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Cristopher Moore

Published podcast speaker

Claims
20
Episodes
1
Shows
1
Named items
0

Claim ledger

What Cristopher said.

20 transcript-backed records

01 / preference

Right? And I guess what I like about computer science is you put on 1 hat and you look for efficient algorithms for things, and then if you fail to find a good algorithm, you can switch hats and try to prove that the problem is hard.

“Right? And I guess what I like about computer science is you put on 1 hat and you look for efficient algorithms for things, and then if you fail to find a good algorithm, you can switch hats and try to prove that the problem is hard.”
Publisher
Machine Learning Street Talk

02 / prediction

And so I don't know. This this to me is a really interesting frontier for AI where you take the problem and you invent on the fly what the what kind of variable you should use to address the problem.

“And so I don't know. This this to me is a really interesting frontier for AI where you take the problem and you invent on the fly what the what kind of variable you should use to address the problem.”
Publisher
Machine Learning Street Talk

03 / evaluation

1 of the reasons why we like sudoku is it's very easy to scan a row, scan a column, and scan a little 3 by 3 box so it fits with how we can address that data structure, if you will.

“1 of the reasons why we like sudoku is it's very easy to scan a row, scan a column, and scan a little 3 by 3 box so it fits with how we can address that data structure, if you will.”
Publisher
Machine Learning Street Talk

04 / belief

You know, you roll the tape over to this part. So, yeah, I I feel I guess for me, mathematically, when I think about computational universality, I think about things like our favorite programming languages and their relationship with the, the theory of partial recursive functions.

“You know, you roll the tape over to this part. So, yeah, I I feel I guess for me, mathematically, when I think about computational universality, I think about things like our favorite programming languages and their relationship with the, the theory of partial recursive functions.”
Publisher
Machine Learning Street Talk

06 / belief

I I agree with you, and yet it seems to work surprisingly well at a lot of things, and we keep moving the goalposts, and we should move the goalposts.

“I I agree with you, and yet it seems to work surprisingly well at a lot of things, and we keep moving the goalposts, and we should move the goalposts.”
Publisher
Machine Learning Street Talk

07 / belief

I mean, I think, you know, we're at this workshop this week where there was a whole discussion yesterday about what do we actually need language for and, you know, and what do we actually need symbolic thinking for because that's where recursion seems to start.

“I mean, I think, you know, we're at this workshop this week where there was a whole discussion yesterday about what do we actually need language for and, you know, and what do we actually need symbolic thinking for because that's where recursion seems to start.”
Publisher
Machine Learning Street Talk

09 / belief

You better be doing it on purpose, but you shouldn't just be doing it because you've heard it before and because it has a high probability in the distribution. So I think his goal as a prose writer was to constantly produce new juxtapositions and in order to, well, to intrigue the reader and to access a space in the, you know, to access a region of the creative space, the writing space, which hadn't been accessed before.

“You better be doing it on purpose, but you shouldn't just be doing it because you've heard it before and because it has a high probability in the distribution. So I think his goal as a prose writer was to constantly produce new juxtapositions and in order to, well, to intrigue the reader and to access a space in the, you know, to access a region of the creative space, the writing space, which hadn't been accessed before.”
Publisher
Machine Learning Street Talk

10 / uncertainty

Therefore, if they were easy, these other problems would be easy too. And a funny thing though is there are a lot of systems where we don't know how to build a computer in them, but they still look really irreducible.

“Therefore, if they were easy, these other problems would be easy too. And a funny thing though is there are a lot of systems where we don't know how to build a computer in them, but they still look really irreducible.”
Publisher
Machine Learning Street Talk

11 / preference

I'm just gonna go solve it anyway. And that's somehow because the real world presents us with examples of these problems where there is so much rich structure to sink your teeth into, whether that's the structure in text, the structure in images, and so on.

“I'm just gonna go solve it anyway. And that's somehow because the real world presents us with examples of these problems where there is so much rich structure to sink your teeth into, whether that's the structure in text, the structure in images, and so on.”
Publisher
Machine Learning Street Talk

13 / recommendation

I think that's a very interesting line of work. So I think that as humans, it would be very good for all of us, especially if we want a democratic society where we're making kind of informed collective decisions about when to use these things, if to use these things, and what settings to use them.

“I think that's a very interesting line of work. So I think that as humans, it would be very good for all of us, especially if we want a democratic society where we're making kind of informed collective decisions about when to use these things, if to use these things, and what settings to use them.”
Publisher
Machine Learning Street Talk

15 / prediction

Then there can also be these interesting middle ranges where you can find the ground truth if you do an exhaustive search. But we actually believe that there is no efficient algorithm that will succeed in that regime because you're wandering around in this high dimensional landscape of possible fits to the data, and the accurate ones are kind of hidden behind what in physics we call an energy barrier.

“Then there can also be these interesting middle ranges where you can find the ground truth if you do an exhaustive search. But we actually believe that there is no efficient algorithm that will succeed in that regime because you're wandering around in this high dimensional landscape of possible fits to the data, and the accurate ones are kind of hidden behind what in physics we call an energy barrier.”
Publisher
Machine Learning Street Talk

16 / evaluation

Because, actually, formal logic is not something that we're built to do. So I guess I I expect I expect these systems to once they can really play with all of these modules, including ones that we don't have, like visualizing things in 7 dimensions, they'll be able to do a lot just as when they start I'm not sure if we should do this, but when we give them access to 3 d printers and fab labs so that they can start building things and seeing whether they work, well, maybe we should solve the alignment problem first.

“Because, actually, formal logic is not something that we're built to do. So I guess I I expect I expect these systems to once they can really play with all of these modules, including ones that we don't have, like visualizing things in 7 dimensions, they'll be able to do a lot just as when they start I'm not sure if we should do this, but when we give them access to 3 d printers and fab labs so that they can start building things and seeing whether they work, well, maybe we should solve the alignment problem first.”
Publisher
Machine Learning Street Talk

17 / evaluation

Turing machines as an architecture, I think, are rather brittle. And, I mean, I think that the partly analog nature of neural networks and, and LLMs, this ability to, I mean, I know that they can be made discrete and so on, but somehow their ability to work in a continuous way with high dimensional vector spaces and embeddings, I I think that that is important to their trainability, even if it's not ultimately important to their cognitive abilities.

“Turing machines as an architecture, I think, are rather brittle. And, I mean, I think that the partly analog nature of neural networks and, and LLMs, this ability to, I mean, I know that they can be made discrete and so on, but somehow their ability to work in a continuous way with high dimensional vector spaces and embeddings, I I think that that is important to their trainability, even if it's not ultimately important to their cognitive abilities.”
Publisher
Machine Learning Street Talk

18 / prediction

If I want to know how some function behaves, I graph it and look at it. And I think once LLMs are given these various playgrounds and given the ability to fire them up to do literally doodle and look at it in a 2 dimensional way, the way we can with our eyes, as opposed to treating everything as 1 dimensional strings of text, I expect that we'll see much more multimodal abilities.

“If I want to know how some function behaves, I graph it and look at it. And I think once LLMs are given these various playgrounds and given the ability to fire them up to do literally doodle and look at it in a 2 dimensional way, the way we can with our eyes, as opposed to treating everything as 1 dimensional strings of text, I expect that we'll see much more multimodal abilities.”
Publisher
Machine Learning Street Talk

20 / preference

Just as maybe a better music maybe a better music or or book recommendation system would challenge you the way a friend challenges you in that wonderful kind of directed way that friends do.

“Just as maybe a better music maybe a better music or or book recommendation system would challenge you the way a friend challenges you in that wonderful kind of directed way that friends do.”
Publisher
Machine Learning Street Talk
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