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Tim Scarfe

Host · Machine Learning Street Talk

Claims
168
Episodes
35
Shows
1
Named items
4

Books, apps, and tools

The evidenced stack.

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tool / uses

MATLAB

“during my PhD, I used Mathematica and MATLAB”

Machine Learning Street Talk · 3 Mar 2026

Evidence receipt · Source ↗

tool / uses

Mathematica

“during my PhD, I used Mathematica and MATLAB”

Machine Learning Street Talk · 3 Mar 2026

Evidence receipt · Source ↗

person / likes

Douglas Hofstadter

“Yes. I I love Douglas Hofstadter. So so there's this kind of self modeling and then second order self modeling and third order self modeling which could be applied to other agents and of course, know, in in the real world, we are computationally bounded.”

Machine Learning Street Talk · 21 Oct 2025

Evidence receipt · Source ↗

person / likes

Andy

“I'm I'm huge fan of Andy's, big hero of mine. You probably didn't think that they would be so relevant later on in your career.”

Machine Learning Street Talk · 18 Oct 2025

Evidence receipt · Source ↗

Claim ledger

What Tim said.

168 transcript-backed records

02 / evaluation

A a lot of people are. And the these agents have an incredible amount of intelligence and flexibility, which means we don't precisely specify what they do.

“A a lot of people are. And the these agents have an incredible amount of intelligence and flexibility, which means we don't precisely specify what they do.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

03 / evaluation

Whereas in the in this sense, we're we're now abstracting them because there's also this orthogonality thesis as well, which is this intelligence of final goals are are, you know, disconnected from each other.

“Whereas in the in this sense, we're we're now abstracting them because there's also this orthogonality thesis as well, which is this intelligence of final goals are are, you know, disconnected from each other.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

05 / belief

I mean, yeah, it is a it is a grotesque metaphor that you can take something which I believe is is a physical property of of stuff in the universe And we can create an abstraction that that seems to work reasonably well for abstract domains like playing chess and and and so on.

“I mean, yeah, it is a it is a grotesque metaphor that you can take something which I believe is is a physical property of of stuff in the universe And we can create an abstraction that that seems to work reasonably well for abstract domains like playing chess and and and so on.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

08 / evaluation

Maybe we can RL train it to refactor it, and we just have to kind of and it's very dangerous keeping going because now we're messing all of our code bases up and we're we're creating all of this slop everywhere.

“Maybe we can RL train it to refactor it, and we just have to kind of and it's very dangerous keeping going because now we're messing all of our code bases up and we're we're creating all of this slop everywhere.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

11 / evaluation

similar context around them. And this is very pertinent because you've got a paper out basically saying that we should predict in the latent space, not the token space.

“similar context around them. And this is very pertinent because you've got a paper out basically saying that we should predict in the latent space, not the token space.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

12 / prediction

Just to kind of play that back just so that everyone The idea is that there is I mean, we're talking about grammar here. But more broadly, we think that there are structured generative processes in the world.

“Just to kind of play that back just so that everyone The idea is that there is I mean, we're talking about grammar here. But more broadly, we think that there are structured generative processes in the world.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

13 / evaluation

I guess the interesting thing for me is that when we think of reinforcement learning algorithms like AlphaGo Zero, it makes sense that they are reward seeking because there is this structured inference process.

“I guess the interesting thing for me is that when we think of reinforcement learning algorithms like AlphaGo Zero, it makes sense that they are reward seeking because there is this structured inference process.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

15 / belief

You mentioned transductive as well which is quite interesting because roughly speaking I think of transduction as you're making a prediction about the specific test instance.

“You mentioned transductive as well which is quite interesting because roughly speaking I think of transduction as you're making a prediction about the specific test instance.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

19 / belief

Like, if we actually had some hypothetical real time adaptive divergent Claude, you know, would it be much better than the Claude that we already have? And, it's also related to the to the work that you guys do, because, the process of intelligence, in my view, is the creation of these coarse grainings, skills.

“Like, if we actually had some hypothetical real time adaptive divergent Claude, you know, would it be much better than the Claude that we already have? And, it's also related to the to the work that you guys do, because, the process of intelligence, in my view, is the creation of these coarse grainings, skills.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

20 / evaluation

Right? And it's a serious problem, because it's all about epistemic subjectivity, which is that you generate things that you don't understand, and it convinces you that it's correct, and you can't see the glitches.

“Right? And it's a serious problem, because it's all about epistemic subjectivity, which is that you generate things that you don't understand, and it convinces you that it's correct, and you can't see the glitches.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

21 / prediction

Because there is some kind of a combinational closure, and that means from the primitives in an LLM, can do hill climbing, and we can build some computational structure to solve problems.

“Because there is some kind of a combinational closure, and that means from the primitives in an LLM, can do hill climbing, and we can build some computational structure to solve problems.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

22 / commitment

Because as you say, like, we're we're we're building the AI and then the AI is helping us build better kernels, better software, better hardware, which then in turn makes the AI better and then, you know, you get this this kind of loop.

“Because as you say, like, we're we're we're building the AI and then the AI is helping us build better kernels, better software, better hardware, which then in turn makes the AI better and then, you know, you get this this kind of loop.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

24 / evaluation

Even that, I think you can argue, maybe not entirely the story, but it's definitely not the story for proteins because that's the hardest problem is the large scale structure.

“Even that, I think you can argue, maybe not entirely the story, but it's definitely not the story for proteins because that's the hardest problem is the large scale structure.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

25 / prediction

There are now structural biologists around the world that can innovate and build new products and save lives potentially because they have access to this protein database.

“There are now structural biologists around the world that can innovate and build new products and save lives potentially because they have access to this protein database.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

26 / evaluation

Physicists, for example, they they work very, low level and they talk about, you know, the the the dynamics of particle systems and and whatnot. And what I'm really fascinated in, I mean, you come at it from an economics perspective, which is traditionally dominated by this agential lens, and you talk about equilibria and incentives and and so on.

“Physicists, for example, they they work very, low level and they talk about, you know, the the the dynamics of particle systems and and whatnot. And what I'm really fascinated in, I mean, you come at it from an economics perspective, which is traditionally dominated by this agential lens, and you talk about equilibria and incentives and and so on.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

27 / evaluation

Well, suppose another thing that doesn't help is that these these systems are like soup, and there's even a field called mechanistic interpretability that tries to kind of dig into the soup and and it's almost like they're they're searching for UFOs.

“Well, suppose another thing that doesn't help is that these these systems are like soup, and there's even a field called mechanistic interpretability that tries to kind of dig into the soup and and it's almost like they're they're searching for UFOs.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

33 / evaluation

You know, and you make this series of decisions, and then you've got people using your application, and then you can't really wind that back. It doesn't matter if you've got the magical automation machine because you can't easily roll that back because there's lots of complexities, you know, do you see ICD testing?

“You know, and you make this series of decisions, and then you've got people using your application, and then you can't really wind that back. It doesn't matter if you've got the magical automation machine because you can't easily roll that back because there's lots of complexities, you know, do you see ICD testing?”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

35 / evaluation

Because the whole reason we created agile software development as a methodology is because it's inconceivable, It's outside our cognitive horizon.

“Because the whole reason we created agile software development as a methodology is because it's inconceivable, It's outside our cognitive horizon.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

36 / belief

I think that you subscribe to the slightly different idea that that we need to be far more open ended and we need to be using evolutionary algorithms and so on.

“I think that you subscribe to the slightly different idea that that we need to be far more open ended and we need to be using evolutionary algorithms and so on.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

37 / evaluation

These are problems that have been solved before in in part or in whole, which means when you look at the epistemic tree, many of the building blocks for solving them are very high up in the tree.

“These are problems that have been solved before in in part or in whole, which means when you look at the epistemic tree, many of the building blocks for solving them are very high up in the tree.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

38 / evaluation

Right? So we're using neural networks because they're incredibly flexible and they understand a lot of things about the world, but they don't have the kind of constraints that we want.

“Right? So we're using neural networks because they're incredibly flexible and they understand a lot of things about the world, but they don't have the kind of constraints that we want.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

40 / belief

Yeah. Because the discriminative learning rate thing is interesting because I I think the received wisdom at the time was when you fine tune a model, if the learning rate is too high, you kind of blow out the representations.

“Yeah. Because the discriminative learning rate thing is interesting because I I think the received wisdom at the time was when you fine tune a model, if the learning rate is too high, you kind of blow out the representations.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

42 / belief

I I think there's a dichotomy though between continual learning, which is when we want to keep training the thing but maintain generality, versus fine tuning a thing to do something specific.

“I I think there's a dichotomy though between continual learning, which is when we want to keep training the thing but maintain generality, versus fine tuning a thing to do something specific.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

44 / belief

You know, I'm so torn on this because I agree with you. And I'm also skeptical of people who say that organizations, they they they converge onto ways of doing things, they no longer need to evolve.

“You know, I'm so torn on this because I agree with you. And I'm also skeptical of people who say that organizations, they they they converge onto ways of doing things, they no longer need to evolve.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

51 / evaluation

I think the counterfactual thing is an important feature here because we could take something which was conscious or something which had agency, and we could just take a trace of the actual path which was found.

“I think the counterfactual thing is an important feature here because we could take something which was conscious or something which had agency, and we could just take a trace of the actual path which was found.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

53 / evaluation

We agents because you have consistent beliefs and ideas and you're not just an impulse response machine that's your being actions aren't determined entirely by this situation, you're a person.

“We agents because you have consistent beliefs and ideas and you're not just an impulse response machine that's your being actions aren't determined entirely by this situation, you're a person.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

54 / preference

I mean, there's a thought experiment that I had a debate with an AI doomer and because I'm a big externalist, I think we're embedded in these cognitive as you do.

“I mean, there's a thought experiment that I had a debate with an AI doomer and because I'm a big externalist, I think we're embedded in these cognitive as you do.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

55 / prediction

Yeah. And what the researchers found is that you train this big dense neural network, and after it's trained, because you need the density for stochastic gradient descent for training tractability, after it's trained, you can prune away 90% of the connections and it still works the same And, maybe evolution and our biological instantiation, maybe it's the same thing.

“Yeah. And what the researchers found is that you train this big dense neural network, and after it's trained, because you need the density for stochastic gradient descent for training tractability, after it's trained, you can prune away 90% of the connections and it still works the same And, maybe evolution and our biological instantiation, maybe it's the same thing.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

56 / evaluation

I think if I remember correctly, at 1 point you drew an imaginary kind of graph where you said on 1 axis we have science realism, which is where our scientific theories actually represent things in the world, then we have empiricism, is the idea that facts we receive tell us something about the world.

“I think if I remember correctly, at 1 point you drew an imaginary kind of graph where you said on 1 axis we have science realism, which is where our scientific theories actually represent things in the world, then we have empiricism, is the idea that facts we receive tell us something about the world.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

58 / evaluation

Of course Pavlov and the dogs, folks at home will know about that. And Newton is still around, we still use that, but we don't use reflex theory anymore.

“Of course Pavlov and the dogs, folks at home will know about that. And Newton is still around, we still use that, but we don't use reflex theory anymore.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

59 / belief

I look up to her very much and certainly thinking back on many of the episodes we've done in 2025, I can see her influence in the questions I ask and how I think about things.

“I look up to her very much and certainly thinking back on many of the episodes we've done in 2025, I can see her influence in the questions I ask and how I think about things.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

62 / evaluation

I interviewed her recently. And she said that 1 of the most pervasive myths in neuroscience is that we use these leaky abstractions and idealizations to talk about cognition.

“I interviewed her recently. And she said that 1 of the most pervasive myths in neuroscience is that we use these leaky abstractions and idealizations to talk about cognition.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

64 / prediction

You start feeling the AGI and you'd be forgiven for thinking this because I've been using Claude code and my god, I feel that there's been more interesting stuff happening in the world of software development in the last 6 months than there has been in the previous 20 years.

“You start feeling the AGI and you'd be forgiven for thinking this because I've been using Claude code and my god, I feel that there's been more interesting stuff happening in the world of software development in the last 6 months than there has been in the previous 20 years.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

67 / belief

Some of the programs which are learned are just really complicated. They had examples of like I think drawing towers and drawing graphs and stuff like that.

“Some of the programs which are learned are just really complicated. They had examples of like I think drawing towers and drawing graphs and stuff like that.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

69 / belief

We just kind of, generate these post hoc confabulations, and then we explain our behavior, and we kind of pretend that that was what we wanted to do, that we had beliefs and so on, but we just kind of make it up as as we go along using this kind of active inference. So I guess the the the question is, like, we do think of ourselves as being like, even though we are emotional and subjective and, like, you know, like, we believe in religion and lots of things that that we presumably made up, but but we have Wikipedia.

“We just kind of, generate these post hoc confabulations, and then we explain our behavior, and we kind of pretend that that was what we wanted to do, that we had beliefs and so on, but we just kind of make it up as as we go along using this kind of active inference. So I guess the the the question is, like, we do think of ourselves as being like, even though we are emotional and subjective and, like, you know, like, we believe in religion and lots of things that that we presumably made up, but but we have Wikipedia.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

71 / belief

I think I think people conflate the machinations of of language models with how we represent them statistically or or abstractly. Because if you look at lot of papers, they they actually represent it like a probability, you know, like a joint probability distribution.

“I think I think people conflate the machinations of of language models with how we represent them statistically or or abstractly. Because if you look at lot of papers, they they actually represent it like a probability, you know, like a joint probability distribution.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

73 / belief

You need to do things a certain way. And even though it's not technically constraining our brains and how we think, like, live in a very, very constrained and weird world now.

“You need to do things a certain way. And even though it's not technically constraining our brains and how we think, like, live in a very, very constrained and weird world now.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

75 / evaluation

Yeah. It's really interesting what you said because the way I read that is things like chat GBT and language models, they are entropy smuggling or agency smuggling.

“Yeah. It's really interesting what you said because the way I read that is things like chat GBT and language models, they are entropy smuggling or agency smuggling.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

76 / evaluation

I I think part of it is it's a kind of acquiescence. So I think you're sequestering your agency when you externalize too much of your cognition, particularly if it's parts of your cognition that are useful in the sense that it has core knowledge which would generalize and help you acquire new knowledge or, it's just the the the proto ability of, you know, discovering knowledge.

“I I think part of it is it's a kind of acquiescence. So I think you're sequestering your agency when you externalize too much of your cognition, particularly if it's parts of your cognition that are useful in the sense that it has core knowledge which would generalize and help you acquire new knowledge or, it's just the the the proto ability of, you know, discovering knowledge.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

77 / evaluation

So for example, someone might come up to me and say, blue swirly thing is over there. And I'll say, well, I don't know what you mean about the blue swirly thing because I've I've never seen 1 before.

“So for example, someone might come up to me and say, blue swirly thing is over there. And I'll say, well, I don't know what you mean about the blue swirly thing because I've I've never seen 1 before.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

78 / evaluation

I mean, because the way I read it in active inference literature, it's a very principled definition of an agent. And there's still a bit of a gap because I think Friston would argue, in the natural world, because of the the laws of physics and particles and and whatnot you get the emergence of things and things become agents when they have a certain you know depth of planning shall we say.

“I mean, because the way I read it in active inference literature, it's a very principled definition of an agent. And there's still a bit of a gap because I think Friston would argue, in the natural world, because of the the laws of physics and particles and and whatnot you get the emergence of things and things become agents when they have a certain you know depth of planning shall we say.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

81 / belief

You had you're talking about Barnes and Noble over there in Seattle, and they they they went up against Jeff Bezos and they said, well, you know, Jeff, we've launched a website and and we think we can do what you do better than they do.

“You had you're talking about Barnes and Noble over there in Seattle, and they they they went up against Jeff Bezos and they said, well, you know, Jeff, we've launched a website and and we think we can do what you do better than they do.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

82 / prediction

They're getting, know, the 10 times bigger every few years and the the bullish people say, oh, we're on an exponential curve and it's just gonna keep going up. But I think that because there is so much groupthink and it's fundamentally the same technology and the same people and there's no fresh new ideas that in a sense it's converged and it's not disruptive anymore.

“They're getting, know, the 10 times bigger every few years and the the bullish people say, oh, we're on an exponential curve and it's just gonna keep going up. But I think that because there is so much groupthink and it's fundamentally the same technology and the same people and there's no fresh new ideas that in a sense it's converged and it's not disruptive anymore.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

84 / evaluation

We we know from experience, right, that when we have experience, we get better at things. And we have this this weird, I don't know whether it's an illusion that all we need to do is just write down our understanding into into a wiki document.

“We we know from experience, right, that when we have experience, we get better at things. And we have this this weird, I don't know whether it's an illusion that all we need to do is just write down our understanding into into a wiki document.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

85 / belief

I think that even though there's this grounding problem, there are many things in the world that can be abstracted and essentialized and we could have this constructive form of understanding, you know, that goes several levels deep.

“I think that even though there's this grounding problem, there are many things in the world that can be abstracted and essentialized and we could have this constructive form of understanding, you know, that goes several levels deep.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

87 / belief

Maybe it just wasn't that hard of a thing to do in the first place. But I think in the case of machine learning, we really need to give credit where it's due here that nobody knew that these superficial statistical regularities have an insane amount of generalization.

“Maybe it just wasn't that hard of a thing to do in the first place. But I think in the case of machine learning, we really need to give credit where it's due here that nobody knew that these superficial statistical regularities have an insane amount of generalization.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

88 / disagreement

We passed the Turing test And we do not have Turing himself, he said that when we passed the Turing test, which is a behaviorist notion of intelligence, something obviously I disagree with, you need to know something about the mechanism.

“We passed the Turing test And we do not have Turing himself, he said that when we passed the Turing test, which is a behaviorist notion of intelligence, something obviously I disagree with, you need to know something about the mechanism.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

89 / belief

If anything, I think that there is more opportunity now for people to work to fix all of the shit which is generated from language models because it's it's mostly garbage.

“If anything, I think that there is more opportunity now for people to work to fix all of the shit which is generated from language models because it's it's mostly garbage.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

92 / evaluation

You you can't seriously tell me that if you simulate fire using a computer program that that the computer would get hot or that you would get hot. It it just doesn't work like that.

“You you can't seriously tell me that if you simulate fire using a computer program that that the computer would get hot or that you would get hot. It it just doesn't work like that.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

94 / disagreement

I mean as before I disagree with the mind uploading thing but I I think there's something to what you're saying that when you look at humans now, we are mostly good.

“I mean as before I disagree with the mind uploading thing but I I think there's something to what you're saying that when you look at humans now, we are mostly good.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

95 / evaluation

Knowledge decays very, very quickly. And that's actually a good thing because it's the way that we can adapt our strategies because things that don't work die off.

“Knowledge decays very, very quickly. And that's actually a good thing because it's the way that we can adapt our strategies because things that don't work die off.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

96 / evaluation

And we'll move to the next level of our evolution. And in a sense, I think, even though I don't agree with it, because I think even with Neuralink, there's bandwidth problems.

“And we'll move to the next level of our evolution. And in a sense, I think, even though I don't agree with it, because I think even with Neuralink, there's bandwidth problems.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

97 / belief

There is a school of thought that our brain works in this way. So we think using, like, the the the symbols and these categories and so on, And then there's and then there's the notion of the universe is a certain way, and we understand the universe with that kind of interface.

“There is a school of thought that our brain works in this way. So we think using, like, the the the symbols and these categories and so on, And then there's and then there's the notion of the universe is a certain way, and we understand the universe with that kind of interface.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

98 / disagreement

Things like intentionality and planning and system 2 and reasoning and stuff like that, I think you're placing the assumption that there's something standard about that.

“Things like intentionality and planning and system 2 and reasoning and stuff like that, I think you're placing the assumption that there's something standard about that.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

99 / prediction

So we want to have understanding which carves the world up by the joints, which represents the important invariances in the world. And the thesis is, I think, that compression might be necessary for understanding.

“So we want to have understanding which carves the world up by the joints, which represents the important invariances in the world. And the thesis is, I think, that compression might be necessary for understanding.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

100 / observation

We we just find that structure. And that's why when I watched your presentation, I was very intrigued when you said that denoising, iterative denoising is is a form of of compression.

“We we just find that structure. And that's why when I watched your presentation, I was very intrigued when you said that denoising, iterative denoising is is a form of of compression.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

103 / belief

If we learned it constructively, so we, you know, you speak about this in your paper, this complexification, the abstract building blocks, and you can do adaptive computation.

“If we learned it constructively, so we, you know, you speak about this in your paper, this complexification, the abstract building blocks, and you can do adaptive computation.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

106 / commitment

You know, there's this path dependence idea. So we need to do supervision because we have the path dependence so we can guide the generation of the language models.

“You know, there's this path dependence idea. So we need to do supervision because we have the path dependence so we can guide the generation of the language models.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

107 / evaluation

Yes, and on that point, I think maybe the most exciting thing about your paper is, you know, we were talking about path dependence and having this understanding which is built step by step, this process of complexification.

“Yes, and on that point, I think maybe the most exciting thing about your paper is, you know, we were talking about path dependence and having this understanding which is built step by step, this process of complexification.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

108 / preference

Because we we've got a we've got a great audience of ML engineers and scientists, and I think working for Socano would be the dream job.

“Because we we've got a we've got a great audience of ML engineers and scientists, and I think working for Socano would be the dream job.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

110 / preference

May I also submit that there could be an additional reason, which is, you know, I love that fractured and tangled representations paper.

“May I also submit that there could be an additional reason, which is, you know, I love that fractured and tangled representations paper.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

114 / evaluation

I think he would, because he he has a bunch of criteria, but 1 of them is is a fundamental coarse graining and reorganization of the micro substrate such that the new phenomena can be described with with a, you know, with within, you know, simple new variable.

“I think he would, because he he has a bunch of criteria, but 1 of them is is a fundamental coarse graining and reorganization of the micro substrate such that the new phenomena can be described with with a, you know, with within, you know, simple new variable.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

116 / observation

I am amenable by the way to this idea of loss of control, you know, which is that we we we start to build systems on top of systems on top of systems, and it's a little bit like the power station.

“I am amenable by the way to this idea of loss of control, you know, which is that we we we start to build systems on top of systems on top of systems, and it's a little bit like the power station.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

118 / prediction

You know, if we had perfect verifiers and and synthetic data generators, probably we wouldn't even need LLMs in the first place, right, because we've already solved all the problems.

“You know, if we had perfect verifiers and and synthetic data generators, probably we wouldn't even need LLMs in the first place, right, because we've already solved all the problems.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

119 / evaluation

You know, when when something is, you know, trivially easy to mechanize, no 1 actually thought it was intelligent. But I think the Turing test is bad because we know that in my opinion, language models aren't actually that intelligent, yet we've passed it with flying colors.

“You know, when when something is, you know, trivially easy to mechanize, no 1 actually thought it was intelligent. But I think the Turing test is bad because we know that in my opinion, language models aren't actually that intelligent, yet we've passed it with flying colors.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

120 / belief

We were talking about semantics earlier. So I I think you've done some work basically proving that semantic censorship for language is is impossible and you related it to the halting problem.

“We were talking about semantics earlier. So I I think you've done some work basically proving that semantic censorship for language is is impossible and you related it to the halting problem.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

122 / prediction

Because I think at some point here soon, I don't know when, next year, 5 years, whatever, personalized, you know, AI models are gonna be a big thing.

“Because I think at some point here soon, I don't know when, next year, 5 years, whatever, personalized, you know, AI models are gonna be a big thing.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

123 / recommendation

Like, you know, they're As part of the Alpha Evolve system, which is very interesting, I recommend people watch that episode, you know, it goes and runs external verifiers.

“Like, you know, they're As part of the Alpha Evolve system, which is very interesting, I recommend people watch that episode, you know, it goes and runs external verifiers.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

126 / uncertainty

I don't know if you've seen the recent couple of papers that are applying it to transformers, you know, where essentially it's it's kind of a step towards probabilistic models where you actually have this uncertainty quantification.

“I don't know if you've seen the recent couple of papers that are applying it to transformers, you know, where essentially it's it's kind of a step towards probabilistic models where you actually have this uncertainty quantification.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

127 / preference

There's this phylogeny of of knowledge, and you need to respect it as much as possible because if you don't respect it, you're not grounded anymore. So it kind of feels to me that intuitively code is great because it means that I'm actually respecting the constraints and and the semantics are correct and it's grounded in in the real world.

“There's this phylogeny of of knowledge, and you need to respect it as much as possible because if you don't respect it, you're not grounded anymore. So it kind of feels to me that intuitively code is great because it means that I'm actually respecting the constraints and and the semantics are correct and it's grounded in in the real world.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

128 / commitment

It was using Sonnet 3.5, and you had about 4 iterations, I think. And and, essentially, you you know, you were working on the ARC challenge, you were producing these programs through evolution.

“It was using Sonnet 3.5, and you had about 4 iterations, I think. And and, essentially, you you know, you were working on the ARC challenge, you were producing these programs through evolution.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

129 / preference

I think I think with SGD because the fascinating thing is that, you know, if you look at all of the FSA algorithms, a a tiny sliver of those algorithms are capable of controlling, you know, a Turing machine and expanding their memory and so on.

“I think I think with SGD because the fascinating thing is that, you know, if you look at all of the FSA algorithms, a a tiny sliver of those algorithms are capable of controlling, you know, a Turing machine and expanding their memory and so on.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

130 / prediction

It's it's Turing complete. And our brains, even though they are finite, they run a Turing complete algorithm, which means our brains know how to expand their memory.

“It's it's Turing complete. And our brains, even though they are finite, they run a Turing complete algorithm, which means our brains know how to expand their memory.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

131 / preference

So on the first 1 as well, you were generating Python programs explicitly. And because of all the things that we're just talking about, I'm a big fan of that because I feel intuitively, and I think you did, that there's something special about Python programs.

“So on the first 1 as well, you were generating Python programs explicitly. And because of all the things that we're just talking about, I'm a big fan of that because I feel intuitively, and I think you did, that there's something special about Python programs.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

133 / evaluation

The reason for that, as we discuss in today's show, is that current AI does not understand the world in a grounded way. It doesn't have a deep abstract understanding of the world, which is why the only way that we can make AI work effectively is by grounding the generation and supervising the training of AI models with human data.

“The reason for that, as we discuss in today's show, is that current AI does not understand the world in a grounded way. It doesn't have a deep abstract understanding of the world, which is why the only way that we can make AI work effectively is by grounding the generation and supervising the training of AI models with human data.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

134 / preference

Absolutely. And I I remember I read in in the first version of your blog post that you were talking about, we need to do this kind of deduction where we synthesize hypotheses, and then we we test them, and we do this kind of generate test loop.

“Absolutely. And I I remember I read in in the first version of your blog post that you were talking about, we need to do this kind of deduction where we synthesize hypotheses, and then we we test them, and we do this kind of generate test loop.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

136 / evaluation

He's talking about things at a level of abstraction which, you know, can refer to anything, but it's beyond most people's cognitive horizon. But there is something to be said for that when when you can respect the history deep down into the epistemic tree, the creative stepping stones you take, because they respect the history, they actually have more evolvability.

“He's talking about things at a level of abstraction which, you know, can refer to anything, but it's beyond most people's cognitive horizon. But there is something to be said for that when when you can respect the history deep down into the epistemic tree, the creative stepping stones you take, because they respect the history, they actually have more evolvability.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

138 / evaluation

I've never been able to completely pin you down, professor Friston, because there have been so many interpretations of the free energy principle that that lean internalist and externalist and even, the the hybrid version, which Maxwell also wrote a paper about.

“I've never been able to completely pin you down, professor Friston, because there have been so many interpretations of the free energy principle that that lean internalist and externalist and even, the the hybrid version, which Maxwell also wrote a paper about.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

139 / evaluation

We'll just turn up the temperature and we'll just sample tokens from the tail. And and you you really get garbage there because you're kind of, you know, you're you're a little bit out of distribution now.

“We'll just turn up the temperature and we'll just sample tokens from the tail. And and you you really get garbage there because you're kind of, you know, you're you're a little bit out of distribution now.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

140 / belief

I think there was a distinction as well that certainly Legg and Hunter, they were very focused on on their simplification of of the model and Occam's razor.

“I think there was a distinction as well that certainly Legg and Hunter, they were very focused on on their simplification of of the model and Occam's razor.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

143 / evaluation

There is just 1 potential objection, which is that you know what a lot of theories of consciousness do is is they kind of they they brush it to 1 side and they treat it as something which is epiphenomenal, which means that it's not like causally embedded in in the system.

“There is just 1 potential objection, which is that you know what a lot of theories of consciousness do is is they kind of they they brush it to 1 side and they treat it as something which is epiphenomenal, which means that it's not like causally embedded in in the system.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

144 / prediction

I guess, in a way, I can't challenge you because you're already saying they need to be biological and and like, you know, physical and real and so because I mean, my my obvious retort to that would be, well, a computer simulation of those things obviously wouldn't be conscious.

“I guess, in a way, I can't challenge you because you're already saying they need to be biological and and like, you know, physical and real and so because I mean, my my obvious retort to that would be, well, a computer simulation of those things obviously wouldn't be conscious.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

145 / prediction

Yeah. And and if I understand correctly, I I think that I've spent years since I read that 2007 paper, but but it it it was about an agent minimizing common complexity, which can, like, do well on a on a the the expected performance on a wide range of environments.

“Yeah. And and if I understand correctly, I I think that I've spent years since I read that 2007 paper, but but it it it was about an agent minimizing common complexity, which can, like, do well on a on a the the expected performance on a wide range of environments.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

147 / belief

Mean, first of all, it was really interesting that political and demographic biases would emerge as coherent utility functions. And I I do take umbrage with this word emergence because I think in the emergence literature, there is a little bit more nuance to how machine learning people use the words.

“Mean, first of all, it was really interesting that political and demographic biases would emerge as coherent utility functions. And I I do take umbrage with this word emergence because I think in the emergence literature, there is a little bit more nuance to how machine learning people use the words.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

148 / belief

Another very interesting thing in your paper because when I think about AI risk in general, we we've thought about this a little bit on the show before, is in terms of, stability and destabilization and the relationship between offense and defense.

“Another very interesting thing in your paper because when I think about AI risk in general, we we've thought about this a little bit on the show before, is in terms of, stability and destabilization and the relationship between offense and defense.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

149 / uncertainty

I mean, 1 thing I was thinking about is so certainly in in enigma about them, we're looking at multistep creative reasoning. And I don't know whether, again, there was some kind of human based methodology for filtering and coming up with ideas, or maybe your frame was, I have some technical principled intuition about what the limitations of AI models are, so I'm gonna lean in in that direction.

“I mean, 1 thing I was thinking about is so certainly in in enigma about them, we're looking at multistep creative reasoning. And I don't know whether, again, there was some kind of human based methodology for filtering and coming up with ideas, or maybe your frame was, I have some technical principled intuition about what the limitations of AI models are, so I'm gonna lean in in that direction.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

151 / evaluation

So as as you've said, we use analogies like, electricity. And I think that's quite a good 1, because as as AI becomes enmeshed in into society, imagine how hard it would be to shut down a power station.

“So as as you've said, we use analogies like, electricity. And I think that's quite a good 1, because as as AI becomes enmeshed in into society, imagine how hard it would be to shut down a power station.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

152 / evaluation

But in Genie 2, there was an ST transformer, so a special temporal transformer, which was conceptually quite similar to like a VIT. And there was a latent action model, which means even from non interactive data, you could infer some low cardinality action space.

“But in Genie 2, there was an ST transformer, so a special temporal transformer, which was conceptually quite similar to like a VIT. And there was a latent action model, which means even from non interactive data, you could infer some low cardinality action space.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

154 / evaluation

We are here at Google DeepMind in London, and you guys have just demoed to me something which I think I'm more impressed with this, I think, than anything I've seen probably ever before.

“We are here at Google DeepMind in London, and you guys have just demoed to me something which I think I'm more impressed with this, I think, than anything I've seen probably ever before.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

156 / evaluation

Perhaps in the future, we might have an outer loop, which makes the system more open ended. But right now, my opinion, Genie 3, like all AI, gives you exactly what you asked for in the prompts and isn't creative on its own.

“Perhaps in the future, we might have an outer loop, which makes the system more open ended. But right now, my opinion, Genie 3, like all AI, gives you exactly what you asked for in the prompts and isn't creative on its own.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

158 / evaluation

I've also noticed that this model is is even better than v o 3 at things like text. It's because you you would think that they would they would dumb down the model to make it interactive and to make it this sophisticated.

“I've also noticed that this model is is even better than v o 3 at things like text. It's because you you would think that they would they would dumb down the model to make it interactive and to make it this sophisticated.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

159 / preference

You know, I'm a huge fan of open endedness, for example. And certainly at the moment, when we prompt models, if we're quite generic in what we put in the prompt, then we tend to get quite simplistic answers.

“You know, I'm a huge fan of open endedness, for example. And certainly at the moment, when we prompt models, if we're quite generic in what we put in the prompt, then we tend to get quite simplistic answers.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

160 / evaluation

You know, it it it feels to me that the missing link is having the correct level of abstraction and being able to do this iterative open ended search. They can't do that because they simply don't have the abstractions.

“You know, it it it feels to me that the missing link is having the correct level of abstraction and being able to do this iterative open ended search. They can't do that because they simply don't have the abstractions.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

161 / evaluation

These are continuous functions because most of the time in a neural network, like, if you if you give it a test sample, which is outside of the training support, it you're in no man's land.

“These are continuous functions because most of the time in a neural network, like, if you if you give it a test sample, which is outside of the training support, it you're in no man's land.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

162 / uncertainty

Duggar. But some people say, I don't know, like polysemanticity or grokking or scale and, you know, that that it just it just appears like the neural network isn't grokking it.

“Duggar. But some people say, I don't know, like polysemanticity or grokking or scale and, you know, that that it just it just appears like the neural network isn't grokking it.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

163 / belief

I mean, there's there's a few points here because I guess like what I was, where I was going with this before is, you take y equals x squared, and the reason why we think of it as robust is for any value of y, it kind of does something it does something reasonable.

“I mean, there's there's a few points here because I guess like what I was, where I was going with this before is, you take y equals x squared, and the reason why we think of it as robust is for any value of y, it kind of does something it does something reasonable.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

164 / commitment

We know that we have certain things we can compose, and we know that we can compose them in certain topologies, and we know that invariably if we follow that trajectory, we will land on interesting things, even though we don't necessarily know exactly what we will land on.

“We know that we have certain things we can compose, and we know that we can compose them in certain topologies, and we know that invariably if we follow that trajectory, we will land on interesting things, even though we don't necessarily know exactly what we will land on.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

165 / belief

We could easily discover something that what that wipes YouTube away. So it's about this epistemic gap between what we really want and what we think we want.

“We could easily discover something that what that wipes YouTube away. So it's about this epistemic gap between what we really want and what we think we want.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk

168 / prediction

It's a divergent, unpredictable, open ended search into the unknown. It's possible that the most important discoveries that we will eventually make will be the ones we aren't even looking for now.

“It's a divergent, unpredictable, open ended search into the unknown. It's possible that the most important discoveries that we will eventually make will be the ones we aren't even looking for now.”
Speaker
Tim Scarfe
Publisher
Machine Learning Street Talk
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