Ilya Sutskever (OpenAI Chief Scientist) - Building AGI, Alignment, Future Models, Spies, Microsoft, Taiwan, & Enlightenment

But I would not underestimate the difficulty of alignment of models that are actually smarter than us, of models that are capable of misrepresenting their intentions. Are you worried about spies? I'm really not worried about debates being leaked.
We'll all be able to become more enlightened because we'd interact with an AGI that will help us see the world more correctly. Like, imagine talking to the best meditation teacher in history.
Microsoft has been a very, very good partner for us. So I challenge the claim that next token prediction cannot surpass human performance.
If your base neural net is smart enough, you just ask it like, what would a person with great insight and wisdom and capability do? Okay, today I have the pleasure of interviewing Elia Sutskavar, who is the co-founder and chief scientist of OpenAI. Elia, welcome to the Lunar Society.
Thank you, happy to be here. First question, and no humility allowed.
There's many scientists, or maybe not that many scientists, who will make a big breakthrough in their field. There's far fewer scientists who will make multiple independent breakthroughs

that define their field throughout their career.

What is the difference?

What distinguishes you from other researchers?

Why have you been able to make

multiple breakthroughs in our field?

Well, thank you for the kind words.

It's hard to answer that question.

I mean, I try really hard.

I gave it everything I got.

And that worked so far.

I think that's all there is to it.

Got it.

What's the explanation for why there aren't more illicit uses of GPT?

Why aren't more foreign governments using it to spread propaganda

or scam grandmothers or something?

I mean, maybe they haven't really gotten to do it a lot. But it also wouldn't surprise me if some of it was going on right now.
Certainly, I imagine they'd be taking some of the open source models and trying to use them for that purpose. Like, I sure would expect this would be something they'd be interested in in the future.
It's, like, technically possible they just haven't thought about it enough? Or haven't, like it at scale using their technology or maybe it's happening but just don't know it. Would you be able to track it if it was happening? I think large-scale tracking is possible yes I mean it requires a small special operation it's possible.
Now there's some window in which AI is very economically valuable on the scale of airplanes let's say but we haven't reached A reached AGI yet. How big is that window? I mean, I think this window, it's hard to give you a precise answer, but it's definitely going to be like a good multi-year window.
It's also a question of definition because AI, before it becomes AGI, is going to be increasingly more valuable year after year. I'd say in an exponential way.
So in some sense, it may feel like, especially in hindsight, it may feel like there was only one year or two years because those two years were larger than the previous years. But I would say that already last year, there've been a fair amount of economic value produced by AI.
Next year is going to be larger and larger after that. So I think this is going to be a good multi-year chunk of time, but that's going to be true, I would say, from now until AGI pretty much.
Okay. Well, because I'm curious if there's a startup that's using your models, right? At some point, if you have AGI, there's only one business in the world, right? It's OpenAI.
How much window do they have, does any business have, where they're actually producing something that AGI can't produce? Yeah, well, I mean, it's the same question as asking how long until AGI. Yeah.
I think it's a hard question to answer. I mean, I hesitate to give you a number.
Also, because there is this thing where effect where people who are optimistic people who are working on the technology tend to underestimate the time it takes to get there. But I think that the way I ground myself is by thinking about the self-driving car.
In particular, there is an analogy where if you look at the, so I have a Tesla, and if you look at the self-driving behavior of it, it looks like it does everything. It does everything.
But it's also clear that there is still a long way to go in terms of reliability. And we might be in a similar place with respect to our models where it also looks like we can do everything.
And at the same time, it will be, we'll need to do some more work until we really iron out all the issues and make it really good and really reliable and robust and well-behaved. By 2030, what percent of GDP is AI? Oh gosh, hard to answer that question.
Very hard to answer the question. Give me an over-under.
Like the problem is that my error bars are in log scale. So I could imagine like, I could imagine like a huge percentage.
I could imagine a disappointing small percentage at the same time. Okay.
So let's take the counterfactual where it is a small percentage. Let's say it's 2030 and, you know, not that much economic value has been created by these elements.
As unlikely as you think this might be, what would be your best explanation right now why something like this might happen? My best explanation, so I really don't think that's a likely possibility. So that's the preface to the comment.
But if I were to take the premise of your question, well, like why were things disappointing in terms of the real world impact? And my answer would be reliability. If somehow it ends up being the case that you really want them to be reliable and they ended up not being reliable.
Or if reliability is now to be harder than we expect. I really don't think that will be the case.
But if I had to pick one, if I had to pick one and you tell me like, hey, like, why didn't things work out? It would be reliability that you still have to look over the answers and double check everything. And that's just really puts a damper on the economic value that can be produced by those systems.
And they'll be technologically mature. It's just a question of whether they'll be reliable enough.
Yeah. Well, in some sense, not reliable means not technologically mature, if you see what I mean.
Yeah. Fair enough.
What's after generative models, right? So before you're working on reinforcement learning, is this basically it? Is this a paradigm that gets us to AGI or is there something after this?

I mean, I think this paradigm

is going to go really, really far

and I would not underestimate it.

I think it's quite likely

that this exact paradigm

is not going to be

quite the AGI form factor.

I mean, I hesitate to say

precisely what the next paradigm will be,

but I think it will probably involve

integration of all the different ideas that came in the past. Is there some specific one you're referring to? I mean, it's hard to be specific.
So you could argue that next token prediction can only help us match human performance and maybe not surpass it. What would it take to surpass human performance? So I challenge the claim that next token prediction

cannot surpass human performance.

It looks like on the surface, it cannot.

It looks on the surface,

if you just learn to imitate,

to predict what people do,

it means that you can only copy people.

But there here is a counter argument

for why it might not be quite so

if your neural net is,

if your base neural net is smart enough.

You just ask it like,

Thank you. but there here is a contra argument for why it might not be quite so if your neural net is if your base neural net is smart enough you just ask it like what would it what would a person with great insight and wisdom and capability do maybe such person doesn't exist but there's a pretty good chance that the neural net will be able to extrapolate how such a person could behave do you see what i mean yes although where would it get that sort of insight about what that person would do if not from? From the data of regular people.

Because if you think about it, what does it mean to predict the next token well enough?

What does it mean actually? It's actually, it's a much, it's a deeper question than it seems.

Predicting the next token well means that you understand the underlying reality that led to the creation of that token it's not statistics like it is statistics but what is statistics in order to understand those statistics to compress them you need to understand what is it about the world that creates those statistics.

And so then you say, okay, well, I have all those people. What is it about people that creates their behaviors? Well, they have thoughts and they have feelings and they have ideas and they do things in certain ways.
All of those would be deduced from next token prediction. And I'd argue that this should make it possible,

not indefinitely, but to a pretty decent degree to say,

well, can you guess what you'd do if you took a person with like this characteristic

and that characteristic,

like such a person doesn't exist.

But because you're so good at predicting the next token,

you should still be able to guess

what that person would do,

this hypothetical imaginary person with far greater mental ability than the rest of us. When we're doing reinforcement learning on these models, how long before most of the data for the reinforcement learning is coming from AIs and not humans? I mean, already most of the data for reinforcement learning is coming from AIs.
Yeah. Well, it's like the humans are being used to train the reward function, but then the reward function in its interaction with the model is automatic and all the data that's generated during the process of reinforcement learning is created by AI.
So like if you look at the current, I would say, technique paradigm, which is in getting some significant attention because of chat GPT, reinforcement learning from human feedback. So there is human feedback.
The human feedback is being used to train the reward function. And then the reward function is being used to create the data which trains them all.
Got it. And is there any hope of just removing the human from the loop and have it improve itself and some sort of alpha go away yeah definitely i mean i feel like in some sense our hopes for like our plan like very much so the thing you really want is for the human teachers that tell you that teach the ai for them to collaborate with an ai so you might want to think about.
So you might want to think about it,

you might want to think of it as being in a world where the human teachers do 1% of the work

and the AI do 99% of the work.

You don't want it to be 100% AI,

but you do want it to be a human-machine collaboration

which teaches the next machine.

So currently, I mean, I haven't had a chance

to play around these models.

They seem bad at multi-step reasoning

and they have been getting better,

but what does it take to really surpass that barrier?

I mean, I think dedicated training will get us there. More improvements to the base models will get us there.
Okay. But, like, fundamentally, I also don't feel like they're that bad at multi-step reasoning.
I actually think that they are bad at mental multi-step reasoning, but they're not allowed to think out loud. But when they are allowed to think out loud, they're quite good.
And I expect this to improve significantly, both with better models and with special training. Are you running out of reasoning tokens on the internet? Are there enough of them? I mean, you know, it's okay.
So for context on this question, like there is, there are claims that indeed at some point we'll run out of tokens in general to train those models. And yeah, I think this will happen one day.
And we'll, by the time that happens, we need to have other ways of training models, other ways of productively improving their capabilities and sharpening their behavior, making sure they're doing exactly, precisely what we want without more data. You haven't run out of data yet? There's more? Yeah, I would say the data situation is still quite good.
There are still lots to go. But at some point, yeah, at some point data will run out.
Okay. What is the most valuable source of data? Is it Reddit, Twitter, books? What would you trade many other tokens of other varieties for? Generally speaking, you'd like tokens which are,

speaking about smarter things,

tokens which are, like, more interesting.

Yeah.

So, I mean, all the sources which you mentioned,

they're valuable.

Okay, so maybe not Twitter,

but do we need to go multimodal to get more tokens,

or do we still have enough text tokens left?

I mean, I think that you can still go very far in text only,

but going multimodal seems like a very fruitful direction.

If you're comfortable talking about this,

like where is the place where we haven't scraped the tokens yet?

Oh, I mean, yeah, obviously.

I mean, I can't answer that question for us,

but I'm sure that for everyone,

there's a different answer to that question.

How many orders of magnitude improvement can we get just not from scale or not from data, but just from algorithmic improvements? Hard to answer, but I'm sure there is some. Is some a lot or is some a little? I mean, there's only one way to find out.
Okay. Let me get your, like, quickfire opinions about these different research directions.
Retrieval transformers. So just, like, somehow storing the data outside of the model itself and retrieving it somehow.
Seems promising. Would you see that as a path forward? I think it seems promising.
Robotics. Was it the right step for OpenAI to leave that behind? Yeah, it was.
Like back then, it really wasn't possible to continue working in robotics because there was so little data. Like back then, if you wanted to work on robotics, you needed to become a robotics company.
You needed to really have a giant group of people working on building robots and maintaining them and having... And even then, like if you're going to have 100 robots, it's a giant operation already, but you're not going to get that much data.

So in a world where most of the progress comes from the combination of compute and data, right? That's where we've been, where it was the combination of compute and data that drove the progress. There was no path to data from robotics.
so back in the day when you made a decision to stop working in robotics, there was no path forward. Is there one now? So I'd say that now it is possible to create a path forward, but one needs to really commit to the task of robotics.
You really need to say, I'm going to build like many thousands, tens of thousands, hundreds of thousands of robots and somehow collect data from them and find a gradual path where the robots are doing something slightly more useful and then the data that they get from these robots and then the data that is obtained and used to train the models and they do something slightly more useful. So you could imagine this kind of gradual path of improvement where you build more robots, they do more things, you collect more data and so on.
But you really need to be committed to this path. If you say, I want to make robotics happen, that's what you need to do.
I believe that there are companies who are thinking about such, doing exactly that. But I think that you need to really love robots and need to be really willing to solve all the physical and logistical problems of dealing with them.
It's not the same as software at all. So I think one could make progress in robotics today with enough motivation.
What ideas are you excited to try, but you can't because they don't work well on current hardware? I don't think current hardware is a limitation. Okay.
I think it's just not the case.

Got it.

So, but anything you want to try, you can just spin it up?

I mean, of course.

Like, the thing, you might say, well, I wish current hardware was cheaper.

Or maybe it had higher, like maybe it would be better if it was higher memory processor bandwidth, let's say.

But, by and large,

hardware is just a limitation.

Let's talk about alignment.

Do you think we'll ever have

a mathematical definition of alignment?

Mathematical definition,

I think is unlikely.

Uh-huh.

I do think that we will instead have multiple,

like rather than achieving

one mathematical definition,

I think we'll achieve multiple definitions that look at alignment from different aspects. And I think that this is how we will get the assurance that we want.
And by which I mean you can look at the behavior. You can look at the behavior in various tests, in various adversarial stress situations.
You can look at how the neural net operates from the inside. I think you have to look at several of these factors at the same time.
And how sure do you have to be before you release a model in the wild? Is it 100%, 95%? It depends how capable the model is. The more capable the model is, the more confident you need to be okay so just say it's

something that's almost AGI where is AGI well depends what your AGI can do keep in mind that AGI is an ambiguous term also like like your average college undergrad is an AGI right but you see what I mean there is significant ambiguity in terms of what is meant by AGI So depending on where you put this mark you need to be more or less confident well you mentioned a few of the paths towards alignment earlier what is the one you think is most promising at this point like i think that it will be a combination i really think that you will not want to have just one approach i think we will want to have a combination of approaches where we you spend a of compute, but adversarially probe it to find any mismatch between the behavior that you want it to teach and the behavior that it exhibits. We look inside into the neural net using another neural net to understand how it operates on the inside.
I think all of them will be necessary. Every approach like this reduces the probability of misalignment.
And you also want to be in a world where your degree of alignment keeps of increasing faster than the capability of the models. I would say that right now, our understanding of our models is still quite rudimentary.
We've made some progress, but much more progress is possible. And so I would expect that ultimately the thing that will really succeed is when we will have a small neural net that is well understood, that's given the task to study the behavior of a large neural net that is not understood to verify it.
By what point is most of the AI research being done by AI? I mean, so today, when you use Copilot, right?

What fraction?

How do you do the,

how do you divide it up?

So I expect at some point

you ask your, you know,

descendant of ChadGPT,

you say, hey, like,

I'm thinking about this and this.

Can you suggest fruitful ideas

I should try?

And you would actually get

fruitful ideas.

I don't think that will make it possible

for you to solve problems

you couldn't solve before.

Got it. But it's somehow just telling the humans, giving them ideas faster or something.
It's not itself interacting with the... One example.
I mean, you could slice it in a variety of ways. But I think the bottleneck there is good ideas, good insights, and that's something which the neural net could help with this.
If you could design some, like a billion dollar prize for some sort of alignment research result or product, what is the concrete criteria for that billion dollar price? Is there something that makes sense for such a price?

it's funny that you asked this

I was actually thinking about this exact question

I haven't come up with an exact criteria

yet maybe something that

with the benefit

maybe a prize where

we could say that

two years later

or three or five years later

we'll look back and say that was the main result. So rather than say that there is a prize committee that decides right away, you wait for five years and then award it retroactively.
But there's no concrete thing we can identify yet as like you solved this particular problem and you made a lot of progress.

I think a lot of progress, yes. I wouldn't say that this would be the full thing.
Do you think end-to-end training is the right architecture for bigger and bigger models or do we need better ways of just connecting things together? I think end-to-end training is very promising. I think connecting things together is very promising.
Everything I think it's promising. So OpenAI is projecting revenues

of a billion dollars in 2024. end-to-end training is very promising.
I think connecting together is very promising. Everything is promising.
So OpenAI

is projecting revenues of a billion

dollars in 2024.

That might very well be correct, but I'm just curious

when you're talking about a new general purpose technology,

how do you estimate

how big a windfall it'll be?

Why that particular number?

I mean, you look at the current,

you look at the, you know, we've already

had a, so we've had a product

for quite a while

now, back from the GPT three days, from two years ago through the API, and we've seen how it grew, we've seen how the response to DALI has grown as well, and so you see how the response to chat GPT is, and I think all of this gives us information that allows us to make a relatively sensible extrapolation to 2024. for maybe would be, that'd be one answer.
Like you need to have data. You can't come up with those things out of thin air because otherwise your error bars will be like off by, your error bars are going to be like 100x in each direction.
I mean, but most exponentials don't stay exponential, especially when they get into bigger and bigger quantities, right? So how do you determine in this case that, I mean, like, would you bet against AI?

Not after talking with you. Let's talk about what like a post-AGI future looks like.
So are people

like you, you know, I'm guessing you're working like 80 hour weeks towards this grand goal that

you're really obsessed with. Are you going to be satisfied in a world where you're basically living

in an AI retirement home? Or like, what is home? What are you concretely doing after AGI comes? I think the question of what I'll be doing or what people will be doing after AGI comes is a very tricky question. I think where will people find meaning? But I think that that's something that AI could help us with.
Like, one thing I imagine is that we'll all be able to become more enlightened because we'd interact with an AGI that will help us see the world more correctly or become better on the inside as a result of interacting. Like, imagine talking to the best meditation teacher in history.
I think that will be a helpful thing.

But I also think that because the world will change a lot,

it will be very hard for people to understand

what is happening precisely and how to really contribute.

One thing that I think some people will choose to do

is to become part AI in order to really expand their minds

and understanding and to really be able to solve the hardest problems that society will face then. Are you going to become part AI? Very tempting.
It is tempting, yeah. Do you think there'll be physically embodied humans in 3,000? 3,000.
Oh, how do I know it's going to happen in 3,000? Like, what does it look like? Are there still like humans walking around on earth? Or have you guys thought concretely about what you actually want this world to look like? 3,000. Well, I mean, the thing is, here's the thing.
Let me describe to you what I think is not quite right about the question. Like, it implies like, oh, like we get to decide how we want the world to look like.
I don't think that picture is correct. I think change is the only constant.
And so, of course, even after AGI is built, it doesn't mean that the world will be static. The world will continue to change.
The world will continue to evolve. And it will go through all kinds of transformations.
And I really have no... I don't think anyone has any idea of how the world will look like in 3000.
But I do hope that there will be a lot of descendants of human beings who will live happy, fulfilled lives where they're free to do as they wish, as they see fit, where they are the ones who are solving their own problems. Like one of the things which I would not want, one world which I would find very unexciting is one where, you know, we build this powerful tool and then the government said, okay, so the AGI said that society should be run in such a way and now we should run society in such a way.

I'd much rather have a world where people are still free to make their own mistakes and suffer their consequences and gradually evolve morally and progress forward on their own through their own strength.

See what I mean?

With the AGI providing more like a base safety net. How much time do you spend thinking about these kinds of things versus just doing the research that...
I do think about those things a fair bit, yeah. I think those are very interesting questions.
So in what ways have the capabilities we have today, in what ways have they surpassed where you expected them to be in 2015? And in what ways are they still not where you would expect them to be by this point? I mean, in fairness, they did surpass what I expected them to be in 2015. In 2015, my thinking was a lot more, I just don't want to bet against deep learning.
I want to make the biggest possible bet on deep learning. Don't know how, but we'll figure it out.
But is there any specific way in which it's been more than you expected or less than you expected? Like some concrete prediction you had in 2015 that's been pronounced? You know, unfortunately, I don't remember concrete predictions I made in 2015. But I definitely think that overall, in 2015, I just wanted to move to make the biggest bet possible on deep learning.
But I didn't know exactly. I didn't have a specific idea of how far things will go in seven years.
Well, I mean, 2015, I did have all these bets with people in 2016, maybe 2017, that things will go really far. But specifics...
So it's like, it's both the case that it surprised me. And I was making these aggressive predictions, but I think maybe I believe them only 50% on the inside.
Well, what do you believe now that even most people at OpenAI would find farfetched? I mean, I think that because we communicate a lot at OpenAI, people have a pretty good sense of what I think. And so, yeah, we reached the point at OpenAI, I think we see eye to eye on all these questions.
So Google has, you know, it's custom TPU hardware. It has all this data from all its users, you know, Gmail and so on.
Does it give it an advantage in terms of training bigger models and better models than you? So I think like when the first, at first when the TPU came out, I was really impressed and I thought, wow, this is amazing. But that's because I didn't quite understand hardware back then.
What really turned out to be the case is that TPUs and GPUs are almost the same thing. They are very, very similar.
It's like, I think a GPU chip is a little bit bigger. I think a TPU chip is a little bit smaller.
It may be a little bit cheaper, but then they make more GPUs than TPUs, so I think the GPUs might be cheaper after all. But fundamentally, you have a big processor, and you have a lot of memory, and there is a bottleneck between those two.
And the problem that both the TPU and the GPU are trying to solve is that by the amount of time it takes you to move one floating point from the memory to the processor, you can do several hundred floating point operations on the processor. Which means that you have to do some kind of batch processing.
And in this sense, both of these architectures are the same. So I really feel like hardware, like in some sense, the only thing that matters about hardware is cost.
Cost per flop. Overall systems cost.
Okay, and there isn't that much difference. Well, I actually don't don't know i mean i don't know how much what what the tpu costs are but i would suspect that probably not if anything probably the views are more expensive because there is less of them when you're doing your work how much of the time is spent you know configuring the right initializations making sure the training run goes well and getting the right hyper parameters and how much And how much is it just coming up with whole new ideas? I would say it's a combination, but I think that coming up with, it's a combination, but coming up with whole new ideas is actually not, it's like a modest part of the work.
Certainly coming up with new ideas is important, but I think even more important is to understand the results, to understand the existing ideas, to understand what's going on. Because normally you'd have this, you know, neural net is a very complicated system, right? And you run it and you get some behavior, which is hard to understand what's going on.
Understanding the results, figuring out what next experiment to run. A lot of the time is spent on that.
Understanding what could be wrong, what could have caused the neural net to produce a result which was not expected. I'd say a lot of time is spent as well, of course, coming up with new ideas, but not new ideas.
I think like, I don't like this framing as much. It's not that it's false, but I think the main activity is actually understanding.
What do you see as the difference between the two? So at least in my mind, when you say come up with new ideas, I'm like, oh, like what happened if you did such and such? Whereas understanding, it's more like, what is this whole thing? Like, what are the real underlying phenomena that are going on? What are the underlying effects? Like, why are we doing things this way, not another way? And of course this is very adjacent

to what can be described as

coming up with ideas. But I think the understanding

part is where the real action

takes place. Does that describe your

entire career? Like if you think back on like ImageNet

or something, was that more a new idea or was that

more understanding? Oh, I was definitely understanding.

Definitely understanding. It was a new

understanding of very old things.

What is the experience of training on Azure been like using Azure? Fantastic. I mean, yeah, I mean, Microsoft has been a very, very good partner for us.
And they've really helped take Azure and make it, bring it to a point where it's really good for ML. And they're super happy with it.
How vulnerable is a whole AI ecosystem do something that might happen in Taiwan? So let's say there's like a tsunami in Taiwan or something. What happens to AI in general? Like it's definitely going to be a significant setback.
It's not going to, like it might be something equivalent to, like no one will be able to get more compute for for a few years but i expect compute will spring up like for example i believe that intel has fabs just of the previous of like a few generations ago so that means that if intel wanted to they could produce something gpu like from like four years ago so yeah it's not the best let's say i'm actually not sure about if my statement about Intel is correct, but I do know that there are fabs outside of Taiwan. They're just not as good.
But you can still use them and still go very far with them. It's just a setback.
Will inference get cost prohibitive as these models get bigger and bigger? So I have a different way of looking at this question. It's not that inference will become cost prohibitive.
Inference of better models will indeed become more expensive. But is it prohibitive? Well, it depends on how useful is it.
Like if it is more useful than it is expensive, then it is not prohibitive. Like to give you an analogy, like suppose you want to talk to a lawyer, you have some case or need some advice or something, you are perfectly happy to spend $500 an hour, right? So if your neural net could give you like really reliable legal advice, you'd say, I'm happy to spend $400 for that advice.
And suddenly inference becomes very much non-prohibitive. The question is, can neural net produce an answer good enough at this cost? Yes.
And you will just have price discrimination, different models. I mean, it's already the case today.
So on our product, the API is sort of multiple neural nets of different sizes. And different customers use different neural nets of different sizes depending on their use case.
Like if someone can take a small model and fine-tune it and get something that's satisfactory for them, they'll use that. But if someone wants to do something more complicated and more interesting, they'll use the biggest model.
How do you prevent these models from just becoming commodities where these different companies, they just spit each other's prices down until it's basically the cost of the GPU run? Yeah, I think there is, without question, a force that's trying to create that. And the answer is you got to keep on making progress.
You got to keep improving the models. You got to keep on coming up with new ideas and making our models better and more reliable, more trustworthy, so you can trust their answers.
All those things. Yeah, but let's say it's like 2025 and the model from 2024 or somebody just offering it at cost and it's like still pretty good.
Why would people use a new one from 2025 if the one from just a year older is, you know, even better? So there are several answers there. For some use cases, that may be true.
There will be a new model from 2025, which will be driving the more interesting use cases. There's also be a question of inference cost like you can if you can do research to serve the same model at less cost so there will be different the same model will be served will cost different some different amounts to serve for different companies i can also imagine some degree of specialization too where some companies may try to specialize in some area and be stronger in a narrower area compared to other companies and i think that too may that may be a response to commoditization to some degree as over time do these different companies do their research directions converge or they diverge are they doing similar and similar things over time or are they doing are they going off branching off into different areas so i'd say the near term, it looks like there is convergence.
Like, I expect there's going to be a divergence-convergence behavior where there is a lot of convergence on the near-term work. There's going to be some divergence on the longer-term work.
But then once the longer-term work starts to yield fruit, I think there will be convergence again. Got it.
When one of them finds the most promising area, everybody just... That's right.
Now, there is obviously less publishing now, so it will take longer before this promising direction gets rediscovered. But that's how I'd imagine it.
I think it's going to be convergence, divergence, convergence. Yeah.
We talked about this a little bit at the beginning, but as foreign governments learn about how capable these models are how do you are you worried about spies or some sort of attack to get your weights or you know somehow abuse these models and learn about them yeah it's definitely something that you absolutely can't discount that yeah and yeah something that we try to guard against the best of our ability but it's going to be a problem for everyone who's building this how do you prevent your weights from leaking well i mean you have like really good security people and like how many people have the if they wanted to just like stage into the weights a machine how many people could do that i mean like what i can say is that the security people that we have they built they've done a really good job so that I'm really not worried about the weights being leaked. Okay, got it.
What kinds of emergent properties are you expecting from these models at this scale? Is there something that just comes about de novo? I'm sure things will come up. I'm sure really new surprising properties will come up.
I would not be surprised. The thing which I'm really excited about

or the thing which I'd like to see

is reliability and controllability.

I think that this will be

a very, very important class of emergent properties.

If you have reliability and controllability,

I think that helps you solve a lot of problems.

Reliability means you can trust the model's output.

Controllability means you can control it.

And we'll see.

But it will be very cool if those emergent properties did exist. Is there some way you can predict it in advance? Like what will happen in this parameter count? What will happen in that parameter count? I think it's possible to make some predictions about specific capabilities, though it's definitely not simple and you can do it in a super fine-grained way, at least today.
But I think getting better at that is really important and anyone who is interested in, who has research ideas on how to do that, I think that can be a valuable contribution. How seriously do you take these scaling laws? If like there's a paper that says like, oh, you just increase, you need this many orders of magnitude more to get all the reasoning out.
Like, do you take that seriously or do you think it breaks down at some point? Well, the that the scaling law tells you what happens as you what happens to your lock to your next word prediction accuracy right there is a whole separate challenge of linking next word prediction accuracy to reasoning capability i do believe that indeed there is a link but this link is complicated and we may find that there are other things that can give us more reasoning per unit effort. Like, for example, some special, like, you know, you mentioned reasoning tokens, and I think they can be helpful.
There can be probably some things. Is this something you're considering, just humans to generate tokens for you or is it all going to come from stuff that already exists out there? I mean, I think that relying on people to teach our models to do things, especially, you know, to make sure that they are well-behaved and they don't produce false things, I think is an extremely sensible thing to do.
Isn't it odd that we have the data we need at exactly the same time as we have the transformer at the exact same time that we have these GPUs? Is it odd to you that all of these things happen at the same time, or do you not see it that way? I mean, it is definitely an interesting situation that is the case. I will say that it is odd, and it is less odd on some level.
Here is why it's less odd. So what is the driving force behind the fact that the data exists, that the GPUs exist, that the transformer exists? So the data exists because computers became better and cheaper.
We've got smaller and smaller transistors. And suddenly at some point it became economical for every person to have a personal computer.

Once everyone has a personal computer,

you really want to connect them with a network.

You get the internet.

Once you have the internet,

you have suddenly data appearing in great quantities.

The GPUs were improving concurrently because you have smaller and smaller transistors

and you're looking for things to do with them.

Gaming turned out to be a thing that you could do.

And then at some point,

the gaming GPU NVIDIA said,

wait a second, Brian.

Thank you. them gaming turned out to be a thing that you could do and then at some point the gaming gpu nvidia said wait a second ryan may turn it into a general purpose gpu computer maybe someone will find it will find it useful turns out it's good for neural nets so it could it could have been the case that maybe the gpu would have arrived five years later or ten years later if but let's suppose gaming wasn't a thing.
It's kind of hard to imagine. What does it mean if gaming isn't a thing? But it could.
Maybe there was a counterfactual world where GPUs arrived five years after the data or five years before the data, in which case maybe things would move a little bit worse. Things would have been as ready to go as there now.
But that's the picture which I imagine. All this progress in all these dimensions is very intertwined.
It's not a coincidence that you don't get to pick and choose in which dimensions things improve, if you see what I mean. How inevitable is this kind of progress? So if, let's say, you and Jeffrey Hinton and a few other pioneers, if they were never born, does the deep learning revolution happen around the same time?

How much does it delay?

I think maybe there would have been some delay, maybe like a year delay.

It's really hard to tell.

Really? That's it?

It's really hard to tell.

I mean, I hesitate to give a longer answer because, okay, well, then you'd have, GPUs

would keep on improving, right?

Then at some point, I cannot see how someone would not have discovered it. Because here's the other thing.
Okay, so let's suppose no one has done it. Computers keep getting faster and better, becomes easier and easier to train these neural nets because you have bigger GPUs.
So it takes less engineering effort to train one. You don't need to optimize your code as much.
You know, when the ImageNet dataset came out, it was huge and it was very, very difficult to use. Now imagine you wait for a few years and it becomes very easy to download and people can just tinker.
So I would imagine that a modest number of years maximum, this would be my guess. I hesitate to give a longer answer, though.
You know, you can't run. You can't rerun the world you don't know.
Let's go back to alignment for a second. As somebody who deeply understands these models, what is your intuition of how hard alignment will be? Like, I think, so here's what I would say.
I think with the current level of capabilities, I think we have a pretty good set of ideas of how to align them but i would not underestimate the difficulty of alignment of models that are actually smarter than us of models that are capable of misrepresenting their intentions like i think i think it's something to to think to think about a lot into research i think this is one area also by the way you know academic researchers ask me, ask me where, what's the best place where they can contribute. And I think alignment research is one place where I think academic researchers can make very meaningful contributions.
Other than that, do you think academia will come up with more insights about actual capabilities or is that going to be just the companies at this point? The companies will realize the capabilities. I think it's very possible for academic research to come up with those insights.
I think it's just, it doesn't seem to happen that much for some reason, but I don't, I don't think there's anything fundamental about academia. Like it's not like academia can't.
I think maybe they're just not thinking about the right problems or something because maybe it's just easier to see

what needs to be done inside these companies.

I see.

But there's a possibility that somebody could just realize.

Yeah, I totally think so.

Like, why would I possibly rule this out?

You see what I mean?

What are the concrete steps by which

these language models start actually impacting

the world of atoms and not just the world of bits?

Well, you see, I don't think that there is a distinction, a clean distinction between the world of bits and the world of atoms and not just the world of bits? Well, you see, I don't think that there is a distinction, a clean distinction between

the world of bits and the world of atoms.

Suppose the neural net tells you that, hey, like here is like something that you should

do and it's going to improve your life, but you need to like rearrange your apartment

in a certain way.

Then you go and you rearrange your apartment as a result.

Did the neural net impact the world of atoms?

Yeah, fair enough.

Fair enough.

Do you think it'll take a couple of additional breakthroughs

as important as the transformer to get to superhuman AI?

Or do you think we basically got the insights

in the books somewhere

and we just need to implement them and connect them?

So I don't really see such a big distinction

between those two cases.

And let me explain why.

Like, I think one of the ways in which progress

has taken place in the past

Thank you. So I don't really see such a big distinction between those two cases.
And let me explain why. Like, I think one of the ways in which progress has taken place in the past is that we've understood that something had a property, a desirable property all along, which we didn't realize.
So is that a breakthrough? You can say, yes, it is. Is it an implementation of something on the books? Also, yes.
So my feeling is that a breakthrough you can say yes it is is that an implementation of something on the books also yes so i am i my feeling is that a few of those are quite likely to happen but that in hindsight it will not feel like a breakthrough everybody's going to say oh well of course like it's totally obvious that such and such thing can and work you see with the transformer the reason it's being brought up as a big as a specific advance is because it's the kind of thing that was not obvious for almost anyone. So people can say, yeah, like it's not something which they knew about.
But if an advance comes from something, like let's consider that the most fundamental advance of deep learning, that the big neural network trained with backpropagation can do a lot of things. Like where's the novelty? It's not in the neural network.
It's not in the backpropagation. But then somehow it's the kind of, but it is most definitely a giant conceptual breakthrough because for the longest time, people just didn't see that.
But then now that everyone sees it, everyone's going to say, well, of course, like it's totally obvious, big neural network. Everyone knows that they can do it.
What is your opinion of your former advisor's new forward-forward algorithm? I think that it's an attempt to brain a neural network without backpropagation. And I think that this is especially interesting if you are motivated to try to understand how the brain might be learning its connections.
The reason for that is that as far as I know, neuroscientists are really convinced

that the... how the brain might be learning its connections.
The reason for that is that as far as I know,

neuroscientists are really convinced

that the brain cannot implement backpropagation

because the signals in the synapses

only move in one direction.

And so if you have a neuroscience motivation

and you want to say, okay,

how can I come up with something

that tries to approximate the good properties of backpropagation without doing backpropagation that's what the forward forward algorithm is trying to do but if you are trying to just engineer a good system there is no reason to not use backpropagation like it's it's it's the only algorithm i guess i've heard you in different contexts talk about the need like using humans as the you know the existing example case that you know agi exists right so at what point do you take the metaphor less seriously and feel don't feel the need to pursue it in terms of research because it is important to you as a sort of existence case like at what point do i stop caring caring about humans as an existence case of intelligence or as the sort of as an example of the model you want to follow in terms of pursuing intelligence in models i see i mean like you gotta i think it's good to be inspired by humans i think it's good to be inspired by the brain i think there is an art into being inspired by humans in the brain correctly because it's very easy to latch on to a non-essential quality of humans or of the brain and i think many people who are in school many people whose research is trying to be inspired by humans and by the brain often gets a little bit specific people get a little bit too okay so like what cognitive like what cognitive science model should we follow? At the same time, consider the idea of the neural network itself, the idea of the artificial neuron. This too is inspired by the brain, but it turned out to be extremely fruitful.
So how do we do this? What behaviors of human beings are essential? That you say like this is something that proves to us that it's possible. What is an essential? No, actually, this is like some emergent phenomenon of something more basic.
And we just need to focus on getting our own basics right. I would say that it's like, I think one can and should be inspired by human intelligence with care.
Final question. Why is there, in your case, such a strong correlation between being first to the deep learning revolution and still being one of the top researchers? You would think that these two things wouldn't be that correlated, but why is that their correlation? I don't think those things are super correlated indeed.
I feel like in my case, I mean, honestly, it's hard to answer the question. You know, I just kept on, I kept trying really hard and it turned out to have sufficed thus far.
Got it. So it's a perseverance.
I think it's a necessary but not a sufficient condition. Like, you know, many things need to come together in order to really figure something out.
like you need to really go for it and also need to have the right way of looking at things and so it's hard it's hard to give him like a really meaningful answer to this question all right um ilia it has been a true pleasure thank you so much for coming on the lunar society i appreciate you bringing us to the offices thank you yeah i Yeah, I really enjoyed it your support. As always, the most helpful thing you can do is to share the podcast.
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