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Why do you think that LLMs will hit a wall in the future?

Again using the replication crisis as an example, you may have noticed the very wide (like, 1 sd or more) average IQ gap between students in most fields which turned out to have terrible replication rates and most fields which turned out to have fine replication rates.

This is rather weak evidence for your claim ("memeticity in a scientific field is mostly determined, not by the most competent researchers in the field, but instead by roughly-median researchers"), unless you additionally posit that fields with terrible replication rates have a higher standard deviation than fields without them (why?). 

I more-or-less agree with Eliezer's comment (to the extent that I have the data necessary to evaluate his words, which is greater than most, but still, I didn't know him in 1996). I have a small beef with his bolded "MIRI is always in every instance" claim, because a universal like that is quite a strong claim, and I would be very unsurprised to find a single counterexample somewhere (particularly if we include every MIRI employee and everything they've ever said while employed at MIRI).

What I am trying to say is something looser and more gestalt. I do think what I am saying contains some disagreement with some spirit-of-MIRI, and possibly some specific others at MIRI, such that I could say I've updated on the modern progress of AI in a different way than they have.

For example, in my update, the modern progress of LLMs points towards the Paul side of some Eliezer-Paul debates. (I would have to think harder about how to spell out exactly which Eliezer-Paul debates.)

One thing I can say is that I myself often argued using "naive misinterpretation"-like cases such as the paperclip example. However, I was also very aware of the Eliezer-meme "the AI will understand what the humans mean, it just won't care". I would have predicted difficulty in building a system which correctly interprets and correctly cares about human requests to the extent that GPT4 does.

This does not mean that AI safety is easy, or that it is solved; only that it is easier than I anticipated at this particular level of capability.

Getting more specific to what I wrote in the post:

My claim is that modern LLMs are "doing roughly what they seem like they are doing" and "internalize human intuitive concepts". This does include some kind of claim that these systems are more-or-less ethical (they appear to be trying to be helpful and friendly, therefore they "roughly are"). 

The reason I don't think this contradicts with Eliezer's bolded claim ("Getting a shape into the AI's preferences is different from getting it into the AI's predictive model") is that I read Eliezer as talking about strongly superhuman AI with this claim. It is not too difficult to get something into the values of some basic reinforcement learning agent, to the extent that something like that has values worth speaking of. It gets increasingly difficult as the agent gets cleverer. At the level of intelligence of, say, GPT4, there is not a clear difference between getting the LLM to really care about something vs merely getting those values into its predictive model. It may be deceptive or honest; or, it could even be meaningless to classify it as deceptive or honest. This is less true of o1, since we can see it actively scheming to deceive [LW · GW].

A recent paper has concluded that this solution (simple probes) doesn't generalize to unintended deception. Do you think this is concerning?

I fully expect LLMs to hit a wall (if not now then in the future), but for any specific claims about timing, it's worth remembering that people frequently claim it's happening soon/has already happened, and will be wrong every time but one. Some past examples:
Facebook's then-head of AI, December 2019: https://www.wired.com/story/facebooks-ai-says-field-hit-wall/
Gary Marcus, March 2022: https://nautil.us/deep-learning-is-hitting-a-wall-238440/
Donald Hobson, August 2022: https://www.lesswrong.com/posts/gqqhYijxcKAtuAFjL/a-data-limited-future [LW · GW]
Epoch AI, November 2022 (estimating high-quality language data exhausted by 2024; in 2024 they updated their projection to 2028): https://epoch.ai/blog/will-we-run-out-of-ml-data-evidence-from-projecting-dataset
Will Eden, February 2023 (thread): https://x.com/WilliamAEden/status/1630690003830599680
Sam Altman, April 2023: https://www.wired.com/story/openai-ceo-sam-altman-the-age-of-giant-ai-models-is-already-over/
Justis Mills, June 2024: https://www.lesswrong.com/posts/axjb7tN9X2Mx4HzPz/the-data-wall-is-important [LW · GW] (he cites Leopold Aschenbrenner for more detail, but Aschenbrenner himself is optimistic so I didn't link directly)

I don't get why you think this is true? EG, it seems like almost no insights about how to train faithful CoT would transfer to systems speaking pure neuralese. It seems to me that what little safety/alignment we have for LLMs is largely a byproduct of how language-focused they are (which gives us a weak sort of interpretability, a very useful safety resource which we are at risk of losing soon).

I think the crux is I think that the important parts of of LLMs re safety isn't their safety properties specifically, but rather the evidence they give to what alignment-relevant properties future AIs have (and note that I'm also using evidence from non-LLM sources like MCTS algorithm that was used for AlphaGo), and I also don't believe interpretability is why LLMs are mostly safe at all, but rather I think they're safe due to a combo of incapacity, not having extreme instrumental convergence, and the ability to steer them with data.\

Language is a simple example, but one that is generalizable pretty far.

It sounds like you think safety lessons from the human-imitation regime generalize beyond the human-imitation regime

Note that the primary points would apply to basically a whole lot of AI designs like MCTS for AlphaGo or a lot of other future architecture designs which don't imitate humans, barring ones which prevent you from steering it at all with data, or have very sparse feedback, which translates into weakly constraining instrumental convergence.

but we're moving away from the regime where such dense feedback is available, so I don't see what lessons transfer.

I think this is a crux, in that I don't buy o1 as progressing to a regime where we lose so much dense feedback that it's alignment relevant, because I think sparse-feedback RL will almost certainly be super-uncompetitive with every other AI architecture until well after AI automates all alignment research.

Also, AIs will still have instrumental convergence, it's just that their goals will be more local and more focused around the training task, so unless the training task rewards global power-seeking significantly, you won't get it.

GPT-4 is rumored to be a 2e25 FLOPs model. With 20K H100s that were around as clusters for more than a year, 4 months at 40% utilization gives 8e25 BF16 FLOPs. Llama 3 405B is 4e25 FLOPs. The 100K H100s clusters that are only starting to come online in the last few months give 4e26 FLOPs in 4 months, and 1 gigawatt 500K B200s training systems that are currently being built will give 4e27 FLOPs in 4 months.

So lack of scaling-related improvement in deployed models since GPT-4 is likely the result of only seeing the 2e25-8e25 FLOPs range of scale so far. The rumors about the new models being underwhelming are less concrete, and they are about the very first experiments in the 2e26-4e26 FLOPs range. Only by early 2025 will there be multiple 2e26+ FLOPs models from different developers to play with, the first results of the experiment in scaling considerably past GPT-4.

And in 2026, once the 300K-500K B200s clusters train some models, we'll be observing the outcomes of scaling to 2e27-6e27 FLOPs. Only by late 2026 will there be a significant chance of reaching a scaling plateau that lasts for years, since scaling further would need $100 billion training systems that won't get built without sufficient success, with AI accelerators improving much slower than the current rate of funding-fueled scaling.

Gave an upvote because you deserve it.

To keep it short as brevity is the soul of wit: check the macro effects in top500 supercomputer speeds. You will see Moore's Law update: it is logarithmic, not linear or exponential. Classical computing of course not speaking on bio or optical or quantum computing, etc.

Some people I know are much more pessimistic about the polls this cycle, due to herding. For example, nonresponse bias might just be massive for Trump voters (across demographic groups), so pollsters end up having to make a series of unprincipled choices with their thumbs on the scales. 

I don't get why you think this is true? EG, it seems like almost no insights about how to train faithful CoT would transfer to systems speaking pure neuralese. It seems to me that what little safety/alignment we have for LLMs is largely a byproduct of how language-focused they are (which gives us a weak sort of interpretability, a very useful safety resource which we are at risk of losing soon).

To put it a different way: human imitation is a relatively safe outer-optimization target, so we should be concerned to the extent that the industry is moving away from it. It sounds like you think safety lessons from the human-imitation regime generalize beyond the human-imitation regime. Maybe I agree that we can derive some abstract lessons like "RL with really dense feedback can avoid instrumental convergence", but we're moving away from the regime where such dense feedback is available, so I don't see what lessons transfer.