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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.

Quick reactions:

1. Re: how over-emphasis on "how radical is my ask" vs "what my target audience might find helpful" and generally the importance of making your case well regardless of how radical it is, that makes sense. Though notably the more radical your proposal is (or more unfamiliar your threat models are), the higher the bar for explaining it well, so these do seem related.
2. Re: more effective actors looking for small wins, I agree that it's not clear, but yeah, seems like we are likely to get into some reference class tennis here. "A lot of successful organizations that take hard-line positions and (presumably) get a lot of their power/influence from the ideological purity that they possess & communicate"? Maybe, but I think of like, the agriculture lobby, who just sort of quietly make friends with everybody and keep getting 11-figure subsidies every year, in a way that (I think) resulted more from gradual ratcheting than making a huge ask. "Pretty much no group– whether radical or centrist– has had tangible wins" seems wrong in light of the EU AI Act (where I think both a "radical" FLI and a bunch of non-radical orgs were probably important) and the US executive order (I'm not sure which strategy is best credited there, but I think most people would have counted the policies contained within it as "minor asks" relative to licensing, pausing, etc). But yeah I agree that there are groups along the whole spectrum that probably deserve credit.
3. Re: poisoning the well, again, radical-ness and being dumb/uninformed are of course separable but the bar rises the more radical you get, in part because more radical policy asks strongly correlate with more complicated procedural asks; tweaking ECRA is both non-radical and procedurally simple, creating a new agency to license training runs is both outside the DC Overton Window and very procedurally complicated.
4. Re: incentives, I agree that this is a good thing to track, but like, "people who oppose X are incentivized to downplay the reasons to do X" is just a fully general counterargument. Unless you're talking about financial conflicts of interest, but there are also financial incentives for orgs pursuing a "radical" strategy to downplay boring real-world constraints, as well as social incentives (e.g. on LessWrong IMO) to downplay boring these constraints and cognitive biases [EA · GW] against thinking your preferred strategy has big downsides.
5. I agree that the CAIS statement, Hinton leaving Google, and Bengio and Hogarth's writing have been great. I think that these are all in a highly distinct category from proposing specific actors take specific radical actions (unless I'm misremembering the Hogarth piece). Yudkowsky's TIME article, on the other hand, definitely counts as an Overton Window move, and I'm surprised that you think this has had net positive effects. I regularly hear "bombing datacenters" as an example of a clearly extreme policy idea, sometimes in a context that sounds like it maybe made the less-radical idea seem more reasonable, but sometimes as evidence that the "doomers" want to do crazy things and we shouldn't listen to them, and often as evidence that they are at least socially clumsy, don't understand how politics works, etc, which is related to the things you list as the stuff that actually poisons the well. (I'm confused about the sign of the FLI letter as we've discussed.)
6. I'm not sure optimism vs pessimism is a crux, except in very short, like, 3-year timelines. It's true that optimists are more likely to value small wins, so I guess narrowly I agree that a ratchet strategy looks strictly better for optimists, but if you think big radical changes are needed, the question remains of whether you're more likely to get there via asking for the radical change now or looking for smaller wins to build on over time. If there simply isn't time to build on these wins, then yes, better to take a 2% shot at the policy that you actually think will work; but even in 5-year timelines I think you're better positioned to get what you ultimately want by 2029 if you get a little bit of what you want in 2024 and 2026 (ideally while other groups also make clear cases for the threat models and develop the policy asks, etc.). Another piece this overlooks is the information and infrastructure built by the minor policy changes. A big part of the argument for the reporting requirements in the EO was that there is now going to be an office in the US government that is in the business of collecting critical information about frontier AI models and figuring out how to synthesize it to the rest of government, that has the legal authority to do this, and both the office and the legal authority can now be expanded rather than created, and there will now be lots of individuals who are experienced in dealing with this information in the government context, and it will seem natural that the government should know this information. I think if we had only been developing and advocating for ideal policy, this would not have happened (though I imagine that this is not in fact what you're suggesting the community do!).

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"Under development" and "currently training" I interpret as having significantly different meanings.

In 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.

For 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.

adversarial 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.

A tiny case of this I wrote about long ago: https://markxu.com/stop-asking-people-to-maximize

Something 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)