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I don't believe that's obvious, and to the extent that it's true, I think it's largely irrelevant (and part of the general prejudice against scaling & Bitter Lesson thinking, where everyone is desperate to find an excuse for small specialist models with complicated structures & fancy inductive biases because that feels right).
Man, that Li et al paper has pretty wild implications if it generalizes. I'm not sure how to square those results with the Chinchilla paper though (I'm assuming it wasn't something dumb like "wall-clock time was better with larger models because training was constrained by memory bandwidth, not compute")
In any case, my point was more "I expect dumb throw-even-more-compute-at-it approaches like MoE, which can improve their performance quite a bit at the cost of requiring ever more storage space and ever-increasing inference costs, to outperform clever attempts to squeeze more performance out of single giant models". If models just keep getting bigger while staying monolithic, I'd count that as pretty definitive evidence that my expectations were wrong.
tlevin on tlevin's ShortformQuick reactions:
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mrcheeze on Why I'm doing PauseAI"Under development" and "currently training" I interpret as having significantly different meanings.
cstinesublime on dkornai's ShortformIn biological organisms, physical pain [say, in response to limb being removed] is an evolutionary consequence of the fact that organisms with the capacity to feel physical pain avoided situations where their long-term goals [e.g. locomotion to a favourable position with the limb] which required the subsystem generating pain were harmed.
How many organisms other than humans have "long term goals"? Doesn't that require a complex capacity for mental representation of possible future states?
Am I wrong in assuming that the capacity to experience "pain" is independent of an explicit awareness of what possibilities have been shifted as a result of the new sensory data? (i.e. having a limb cleaved from the rest of the body, stubbing your toe in the dark). The organism may not even be aware of those possibilities, only 'aware' of pain.
Note: I'm probably just having a fear of this sounding all too teleological and personifying evolution
Pithy sayings are lossily compressed.
gwern on We are headed into an extreme compute overhangFor example, 70B model trained on next-token prediction only on the entire 20TB GenBank dataset will have better performance at next-nucleotide prediction than a 70B model that has been trained both on the 20TB GenBank dataset and on all 14TB of code on Github.
I don't believe that's obvious, and to the extent that it's true, I think it's largely irrelevant (and part of the general prejudice against scaling & Bitter Lesson thinking, where everyone is desperate to find an excuse for small specialist models with complicated structures & fancy inductive biases because that feels right).
Once you have a bunch of specialized models "the weights are identical" and "a fine tune can be applied to all members" no longer holds.
Nor do I see how this is relevant to your original claim. If you have lots of task-specialist models, how does this refute the claim that those will be able to coordinate? Of course they will. They will just share weight updates in exactly the way I just outlined, which works so well in practice. You may not be able to share parameter-updates across your protein-only and your Python-only LLMs, but they will be able to share updates within that model family and the original claim ("AGIs derived from the same model are likely to collaborate more effectively than humans because their weights are identical. Any fine-tune can be applied to all members, and text produced by one can be understood by all members.") remains true, no matter how you swap out your definition of 'model'.
DL models are fantastically good at collaborating and updating each other, in many ways completely impossible for humans, whether you are talking about AGI models or narrow specialist models.
gallabytes on Ironing Out the Squigglesadversarial examples definitely still exist but they'll look less weird to you because of the shape bias.
anyway this is a random visual model, raw perception without any kind of reflective error correction loop, I'm not sure what you expect it to do differently, or what conclusion you're trying to draw from how it does behave? the inductive bias doesn't precisely match human vision, so it has different mistakes, but as you scale both architectures they become more similar. that's exactly what you'd expect for any approximately Bayesian setup.
the shape bias increasing with scale was definitely conjectured long before it was tested. ML scaling is very recent though,and this experiment was quite expensive. Remember when GPT-2 came out and everyone thought that was a big model? This is an image classifier which is over 10x larger than that. They needed a giant image classification dataset which I don't think even existed 5 years ago.
mark-xu on Failures in KindnessA tiny case of this I wrote about long ago: https://markxu.com/stop-asking-people-to-maximize
yanni-kyriacos on yanni's ShortformSomething someone technical and interested in forecasting should look into: can LLMs reliably convert peoples claims into a % of confidence through sentiment analysis? This would be useful for Forecasters I believe (and rationality in general)