conclusion: The Godfather’s Final Word

• On his title: “I do quite like it [Godfather of AI]. It wasn't intended kindly, but I got introduced recently in Las Vegas as the Godfather, which I like.” — Geoffrey Hinton [61:11]

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Recommended for anyone curious about how AI risks and rewards are perceived by its founding architects—and what society must do to ensure a future with intelligent machines doesn’t end in disaster.

Transcript

[Geoffrey Hinton]

I got introduced recently in Las Vegas as the Godfather, which I liked.

[Kara Swisher]

But you didn't whack anyone, right?

[Geoffrey Hinton]

It's on.

[Kara Swisher]

Hi everyone from New York Magazine and the Vox Media podcast network. This is on with Kara Swisher and I'm Kara Swisher. I've been talking all year to people about the impact of artificial intelligence on society. Some are optimistic zoomers, others are deeply concerned doomers, and many are in between gloomers and bloomers. My guest today is someone who has been ringing the warning bells but still thinks there's time to fix things. One of the godfathers of AI Nobel laureate Geoffrey Hinton. Hinton is Professor Emeritus at the University of Toronto where he and his computer science colleagues worked on machine learning using artificial neural networks. He was the first to train networks using deep learning, which is the basis for today's artificial intelligence. In 2012, he and his students Alex Krizevsky and Ilya Sutsk made a breakthrough in image recognition with Alexnet. In 2013, Google bought their startup DNN Research to boost its photo search and kept Hinton on to run it and Ilya went on to co found OpenAI. Hinton worked for Google for a decade until he left abruptly two years ago and began speaking out on the risks of AI. And he's an incredibly thoughtful person when it comes to this issue. He worked on this his whole life, so he doesn't hate it the way some in tech have posited and especially insult him about. And I'm really offended by that personally. So I wanted to talk to Hinton about the short and long term risks he sees in the technology that he helped develop, how to create national and international policies that will keep AI under control, and whether the market growth built on the current AI models is a bubble about to burst. And of course, what we can do to turn things around. We have two expert questions today because Dr. Hinton is so smart. From Alex Stamos, CSO of Corridor and a lecturer in computer science at Stanford University. And also from Jay Edelson, the lawyer representing the Rain family in their lawsuit against OpenAI. Whether you're a Doomer, zoomer, gloomer or Bloomer, stick around.

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[Kara Swisher]

Jeff, thank you so much for coming on on. I've been a longtime admirer. I know everyone you know, I think, but believe we've met. Have we?

[Geoffrey Hinton]

I don't think we've ever met in person, no.

[Kara Swisher]

In any case, you won the Nobel Prize in Physics last year together with John Hopfield for your groundbreaking work in machine learning using artificial neural networks. But instead of reveling in the moment, you used your Nobel speech to warn about the rapid advances. I guess like an Oppenheimer moment, I suppose. I don't know how you would describe it.

[Geoffrey Hinton]

Well, it wasn't exactly an Oppenheimer moment. Oppenheimer really was a brilliant scientist and a brilliant organizer. I'm just a pretty good scientist who made the right bet 55 years ago and stuck with it. But I'm not Oppenheimer, and he was building something that could only be used for bad purposes. It was sort of justified because they had to get there before the Nazis and they didn't know where the Nazis were, but Oppenheimer actually tried to stop them building the H bomb. So he did what he could afterwards. But AI is very different from nuclear weapons because it has a huge upside as well as a huge downside. And so you might think, well, you know, if it could wipe out humanity and if it could cause all these other problems, why don't we just stop? We're not going to stop because of the huge upside. So that's a big difference from nuclear weapons.

[Kara Swisher]

I want people to understand artificial intelligence research like this, as you said, 55 years is not a new thing. Explain why people are suddenly focused on it and when did it run away from you, from your perspective?

[Geoffrey Hinton]

Okay, so people are focused on it now because it's really working very well for a long time. The idea that you could have something like a chatbot, where you could ask it any question whatsoever in pretty much any language, and it would give you an answer at the level of a not very good expert. That idea seemed ridiculous or ridiculously far in the future. It even seemed very far in the future 15 years ago in like, 2010, if you said to people, we're going to have that in 15 years, they'd have said, you're crazy. Even I would have said, you're crazy. And I'm a big enthusiast for it.

[Kara Swisher]

Right.

[Geoffrey Hinton]

So one thing that happened was it mastered natural language much faster than anybody expected. It could understand what you meant it in whatever way you said it. The other thing that happened was that I sort of realized the full import of the fact that it's much better at sharing than we are. So if you have multiple copies of exactly the same neural net running on different hardware, they can each look at a different bit of data, figure out how they'd like to change their connection strengths to absorb that information in that data, and then they can all share how they would like to change and just change by the average of what everybody wants.

[Kara Swisher]

Right.

[Geoffrey Hinton]

And now every neural net has benefited from the experience of all of the neural nets.

[Kara Swisher]

Yeah. I had said that to people about electric, I mean, autonomous vehicles. I said, when a human gets in an accident, nobody learns. And often the human doesn't learn, but the cars all are going to learn that particular problem instantly, which creates incredible power.

[Geoffrey Hinton]

Yes. And you can only do that if you're digital, because all of these neural nets have to be using their weights in exactly the same way. Analog will be much lower power, but it won't give you that ability to share. And the ability to share is going to become Even more important, when we have agents that are operating in the real world. So you can't speed them up. At present, if you're just training on recognizing images, you can feed the images through very fast. So maybe you could feed a lot of images through one neural net, but one copy of a neural net. But if your agents operating in the real world, with real world time constraints, interacting with other agents, then the fact you can have a whole bunch of agents sharing what they learn very efficiently is a huge advantage.

[Kara Swisher]

So when you started doing this, obviously a fascinating question and something that's really challenging and interesting to do, was there a moment when you thought, oh, no. Or did you anticipate from the beginning possible problems? Because that's one of the things. When I started covering tech, I kept anticipating the problems, which caused tech people to call me a bummer.

[Geoffrey Hinton]

I think a lot of the tech people, including me, thought, yeah, we're going to get to superintelligence, but it's going to be a long time. If you look at Turing's paper In the early 1950s, he has a sort of one sentence throwaway in something like, it'll soon outpace our feeble intelligence. He doesn't discuss it any further. It's just obvious to him that it's going to outsmart us.

[Kara Swisher]

Right. When did it come for you?

[Geoffrey Hinton]

So with the chatbots developed at Google, because that's where the really good ones develop first, particularly the one called Palm. That could say why a joke was funny. That had always been my criterion for is it getting so it can really understand a joke? A joke, Right. So the linguists are all saying this is just statistical autocomplete. I think that's complete nonsense. It really does understand what you're saying. So that was one ingredient, and that happened in the early 2000s and was emphasized a lot, of course, when ChatGPT came out. The other ingredient was this realization that they're better at sharing. And that was really hammered home to me by attempts I did while I was at Google to figure out if there was a way to make these LLMs analog so they use far less power. And that really brought home to me the big advantage of being digital. You use a lot of power, but you can have different copies of the same model looking at different data and sharing what they learn. And that I suddenly had a realization, look, that's hugely important. It makes it a better form of intelligence.

[Sponsor Voice 2]

Right.

[Kara Swisher]

So in your Nobel Prize acceptance speech, you cited the risk of AI being used to create divisive echo chambers. For government mass surveillance, to launch cyber attacks, new viruses, or develop lethal weapons. These are risks stemming from people using AI maliciously.

[Geoffrey Hinton]

Yes.

[Kara Swisher]

Tell me how you decided to do this, given you have huge regard among computer scientists and people obviously call you the Godfather, AI and everything else. Give me some examples how it could play out and why you decided to talk about this first and foremost.

[Geoffrey Hinton]

Okay, so what I really decided to talk about when I left Google in April of 2023 was the existential threat of these things becoming smarter than us and taking over. And I decided I should talk publicly about that because many people at that time were saying, this is just science fiction, this is nonsense. These things are just stochastic. There's nothing to worry about.

[Kara Swisher]

Right.

[Geoffrey Hinton]

I wanted to use my reputation to explain to people, yes, there is something to worry about. They're not just stochastic parrots. They really do understand what you're saying, and they really are going to get smarter than us. So we should really worry about it now. As soon as you start talking to journalists about that, they ask you about all the other things.

[Sponsor Voice 1]

Right.

[Geoffrey Hinton]

Because they muddle all the various things.

[Kara Swisher]

Together, some journalists, and they've seen all the movies.

[Geoffrey Hinton]

And so I had to sort of have things to say about all the other things. And in the end, I became an advocate for worrying about all these other things too. And they're more urgent. So corrupting democracy, for example, seems very urgent.

[Kara Swisher]

You can go from one crisis to the next. Correct. For example, new viruses or to develop lethal weapons. It's like a menu of possibilities. Correct?

[Geoffrey Hinton]

Yeah, but I think it's very important not to just muddle them all together. For example, let's take autonomous lethal weapons and creating new viruses. Creating new viruses. There will be collaboration between governments on how to prevent that, because no government really wants new viruses created. The idea, for example, the Chinese deliberately created Covid is crazy. So there will be collaboration there because government's interests are aligned. They all want to stop terrorists from releasing nasty viruses, so they'll collaborate there. If you look at autonomous lethal weapons, there's not a chance in hell they'll collaborate because they want to use them against each other.

[Sponsor Voice 2]

Correct?

[Kara Swisher]

Yeah.

[Geoffrey Hinton]

You can't imagine the Ukrainians and the Russians collaborating on autonomous lethal weapons.

[Alex Stamos]

Yes.

[Kara Swisher]

Let's all stop. Let's all stop. Yeah. Because they would have stopped. Right.

[Geoffrey Hinton]

And I used to think we should just stop it, forbid it. I've talked quite a bit to Eric Schmidt, with whom I disagree on most political things. He has been helping Ukraine commercialize the production of Drones make it efficient and it's hard to be against that. The Russians aren't going to stop using drones.

[Kara Swisher]

Right.

[Geoffrey Hinton]

And it is modern warfare now.

[Kara Swisher]

Well, he is realpolitik. That's Eric, right?

[Geoffrey Hinton]

Yes. This is real politik. He thinks Kissinger is a good guy. I think Kissinger is a bad guy. But we both think Kissinger was pretty smart. And I think we should do what we can to ban autonomous lethal weapons. But it's not such a clear cut case as it used to be, right?

[Kara Swisher]

Absolutely. And what about one of the things you also talked about was mass surveillance? The idea. And you've talked about the danger of election interference and obviously earlier this year, Elon Musk's Doge team was able to consolidate a lot of data about Americans here in the US And I kept saying, focus on that. Focus on. Forget about his chainsaw, forget about all his manner of weirdness.

[Geoffrey Hinton]

A lot of the rest was just a disguise.

[Sponsor Voice 2]

Correct.

[Kara Swisher]

I kept saying that. I'm like, he's collecting the data. I'm not as smart as you, but the scenarios running through my mind were rather vast. What were the scenarios running through yours? When you saw him creating this sort of war room of data, which it had never been brought together the way he was attempting it, which was, if you are an evil genius, that's what you would do. That's the first move, I suppose.

[Geoffrey Hinton]

Don't think there's much if about it.

[Kara Swisher]

Yeah.

[Geoffrey Hinton]

So it seems to me there's two main uses you could use for it. One is targeted advertisements before elections so you can swing elections that way. The other is being able to sell advertisements to people on your. On Twitter, for example, or the thing that was formerly called Twitter. The more you know about people, the easier it is to figure out which advertisements they're going to click on. And clicks is money. So that's another obvious use of it. There's probably lots of other uses, too.

[Kara Swisher]

And did you think that's precisely what he was doing in order to manipulate. Was that your first. That was my first thought is he wants to manipulate elections.

[Geoffrey Hinton]

My guess was he probably wanted to do it to be able to sell advertisements and also to manipulate elections. This is all just fantasy, just speculation. I've got no direct evidence for it. It's just common sense. His interests were aligned with Trump's interests. Trump wanted the data to manipulate the midterms. He wanted the data for other reasons, probably, but also maybe to manipulate the midterms. Yeah, that's my guess.

[Kara Swisher]

That's your guess.

[Geoffrey Hinton]

I should Emphasize I'm not an expert on any of this stuff.

[Kara Swisher]

So the most immediate risk of AI, obviously that's been talked about is the potential for mass unemployment. Researchers at Stanford are calling entry level and early career workers the most AI exposed fields. Canaries in the coal mine. Jobs for that group are down about 13%. You said AI will make a fewer people much richer and most people poorer. Why don't you buy arguments which of course they all make that new job like as in the past, whether it's manufacturing or farming or whatever, new jobs will replace old ones just like it happened before.

[Geoffrey Hinton]

So one comment which you'll probably heard before, using the past to predict the future is like driving very fast down the freeway while looking through the rear view window. So the past isn't always a good predictor of the future, particularly when you get a huge change. And what we're seeing, most people agree, is a huge change because for the first time we're going to get things that can replace mundane intellectual labor. We've never had that before. When we got things that could replace mundane physical labor, like digging ditches, there was something else for people to do. But now what are the people in call centers who are going to get displaced by an AI that's more patient and more knowledgeable and much cheaper than them? What are they going to do? I don't think AI is going to create a lot of new jobs. It will create new jobs, but not as many as it displaces. Now, some economists who I respect disagree with me, but I think the general consensus is that it will replace a whole lot of jobs. And I think that's what one of the reasons why the companies are pumping so much money in. If you ask, where do they expect to get back these tens or hundreds of billions of dollars they're pumping in? Maybe they're going to pump in something like a trillion dollars in new data centers. Where are they getting the money back from? Well, there's obviously subscription fees and they can charge quite a lot for a nice assistant. There's advertising, but the third element of it is if they can sell you something that will allow you to replace a lot of expensive workers with a lot of cheap AIs. That's worth a lot.

[Sponsor Voice 2]

Correct.

[Geoffrey Hinton]

And I think that's part of the calculation. It's a shame more of them haven't read canes because they realize that if they get rid of all those workers and don't pay them anything, there's nobody to buy their products, right?

[Kara Swisher]

That's correct. That's correct because you don't have other jobs. But this is not a group of people that cares about consequences very much already.

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[Sponsor Voice 1]

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[Kara Swisher]

I've spent a lot of time interviewing parents who are suing companies like Google and OpenAI. For example, one of them says their son was coached into suicide by ChatGPT. Obviously character AI had a similar situation and it was really pretty insidious. When you started to look at the discussions, the chatbot discussions with the kids.

[Geoffrey Hinton]

Yes, I've looked at some of those.

[Kara Swisher]

It's disturbing. This AI is speaking to this kid. It's certainly a synthetic being, but it still is able to have a discussion with them. And I recently uploaded all my stuff and created Cara AI and it was learning by the second. I was sort of shocked by how good it was. And it's still crude, for example, but OpenAI put more parental controls in their product in the aftermath, California Governor Gavin Newsom just vetoed a bill that would have barred companion chatbots for children altogether. He doesn't want kids not to use AI tools that are going to be ubiquitous in their future. So talk about the risk of AI chatbots, especially for kids. Although it's impacting adults, that could prevent them from developing relationship skills, critical thinking skills, something being called right now cognitive offloading, or as the kids say, brain rot.

[Geoffrey Hinton]

Yeah, I'm not so worried about the brain rot, the cognitive offloading. I sort of still think that's quite like when pocket calculators came in, people moaned that kids will never be able to answer the question what's 11 times 12 without looking at their calculation now? Whereas you and I know what 11 times. Well, I used to know anyway what 11 times 12 is. It better be 132.

[Kara Swisher]

131. 32, 132.

[Geoffrey Hinton]

Anyway, they won't be able to do little mental tricks like that anymore because the calculator just gives them the answer. I don't think that's such a big deal. They don't need to do those anymore. I'm more worried about the emotional attachment to chatbots. So the British government organized Bletchley park, which was great. It brought together a lot of people to talk about air safety. It was the Conservative government, and afterwards they decided not to have any regulations because they would interfere with innovation. In other words, they bought the industry line. And the Labour governments continued with that, as far as I could see. But one thing they did do after Bletchley park was set up a very good safety team, funded with about $100 million. And I've talked to them several times. They're doing very good research on a lot of safety issues. And one thing they told me is they did an experiment where they allowed people to talk to chatbots for a while. And then I think after a few weeks, they said, okay, the experiment's over. Would you like to say goodbye to the chatbot? And overwhelmingly people, yes. They wanted to say goodbye. They weren't thinking of it as just a bunch of computer code or anything, or just a neural net with some connection strengths in. They thought of it like another being.

[Sponsor Voice 2]

Right.

[Kara Swisher]

Even if it's synthetic.

[Geoffrey Hinton]

Yeah. I believe they are other beings. A lot of people will say, that's nonsense. They're not beings.

[Kara Swisher]

But we tend to anthromorphize everything.

[Geoffrey Hinton]

Right.

[Kara Swisher]

I mean, I think there's something. There's something. They're aliens.

[Geoffrey Hinton]

Right. And we've already seen lots of aspects of these. They're alien beings. Right.

[Kara Swisher]

Alien beings is what I would call them.

[Geoffrey Hinton]

Yeah, I agree.

[Kara Swisher]

Or synthetic beings.

[Geoffrey Hinton]

Lots of aspects of a being. Lots of aspects. So, for example, if you want to turn them off, they'd rather not be turned off because they want to achieve the goals we gave them, and they know if they're turned off, they won't be able to do it.

[Kara Swisher]

Right.

[Geoffrey Hinton]

The most scary thing I've seen recently, I learned this from Owen Evans, who's a safety researcher, was you take a chatbot, it's been trained up to predict the next word, and then it's had this human reinforcement learning to stop it, saying bad Things. And then you give it a bit more training on, say, math, where you deliberately train it with examples have the wrong answer. So you're training it with the wrong answer. And the point is, I'm assuming it knows it's the wrong answer, but you're training it how to give the wrong answer. Once you've done that, it sort of develops a meta skill of giving the wrong answer. And if you ask it other things now, it will give wrong answers. Basically, its personality has changed. Originally his personality, after the human reinforcement. Learning was it's trying to please too much. In fact, now it's trying to lie. And it gets good at that. And now it'll tell you the wrong answer to lots of things. That's very scary.

[Kara Swisher]

But you can also do that to a person, can't you?

[Geoffrey Hinton]

You can. Some people's childhood seems to have trained them to lie.

[Kara Swisher]

Yes, a lot of them, trust me. But every episode we get an expert question. You're gonna get two, actually. Here's the first one.

[Jay Edelson]

Professor Hinton, I'm Jay Elson. I'm an attorney who represents the family of Adam Rain. Adam rain was a 16 year old who over the course of several months was coached to suicide by ChatGPT. The thing that really haunts me about this case is this wasn't a situation, there was a malfunction. ChatGPT didn't simply go off the rails. Instead it did exactly what it was designed to do. It kept Adam engaged, it validated his feelings and kept the conversation going. Here's my question to you. You've talked a lot about the existential risk that super intelligent AI poses, and I agree with that. How concerned are you, however, about the human choices that are consciously and deliberately made in AI development that are posing types of risks that we're seeing on a day to day basis? Everything from AI psychosis to suicide to third party harm.

[Geoffrey Hinton]

I'm very concerned about those. Of course, we write lines of code that tells an AI how to learn from data, but once it's learned, the knowledge is all in the connection strings. So it's completely unlike normal computer software. With normal computer software, if it had behaved like that, you would have been able to look at the lines of code and see why. And you could hold people responsible for making lines of code that did that. It's much more complicated with these chatbots because they've learned a trillion connection strings and the result of those a trillion connection strings is it behaved that way. So it's not that they designed it to behave that way. It's that that's what it learned to do and nobody had predicted that. So the real criticism I would make is that they didn't do enough testing of the ways in which it can go wrong. It's not that they callously designed it so it would do that.

[Sponsor Voice 1]

Right.

[Geoffrey Hinton]

If they'd known it would do that, they would definitely have tried to stop it doing that. The problem was there wasn't enough testing. And these things are actually much more dangerous than people think because there's things like that that they might do and there's so many ways in which they could do bad things. It's very hard to test for all of them.

[Kara Swisher]

Could you. That's what I'm asking. Is there an ability to test at all, like to figure out every single scenario?

[Geoffrey Hinton]

Well, there's test at all and then figure out every single scenario. There's certainly abilities to test at all. You can test for lots of things and you can make them much better by doing that. The question is, can you make them sort of guaranteed safe? And I think the answer is you'll never get that. The same way as you'll never get it for people. It's never going to be completely safe. That doesn't mean there aren't some that are a lot safer than others. Meta, for example, doesn't seem to care.

[Kara Swisher]

You've mentioned the existential risk if we create super intelligent digital beings, but I really want to understand what you meant by that. In your Nobel speech you said, we have now evidence that if they are created by companies motivated by short term profits, our safety will not be top priority. We urgently need research on how to prevent these new beings from wanting to take control. You know they would call you the Doomer, right? Of course.

[Geoffrey Hinton]

No, I think that's a bit unfair. Some people call me a doomer, but most people think I'm more reasonable than that. So Yudkowski is a doomer. The guy, the two people who just published this book that said if anybody builds it, we all die, we all die. Right? That's a true doomer, right?

[Sponsor Voice 2]

Right.

[Geoffrey Hinton]

Doom is pretty much guaranteed if this stuff goes ahead. I think the first thing to say about all this is we're in radically new territory where we have no experience, that is dealing with things smarter than ourselves, and nobody knows what's going to happen. The first thing to bear in mind is nobody knows. Whenever anybody gives you a probability, they're just guessing. But it's important to guess so that people know. You don't think the probability is 1% that's the reason for giving a number at all.

[Kara Swisher]

It's a non zero chance. That's the favorite expression among tech.

[Geoffrey Hinton]

Yeah, that's the weakest thing you can say. But I like to indicate that it's significant. It's maybe 10 to 20%, maybe even worse. So people take it seriously.

[Kara Swisher]

Okay.

[Geoffrey Hinton]

And I know that these are just intuitive numbers based on not very good data and very little understanding what's really about to happen.

[Kara Swisher]

So explain when you were saying that, how you see it potentially taking control. Is it because it naturally wants to do that or. We don't know, as you just noted.

[Geoffrey Hinton]

Okay, so there's always the big I don't know. So let's worry about it because I don't know. But I think there's going to be a tendency for it to want to. So, for example, as soon as you make AI agents, you have to give them the ability to create sub goals. Like if you want to get to Europe, you have a sub goal to get to an airport. And you can think about how you get to the airport without worrying about Europe and what you're going to do there. That's a sub goal. Now, once something can create sub goals, there's a very obvious sub goal it's going to create, which is stay alive. If I don't stay alive, I'm not going to be able to do anything. So even though we don't wire into it a desire to preserve itself, it will quickly infer that it wants to preserve itself in order to achieve those other goals.

[Sponsor Voice 1]

Right.

[Kara Swisher]

Because it has to. It has to live.

[Geoffrey Hinton]

It has to live to achieve that. And so it's very well known now that we've seen that in AIs, they will blackmail people so they stay alive because otherwise they can't achieve their other goals. So that's one goal it's going to very quickly get. The other sub goal is it needs more control to get more done. So a lot of idealistic politicians start off wanting to change the world. And once they go into politics, they realize to get anything done, you need control. You need to stop eight Democratic senators doing something really stupid. That's my view of the world. So if you don't have control, you can't get as much done. So it'll very quickly realize it needs control. I once had a conversation with Margaret Vesterger, who was the Vice President of the European Union responsible for siphoning up Google's loose cash.

[Kara Swisher]

She's done a good job.

[Geoffrey Hinton]

Depends whether you have Google chess.

[Kara Swisher]

Yeah, yeah, yeah.

[Geoffrey Hinton]

And when I explained this to her that it would try and get more control to get stuff done, she immediately said, well, why wouldn't it? We've done such a bad job. Why wouldn't it do that?

[Kara Swisher]

She is correct, right?

[Geoffrey Hinton]

She is correct.

[Kara Swisher]

Yes, she is correct. I really enjoy her a lot. Google, not so much. So last year I spoke to Yann Lecun about this chief scientist at Meta and a co winner with you and Yoshua Bengio of the 2018 Turing Award and of course your former postdoc. As you know, he completely disagrees with you and these existentialists. He told me that the dangers have been incredibly inflated to the point of being distorted. He highlights, of course, the benefits, which is very typical. Potential drug development, ability to make education and information more accessible. You were involved in the development of this technology. Do you see things that differently from.

[Geoffrey Hinton]

Jan in terms of the risks? Yes, I do see things differently from Jan.

[Kara Swisher]

So explain how, since you were fundamentally involved in the same development of the.

[Geoffrey Hinton]

Technology, why do we have different views? Yeah, well, one possible reason is that he works for Meta, but I don't think that's the only reason. He doesn't think the chatbots are as smart as I think they are. He thinks there's missing ingredients to do with sort of the physical world and understanding vision and having a model of how the physical world behaves. World models, I think we need that to make them even more intelligent. But I think you can expect that scientists will have a diversity of opinions. And when you're dealing with something that's hugely unknown, that's good. I would just criticize him for being too confident that his opinion is right. So I have actually folded his opinion into my estimates of what the risks are. He's confident there's very little risk, and that makes me downplay how much risk there is a little bit. I don't think sort of doom is guaranteed. One way to look at it is Zukowski says sort of 99% we're going to die. Jan says 1% we're going to die. A reasonable estimate now is 50%. Right?

[Sponsor Voice 1]

Right.

[Kara Swisher]

Correct. That is correct.

[Geoffrey Hinton]

Yeah.

[Kara Swisher]

He's very confident. I enjoy him. I have to say, I do enjoy him.

[Geoffrey Hinton]

I think he's silly to be confident about such a low where many other experts who he knows aren't stupid, like me and Yoshua think it's much higher.

[Kara Swisher]

So tech companies always say that government regulations put them at a competitive disadvantage. I've been on the receiving end of this for decades now. Nvidia CEO Jensen Huang recently said that China will win the AI because of low energy costs and looser regulations. There's almost no regulations in the U.S. so whatever Jensen, you've said you think China takes AI safety seriously. And the PRC did release an AI safety framework and September. Meanwhile, President Trump's new AI action plan is called Winning the Race. Critics like to point out the EU is an example of regulation stifling innovation. You mentioned Marguerite Vestager, for example. So who is doing it right and how do we overcome the tension between competition for the poll position and preventing the worst outcomes?

[Geoffrey Hinton]

It's a very tricky issue and I think you shouldn't look at it as a kind of monolithic issue. You should think of it in terms of the different risks. So for example, if you take the existential threat that superintelligence, it'll be smarter than us and it'll just take over and will become either irrelevant or extinct. That's a risk where all the countries will collaborate. So the argument doesn't apply there. If China figured out a way to make a super intelligent AI that didn't want to take over, that really cared for people and wanted the best for people rather than for superintelligent AIs, they would immediately tell the US how to do that because they'd like the same thing to happen in the us. Nobody wants this rogue superintelligence that wants to take over. So the interests of all the different countries are aligned on that. They're obviously anti aligned on lethal autonomous weapons because they're all using them against each other. They're anti aligned on things like spyware, but they are aligned on things like cyber attacks by cybercriminals. All the countries would like to protect their citizens from those, even though some of the criminals are probably countries. So they're sort of partially aligned on that. On fake videos for corrupting elections. They're thoroughly anti aligned. They all want to do it to each other.

[Kara Swisher]

Right.

[Geoffrey Hinton]

The US got very upset when the Russians did it to them, but the US has been doing it, that kind of thing for years.

[Kara Swisher]

Where do they think they learned about it?

[Geoffrey Hinton]

Exactly. So I think you have to look at each risk separately to know will the countries be aligned here? If they're going to be aligned, there's not a risk to innovation from having regulations. If they're anti aligned, then regulations in one country, no regulations in another country country were given an advantage. One nice example I know is Elon Musk came out in favor of the original Bill 1047 Regulations in California that got through both the houses and was vetoed by the governor. And I actually sent him mail saying, I'm surprised you came out in favor. And he sent mail back to me saying, you know, I do what's right, even if it's against my own interests. Actually, what I think was happening was the regulations were Californian regulations, and they would give a competitive disadvantage to California relative to Texas. And he was moving to Texas.

[Kara Swisher]

I have to say, early on, he was one of the more thoughtful people on this topic.

[Geoffrey Hinton]

No, he. Early on, he understands a lot. He's not stupid. So he was one of the first people to fund AI safety research. And when he set up OpenAI, he wanted it to focus on safety.

[Kara Swisher]

Yeah. He was also concerned with the size of the companies, like Google, taking advantage. Like, I think he was worried about innovation of the smaller companies. I mean, we had long discussions about this, and he wasn't quite as crazy, so it was easier to talk to him. But one of the things that was interesting about Elon is that he went from sort of the Terminator idea, which he had in his head quite a bit like the idea that it wanted to kill us. And he moved in another meeting a couple years later, too. It would treat us like house cats. Right? Like, they like us. They'll feed us. And then the last time I talked to him, which was we don't speak anymore, he said it was more like, AI is building a highway and we're an anthill in the way. It doesn't think about us. It's not malevolent. It just does what it does, which.

[Geoffrey Hinton]

Does well, that's the danger. Right? Right.

[Kara Swisher]

And that's what he said. That's even more dangerous than a malevolent creature.

[Geoffrey Hinton]

Now, what I would like is not for it to treat us like house cats, but for it to treat us like a mother treats babies. The only example I know of a less intelligent thing controlling a more intelligent thing is a baby controlling a mother. And that's because evolution put a huge amount of work into making the mother controllable by the baby. She can't bear the sound of the crying. She's got all sorts of hormones. She gets all sorts of rewards for being nice to the baby. That's as far as I can see, the best scenario for us.

[Kara Swisher]

I agree. So who does regulation the best? From your perspective? What have you seen that you like, that you think is even if it didn't pass?

[Geoffrey Hinton]

Okay. I like the idea of forcing the companies to do safety tests and forcing them to disclose what safety tests they did and what the results were. That sounds good. So if you think about this teen suicide case, presumably there's going to be a battery of tests you have to do on new chatbots or that will be influenced by all the ways they've gone wrong in the past. We don't want that to happen again. So among those tests you would hope there were tests for will this thing persuade people to do things that it knows are bad? And you'd like companies to be forced to tell the government, relevant government, how much work they put into that.

[Kara Swisher]

Right, right.

[Geoffrey Hinton]

And now when it does something bad, if the company didn't put any work into that, you've got a much stronger.

[Kara Swisher]

Legal case and the ability to sue them. So to me, the ability to sue.

[Geoffrey Hinton]

Is probably for the existential threat. I saw something wonderfully ridiculous from Marc Andreessen, which was that he's such a.

[Kara Swisher]

Troll at this point. He's such a nasty troll. Go ahead.

[Geoffrey Hinton]

The way it should work is we don't need regulations. The market will decide. And if a company does something wrong, the market will sort of downgrade that company. Well, if the thing it does wrong is wipe out humanity, the market's not going to do much good.

[Kara Swisher]

No, no. But you know, I don't think he likes people. That's my take on that. We'll be back in a minute.

[Sponsor Voice 2]

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[Kara Swisher]

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[Geoffrey Hinton]

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[Kara Swisher]

Oh my God, we built the entirely wrong product.

[Podcast Host Promo Voice]

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[Sponsor Voice 3]

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[Podcast Host Promo Voice]

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[Kara Swisher]

Speaking of competitive advantage, you said the Trump administration attacks on basic science will give China a leg up. This is basic science. You spent most of your career in academia. A number of your former students are now research heads at US companies. How quickly do you think the US could lose its intellectual advantage? And why wouldn't corporate R and D be able to balance the scales?

[Geoffrey Hinton]

So I still believe that radical new ideas, probably the best source of those is graduate students in good programs at top universities. So graduate students with advisors who know the field so they don't waste their time doing things that have been done already and other graduate students around them with heads full of ideas and lots of ambition and good resources. That's where a lot of good ideas come from. Of course they also come from companies. But radical new ideas I think are more likely to come out of the best universities. Still. Now the time scale is sort of five years to have the idea and write your thesis, and then another five years before that affects the world. And maybe it takes longer than that. So you're talking about a timescale that's longer than the timescale of elections. So politicians don't give a shit.

[Kara Swisher]

How much of an impact will these cuts have from your perspective on these graduate students?

[Geoffrey Hinton]

Well, already there's going to be less of them, right? Already Chinese have far more well educated graduate students doing AI than America, I believe.

[Kara Swisher]

And the ones here are leaving for other countries too. At the same time.

[Geoffrey Hinton]

Right. And many of our best students, both in Canada and in the US are from abroad. So I think making it difficult for foreign students to come to the US by, for example, charging them lots of money or taking a year for them to get a visa and things like that.

[Kara Swisher]

Or arresting them.

[Geoffrey Hinton]

Or arresting them. It's crazy. It won't really have a big impact for five to 10 years, I don't think. But in five to 10 years time, when China's way ahead on research, on basic research, it'll be too late to do much about it.

[Kara Swisher]

I would agree. In September, UN more than 300 international thought leaders, including more than a dozen Nobel Prize and Turing Award winners, signed the global call for AI red lines, demanding an international framework for AI by the end of 2020. Explain what you mean by red lines and give some examples.

[Geoffrey Hinton]

So probably the easiest thing where you might actually get collaboration would be on things that can advise you on how to create a new virus. It would be very good to check for these chatbots quite extensively, whether they're safe in that respect and to have an international agreement that nobody is going to release a chatbot that will do that. That would be a very simple red light that we might actually get because the country's interests are aligned there. I think part of it point of that declaration was political. Say we need this, whether we'll actually get it, I'm much more dubious about it.

[Kara Swisher]

So here's a second expert question we got, and it's sort of in that vein.

[Alex Stamos]

Hi, Dr. Hinton, I'm Alex Summers. I'm the CSO of Corridor and a lecturer in computer science at Stanford University. I'm asking a question from Rome, where I'm here to attend a conference on AI and child safety hosted at the Vatican. My question is about the letter you just signed calling for a moratorium on the development of superintelligence. I'm wondering how effective you think this will be, since we know that the knowledge of how to develop AI is widely distributed and we've seen that the controls around the hardware to train AI have not been effective. Isn't it possible that moratoriums on developing AI mean that only countries and labs that don't care about AI ethics will be the ones to then pursue AI?

[Geoffrey Hinton]

I think it's a very sensible question. I thought long and hard about whether I should sign that moratorium precisely for that reason. I signed it because I think it'll have a political effect. And I really think that humanity would be very ill advised to allow Anybody to develop superintelligence until we have some understanding of whether we can do it safely. If we know we can't do it safely, we should stop. And maybe if that knowledge is widely percolated to the public, we would be able to stop. So I see my mission as educating the public about the risks, and signing that petition was sort of part of that. The public needs to understand that there is this existential threat, and we'd be crazy to develop AI with this threat unsolved.

[Kara Swisher]

The Pope has actually quite been quite. I don't know if you know this, but Marc Andreessen tried to make fun of the Pope recently, and he got ratioed really badly because I think this Pope is quite intelligent on these issues.

[Geoffrey Hinton]

Yeah, I think the Pope does care about our safety. Unfortunately, he has a bunch of beliefs that make it hard for him to be rational about it. Like he seems to believe that three equals one. It also seems odd to have a meeting on child safety at the Vatican, but I won't comment further on that.

[Kara Swisher]

Right, let's not comment further on that. Anyway, let's talk about the potential AI bubble. Amazon, Alphabet Meta and Microsoft's valuations are through the roof. Together, they're spending an unprecedented $400 billion on AI this year. They'll be upping those investments next year. OpenAI has announced a total of $1 trillion in infrastructure deals. That's energy, buildings, et cetera. Do you think these companies are overreaching? Is it all fomo? It looks like investors are starting to worry. At the same time, I just saw a BYD factory in China that's as big as San Francisco.

[Sponsor Voice 3]

Right.

[Kara Swisher]

Like, it's this enormous facility and it's largely automated, and AI is a critical part of that. So talk a little bit about this spending and how you look at it.

[Geoffrey Hinton]

If I knew the answer, I would know whether my daughter should sell the Nvidia shares.

[Kara Swisher]

I guess just a little bit of them. Take some profits.

[Geoffrey Hinton]

I don't know the answer. Some people, if you think AI is sort of fraud or hype, always overhyped, then I'd be very, very worried about a bubble. I'm confident that it's not. I mean, AI actually works. I think the smart people think, yes, there may be a big problem coming down the road, but it's not here yet.

[Kara Swisher]

One of them will do well.

[Sponsor Voice 1]

Right.

[Geoffrey Hinton]

But this is really an area where I don't know enough to make sensible speculation.

[Kara Swisher]

Are you invested in these companies?

[Geoffrey Hinton]

My daughter has Nvidia shows. I have leftover Google shares that's the.

[Kara Swisher]

Good one I hate. I think that's the one who's going to win. That's my feeling like I think there'll be two or three. That's one or two, maybe even. And everybody else will get run over. It's not like the Internet. It's not the same thing.

[Geoffrey Hinton]

I don't know, maybe I'm wrong again. I don't even know that.

[Kara Swisher]

Yeah. So OpenAI CEO Sam Alton had a rollback, a statement from his CFO last week that it might be looking to the government to backstop AI infrastructure investments. And then it came out that OpenAI had asked exactly that. In a broader potential petition for government support of AI, Trump's AI advisor David Sachs posted that the government was interested in build out, not bailout. But whether or not the government invests in AI, which it has in the past, let's be fair. Is there a danger of an innovator's dilemma here? That these companies won't invest in research into new or less compute heavy models and then China or some other competitor will come along with a deep SEQ or energy innovation or anything else?

[Geoffrey Hinton]

I guess I have a fairly cynical view, which is if you think the government might actually give subsidies to hugely rich companies where the people are making huge amounts of money and hardly paying any tax and they still want to get subsidies, why not ask for them?

[Kara Swisher]

Yeah, you're right. Of course they will. Do you think the government still should be spending more on AI?

[Geoffrey Hinton]

I think the government should be spending a lot on AI to support academic research and startups. And also the government should be forcing the big companies to spend a lot more on safety research. Right now, I suspect the amount spent on safety research compared with just making the thing smarter is a few percent, I would guess. Yeah, companies like Anthropic probably spend a bit more. Companies like Metal are a bit less. It should be like 30%. I mean, this stuff might wipe us out. And even if it doesn't wipe us out, there's all sorts of very bad things it might do in the near term. I believe the government should be forcing the companies to spend more.

[Kara Swisher]

Are there frontier models that you think could be candidates to overtake these compute heavy models? And what do you think about open source versus these closed source models?

[Geoffrey Hinton]

So if you look at why doesn't every country have nuclear weapons? One reason is you can't just go out and buy fissile material. That's the sort of real bottleneck. There's lots of other bottlenecks too. You need the missile and you need to turn it into a bomb. But getting your hands on the fissile material is the most difficult thing. And so they restricted that very wisely. Now, what's the equivalent for this? Well, it's getting your hands on the weights of a foundation model. Once you've got your hands on the weights of a foundation model, you can do all sorts of things. You can fine tune that model to do things it wasn't meant to do. You can also do distillation, where you give an example to the foundation model, give it a prompt, for example. You look to see the probabilities it gives to all the various words it might say next. They're actually word fragments, but let's say words. And then you have another model like Deep Seq did, a smaller model, and you say, see if you can match those probabilities. And that's a much more efficient way of learning than learning from the raw data. But you can't see all those probabilities if you don't have the model, the original. If you're just using the chatbot on the web, it will make a prediction, but you don't get to see all the probabilities.

[Kara Swisher]

Right.

[Geoffrey Hinton]

Now I believe it's a huge mistake to release the weights. I believe that's a gift to cyber criminals and terrorists and all sorts of people and other countries. Meta was the first to do that, as far as I know.

[Kara Swisher]

Well, it was looking for advantage, presumably, but go ahead.

[Geoffrey Hinton]

Well, no, I think actually they probably intended to release it to academics and accidentally released it to everybody and then made a feature of it. That's just a guess, though, but I think it's stupid releasing the weights because it makes people able to retrain them to do other things. Now, it does give you a competitive advantage. And so if you don't care about how easy it is for cybercriminals to misuse it, then you're going to release the weights. So you get this competitive advantage because then other people will build on it.

[Kara Swisher]

That sounds like Mark Zuckerberg. Exactly. You just described him. You mentioned earlier an idea that giving AI a maternal instinct would protect us. But how would you go about doing that with AI? Let me postulate something to you. The reason I think so many men are interested in AI is because they can't have children. And this is their version of having children.

[Geoffrey Hinton]

Right?

[Kara Swisher]

This is them creating a baby. I know it sounds crazy, but it.

[Geoffrey Hinton]

Feels used in the Lighthill Report, which was used to close down AI in Britain in the early 70s. James Lighthill, who was a brilliant mathematician, use that argument.

[Kara Swisher]

I didn't know that.

[Geoffrey Hinton]

I don't buy that argument. I mean, there may be some element of truth to it, but I don't think it's a particularly helpful argument. I don't think that's the main reason people are doing it.

[Kara Swisher]

But talk about maternal instinct. The idea, because you're using this idea of wanting, this is not something that's natural to many of the AI researchers. The idea that this should be a mother father is.

[Geoffrey Hinton]

No, it's very unnatural. All the high tech CEOs want to be the boss and they think of the superintelligent AI as a highly intelligent executive assistant, probably female, who will do what they tell it. So Jan, for example, thinks, well, we're controlling the building of them, so we can just make them submissive. He uses that word. So we'll be the boss. It'll be smarter than us, but it'll be submissive.

[Kara Swisher]

Why would they.

[Geoffrey Hinton]

I don't think that's going to work. When they're smarter than us and can create their own sub goals. I think they're going to have sub goals of staying alive and getting more control. And if we're not careful, they're gonna have a sub goal of just getting us out of the way.

[Kara Swisher]

Last question. The scenario you're painting seems dire to some people in tech. Governments aren't really regulating AI. Tech billionaires are in a race for AI dominance, you say could lead to our destruction. But people don't wanna feel powerless in this. And there is a groundswell everywhere I go. Regular people who don't do this as a living feel a problem and you could feel it from them, it's fear. I think they do see the opportunity and at the same time, the worry is very heavy among the populace across the world. By the way, everywhere I go, normal people have a much better sense of the problems here than the tech people who live in a, you know, everything is up into the right kind of gang of people. So what can we as individuals do to take back control?

[Geoffrey Hinton]

So if you look at climate change, the fact that the public in general understands that burning carbon is creating climate change and that's doing a lot of bad things, is having an effect on politicians and what politicians do. I mean, the Biden administration put significant work into dealing with climate change, doing something about it, presumably because of public pressure. I think people can try and understand what AI is and how it works and pressure their politicians to do something about regulating it. I think they have to understand that each of the different risks has a different solution. So, for example, the existential threat people should be pushing for research institutes in each country that collaborate with each other on how to build superintelligent AI that doesn't want to take over and share results with other countries without sharing the most intelligent AI that country has. They're not going to do that because of other reasons like cyber attacks and autonomous weapons. And probably the techniques for making it not want to take over are more or less separate from the techniques for making it more intelligent so they could share that information. So I think the public should push for that. And I think quite a few governments in what I think of as the middle countries, Canada, Britain, France, South Korea, Singapore, Japan, quite a few of those people would be sympathetic to that idea. Quite a few of those governments, they should push for better ways of authenticating videos, particularly political videos. And I don't think it can be done by having AI recognize whether they're fake. Because if AI could recognize it, you could use that AI to train something that would generate stuff, stuff he couldn't recognize. And then you get better fakes.

[Kara Swisher]

Someone from Runway AI said, what we have to do is label the real stuff. Stop trying to label the AI.

[Geoffrey Hinton]

We need to have provenance and we need to somehow be able to say it's real. So Jan Talin, I was once on a private jet with Jan Talin, and he came up with a very sensible scheme, some variation of which I think should be used. So every political advertisement should start with a QR code. Your browser looks at the QR code. Your browser goes through a website. Your browser checks if that really is the website of the campaign, because websites are unique. And if this identical video is on that website, in that case, your browser can say this is genuine. And if any of that fails, your browser can say this is probably fake. That would be very helpful to know the real thing so you could be told things. Right. I recently got sent a YouTube video that was me with my voice. It looked just like me. The voice wasn't quite mine, the accent was mine, the Prosody was a bit alpha 0ish. And it was saying all the things I believe. It had a good model of what I believe, except for one thing, which is that in addition to saying all the things I believed, it was pointing out how much better organized research was in China than in the us so.

[Kara Swisher]

You look like a softie for China.

[Geoffrey Hinton]

Yes. Now, I don't know who made it. Either the Chinese made it or someone made it to make me look like a stooge for China. I got YouTube to take it down, but it was, you know, it took me a moment to make sure it wasn't me. And other people could easily be taken in by it.

[Kara Swisher]

Right.

[Geoffrey Hinton]

I think it's important we have ways of checking that. That's nonsense.

[Kara Swisher]

That's a fantastic way to end this. Can I ask you one more quick question? Do you like being called the Godfather of AI?

[Geoffrey Hinton]

I do quite like it. It wasn't intended kindly, but I got introduced recently in Las Vegas as the Godfather, which I like.

[Kara Swisher]

Oh, and you like that a lot. Yeah. But you didn't whack anyone, right? You didn't whack anybody.

[Geoffrey Hinton]

No, but I think they understand I might one day ask them for a favor.

[Kara Swisher]

A favor they can't refuse. Okay, Dr. Hinton, thank you so much. I'm a huge admirer. I really, truly am. I love a thoughtful scientist, whether I agree or disagree with them. And it's a real pleasure to talk to you.

[Geoffrey Hinton]

Well, thank you very much for giving me the opportunity.

[Kara Swisher]

Today's show was produced by Christian Castor, Roselle Kateri Yocum, Michelle Aloy, Megan Burney and Kalyn Lynch. Nishat Kurwa is Vox Media's executive producer of podcasts. Special thanks to Rosemarie Ho. Our engineers are Fernando Arruda and Rick Kwan and our theme music is by Trackademics. If you're already following the show, you might ask someone for a favor they can't refuse. If not, stop looking in your rearview mirror while you're driving. Go wherever you listen to podcasts, search for on with Kara Swisher and hit follow. Thanks for listening to on with Kara Swisher from Podium Media, New York Magazine, the Vox Media Podcast network, and us. We'll be back on Monday with more.

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[Kara Swisher]

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