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Well the counter argument is that with an equal amount of compute, specialized models will win over generalized ones. The reason is that generalized ones have t

by nuz 3y ago

Well the counter argument is that with an equal amount of compute, specialized models will win over generalized ones. The reason is that generalized ones have to spread out their weights to be able to do a ton of irrelevant things to the niche subject that a finetuned model can accomplish with fewer and more focused weights.

rschiavone 3y ago
I'd argue that yours is not a counter argument, but the same thing said by grandparent rephrased. It's obvious that with the same amount of computing power, specialized > generalized. But in 2-5 years, the future generalized models can beat the current specialized ones.
- goldenkey 3y ago
  The future of this is the same as what happened with PCs. The specificity will initially be thrown away, added back later as an accelerator, and eventually, brought back into the fold as an automatically used accelerator. It all comes full circle as optimizations, hinting, tiering, and heuristics. AI will be used to select the net to automatically load. Nets will be cached, branch predicted etc. The future of AI software and hardware doesn't yet support the scale we need for this type of generalized AI processor (think CPU but call it an AIPU.) And no, GPUs aren't an AIPU, we can't even fit whole some of the largest models on these things without running them in pieces. They don't have a higher level language yet, like C, which would compile down to more specific actions after optimizations are borne (not PTX/LLVM/Cuda/OpenCL.)
- marcosdumay 3y ago
  No, the GP is stating that the short term advantage will always exist, for fundamental reasons. It's just short term because it has to be reacquired often, not because there stops being an advantage. In 5 years, the specialized model would still beat the generalized ones. They would just be different from the ones today.
  - PaulHoule 3y ago
    Couldn't you apply the same prompt engineering tricks to the specialized model that their system beats?
    - marcosdumay 3y ago
      Well, I'm not sure how valid is the entire thing. It's not clear how much cross-polination there is between those datasets and the ChatGPT training set (IMO, if I was training something like it, I'd take any specialized dataset I can find), or how useful answering those questions is to any real task. Also, it's not only the prompt engineering. Training ChatCPT was a decades long process, with billions of dollars invested, and it takes a small cluster to run the thing. How would the other models compare if given similar resources? Besides that, on the context of this thread, the bitter lesson has absolutely no relation to the specialized vs. general purpose model dichotomy. It's about the underlining algorithms, that for this article are all very similar. (And it's also not a known truth, and looking less and less as an absolute truth as deep learning advances, so take it with a huge grain of salt.)
fauigerzigerk 3y ago
That's probably true in terms of benchmark problems, but at the same time, losing knowledge of seemingly irrelevant things could make the specialised model less creative when it comes to looking for unexpected causes and links. It would also be less robust in the face of exceptional situations that should make it doubt the reliability and relevance of its own training. For instance, an autonomous driving system that doesn't know the first thing about the zombie apocalypse could put its passengers' lives at risk by refusing to run over "pedestrians". A specialised diagnostic system might not notice signs of domestic violence that a GP would see. Being able to connect the dots beyond the confines of some specialised field is extremely useful in many situations.
- notahacker 3y ago
  > For instance, an autonomous driving system that doesn't know the first thing about the zombie apocalypse could put its passengers' lives at risk by refusing to run over "pedestrians". I think that argument works better the other way. I'm outspoken in arguing many of the edge cases in driving are general intelligence problems we're not about to solve by optimising software over a few billion more miles in a simulator, but I don't want that intelligence so generalised that my car starts running down elderly pedestrians because there's been a lot of zombie literature on the Internet lately. I'm pretty confident there's more risk of people dying from that than a zombie apocalypse.
- skinner_ 3y ago
  > For instance, an autonomous driving system that doesn't know the first thing about the zombie apocalypse could put its passengers' lives at risk by refusing to run over "pedestrians". I'd prefer my self-driving car not to learn about the zombie apocalypse, lest it starts running over pedestrians.
ryanjshaw 3y ago
Wouldn't it become more and more difficult to improve the specialist model? And wouldn't the ultimate generalized models (AGI) eventually be able to improve themselves automatically? Or am I talking about something different here?

by nuz 3y ago

rschiavone 3y ago

I'd argue that yours is not a counter argument, but the same thing said by grandparent rephrased. It's obvious that with the same amount of computing power, specialized > generalized. But in 2-5 years, the future generalized models can beat the current specialized ones.

goldenkey 3y ago
The future of this is the same as what happened with PCs. The specificity will initially be thrown away, added back later as an accelerator, and eventually, brought back into the fold as an automatically used accelerator. It all comes full circle as optimizations, hinting, tiering, and heuristics. AI will be used to select the net to automatically load. Nets will be cached, branch predicted etc. The future of AI software and hardware doesn't yet support the scale we need for this type of generalized AI processor (think CPU but call it an AIPU.) And no, GPUs aren't an AIPU, we can't even fit whole some of the largest models on these things without running them in pieces. They don't have a higher level language yet, like C, which would compile down to more specific actions after optimizations are borne (not PTX/LLVM/Cuda/OpenCL.)
marcosdumay 3y ago
No, the GP is stating that the short term advantage will always exist, for fundamental reasons. It's just short term because it has to be reacquired often, not because there stops being an advantage. In 5 years, the specialized model would still beat the generalized ones. They would just be different from the ones today.
- PaulHoule 3y ago
  Couldn't you apply the same prompt engineering tricks to the specialized model that their system beats?
  - marcosdumay 3y ago
    Well, I'm not sure how valid is the entire thing. It's not clear how much cross-polination there is between those datasets and the ChatGPT training set (IMO, if I was training something like it, I'd take any specialized dataset I can find), or how useful answering those questions is to any real task. Also, it's not only the prompt engineering. Training ChatCPT was a decades long process, with billions of dollars invested, and it takes a small cluster to run the thing. How would the other models compare if given similar resources? Besides that, on the context of this thread, the bitter lesson has absolutely no relation to the specialized vs. general purpose model dichotomy. It's about the underlining algorithms, that for this article are all very similar. (And it's also not a known truth, and looking less and less as an absolute truth as deep learning advances, so take it with a huge grain of salt.)

fauigerzigerk 3y ago

That's probably true in terms of benchmark problems, but at the same time, losing knowledge of seemingly irrelevant things could make the specialised model less creative when it comes to looking for unexpected causes and links. It would also be less robust in the face of exceptional situations that should make it doubt the reliability and relevance of its own training. For instance, an autonomous driving system that doesn't know the first thing about the zombie apocalypse could put its passengers' lives at risk by refusing to run over "pedestrians". A specialised diagnostic system might not notice signs of domestic violence that a GP would see. Being able to connect the dots beyond the confines of some specialised field is extremely useful in many situations.

notahacker 3y ago
> For instance, an autonomous driving system that doesn't know the first thing about the zombie apocalypse could put its passengers' lives at risk by refusing to run over "pedestrians". I think that argument works better the other way. I'm outspoken in arguing many of the edge cases in driving are general intelligence problems we're not about to solve by optimising software over a few billion more miles in a simulator, but I don't want that intelligence so generalised that my car starts running down elderly pedestrians because there's been a lot of zombie literature on the Internet lately. I'm pretty confident there's more risk of people dying from that than a zombie apocalypse.
skinner_ 3y ago
> For instance, an autonomous driving system that doesn't know the first thing about the zombie apocalypse could put its passengers' lives at risk by refusing to run over "pedestrians". I'd prefer my self-driving car not to learn about the zombie apocalypse, lest it starts running over pedestrians.

ryanjshaw 3y ago

Wouldn't it become more and more difficult to improve the specialist model? And wouldn't the ultimate generalized models (AGI) eventually be able to improve themselves automatically? Or am I talking about something different here?