11 ms·

Prediction Games

69 points by todsacerdoti 2y ago

pitt1980 2y ago
How many $1 million prizes were given out?
optimalsolver 2y ago
>This is a bitter lesson about the interplay between techlash activism and big tech power structures. Twenty years of privacy complaints have only made tech companies more powerful. So we should have done what, exactly, Ben?
derbOac 2y ago
I have a hard time keeping up with the literature on this and it's not exactly my area of research, but the "overfitting is ok" always seemed off and handwavy to me. It violates some pretty basic information-theoretic literature, for one thing. I guess it seems like parameters need to be "counted" differently or there's something misunderstood about what a parameter is, or whether and how it's being adjusted for somewhere. Some of the gradient descent literature I've read, makes it seem like there are sometimes adjustments for parameters as part of the optimization process, so talking about "overfitting doesn't mean anything" is misleading. It just seems like something where there's a lot of imprecision in terms that is critically important, no definitive explanations for anything, and so forth. The results are the results, but then again we have hallucinations and weird adversarial probe glitches suggestive of overfitting (see also e.g., http://proceedings.mlr.press/v119/rice20a http://proceedings.mlr.press/v119/rice20a). I might even suggest the definition of overfitting in a DL context has been poorly operationalized. Sure you can have a training and a test set, but if the test set isn't sufficiently differentiated from the training set, are you going to identify overfitting? I can take training and test sets with a traditional statistical model and if I define the test set a certain way, minimize overfitting results. I guess I just feel like a lot of overfitting discussions tend to feel kind of handwavy or misleading and I wish they were different. The number of parameters has never really been the correct metric when talking about overfitting, it just happens to align nicely with the correct metric in conventional models.
- sdwr 2y ago
  How does "overfitting is ok" violate information theory? How are hallucinations suggestive of overfitting? Overfitting is a tactical term, not a strategic one, and is heavily coupled to the specific implementation.
  - AlotOfReading 2y ago
    I suspect they're trying to relate the pigeonhole principle to overparameterization, but those pieces don't really fit together into a coherent argument for me.
  - genewitch 2y ago
    What is the difference between a strategy and a tactic?
    - sdwr 2y ago
      Strategy is the big-picture plan, tactics are "in the moment" actions. Scamming seniors over the phone is a strategy, pretending to be their grandson is a tactic.
- freeone3000 2y ago
  The definition of overfitting is handwavy. It’s a failure to generalize outside of the observed data. The current batch of LLMs is trained on essentially all of the internet; what would something outside of the observed data even look like? What does it mean there? On the contrary, if a printing press controller “overfits” to the printing press it’s installed on, that is actually pretty desirable! So what are you actually trying to prevent when you want to prevent “overfitting”, and why?
  - esafak 2y ago
    All of the Internet does not include everything you can extrapolate from it. When I ask it to help with my code or writing, I am not asking it to reproduce anything.
kombine 2y ago
I recently read another great post by the same author about the connection between optimisation with constraints and backpropagation algorithm https://archives.argmin.net/2016/05/18/mates-of-costate/ https://archives.argmin.net/2016/05/18/mates-of-costate/ Apparently based on older LeCun's paper.
jfkrrorj 2y ago
> Netflix launched an open competition ... in-house recommendation system by 10%. It worked great for them. Current masterpiece from Netflix has 13 Oscar nominations! Every AI company should learn and apply this lesson!
- hobs 2y ago
  They pretty quickly publicly abandoned that algorithm, so they may have recreated it (since the core stuff is pretty reproducible as the blog states) but yeah, that competition being brought up without bringing up that they abandoned it is interesting.
esafak 2y ago
Where's the beef? This is old hat and you could have read it at https://en.wikipedia.org/wiki/Netflix_Prize https://en.wikipedia.org/wiki/Netflix_Prize

69 points by todsacerdoti 2y ago

pitt1980 2y ago

How many $1 million prizes were given out?

>This is a bitter lesson about the interplay between techlash activism and big tech power structures. Twenty years of privacy complaints have only made tech companies more powerful. So we should have done what, exactly, Ben?

derbOac 2y ago

I have a hard time keeping up with the literature on this and it's not exactly my area of research, but the "overfitting is ok" always seemed off and handwavy to me. It violates some pretty basic information-theoretic literature, for one thing. I guess it seems like parameters need to be "counted" differently or there's something misunderstood about what a parameter is, or whether and how it's being adjusted for somewhere. Some of the gradient descent literature I've read, makes it seem like there are sometimes adjustments for parameters as part of the optimization process, so talking about "overfitting doesn't mean anything" is misleading. It just seems like something where there's a lot of imprecision in terms that is critically important, no definitive explanations for anything, and so forth. The results are the results, but then again we have hallucinations and weird adversarial probe glitches suggestive of overfitting (see also e.g., http://proceedings.mlr.press/v119/rice20a http://proceedings.mlr.press/v119/rice20a). I might even suggest the definition of overfitting in a DL context has been poorly operationalized. Sure you can have a training and a test set, but if the test set isn't sufficiently differentiated from the training set, are you going to identify overfitting? I can take training and test sets with a traditional statistical model and if I define the test set a certain way, minimize overfitting results. I guess I just feel like a lot of overfitting discussions tend to feel kind of handwavy or misleading and I wish they were different. The number of parameters has never really been the correct metric when talking about overfitting, it just happens to align nicely with the correct metric in conventional models.

sdwr 2y ago
How does "overfitting is ok" violate information theory? How are hallucinations suggestive of overfitting? Overfitting is a tactical term, not a strategic one, and is heavily coupled to the specific implementation.
- AlotOfReading 2y ago
  I suspect they're trying to relate the pigeonhole principle to overparameterization, but those pieces don't really fit together into a coherent argument for me.
- genewitch 2y ago
  What is the difference between a strategy and a tactic?
  - sdwr 2y ago
    Strategy is the big-picture plan, tactics are "in the moment" actions. Scamming seniors over the phone is a strategy, pretending to be their grandson is a tactic.
freeone3000 2y ago
The definition of overfitting is handwavy. It’s a failure to generalize outside of the observed data. The current batch of LLMs is trained on essentially all of the internet; what would something outside of the observed data even look like? What does it mean there? On the contrary, if a printing press controller “overfits” to the printing press it’s installed on, that is actually pretty desirable! So what are you actually trying to prevent when you want to prevent “overfitting”, and why?
- esafak 2y ago
  All of the Internet does not include everything you can extrapolate from it. When I ask it to help with my code or writing, I am not asking it to reproduce anything.

kombine 2y ago

I recently read another great post by the same author about the connection between optimisation with constraints and backpropagation algorithm https://archives.argmin.net/2016/05/18/mates-of-costate/ https://archives.argmin.net/2016/05/18/mates-of-costate/ Apparently based on older LeCun's paper.

jfkrrorj 2y ago

> Netflix launched an open competition ... in-house recommendation system by 10%. It worked great for them. Current masterpiece from Netflix has 13 Oscar nominations! Every AI company should learn and apply this lesson!

hobs 2y ago
They pretty quickly publicly abandoned that algorithm, so they may have recreated it (since the core stuff is pretty reproducible as the blog states) but yeah, that competition being brought up without bringing up that they abandoned it is interesting.

esafak 2y ago

Where's the beef? This is old hat and you could have read it at https://en.wikipedia.org/wiki/Netflix_Prize https://en.wikipedia.org/wiki/Netflix_Prize