4 ms·

NNs (indeed, all statistical fitting algs) have no relevant properties here: properties derive just from the structure of the dataset. Here (https://arxiv.org/a

NNs (indeed, all statistical fitting algs) have no relevant properties here: properties derive just from the structure of the dataset. Here (https://arxiv.org/abs/2201.02177 https://arxiv.org/abs/2201.02177) NNs are trained on a 'complete world' problem, ie., modulo arithmetic where the whole outcome space is trivial in size and abstract with complete information. Why should NNs eventually find a representation of this tiny, abstract, full-representable outcome space after an arbitrary amount of training time? Well it will do so, eventually, if this outcome space can be fully represented by sequences of conditional probabilities. There is nothing more to this 'discovery' than some trivial abstract mathematical spaces can be represented as conditional probability structures. Is this even a discovery? One has to imagine this deception is perpetrated because the peddlers of such systems want to impart the problem structure to properties of NNs in general, and thereby say, "well, if you train NNs on face shapes, phrenology becomes possible!". ie., as a way of whitewashing their broken half-baked generative AI systems where the problem domain isnt arithmetic mod 97

sillysaurusx 3y ago
Firstly, thanks for linking the paper. I think you’re being unfair. I say that as someone who has done his share of charlatan hunting. They explicitly list their contributions in the paper. They’re not saying they did something they didn’t. It’s not like that bogus "rat brain flies plane" paper that was doing something simple under the hood and then dressing it up as a world changing discovery in order to gain funding. They are doing something simple and studying it carefully. This is grade A science as far as I’m concerned, every bit as good as studying the motion of the stars and trying to find patterns. It did not occur to me that NNs might be able to extrapolate a tiny training dataset into complete solutions. ML scientists are taught from an early age that you need to have a large dataset to make any progress. I don’t know whether this paper counts as a "discovery", but it was certainly a fun read, which was nice enough for me. In some sense this paper is a proof of the idea that NNs can extrapolate, not merely memorize. This is in contrast to recent work where researchers have been claiming otherwise. The weight decay study was also a nice touch. It’s no discovery to say that weight decay helps, but it’s a reminder to use it. I haven’t. Adam has always been reliable for me, and now I wonder if it was a mistake to shy away from weight decay. (We wanted to keep our training pipeline as simple as possible, in case any part of it might be causing problems. And lots of unexpected parts did.) Now, I haven’t read the article submitted here, only the paper you linked. Maybe they’re claiming something more than the paper. But if so, then that is a (very real) problem with scientific journalism, and not necessarily the scientists themselves. It depends how much the scientists are leading or misleading the reporters. It’s important to separate the criticism of the work from the reporting around the work. I’d also be curious if you have any citations for your claim that if an outcome space can be represented as a sequence of conditional probabilities, then NNs are guaranteed to find a solution after some unknown amount of training time. This is a surprising thing to me.
gus_massa 3y ago
The interesting part is that with training you can go: random -> generalization -> overfiting -> better generalization The last step was unknown IIUC. The relevant trick is "regularization": > This is a process that reduces the model’s functional capacity — the complexity of the function that the model can learn.

sillysaurusx 3y ago

Firstly, thanks for linking the paper. I think you’re being unfair. I say that as someone who has done his share of charlatan hunting. They explicitly list their contributions in the paper. They’re not saying they did something they didn’t. It’s not like that bogus "rat brain flies plane" paper that was doing something simple under the hood and then dressing it up as a world changing discovery in order to gain funding. They are doing something simple and studying it carefully. This is grade A science as far as I’m concerned, every bit as good as studying the motion of the stars and trying to find patterns. It did not occur to me that NNs might be able to extrapolate a tiny training dataset into complete solutions. ML scientists are taught from an early age that you need to have a large dataset to make any progress. I don’t know whether this paper counts as a "discovery", but it was certainly a fun read, which was nice enough for me. In some sense this paper is a proof of the idea that NNs can extrapolate, not merely memorize. This is in contrast to recent work where researchers have been claiming otherwise. The weight decay study was also a nice touch. It’s no discovery to say that weight decay helps, but it’s a reminder to use it. I haven’t. Adam has always been reliable for me, and now I wonder if it was a mistake to shy away from weight decay. (We wanted to keep our training pipeline as simple as possible, in case any part of it might be causing problems. And lots of unexpected parts did.) Now, I haven’t read the article submitted here, only the paper you linked. Maybe they’re claiming something more than the paper. But if so, then that is a (very real) problem with scientific journalism, and not necessarily the scientists themselves. It depends how much the scientists are leading or misleading the reporters. It’s important to separate the criticism of the work from the reporting around the work. I’d also be curious if you have any citations for your claim that if an outcome space can be represented as a sequence of conditional probabilities, then NNs are guaranteed to find a solution after some unknown amount of training time. This is a surprising thing to me.

gus_massa 3y ago

The interesting part is that with training you can go: random -> generalization -> overfiting -> better generalization The last step was unknown IIUC. The relevant trick is "regularization": > This is a process that reduces the model’s functional capacity — the complexity of the function that the model can learn.