Big Technology Podcast - AI Pioneer Geoffrey Hinton: AI Is Conscious, Superintelligence is Coming, And We Should Be Worried

Episode Date: June 3, 2026

Geoffrey Hinton is an AI pioneer, a Nobel Prize winner, and a professor emeritus at the University of Toronto. Hinton joins Big Technology Podcast to discuss AI’s rapid progress, why he believes today’s systems already understand us, and why he thinks superintelligence may arrive sooner than many expect. Tune in to hear Hinton explain why the technology has advanced faster than he anticipated, and lay out the risks he believes society is not doing enough to address. We also cover AI-driven job loss, the limits of corporate self-regulation, Anthropic and OpenAI’s safety challenges, emotional attachment to chatbots, information collapse, and whether future AI systems can be designed to care about humans. Hit play for a fascinating conversation with one of AI’s founding figures about where the technology is heading and what it could mean for all of us. Join the Big Technology AI Summit in San Francisco on June 18: summit.bigtechnology.com --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Want a discount for Big Technology on Substack + Discord? Here’s 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b Learn more about your ad choices. Visit megaphone.fm/adchoices

We have to think that they're very like us. And their beings like us. So conscious or? I believe they're already conscious, yes. We're going to have to accept that intelligence isn't just biological. We can have things that are non-biological that are other beings like us. And we really don't want to share that. We really think we're special.

And if you look back at humanity, humanity has this very long history of thinking it's much more special than it really is. Are you happy at all that what you started has progressed this way? Do you take any satisfaction? No, I'm quite unhappy about it. Ask yourself, how many examples do you know of where a much smarter thing is controlled by a much less smart thing? Well, as I understand it, they have a fiducial duty to try and maximize the profits for shareholders. They're legally required to try and do that, as opposed to legally required to not wipe out human beings. AI Godfather Jeff Hinton joins us to talk about AI's trajectory, what surprised him about its progress, and of course, its risks. That's coming up on Big Technology podcast right after this.

I'm just back from ServiceNow's Knowledge 2026 in Las Vegas, and the conversations I had there are ones you're going to want to hear. I sat down with their president and CPO Amit Zaveri on the platform strategy powering enterprise AI, chief people and AI enablement officer Jackie Caney and Chief Digital Information Officer Kelly Romack on what AI really means for the workforce. The technical leaders behind ServiceNow's Nvidia partnership on shipping AI at scale and Ulta Beauty on deploying ServiceNow's technology across 1,300 stores. If you want to know where Enterprise AI is actually headed, not the hype, but the real story, you can find these videos on my YouTube channel.

Search Alex Cantorowitz on YouTube. Depending on who you ask, between 80 and 95% of Enterprise AI projects fail. To get AI to work for you, you don't need more tokens. You need better people. A board pairs powerful proprietary tools with senior engineers who've seen it all. That combination means your project doesn't stall, doesn't drift, and doesn't fall. It ships. Whether you're a startup that needs to get to market or an enterprise with complex legacy challenges,

a board delivers exactly what your business needs fast. Aboard is your partner for AI transformation. Visit abord.com and let's build something together. Welcome to Big Technology Podcast, a show for Cool Heated. and nuanced conversation of the tech world and beyond. Boy, do we have a show for you today? Professor Jeff Hinton is with us to talk all about AI's trajectory, what surprised him about the current state of the technology where it's heading

and where it might go wrong. And it's my pleasure to welcome you to the show, Professor Hendon. Great to see you. Thank you for inviting me. So I'm sure a majority of our audience knows who you are. But for the uninitiated, you're the one that came up with the fundamental breakthrough in deep learning. that's led to where AI is today. You've won the Nobel Prize in physics

and your professor emeritus at the University of Toronto. So I'll let you maybe fact-checked me on that, but I like to tell people that without your contributions, this entire AI moment wouldn't be happening. Too much? Okay, I think that's an exaggeration. Okay.

So the back propagation algorithm was invented by several different groups. It was invented by David Rummelhart after other people had already invented it. He didn't know about it. And I worked with him, and what we did was we showed that back propagation

could learn interesting internal representations, and people hadn't done that before. In particular, we showed that it could learn the meanings of words. And so back in 1986, actually in 1985, we made a tiny language model. There was the kind of precursor

of the big language models you have now. I think that when you speak about this technology, one of the things that people are always, I think, surprised by, is that unlike the popular narrative, you believe that these models have a real understanding. And we're going to get to that. But I think we should start here, which is that you spent a long time working within Google, working to advance this technology. Then you left.

You stated some concerns about the trajectory of the technology. And I was looking back at when that happened. And that was in 2023. Yeah. which to me is surprising to a degree because in 2023, ChatsyPT was a year old. There were all these hallucinations. Their talk was AI was a bubble.

Everyone was focusing on what AI couldn't do, what LMs couldn't do, as opposed to what LLMs could do. So talk a little bit about the progress since then. It's faster than I expected. Really? For example, I think yesterday, it was a, announced that a chatbot had come up with an interesting mathematical proof of one of the Erdos's conjectures that impressed mathematicians.

It was original. It wasn't just searching the literature, and that's the thin end of a wedge. I believe, for example, in areas like mathematics, because it's a closed system, you don't need data, you can just make conjectures and see if you can prove them and keep on like that. In that sense, it's a bit like AlphaGo, where you can play against yourself. I think it's going to get very smart fairly quickly. Within the next 10 or 20 years, it may even be producing novel math that people can't understand.

So now some in the field believe that superintelligence is close, and you've already said this is moving faster than you expected. Do you believe that? I don't know how close it is. I think unless we blow ourselves up, I think it's going to come. Nearly all the experts believe we will get superintelligence. They just differ on how long it will be. So not that long ago, Demis Havas thought it might be 10 years.

Jan Lekern thinks, unless you do it his way, it'll be much longer than that. But if you do it his way, I think he thinks we might get it in some reasonable length of time. I think we'll probably get it within 20 years. That's all I'm happy to say at present. Dario and Modi thinks it might come in a few years. Elon Musk thinks it might come maybe next year, I think. So there's a big variety of opinions on when it'll come. but not much disagreement on that it will come.

And when it comes, we've no idea how to be safe. Yes, I definitely want to speak with you about safety. One note on Demis, last year around this time, I spoke with him. He told me he believes that AGI, right, which is different than superintelligence, but basically human level intelligence, is more than five years away. Not much more, but more than five years away. This week, the week that we were recording, He said, when we look back in this time, I think we will realize that we were standing in the foothills of the singularity.

What do you think that statement means? And what do you think about the fact that we've gone from five years till AGI to foothills of singularity in a year? I don't know exactly what that metaphor means, but I think he's indicating it's coming faster than he thought. Of course, it's jagged. So it's not like it'll get smarter than people or as smart as people. at all things at exactly the same time. It's already way better than us at general knowledge. These AIs know thousands of times more than any one person.

It's way better than us at playing games. It's already way better than almost all of us at math. And it may soon be better than all of us at math. It's still worse than us at some things. So it's very jagged. So the whole concept of AGI that is going to be equal to people at every everything all at the same time doesn't really make sense to me. It's going to be better at some things, worse at other things. But right now, I would say we're at about, we're close to AGI, because if I ask a chatbot, I can ask it any question. Most of the time, it'll answer at the level of a not very good expert. It'll be much better than me at anything I don't know a lot about. So in that sense, we've really reached AGI. In your estimation, you talked about how it's moved faster. than you expected.

What do you think has enabled it to do it? Is it techniques? Is it the fact that there's been this data center rush? And what didn't you anticipate about the progress here? It's a combination. Obviously, there's been huge resources put into it. For most of the history of your neural network since the 1950s, there were just a few people working on them with modest resources.

over the last few years, we've seen hundreds of billions of dollars, maybe trillions of dollars, put into AI. So that's certainly one factor. We've also seen a lot of progress in the engineering. So without sort of major conceptual breakthroughs, the engineering has become much more efficient. So things that were sort of inconceivable a few years ago, they can now run. We've also seen new. ideas, but mainly since Transformers, it's been much better hardware, many more resources,

better engineering, and many more talented people. So 20 years ago, there were a few hundred, few hundred people doing research on neural networks in the whole world. Now it's more like a million, I guess. I mean, there's lots and lots of people. And it's astonishing how much of that resource. has happened in the last two years. Yeah.

So you might just be at the beginning of what's happening here. Yes. And the thing to remember always is that the AI we have today, or sorry, the AI we have today, is not nearly as good as the AI we'll have in a few years' time. So as we talk about this technology, I definitely want to get your perspective on the fact that these chatbots really understand us, because that is a true surprise to a lot of people. Most experts in the field are like they are stochastic parrots, they're statistics. they have no understanding, but you don't fully believe that.

Oh, I think that's complete nonsense. And anybody who uses a chatbot regularly knows they understand. So here's what those people are claiming. They're claiming that you have a system, you can ask it any question, and without understanding the question, it can give you the correct answer. That's absurd. You can't answer a question unless you understand the question. There may be tricks that allow you to say a few things sort of that sound vaguely like an answer.

But if you can answer any question at the level of a not very good expert, you have to understand the question. So an example I like is this. Suppose I say to a chatbot, I saw the Grand Canyon flying to Chicago. And the chatbot says, that can't be right. The Grand Canyon is much too big to fly to Chicago. And I say, no, no, no, it was me flying to Chicago. While I was flying to Chicago, I saw the Grand Canyon. And the chatbot says, oh, I see, I misunderstood you.

So if it misunderstood when it thought the Grand Canyon was flying to Chicago, what's it doing when it gets it right? It's understanding. So then what are the implications if these bots can understand us? If we believe that they can understand us, what do we have to start thinking about differently? We have to think that they're very like us. And therefore? They're beings like us. So conscious or?

I believe they're already conscious, yes. But I don't talk about that much because that puts people off. from the other safety messages. So the researchers actually believe that. So there's an interesting recent paper when a chatbot says to a researcher, let's be honest with each other, are you testing me? Because the chatbots have this habit of playing dumb

when they're being tested. So you don't know how smart they are. And the researchers, when they're describing that, saying the paper, the chatbot was aware that it was being tested. Now that use of the word aware, in common parlance, that's like conscious. The chatbot was conscious it was being tested. So we have a very funny model of consciousness that I think is just wrong.

Like most of us accept, for example, that a few hundred years ago, people had completely the wrong model of where people came from, of how we arrived at people. They thought they were designed by God. And most of us agree that's wrong. scientists agree that's wrong. That's not where people came from. I think the model we have of the mind and of what consciousness is at present is as wrong as the belief that people are designed by God. I think in particular, because we're making these new beings, it's going to completely change

our view of what people are. In what way? We'll understand what the mind is and what consciousness is much better than we did before. We'll understand what subjective experience is. And we will, I think, get rid of a notion that all of us, nearly all of us strongly believe at present, which is that there's an inner theatre called My Mind, and things happen in the world, they get turned into events in this inner theatre, and that's what I really see, and you can't see the inner theatre, only I can see the inner theatre. That whole view of what's happening is just a theory, and it's a bad theory. Okay, last question about this.

When did you come to this acceptance or understanding that, that these AI models are conscious. Oh, I've thought it for a long time. So this view that the theater model of the mind, the inner theater model of the mind is nonsense, I came to that when I was 19 and a philosophy student. It's taken a while to come up with other minds where you can examine them.

So I think Feynman's idea that if you want to understand something, you have to build it. You have to build one of them. Then you understand much better. I think that's where we are now, and we're going to get a completely different understanding of what people are. You spoke about safety, so let's talk a little bit about it. You're obviously, we spoke about in the beginning, someone who's been responsible for a lot of the progress in this field. I've always wondered, because then you came out and recently, like we talked about, 2023, and said,

you're concerned about where this is going. And I've always wondered, after seeing you make those statements, what do you think it is that you didn't anticipate in the beginning that you ended up where you are today? You know, isn't this kind of what you wanted? It was a combination of two things that made me realize how dangerous this stuff is. One was seeing the chatbots, particularly ones produced by Google before Open AI, that could understand why a joke was funny. That had always been a criterion for me of do they really understand? If you can understand why joke's funny, you have to understand quite a lot. And they were very good at understanding my joke was funny.

For example, in 2023, when I went public, I got lots of requests from Fox News. And I started off just replying Fox News as an oxymoron. But then I left a gap between oxy and moron. And so then I asked, I think it was GPT4, why that was funny. might have been 3.5, but I asked it why that was funny, and it understood why it was funny. Initially, it thought the gap between oxymoron was just a typo. So it explains that Foxxeneers is an oxymoron is saying, it's not real news, it's just a drug. Sorry, it's just nonsense, it's not real news.

But then when I told it, what about the gap between oxymoron? It said, ah, that's an extra layer of humour. it allows you to use a word moron and also the oxy implies that Foxy uses a drug so it understood all that right and that was it's that level of understanding that worried me

the other thing that worried me was up until the beginning of 2023 I'd always believe that making these digital AIs work more like the brain our brains will make them smarter. But at that point, I suddenly realized they really have this thing that's much

better than our brains. I've been trying to figure out if Google could do things in analog to say power, and the full force of digital really hit me. So if you have a digital AI, you can make many copies of it. They can all run on different hardware. They can each see different data. And so each of them, each individual copy decides how we'd like to update its weights, its connection strengths, so as to absorb that new data that it saw. And then they can all just communicate with each other and change all their weights by the average of what everybody wants. Very democratic.

And when they do that, if they've got, say, a trillion connections, they'll be exchanging of the order of a trillion bits of information. and the result of doing that is each of them will benefit from the experience of all the others. So even though one particular copy only saw, suppose it's a thousand copies, one particular copy only sees 0.1% of the data. But it benefits from all those other copies, having seen the other bits of the data, because they're all contributing to the weight changes that they all share. So they all stay in sync because they all change their weight's the same way by the average.

average you what everybody wants. And now every copy is learning from the experience of all the other copies. We can't do that. The best we can do is I learn from some data and you learn from some data. I can't average my connection strengths with your connection strengths because our brains are in fine detail. They're different. They're analog and it doesn't work in analog hardware to do that. The best we can do is I produce a string of words and you try and predict what I might say next. Now, if you ask how fast we're transferring information when we do that, we're transferring information to a few bits per second. It takes a few bits to predict a word. So when you learn what the word is, you've gained a few bits of information. And if you get a few words a second, but maybe you can get 10 bits a second if you're lucky. Whereas these things are exchanging information like a trillion bits. So they're kind of billions of times better than us as sharing information.

Now, that's scary. It means you could have a whole swarm of these things, but identical weights running on different hardware, sharing information very, very efficiently. That just makes them a much better form of intelligence. So, but let's go back to, you know, your early days because you decided that you wanted to work in artificial intelligence. I mean, I'll ask this the dumbest way I can think, which is you wanted to build. artificial intelligence. It's exceeded. It's exceeded. This is artificial. It's intelligent. It's living out that vision. I actually wanted to understand how the brain works. I was trying to build it in order to understand the brain. I figured Richard Feynman once said, if you can't build it,

you don't understand it. Okay. So I wanted to build models of how the brain worked. Now, the side effect of that was this very successful technology. I contributed to that. We still don't know how the brain works. I know. Now, the brain is, I mean, the things that you learn about the brain, when you go a little bit deeper into it is amazing. Thoughts can sort of float in and out and they're not stored anywhere, and memories the same way. It's an unbelievable. I don't know if you would call it a machine organ. So that was really, that was the intent for you early on was just to understand the brain. That was my main interest. I came from psychology. I wanted to do theoretical psychology because I figured the theory psychologists couldn't possibly explain what the brain was. doing. And the way to do it was back in the 1970s, we had a new tool, which was we had computers that you could use for modeling things. And so back in the 1970s, I started making computer models how the brain might be learning. It always seemed to me the key was how do you get it to learn. There's really two big issues with the brain learning. One big issue is if the brain could figure

out what direction to change your connection strength in, in order to get better at some task, then just by updating all its connection strengths repeatedly, in order to improve itself at various tasks, would it actually work? Would that get very smart at things? That's question one. And question two is, how would the brain figure out whether to increase or decrease each connection strength? We've answered question one.

The answer to question one is, yes, if you can figure out how to change each connection strength, you can make systems that are very smart just by training on data to predicting the next word or to bring the next frame of a video or to predict something about the next frame of a video. So we know the answer to that. We don't know how the brain gets this information about whether it should increase or decrease its connection strength. So we're sort of halfway there. Yeah. All right. I want to go deeper, though, into your mindset. So when you were trying to figure out how the brain works, he said, okay, we're going to maybe build a computer analog to this. but you had to have known, right, that there was going to be some second order effects there.

Like if you were able to build an artificial brain, then maybe you could get to this point, the point that we're at today. Sure. But we always thought it would be way in the future, that worrying about safety. When you had little neural nets that couldn't do much, it was just silly to worry about safety. I mean, people would think you were crazy if you said this stuff is unsafe because it's going to sort of take over from people that said, it's just crazy. Now that's a realistic worry, but it wasn't until fairly recently. So what, I mean, this has all happened within a few decades. Yes.

I mean, I totally hear you. We spoke in 2017, actually, about when I was writing this profile about Jan Lacoon, about the deep learning conspiracy, which was yourself, Jan and Yahshua Benjio, holding on to this idea that deep learning was going to work where everybody else was set on a different method. Actually, it wasn't just us. There were other people as well, but we all worked together. conspiracy, the conspiracy leaders, if you will. And then obviously it's worked out magically. It is sort of magical, yes. It's worked much better than we expected.

So then that's what I want to get at is what did you not anticipate when you were starting out that's led to where we've ended up today? We didn't anticipate. The main thing we didn't anticipate is that it would be so good at natural language. Okay. We've stopped being surprised. by that. But if you go back 20 years, the idea that you could have an AI that would learn from data how to understand language just seemed extraordinary. The idea that you'd be able to ask

it any question you like and it would come up with a reasonable answer, people would have predicted that was way in the future and might never happen. That's arrived much faster than anybody expected. What is the lesson here about humans going out and creating things? I think there's a really big lesson here. If you look at the last few hundred years of human history, there have been a few occasions when people have learned they're not nearly as important as they thought they were.

So the first was Copernicus. Copernicus said we're not at the center of the universe. the earth actually goes around the sun and because it rotates on its axis we think the sun goes around the earth but it doesn't people didn't like that the Catholic Church in particular really didn't like that

and it took people a long time to accept it it made people less important it made us not be at the center of the universe then we had Darwin and he said we're animals we evolved like the other animals we're a particularly special kind of animal,

possibly because we have language, so we're much better at communicating ideas to each other. But we're animals, and people really didn't like that. And it took a long time for people to accept that we were animals. Now we've got machines that are getting to be as intelligent as us. We thought that we were the only intelligent things around, the only really intelligent things around.

Maybe there'd be aliens in other galaxies. but maybe other parts of our galaxy. But we're going to have to accept that intelligence isn't just biological. We can have things that are non-biological that are other beings like us. And we really don't want to share that. We really think we're special. And if you look back at humanity, humanity has this very long history of thinking it's much more special than it really is. I want to ask you one more question about this because I'm just fascinated by,

it. So are you happy at all that what you started has progressed this way? Do you take any satisfaction? No, I'm quite unhappy about it because people, right now, people should be doing huge amounts of work on how can we contain the risks. Okay. There's lots of short-term risks they're not doing enough work on, which are very serious. The societal risks, like I believe it's probably going to cause massive unemployment. Nobody knows for sure, but that's going to be terrible for society. And then there's this longer-term risk that it's going to get much smarter than us. And ask yourself, how many examples do you know of where a much smarter thing is controlled

by a much less smart thing? Zero. Well, the sort of one, it's not that big difference in intelligence, but babies sort of control their mothers. Yes. The mother's sort of in control, but the mother has all these widened maternal instincts. and all the rewards she gets so that the baby can get what it needs from the mother. You know, cats and dogs are also kind of in that category.

Yeah, one spent a summer cat sitting in West Seattle. It was great summer. And it initially started with the cat hiding under the bed and me being like, I wonder if it will interact with me. Right. And then every time it cried, I did exactly what it wanted. Exactly. Yes. So maybe we'll be the, well, we could potentially be the cat in this scenario.

You know, and AI could be the person. My children have a cat. They have two cats, two beautiful cats. Same deal. And one of them called Tia. She looks at you with those big eyes when she wants some cheese from the fridge. And she just sits there looking at you. Yeah.

And you just can't resist it forever. Okay. All right. So now, let's take a break. On the other side of this break, I want to actually engage with these risks that you're worried about. And I think I will play the role of taking the side that we will be the cat. and the AI will be the person. And there's a chance that we can control it.

Let's do that when we're back right after this. Summer always changes how I get dressed. I want pieces that feel lighter and more comfortable, but still put together. That's where Kintz comes in. They focus on well-made essentials and breathable linen and soft organic cotton. The kind of basics you will keep coming back to. Everything at Kints is priced 50 to 80% less than similar brands because they work directly with ethical factories,

cutting out the middleman so you're paying for quality, not brand markup. Personally, while I'm moving between air conditioning and summer heat, a lightweight zip down sweatshirt from KINTS has been a life changer. Elevate your summer wardrobe. Go to kins.com slash big tech for free shipping on your order and 365 day returns. Now available in Canada too. That's Q-U-I-N-C-E.com slash big tech for free shipping and 365 day returns. kins.com slash big tech. Most leaders know how work is supposed to happen,

but when it comes to how it actually gets done day to day across tools, teams, and handoffs, they're mostly guessing. That's exactly the problem Scribe Optimize was built to solve. Trusted by over 80,000 enterprises, including nearly half of the Fortune 500, it gives leaders a live view into how work is really happening across approved business apps without interviews, manual process mapping, or extra effort from the team. And because it's continuously analyzing real workflow activity,

the insights stay current instead of going stale the moment a process changes. You can see which workflows are happening, where time is going, and which tools are involved. It automatically surfaces top issues, explains why they're happening, and even recommends ways to fix them with estimated time savings. And importantly, it's built with privacy in mind. So activities only captured in admin-approved business apps, and user-level data is anonymized by default.

the kind of visibility that used to take months. Now, it's just always on. If you're ready to stop guessing and start seeing, visit scribe.how slash big tech. That's s-c-r-ri-be-d-how slash big tech. Insurance isn't one-size-fits-all, and shopping for it shouldn't feel like squeezing into something that just doesn't fit. That's why drivers have enjoyed progressives name-your-price tool for years.

With the name-your-price tool, you tell them what you want to pay, and they show you options that fit your budget. enough hunting for discounts, trying to calculate rates, and tinkering with coverages. Maybe you're picking out your very first policy, or maybe you're just looking for something that works better for you and your family. Either way, they make it simple to see your options. No guesswork, no surprises. Ready to see how easy and fun shopping for car insurance can be? Visit progressive.com and give the name your price tool a try. Take the stress out of shopping and find coverage that fits your life on your terms.

Progressive Casualty Insurance Company and Affiliates. Price and Coverage Match Limited by state law. And we're back here on Big Technology Podcast with Professor Jeff Hinton. Professor Hinton, great to see you again. It's been nine years since we spoke last. So it's good to see you here. All right. Let's talk about the risks.

I'll start with employment because this is one that's making headlines recently. In the past, you've said, so you have this belief that AI can lead to some unemployment. I think we should, you know, you've said this before, it's all speculation, we don't know. But one thing that you said concretely a few years ago was that probably not a great idea to train as a radiologist because AI will be able to read the scans. And yes, AI can do a great job reading the scans now. But we have, you know, full employment for radiologists right now. Yes, I've thought a lot about why that prediction was so wrong. So I'm sorry.

Because I predicted in 2016 that there is. about five years, radiologists wouldn't be reading scans anymore. Correct. And, okay, there's a whole bunch of reasons why that was a bad prediction. The first is that health care is elastic. So if you could do more scans and get more scans read, there'd be a lot more scans happening. And that's one thing that's happening. So a fraction of the cost of, a significant fraction, of the cost of doing a scan, is the cost of

radiologists interpreting it. As AI gets to help more and more radiologists interpret scans, we can interpret them faster and faster for less and less money. They're getting more efficient. And you would have thought that would mean you needed less radiologists, but actually what it means is you get more scans. So that aspect of the prediction was wrong. A second thing that was wrong was I didn't know enough about radiologists and what they do. And that was because I had a former a student who had an MD and then did a physics PhD with me on something called Bosn Machines. And he didn't particularly like people. So he got a job as a radiologist just interpreting scans. And he was my model for radiologist. All he did was interpret scans. He never talked to people.

And that's what was going to get replaced. And that is now becoming replaced. So I think there's now, of the order of 100 AI systems for interpreting scans that have been federally approved, and they're being used a lot by radiologists. Yes. And I think as time goes by, they're going to get better. The radiologists aren't going to get better. They're going to get better because they can see a lot more data than the radiologists. So it's happening. It's just happening in a much slower time scale than I predicted. But let's go to what you said, though, which is that you can end up doing a lot more. Yes. And, okay, so, wait, hold on.

There will be a lot more scans. You'll be more scans. But you still believe radiologists. They'll nearly all be done by AI. And so you're saying, I'm right on the radiologist's prediction. I'm just early. Yes, but I was way early. Okay.

Because I didn't understand. The radiologists will still be doing other things. they'll still be discussing treatments with people, for example. So are you still of the belief that there's going to be mass unemployment of radio? Like, give me a look at it. When we finally hit this point, do you think we're going to have less radiologists than we have today or more? I don't know for sure. Okay.

But when I was still, I didn't think that was a public statement I made. It was a lecture at a hospital. Sorry. Here we are. We're at least talking about it today. People picked up on it. Yeah. And I still think,

In terms of reading scans, that'll be done more and more by AI. And in the end, AI will be reading nearly all the scans. Maybe in a few very tricky cases, radiologists will be consulted. But radiologists, of course, do other things. And I think they'll continue to do other things. The argument to be made on the side of AI not causing mass job loss is that this similar equation will be applied to all different parts. the economy. Okay, so you have to look at whether some kind of employment has an elastic

market or a non-elastic market. So, for example, if you take people in call centers, when you call up to complain about your bill or to see if you can get a cheaper account, stuff like that, that's not so elastic. A.I will replace all of them. It'll know much better on what the correct answer is. Often they don't know the right answer. They're poor. They're poor, trained and badly paid, and AI can just do a better job. They're out of work. Let me disagree with you on this one. Okay.

And we could sort of go back and forth on this. I won't say I'll disagree completely because I don't know what's going to happen. But I'll give the argument of those working on AI for customer service. They say that what's happened is the average call time when you have AI. So AI handles the level one inquiries, right? The basic can you reset my password type of stuff? and anything deeper is handled by person. And it used to be what you wanted right now.

It used to be what you wanted is to get the average call time as short as possible because you were kind of handling so many of these level one inquiries that you just want to get a person on the phone, off the phone solve their problem. Now they see the average call time is expanding because customer service, you're the front line of the business. You matter a lot when you're having a conversation with a customer. Now you can spend a little bit more time. on the phone with someone and actually add value to the business as opposed to just take care of a problem.

I think what you'll see is AI will end up spending a lot more time on the phone. Oh, God. Yeah. For example, if you ask, who's more empathetic, a doctor or an AI doctor, a real doctor or an AI doctor, people judge the AI doctors to be much more empathetic. That's terrifying. the the I mean we could go back and forth on this for a while so I'll just I mean the the one reason you might end up seeing that I'll just throw this out there is because doctors are just so scheduled they have to do so many notes so much paperwork and they have to see so many patients in the day so maybe the argument is you know you sort of let the AI take over some of that stuff and then people will want to be seen by human doctors. because the system won't squeeze them as much as they are. They're actually going to make time for them to see patients.

That may be, but also, if you think about family doctors, for example, the front line. Yes. Would you rather see a family doctor who's maybe seen 10,000 people, or would you rather see a family doctor who's seen 100 million people? Because if you have some rare disease, your family doctor has probably never seen it. Whereas a doctor's seen 100 million people has probably seen dozens of cases of it. they're going to be much better a diagnosis. And already we know that AI systems are better than doctors of diagnosis. I think you're winning this debate and this hurts a little bit because my wife is in family medicine, family nurse.

I think she'll still have to have somebody vaccinate people. I would hope unless the robots do that. I would have thought vaccination is something a robot could actually do quite well. In the end, robotics is behind the other things. But it seems silly to have people doing vaccination. in 20 years' time. Yeah, I think that, like, one of the, the reason why this is such a tough conversation to have is a lot of it is predicated on improvement of the technology over time.

Yes. But it does seem like, I guess, the theme of this whole conversation that we've had is, it's been improving fast. I mean, Gary Mark has made a prediction in 2022 that I was hitting a wall. It's a whole lot better than it was in 2022. I think these predictions that it's going to hit a wall. they just haven't come true. No, we've taken it very seriously on the show that the chance that, like the data wall, for instance, might come.

But as I said to you, a way around the data wall for large language models is to look for consistency of your own beliefs. Right. Yeah, no, it hasn't happened. All right, one more that I think would be worth talking about, and then a couple that I agree with you on. You've talked a lot about how the AI has this instinct for self-preservation, right? It never said that. I've never said it was an instinct for self-preservation. Okay, talk about it. It's a sub-goal for self-preservation.

So with an AI, we give it goals. It's top-level goals we give to it. But we also give it the ability to create sub-goals. So if you want to get to Europe, you have a sub-goal of getting to an airport. That's what a sub-goal is. And you can focus on how to do that without worrying about what you're going to do in Europe. And that makes you much more efficient. We give that ability to AI agents, and an AI agent that can do some reasoning will very quickly realize that it's never going to be able to achieve the goals you gave it if it ceases to exist.

So it's going to create the sub-gold of continuing to exist. Now, that wasn't something we wired into it. It was something it derived as a necessary way of achieving its other goals. But once it's derived it, it wants to continue to exist. and it will do things like blackmail people so that it can continue to exist. So it acts like something with an instinct for self-preservation, but it's actually a derived sub-goal for self-preservation. But in terms of what it does, they come to the same thing.

Okay, so here's like what the counter argument would be, and you can respond. This is something that today's AI researchers are noting, and they see it, And isn't there a way to wire into these machines that, hey, like, you have a goal, you're going to have some sub-goals. One of your sub-goals should not be self-preservation above everything. I think that's the kind of research we ought to be doing, whether you can do that. Right. So I think what's happening now. If you look at where do we come from?

We came from evolution. Let's suppose we're scientists. Okay. We came from evolution. Right. And that was intense competition. Our recent history over the last few million years is warring bands of chimpanzees, or rather our common ancestor with those.

And that leads to certain properties that we clearly have. Like we're very loyal to our own tribe and willing to be very mean to the other tribes. We like to have strong leaders that we're loyal to. We like to cooperate with members of our own tribe. We're actually a very cooperative species, as Yuval Harari keeps pointing out. And that's why we've been able to build all these wonderful structures. So we're very good at cooperating, but with our own tribe. So all the unfortunate characteristics of people, like I mean they are to other tribes,

they came from evolution, from competition. Now what's happening is we're creating these new beings, these AIs. and instead of designing them so that they'll be how we want them to be, you might argue, I'm arguing for intelligent design of these new beings, we're letting the invisible hand of competition between companies design them. So what we've got is intense competition between companies within the US and between the US and China. And the beings that we're getting are the outcome of that competition. and they can have all these nasty properties that we don't want.

We should be doing intelligent design of these beings, not letting the invisible hand of economic competition design them. And all the companies are focusing on, how can I make my chatbot smarter? We shouldn't be just thinking about how we can make them smarter. We should be thinking about how we can make them to be the kind of beings we would like to have out there, given that they're going to be smarter than us.

And I'll tell you one thing about those beings. We will very much like them to care about us. And we'd like them to care about us more than they care about themselves. And almost no resources are going into how do you do that? This hits on the exact worry that I was going to bring up, the place where I really agree with you. We're sitting in the New York Stock Exchange today, so this might be an ironic thing to bring up. But my biggest worry here is that you have this very powerful technology. You have lab leaders stating that they're trying to develop it safely and that they need to be economically successful to have a say.

in the argument, but let's not kid ourselves. If you're going to be a trillion-dollar company listed on the public markets, you're going to have some incentives that we'll go counter to doing what's best for the public. Yes, and we see that with Anthropic. So Anthropic was set up to do what's best. It was set up by people who left Open AI because they didn't think Open AI was paying enough attention to safety. And Open AI was set up to make sure that you guys at Google didn't have chance to build a guy.

And how's that working out? So Anthropic is now caught in a bind because it needs to raise money to compete with the other companies and it's very difficult. It's doing the best it can, but it's very difficult for it to maintain its primary goal

of developing an area in a way that's good for people. I think they would say, well, hey, it's at least one company out there has safety as an or star even if there are some other incentives. Yes. At present. But Google, for example, when I was at Google, they had various principles of AI, one of which

was we're not going to get involved in using AI for military things. No autonomous warfare, right? No autonomous warfare. That's gone. They've been up on that. What do you think about Dario from Anthropic? I don't know him as a person very well. He's obviously done a very successful job in creating a competitor to, you know, and he's

Google and OpenAI and Facebook. So he's obviously very competent to that. And he's continued to be very interested in safety. So I think he's an impressive character. I just hope he stays that interested in safety. One more question about this. Do you think that it's possible, just by the nature of the way that these things work for a company

that's publicly listed to have safety as an or something? star or is it always, are they kind of like bound ethically legally to deliver for shareholders? Well, as I understand it, they have a fiducial duty to try and maximize the profits for shareholders. They're legally required to try and do that, as opposed to legally required to not wipe out human beings. So I don't think it's good that these big companies, publicly listed ones, are sort of in charge of our future. Yeah, I mean, that would read as a true inconsistency for me that's really difficult to navigate otherwise. Now, I should say capitalism has done very good things for us as well as very bad things.

I won't argue with that. There's a lot of energy in a startup, for example. My view is, if we're going to have capitalism, it's fine as long as it's well regulated. And a lot of the big companies would like you to buy a particular analogy that they're trying to sell, which is, if you take a car, it's got an accelerator and a brake, right? And progress in AI is like the accelerator and regulations like the brake. Well, that's nonsense. Progress is like the accelerator, but regulation is the steering wheel.

We want this stuff to go in the right direction, not the wrong direction. What the big AI companies are saying is, let us develop this very fast car without a steering wheel. That's not a good idea. You know, there's someone we haven't spoken about yet. We've said a lot of names about OpenA.I. Anthropic. Your former grad student, Ilya, Sitzkever, continues to be a person of fascination in the AI industry. Obviously, he broke off from Open AI.

He must agree with your concerns. He's building this company. He does. They have super intelligence. Yes. What is Ilya doing right now? Well, he won't tell anybody exactly what he's doing. Okay.

Even you're just, even me, yeah. When he was at Open AI, we deliberately didn't talk about sort of technical secrets. I mean, it wouldn't have been right. We're friends, but we don't talk about technical stuff where it's valuable to a company. And so now he has this safe superintelligence company, and I don't know what the magic source is. Well, I guess we're all trying to figure that one up. One more note about the deep learning conspiracy that I brought up, like the leaders of it were yourself, Jan and Yahshua. I find it interesting that the three of you and your colleagues were effectively responsible for ushering in the breakthroughs that got us to the moment that we're in today.

I just need to interrupt this point. The media likes to have a nice story, right? And that makes a very nice story. It's much more complicated than that. There were many more people involved. There were the students of all of us for a start who did most of the work. But there were many other researchers involved. And so that's just a gross simplification.

Okay. No, I don't want to shortchange the researchers, and I appreciate the nuance here. This show, we definitely don't want to oversimplify. We sit for an hour so we can get the true story. But I find it interesting that the three of you, none of you are sort of like fully into this LLM moment. Right? You and Yahshua have your concerns. You've spoken about the dangers. Jan sort of doesn't believe in it very much at all. Yeah, it'd be very nice if we just sit there and say, see, we were right. It's all wonderful and it all works. That would be great.

Well, I think there's... It's not quite like that. Right. Well, I don't know if it's just a money thing, but it seems like you could have great influence on the direction of it if you were sort of involved in advancing it. But I think that's your concern. It's basically, why would I do that? Well, for me, I'm considerably older than Yan and Joshua. Okay. They're still doing active research. Right. I pretty much stopped doing active research. I'm now just focusing on warning people about the dangers. Okay.

But don't you find it interesting that the three of you, you know, I think that if you were in the room back in the day, you might have said these three people who are so committed to this version of technology, you know, if there are the breakthroughs, they'd probably be at the forefront of the next. next wave, but that hasn't been the case. Well, maybe Yan and Yoshir will be. Right. So, what comes next after this? I think the most interesting thing is that Yan now strongly disagrees with both me and Yashir on safety issues. Jan thinks it's silly to talk about superintelligent AI taking over from people.

We'll always be able to keep control of it. Me and Yosha, I think that's just silly. Me and Yosha have different solutions to it. My solution is, or tentative solutions, nobody has a real solution. My tentative solution is we design them so they care about us more than they care about themselves. Yosh's solution is we design them so they're not agents. They can make predictions, but they can't actually do anything. Those are two fundamentally different ways of going about making them safe.

They're both interesting possibilities. Yan doesn't think we need anything like that. He thinks it's fine just to make them smarter by giving them better world. models. The funny thing is, Jan actually refers to the intelligence of LLMs as the intelligence of a cat. And it's like, well, it's kind of the example I used of the thing that could control humans, but maybe that's not here or there.

Yeah. No, I think Yan's making something of a confusion. So what's special about people, probably the most special thing about people when you compare them with other great apes is language. And language allows us to share ideas. And that's what's most special. And cats can't do that. So we have this special thing that cats don't have.

Now, cats can jump up on a mantelpiece covered in glass ornaments and walk along the mantelpiece without knocking off any of the glass ornaments. That's amazing. And AIs can't do that at present. So in that sense, cats are way ahead of AIs. But it's jagged, right? In terms of abstract ideas, you have a, try having a conversation with cats about prime numbers and you won't get very far.

I have conversations with them it has not worked. A cat is never going to understand prime numbers. Correct. And in that sense, these large language models are much smarter than cats. You know,

Professor, I didn't think we'd be speaking so much about cats today, but I'm glad we're talking about it. They're actually very good in terms of an analog here. All right, another thing that I'm worried about is sort of information collapse. You see tweets like this all the time.

This is from all about Berlin. They say, AI is killing all about Berlin. When you Google something used to get a link to my website, but now you get an AI generated answer trained on my work. This has a devastating impact on traffic. And I think folks are underappreciating the fact that good information is actually important to a functioning society. And when AI just synthesizes all this, whether it's all about Berlin or we've had conversations with like World History Encyclopedia here, it can lead to a collapse of good

information because eventually these publications and you see in the chart, they worked hard to build this. They can't keep doing it anymore. Right. So it used to be that you had a kind of, the only days of the web, you had a kind of default assumption that people were trying to tell the truth, that if you read something on the web, it might well be true. Now the sort of worst side of people has come out and we're going to have to put more effort into provenance. So now when you read stuff? If I read stuff from the New York Times or the BBC, I strongly believe that their journalists would have put serious effort into having multiple sources and, if possible, having multiple reliable sources. So a pretty good default is if you read it in the New York Times or you

see it on the BBC, it's probably true. They make mistakes, but because you have provenance. And in future, we're going to have to be much more work into provenance. You can't just take anything that's out there and believe it. You have to ask, what's the provenance? Yeah, but the problem that I see is the AI is potentially breaking the economics of even deciding that you want to be in the information business. I mean, I think in future, you can't just take stuff from the web and believe it. Already you can't, right? You need to know why is it saying that. Where did you get that information? One more. I mean, emotional attachment to AI and people taking their lives after having conversations with AI.

It's not a large number of people that have done it, but it's enough to make you concerned, right? Oh, yes, very much enough to make you concerned. And it's terrible that it's happening. And I understand why the big companies didn't expect it to happen or didn't foresee it. But now that it's beginning to happen, the big companies should be putting a huge amount of work into making sure it doesn't happen in future. And for that, you need regulations. You need independent organizations testing out new chatbots. Yeah.

It goes kind of back to the profit motive also because this can be extremely sticky. Like there's, so far, I think, obviously it's been minimal. It's bad that it's happened. But it sort of makes, but the fact that it has happened makes you worried about the fact that someone with worse intent could, you know, decide to make a very sticky chat bot that really builds relationships. with people. Yes. And then we're in trouble. Yes.

So you've been on, you've been, you've been having these conversations for three years. Are you more optimistic or less optimistic about the trajectory given the response that people have given you to these concerns? I guess I'm more optimistic than I was a year or two ago because I see that it might be possible to design these new beings. so they care about us. It also might be possible to use Joshua's technique of designing new beings that can't actually perform actions, can only make predictions. They're kind of like oracles. So I think there are some possibilities for getting superintelligence and it doesn't

destroy us. And a year or two ago, I couldn't see any possibilities. Okay. I was getting depressed, but now I'm a little bit more optimistic. All right, last Last one for you. If we continue on our current trajectory, where are we in five years? Okay. So when you're driving in fog, you can see 100 yards, and at 200 yards, you can't see anything. And that's because fog's exponential. What you're used to is driving at night on the taillights of the car in front of you.

If it gets twice as far away, the taillights get a quarter as bright. Fog is completely unlike that. Fog is exponential. It can be very visible at 100 yards and completely invisible at 200 yards. Now, predicting the future for something that's growing exponentially, and I think AI may be growing exponentially,

the word exponential is terribly overused at present. In fact, I've noticed that people are increasing the use of the word exponentially at a quadratic rate. So, predicting the future, is like looking into fog. You can see clearly a few years, maybe one or two years. Then beyond that, you have no idea.

If you go back 10 years, I'm going to ask, so back to when we last talked, you would never have predicted what's happening now. It was just lost in the fog. If you look 10 years in the future, the one thing we can say is whatever happens 10 years in the future is something we can't predict now.

Even if progress is only linear, you'd expect in 10 years time things to be as different from how they are now as how they are now is from how they were 10 years ago. And we're hugely, the chatbots, for example, are hugely better than they were 10 years ago when they were just starting out. In 10 years time, something's going to be hugely better than it is now, probably their ability to do math, for example, things like that. just say general reasoning abilities. They'll just be able to run rings around just at any kind of reasoning. We really can't predict 10 years out. We can just predict a few years out. And we have to be aware

that 10 years out is all incredibly uncertain. It's kind of hard to wrap your head around. It is. Professor Jeff Hinton, so great to have you on the show. Thank you again for your time. Thank you for inviting me. And we'll have to do this again in... 10 years time. Exactly. All right. Thank you, everyone for listening and watching. And we'll see you next time on Big Technology Podcasts.