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

We have to think that they're very like us.

And therefore They're beings like us.

So conscious or Um I believe they're already conscious, yes. We going to have to accept that

yes. We 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 [music] 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 quite unhappy about it. Ask yourself,

how many examples do you know of where a much smarter thing is controlled by much less smart thing?

Well, as I understand it, they have a fiduciary duty to try [music] and maximize the profits for shareholders.

Um they're legally required to try and do that. As opposed to legally required

do that. As opposed to legally required to not wipe out human human beings.

AI Godfather Jeff Hinton joins us [music] to talk about AI's trajectory, what surprised him about his progress, and of course its risks. That's coming

up on Big Technology Podcast right after this. Welcome to Big Technology Podcast,

this. Welcome to Big Technology Podcast, a show for cool-headed and nuanced [music] conversation of the tech world and beyond. Boy, do we have a show for

and beyond. Boy, do we have a show for you today. Professor Jeff Hinton is with

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, [music] and where it might go wrong, and it's my pleasure to welcome you to the show,

Professor Hinton. Great to see you.

Professor Hinton. Great to see you.

Thank you for inviting me.

So, I'm sure a majority of our audience knows who you are, but for those for the uninitiated, um 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

today. You've won the Nobel Prize in physics, and you're Professor Emeritus at the University of Toronto. So,

I like I I'll let you maybe fact-check 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 backpropagation algorithm was invented by several different groups.

Um it was invented by David Rumelhart 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

him. And what we did was we showed that backpropagation could learn interesting internal representations. And people

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 1985, we made a tiny language model um that was a kind of precursor of the

big language models we have now.

I think that when you speak about this technology, one of the things that people are always, I think, surprised by surprised by is that, unlike the popular narrative, you believe that these models

have a real understanding. Um and we're going to get to that. Um 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 and at when that happened. And that was in 2023.

happened. And that was in 2023.

Yes.

Um which to me is surprising to a degree because in 2023, ChatGPT was a year old.

There were all these hallucinations.

Their talk was AI was a bubble. Everyone

was focusing on the what AI couldn't do, what LLMs do, as opposed to uh what LLMs could do. So, talk a little bit about

could do. So, talk a little bit about the progress since then.

It's faster than I expected.

Really?

Um for example, I think yesterday it was announced that um a chatbot had come up with an interesting mathematical proof of one of

the Erdős's conjectures that um impressed mathematicians. It was

impressed mathematicians. It was original. Um it wasn't just searching

original. Um it wasn't just searching the literature.

And that was that's the thin end of a wedge. I believe for example in areas

wedge. I believe for example in areas like mathematics, because it's a closed system, um, 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.

Um, I think it's going to get very smart fairly quickly. Within the next 10 or 20

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?

you expected. Do you believe that?

Um, I don't know how close it is. I

think unless we blow ourselves up, um, I think it's going to come.

Nearly all the experts believe we will get superintelligence. They just differ

get superintelligence. They just differ on how long it will be.

So, not that long ago Demis Hassabis thought it might be 10 years.

Um, Yann LeCun thinks, um, 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

years. That's all I'm happy to say at present. Dario Amodei thinks it might

present. Dario Amodei thinks it might come in a few years. Elon Musk thinks it might come maybe next year, I think. Um,

so there's a big variety of opinions on when it'll come, but not much disagreement on that that it will come.

Right.

And when it comes, we've no idea how to be safe.

Yes, I definitely want to speak with you about about safety. Uh, one note on Demis, last year around this time I spoke with him. He told me he believes that AGI,

him. He told me he believes that AGI, right, which is different than superintelligence, but basically human-level intelligence is more than 5 years away.

Not much more, but more than 5 years away.

This week, the week that we're recording, um, 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?

Um and and what do you think about the fact that we've gone from 5 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. Um

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.

Um it's way better than us at playing games.

It's already way better than almost all of us at math.

Um and it may soon be better than all of us at math.

Um it's still worse than us at some things.

So it's it's very jagged. Um so the whole concept of AGI that it's going to be equal to people at everything all at the same time doesn't really make sense to me. It's going to be better at some

to me. It's going to be better at some things, worse at other things.

But right now we're we're I would say we're at about we're close to AGI because if I ask a chatbot, I can ask it any question and 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.

Mhm.

So in that sense we've really reached AGI.

In in your estimation, um you talked about how it's moved faster than you expected.

Um uh what do you think has enabled it do it? Is it techniques? Is it the fact

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?

Um it's a combination. Obviously there's

been huge resources put into it.

For most of the history of neural networks since the 1950s, there were just a few people working on them with modest resources.

Um over the last few years we've seen um hundreds of billions of dollars, maybe trillions of dollars put into AI.

Um so that's certainly one factor. We've

also seen a lot of progress in the engineering. So

engineering. So without sort of major conceptual breakthroughs, the engineering's become much more efficient. So things that were sort of inconceivable a few years ago, they can now run.

Um We've also seen new ideas, but but mainly since transformers, it's been much better hardware, many more resources, um

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.

Um now it's more more like a million, I guess. I mean, there's lots and lots of

guess. I mean, there's lots and lots of people.

And it's astonishing how much of that resource addition has happened in the last 2 years.

Yeah.

So we might just be at the beginning of what's happening here.

Yes, and the thing to remember always is that the AGI we have today, or sorry, the AI we have today, is um 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

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. So

anybody who uses a chatbot regularly knows they understand.

So here's what those people are claiming. They're claiming

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.

Um [snorts] suppose I say to a chatbot, "I saw the Grand Canyon flying to Chicago."

Chicago." And the chatbot says, "That can't be right. The Grand Canyon's much too big

right. The Grand Canyon's much too big to fly to Chicago." And I say, "No, no, no, no. Um it was me flying to Chicago

no, no. Um it was me flying to Chicago Chicago. While I was flying to Chicago,

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 Um I believe they're already conscious, yes. But, I don't talk about that much

yes. But, I don't talk about that much because that puts people off from the other safety messages. So, and the researchers actually believe that. So,

there's an interesting recent paper when a chatbot says to a researcher, um "Let's be honest with each other each other. Are you testing me?"

other. Are you testing me?"

Cuz the chatbots have this habit of playing dumb when they're being tested, so you don't know how smart they are.

Um and the researchers, when they're describing that, say in 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

people. They thought they were designed by God. And most of us agree that's

by God. And most of us agree that's wrong. Most scientists agree that's

wrong. Most scientists agree that's wrong. That's not where people came

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 were designed by God. I think and in particular because we're making these

new beings, it's going to completely change our view of what people are.

In what way?

Um we'll understand um 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 strong nearly all of us strongly believe at present, which is that there's an inner

theater called my mind and this things happen in the world, they get turned into events in this inner theater and that's what I really see and you can't see the inner theater, only I can see the inner theater. 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 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.

Okay.

Um 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.

Um 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.

[snorts] Uh you spoke about safety. So, let's

talk a little bit about it.

Uh you're obviously, we spoke about it in the beginning, someone who's been responsible for a lot of the progress in this field. Uh

this field. Uh I've always wondered cuz 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 culmination of two things that made me realize how dangerous this stuff is.

One was seeing the chatbots, particularly ones produced by Google before OpenAI, that could understand why a joke was funny.

Um that had always been a criterion for me of do they really understand? If you

can understand why a joke's funny, you have to understand quite a lot.

Oh, yeah.

And they were very good at understanding why a joke was funny.

For example, um in 2023 when I went public, I got lots of requests from Fox News.

And I started off just replying Fox News is an oxymoron.

Um but then I left a gap between oxy and And so then I asked um I think it was GPT-4 why that was funny.

Might have been 3.5, but I asked it why that was funny.

[snorts] And it understood why it was funny.

Initially, it thought the gap between oxy and was just a typo.

So it explains that Fox News is an oxymoron is saying it's not real news, it's just a drug. Um [snorts] sorry, it's just um nonsense. It's not real news.

But then when I told it, "What about the gap between oxy and moron?"

It said, "Ah, that's an extra layer of humor."

humor." Um it allows you to use the word Um and also the oxy implies that Fox News is a drug.

Mhm.

So it understood all that.

Right.

And that was um it's that level of understanding that worried me.

Um the other thing that worried me was up until the beginning of 2023, I'd always believed that making um 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 save 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 it would 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.

Um, 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 um, suppose there's a thousand copies, one particular copy only sees 0.1% of the data, um, 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'll stay in sync cuz they'll change their weights the same way by the average of 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.

You I can't average my connection strengths with your connection strengths cuz our brains are in fine detail they're different. Um,

they're analog and it doesn't work in analog hardware to do that. Um, 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

next. Now, if you ask how fast we're transferring information when we do that, we're transferring information at a few bits per second.

It takes a few bits to predict a word.

Um, so when you learn what the word is, you've absorbed you've gained a few bits of information.

And if you get a few words of a second, but maybe you can get 10 bits a second if you're lucky.

Whereas these things are exchanging information at like a trillion bits.

So, they're kind of billions of times better than us at sharing information.

Now, that's scary. It means you could have a whole swarm of these things that are in identical weights running on different hardware, sharing information um, very, very efficiently.

That [snorts] just makes them a much better form of intelligence.

So, but let's go back to, you know, your early days cuz you decided that you wanted to work in artificial intelligence. I mean, I'll ask this the

intelligence. I mean, I'll ask this the dumbest way I can think, which is you wanted to build artificial intelligence.

It succeeded. It succeeded. This is

artificial. It's intelligent. It's

living out that vision.

Uh so actually wanted to understand how the brain works. I always tried to build it

brain works. I always tried to build it in order to understand the brain. I

figured um, Richard Feynman once said, "If you can't build it, you don't understand it."

understand it." Okay.

so I wanted to build models of how the brain worked. Now, the side effect of

brain worked. Now, the side effect of that was this very successful technology. I contributed to that. We

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.

Um, thoughts can sort of float in and out and they're not stored anywhere and memory's the same way.

Um, it's an unbelievable, I don't know if you'd call it a machine organ.

Um, so that was really that was the the intent for you early on was just to understand the brain.

That was my main interest. I came from psychology. I wanted to theoretical

psychology. I wanted to theoretical psychology um, because I figured the theories psychologists had couldn't possibly explain what the brain was doing.

Um, 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 of what how the brain might be learning.

Right.

It always seemed to me the key was how do you get it to learn? There's really

two two big issues with the brain learning. One big issue is

learning. One big issue is if the brain could figure out what direction to change a 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

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?

Mhm.

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 predict the next word or to predict the next frame of a video

or to predict something about the next frame of a video.

So, we know the answer to that. Um, we

don't know how the brain gets this information about whether it should increase or decrease each connection strength.

Mhm.

So, we're sort of halfway there.

Yeah.

All right, I want to go deeper though into your mindset. Um,

so when you were trying to figure out how the brain works, you said, "Okay, we're going to maybe build a computer analog to this."

Um, but you had to have known, right, that there was going to be some second order effects there? Like if you were able to

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. The worrying about safety when you had little neural nets that couldn't do much.

Right.

It was just silly to worry about safety.

I mean, people would think you were crazy if you said, "This stuff is unsafe." But

unsafe." But because it's going to sort of take over from people that are saying, "You you're just crazy."

just crazy." Now, that's a realistic worry, but it wasn't until very recently.

So, what I mean, this has all happened within a few decades. I mean, and I totally hear you. We spoke in 2017 actually about when I was writing this profile about Yann LeCun about the deep

learning conspiracy, which was yourself, Yann, and Yoshua Bengio holding onto 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 were the conspiracy conspiracy leaders, if you will. Um and and then obviously it's worked out magically. Uh

It is sort of magical, yes. It's worked

much better than we expected.

So, then what I That's what I want to get at is what did you not anticipate when you were starting out um that's led to the where we've ended up today?

We didn't anticipate The main thing we didn't anticipate is that it'd be so good at natural language.

Okay.

We've stopped being by that, but if you go back 20 years, um the idea that you could have an AI that would learn from data how to

understand language um 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. Um

it's That's arrived much faster than anybody expected.

What is the lesson here about humans going out and creating things?

I think there's um there's a really big lesson here. If you look at the last few

lesson here. If you look at the last few hundred years of human history um there've 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.

Um, the Earth actually goes around the sun. Um,

sun. Um, and because it rotates on its axis, we think the sun goes around the Earth, but it doesn't.

Um, 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, um, we're animals.

We we evolved like the other animals.

We're a particularly special kind of animal, possibly cuz we have language, so we're much better at communicating ideas to each other.

Um, 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.

Um, [snorts] now we've got machines that are as in 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 would be aliens in other galaxies, but or maybe other parts of our galaxy.

But um 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 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 cuz I'm just fascinated by it. Um, so

it. Um, so are you happy at all that what you started has progressed this way? Do you

take any satisfaction unhappy about it. Because people right now people should do be doing huge amounts of work on how can we contain the risks?

Okay.

Um, there's lots of short-term risks they're not doing enough work on, which are very serious.

Um, there's societal risks like I believe it's probably going to cause massive unemployment. Nobody knows for

massive unemployment. Nobody knows for sure, Okay. but that's going to be terrible

Okay. but that's going to be terrible for society.

Um 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, there's sort of one. It's not that a big difference in intelligence, but babies sort of control their mothers.

Um Yes.

The mother's sort of in control, but the mother is has all these wiring in maternal instincts.

And all the rewards she gets so that um the baby can get what it needs from the mother.

You know, cats and dogs are also kind of in that category. Yeah, I once spent a summer cat sitting in uh 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 maybe we'll be the Well, we could potentially be the cat in this scenario and AI could be the the person.

My children have a cat. They have two cats, two beautiful cats. Same deal. Um

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 I'm going to 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.

And we're back here on Big Technology podcast with Professor Geoff Hinton.

Professor Hinton, great to see you again. It's been

again. It's been nine years since we spoke last, so it's good to see you here. Um all right. Talk

Let's talk about the risks.

Um I'll start with employment because this is one that's been making headlines recently.

Um in the past, you've said so you have this belief that AI can lead to some unemployment. Um, I think we should

some unemployment. Um, I think we should you know, you've said this before, it's all speculation. We don't know. Uh, but

all speculation. We don't know. Uh, but

one thing that you said concretely a few years ago was that um probably not a great idea to train as a radiologist. Because AI will be able to

radiologist. Because AI will be able to read the scans. And yes, AI can do great job reading the scans now. But we have um you know, full employment for radiologist right now.

Yes, but I've thought I've thought a lot about why that prediction was so wrong.

So I'd like to hear cuz I predicted in 2016 that in about 5 years um radiologist wouldn't be reading scans anymore. Correct.

anymore. Correct.

And Okay, there's a whole bunch of reasons why that was a bad prediction. Um

the first is that healthcare 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. And that's one thing that's happening. So if

happening. So if a fraction of the cost of a significant fraction of the cost of doing a scan is the cost of the radiologist interpreting it. As AI gets to help more and more

it. As AI gets to help more and more radiologist interpret scans, we can interpret them faster and faster for less and less money.

Um they're getting more efficient. Um

and you would have thought that would mean you needed less radiologist. 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 radiologist and what they do. And that was because I had a former student who had an MD and then

did a um a physics PhD with me on something called Boltzmann machines.

And he didn't particularly like people.

So he got a job as a radiologist just interpreting scans. And he was my model

interpreting scans. And he was my model for radiologist. All he did was

for radiologist. All he did was interpret scans. He never talked to

interpret scans. He never talked to people. Um and that's what was going to

people. Um and that's what was going to get replaced. And that is now becoming

get replaced. And that is now becoming replaced. So, I think there's now of the

replaced. So, I think there's now of the order of a hundred 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, um they're going to get better. The radiologists

aren't going to get better.

Um they're going to get better cuz they can see a lot more data than the radiologists. So,

radiologists. So, it's happening. It's just happening in a

it's happening. It's just happening in a much slower timescale than I predicted.

But they but I but let's get to what you said though, which is that you can end up doing a lot more.

Yes.

and okay, so so uh wait, hold on.

You said be a lot more scans. There will be a lot more scans, but they'll nearly all be done by um AI.

And so you're just saying I'm early on the radiologist prediction. I'm just early.

radiologist prediction. I'm just early.

Yes, but I was way early. Because I

didn't understand enough. The

radiologists will still be doing other things. They'll still be discussing

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 or like give me give me a look as But radiologists 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

Good.

when I was still I I didn't think that was a public statement I made. It was a a lecture at a hospital.

Sorry.

Um and Here we are. We're at least talking about it today.

People picked up on it.

Yeah.

And um I still think in terms of reading scans, um that'll be done more and more by AI.

And in the end, AI will be doing reading nearly all the scans. Maybe in a few very tricky cases, radiologists will be consulted. Um

consulted. Um but radiologists, of course, do other things. So, I think they'll continue to

things. So, I think they'll continue to do other things.

The argument to be made on the side of AI not causing mass job losses that this similar uh equation will be applied to

all different parts of the economy.

Okay, so you have to look at whether some um 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, um

that's not so elastic. AI will replace all of them. It'll know much better um what the correct answer is. Um often

they don't know the right answer. But

they're poorly trained and badly paid.

Um and AI can just do a better job.

They're out of work.

Well, let me disagree with you on this one. And and we could sort of go back

one. And and we could sort of go back and forth on this. All right, I won't say I'll disagree completely cuz 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

right? The basic, can you reset my password type of stuff. And anything

deeper is handled by person. And it used to be that 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

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 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.

[laughter] Um Yeah.

For example, if you ask who's more empathetic, a doctor or an AI doctor, a real doctor or an AI doctor?

Um people judge the AI doctors through much more empathetic.

That's That's terrifying.

The The I mean, we could go back and forth on this uh for a while.

Um so, I'll just say 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

paperwork. And they have to see so many patients in a day.

So, maybe the argument is uh you know, you sort of let the AI take over some of that stuff, and then people will want to see be seen by human doctors cuz the system won't squeeze them as much as they are. They're actually going to make

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.

Um 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?

Mhm.

Because if you have some rare disease, your family doctor's probably never seen it. Whereas a doctor who's seen 100

it. Whereas a doctor who's seen 100 million people has probably seen dozens of cases of it. They're going to be much better at diagnosis. And already we know that AI systems are better than doctors at diagnosis.

I think you're winning this debate, and this hurts a little bit cuz my wife is in family medicine.

Family nurse. Um I think she'll still You'll still have to have somebody vaccinate people. I would hope unless

vaccinate people. I would hope unless the robots do that.

I would have thought vaccination is something a robot could actually do quite well.

[snorts] In the end, robotics is behind the other things, but I It seems 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 Marcus made a prediction in 2022 that AI was hitting a wall.

Um [snorts] It's a whole lot better than it was in 2022.

Yeah.

I think these predictions that it's going to hit a wall, they just haven't come true.

Now, we've taken it very seriously on the show that the chance that like the data wall, for instance, might come. And

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. Um

you've talked a lot about how the AI has this instinct for self-preservation, right? That if

right? That if I've never said that. I've never said it was an instinct for self-preservation.

Okay, talk about existence goal for self-preservation.

goal.

So, um with an AI, we give it goals. It's

top-level goals we give to it.

Um 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.

Um 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

in Europe. And that makes you much more efficient.

Um 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 goal of continuing to exist.

Now, that wasn't something we wired into it. It was something it derived as a

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.

[snorts] 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 here's like the what the counterargument would be and you can respond. Um

this is something that today's AI researchers are uh 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 did we come from? We came

from evolution.

Let's let Don't have to tell that to your listeners, say Let's Let's suppose we're scientists.

Okay.

We came from evolution.

Right.

And that was intense competition. Um

our recent history over the last few million years is warring bands of chimpanzees or rather a common ancestor with those.

Um 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.

Um we we like to have strong that we're loyal to. Um we like to cooperate with

loyal to. Um we like to cooperate with members of our own tribe. We're actually

a very cooperative species as Yuval Harari keeps pointing out.

Yes.

Um and that's why we've been able to build all these wonderful structures.

Um so, we're very good at cooperating but with our own tribe.

Um so, all the unfortunate characteristics of people like how mean they are to other tribes, um 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 um how we want them to be, you might argue I'm arguing for intelligent design of these new beings.

Um we're letting the invisible hand of competition between companies design them. So, what we've got is intense

them. So, what we've got is intense competition between companies within the US and between the US and China.

Um 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 would very much like them to

beings. We would very much like them to care about us.

And we'd like [snorts] 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 things where 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 it 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 will 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.

Um it was set up um by people who left Open AI cuz 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 a chance to build an AI.

Indeed. And how's that working out? Um

so Anthropic is now caught in a bind cuz it needs to raise money to compete with the other companies.

And it's um very difficult. It's it's doing the best it can, but it's very difficult for it to maintain its primary goal of developing AI in a way that's good for people.

I I they would say, well, hey, it's at least one company out there has safety as an North Star, even if there are some other incentives."

Yes.

a present But Google, for example, when I was at Google, um they had various principles of AI, one of which was it we're not going to 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 given up on that.

What do you think about Dario?

From Anthropic.

Um I don't know him as a person very well.

He's obviously done a very successful job in creating a competitor to Google and Open AI and Facebook. Um

Facebook. Um so he's obviously very competent at that.

And he's continued to be very interested in safety.

Um so I think he's an impressive character. Um I just hope he stays that

character. Um I just hope he stays that interested in safety.

One more question about this. Do you

think that it's possible just by the the nature of the way that these things work for a company that's publicly listed to have safety as an North 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 fiduciary duty to try and maximize the profits for shareholders.

Um they're legally required to try and do that. As opposed to legally required

do that. As opposed to legally required to not wipe out human human beings. Um

so I don't think it's good that these big companies, um 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's done very good things for us as well as very bad things.

argue with that.

Um there's a lot of energy in a startup, for example.

Um my view is um 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 regulation is like the brake.

Well, that's nonsense.

Um 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 um let us develop this very fast car without a steering wheel.

That's not a good idea.

You know, the song we haven't talked spoken about yet. We've said a lot of names about OpenAI and Anthropic. Um

your former grad student, Ilya Sutskever, continues to be a uh person of fascination in the AI industry. Uh

obviously, he broke off from OpenAI. He

must agree with your concerns. He's

building this company He does.

Safe Superintelligence.

Yes.

What is Ilya doing right now?

Well, he won't tell anybody exactly what he's doing.

Okay.

Because even if that's his even me, yeah. Um

yeah. Um when he was at OpenAI, we deliberately didn't talk about sort of technical secrets. I mean

secrets. I mean it wouldn't have been right.

Um we're we're we're friends, but we don't talk about technical stuff where it's valuable to a company. And so now he has this um Safe Superintelligence company.

And I don't know what the magic sauce is.

Hm. [snorts]

Well, I guess we're all we're all trying to figure that one out.

Um one more note about the deep learning conspiracy that I brought brought up, like that the leaders of it were yourself, Jan, and Joshua. 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.

But I just need to interrupt Yes. At this point the media likes to

Yes. At this point the media likes to have a nice story, right?

Okay.

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. Um but

there were many other researchers involved. And so that's just a gross

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. Um

nuance here. Um this show we definitely we don't want to oversimplify. We sit for an hour so we

oversimplify. We sit for an hour so we can get like Okay.

the the true story. Um but I find it interesting that the three of you none of you are sort of like fully into the this LM moment, right? You and

Yoshua 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

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 in advancing it. But

I think that's your concern. It's

basically, "Why would I do that?"

Well, for me, I'm I'm considerably older than Jan and Yoshua.

Okay.

They're still doing active research.

Right.

I pretty much stopped doing active research. I'm now just focusing on

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 have, you know, I think that if you were in the room back in the day you might have said these these three people who are so committed to this

version of technology, you know, if they are the breakthroughs, they'd probably be at the forefront of the next wave.

But that hasn't been the case.

Um well, maybe Jan and Yoshua will be.

Right.

What's next after this?

I think the most interesting thing is that um Jan now strongly disagrees with both me and Yoshua on safety issues. Jan

thinks it's silly to talk about um super intelligent AI taking over from people.

We'll always be able to keep control of it. Um

it. Um me and Yoshua think that's just silly.

Me and Yoshua have different solutions to it. My solution is or tentative

to it. My solution is or tentative solutions, nobody has a real solution.

Um my tentative solution is we design them so they care about us more than they care about themselves.

Yoshua's solution is we design them so they're not agents. They can make predictions, um but they can't actually do anything.

Um those are two fundamentally different ways of going about making them safe.

They're both interesting possibilities.

Jan doesn't think we need anything like that. He thinks it's fine just to make

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 I was [snorts] like, well, that's the kind of example [clears throat] I used of the thing that could control humans, but maybe that's not Maybe that's not here or there.

Yeah, no.

I think Jan's making something of a confusion. So, what's special about

confusion. So, what's special about people that 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

don't have. Now, cats can jump up on a mantelpiece covering glass ornaments and walk along the mantelpiece without knocking off any of the glass ornaments.

That's amazing and um AIs can't do that at present. Um so, in that sense, cats

at present. Um so, in that sense, cats are way ahead of AIs, but it's jagged, right? In terms of abstract ideas, um

right? In terms of abstract ideas, um you have a Try having a conversation with cats about prime numbers and you won't get very far.

A cat's never going to have conversations with them and that's not working. A cat is never going to

working. A cat is never going to understand prime numbers.

Correct.

Um 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 we're talking about it. They're actually very good in terms

it. They're actually very good in terms of an analogy here.

All right. Another thing that I'm I'm worried about is sort of information collapse.

You see tweets like this all the time.

This is from All About Berlin. Uh they

say AI is killing All About Berlin. When

you Google something, you 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

traffic. And I think folks are under-appreciating the fact that good information is actually important to a functioning society. And when AI just

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 uh here, it it 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 They can't keep doing it anymore.

Right. So, it used to be that you had a kind of the early days of the web, you had a kind of default assumption that people trying to tell the truth.

Um that if you read something on the web, it might well be true.

Um now the sort of worst side of people has come out, and um 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 a pretty good

reliable sources. So, a 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 um because you have provenance.

And in future, we're going to have to put 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 the problem that I see is the AI is is breaking potentially breaking the economics of our even deciding that you want to be in the information business.

You're trying I mean, I think Yeah.

in future, you can't just take stuff from the web and believe it.

Yeah.

Already you can't, right? You need to know why is it saying that? Where did it get that information?

One more. Uh

emotional attachment to AI.

And um we and people taking their lives after having conversations with AI. Now,

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

concerned. And it's terrible that it's happening. And I understand why the big

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

future. And for that, you need regulations. You need independent

regulations. You need independent organizations testing out new chatbots.

Yeah. It goes kind of back to the profit motive also because Yes.

This can be extremely sticky. Like

there's the 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 uh you know, decide to make a very sticky chatbot that really builds relationships with people.

Yes.

we're in trouble.

Yes.

Um So, you've been on you've been you've been having these conversations for 3 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 Okay.

I I see that it might be possible to design these new beings so they care about us.

It also might be possible to use Yoshua's technique of designing new new beings that can't actually perform actions um can only make predictions. They're

they're kind of like oracles.

Um so I think there are some possibilities for getting superintelligence that 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. Um if we continue on our current trajectory, where are we in 5 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 cuz fog's exponential. What

you're used to is driving at night on the tail lights of the car in front of you.

If it gets twice as far away, the tail lights 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 [snorts] the future for something that's growing exponentially, um and I think AI may be growing exponentially. The word exponential is

exponentially. The word exponential is terribly overused at present. In fact,

I've noticed that people are increasingly using the word exponentially at a quadratic rate.

Um so predicting the future is like looking into fog. You can see clearly a few

into fog. You can see clearly a few years, maybe 1 or 2 years. Then beyond

that, you have no idea. If you go back 10 years, um and ask, so back to when we last talked um 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, Right.

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. Um

starting out. Um 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. Um maybe just their

general reasoning abilities. They'll

just be able to run rings around us at any kind of reasoning.

Um 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 Geoff 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, 2036, exactly.

All right, thank you everyone for listening and watching and we'll see you next time [music] on Big Technology Podcast.