TurnTrout's shortform feed

TurnTrout

This is a special post for quick takes by TurnTrout. Only they can create top-level comments. Comments here also appear on the Quick Takes page and All Posts page.

The meme of "current alignment work isn't real work" seems to often be supported by a (AFAICT baseless) assumption that LLMs have, or will have, homunculi with "true goals" which aren't actually modified by present-day RLHF/feedback techniques. Thus, labs aren't tackling "the real alignment problem", because they're "just optimizing the shallow behaviors of models." Pressed for justification of this confident "goal" claim, proponents might link to some handwavy speculation about simplicity bias (which is in fact quite hard to reason about, in the NN prior), or they might start talking about evolution (which is pretty unrelated to technical alignment, IMO).

Are there any homunculi today? I'd say "no", as far as our limited knowledge tells us! But, as with biorisk, one can always handwave at future models. It doesn't matter that present models don't exhibit signs of homunculi which are immune to gradient updates, because, of course, future models will.

Quite a strong conclusion being drawn from quite little evidence.

5Thane Ruthenis3y

Yeah, for sure. A training procedure that results in an idealized predictor isn't going to result in an agenty thing, because it doesn't move the system's design towards it on a step-by-step basis; and a training procedure that's going to result in an agenty thing is going to involve some unknown elements that specifically allow the system the freedom to productively roam. I think we pretty much agree on the mechanistic details of all of that! [...] — yep, I was about to mention that. @TurnTrout's own activation-engineering agenda seems highly relevant here. [...] But I still disagree with that. I think what we're discussing requires approaching the problem with a mindset entirely foreign to the mainstream one. Consider how many words it took us to get to this point in the conversation, despite the fact that, as it turns out, we basically agree on everything. The inferential distance between the standard frameworks in which AI researchers think, and here, is pretty vast. Moreover, it's in an active process of growing larger. For example, the very idea of viewing ML models as "just stochastic parrots" is being furiously pushed against in favour of a more agenty view. In comparison, the approach we're discussing wants to move in the opposite direction, to de-personify ML models to the extent that even the animalistic connotation of "a parrot" is removed. The system we're discussing won't even be an "AI" in the sense usually thought. It would be an incredibly advanced forecasting tool. Even the closest analogue, the "simulators" framework, still carries some air of agentiness. And the research directions that get us from here to an idealized-predictor system look very different from the directions that go from here to an agenty AGI. They focus much more on building interfaces for interacting with the extant systems, such as the activation-engineering agenda. They don't put much emphasis on things like: * Experimenting with better ways to train foundational mo

I think what we're discussing requires approaching the problem with a mindset entirely foreign to the mainstream one. Consider how many words it took us to get to this point in the conversation, despite the fact that, as it turns out, we basically agree on everything. The inferential distance between the standard frameworks in which AI researchers think, and here, is pretty vast.

True!

I expect that if the mainstream AI researchers do make strides in the direction you're envisioning, they'll only do it by coincidence. Then probably they won't even realize wh

... (read more)

1RogerDearnaley3y

I think the shoggoth model is useful here (Or see https://www.alignmentforum.org/posts/vJFdjigzmcXMhNTsx/simulators). An LLM learning to do next-token prediction well has a major problem that it has to master: who is this human whose next token they're trying to simulate/predict, and how do they act? Are they, for example, an academic? A homemaker? A 4Chan troll? A loose collection of wikipedia contributors? These differences make a big difference to what token they're likely to emit next. So the LLM is strongly incentivized to learn to detect and then model all of these possibilities, what one might call personas, or masks, or simulacra. So you end up with a shapeshifter, adept at figuring out from textual cues what mask to put on and at then wearing it. Something one might describe as like an improv actor, or more colorfully, a shoggoth. So then current alignment work is useful to the extent that it can cause the shoggoth to almost always put one of the 'right' masks on, and almost never put on one of the 'wrong' masks, regardless of cues, even when adversarially prompted. Experimentally, this seems quite doable by fine-tuning or RLHF, and/or by sufficiently careful filtering of your training corpus (e.g. not including 4chan in it). A published result shows that you can't get from 'almost always' to 'always' or 'almost never' to 'never': for any behavior that the network is capable of with any probability >0 , there exists prompts that will raise the likelihood of that outcome arbitrarily high. The best you can do is increase the minimum length of that prompt (and presumably the difficulty of finding it). Now, it would be really nice to know how to align a model so that the probability of it doing next-token-prediction in the persona of, say, a 4chan troll was provably zero, not just rather small. Ideally, without also eliminating from the model the factual knowledge of what 4chan is or, at least in outline, how its inhabitants act. This seems hard to do by fin

TurnTrout's shortform feed

28

Ω 10

TurnTrout's shortform feed

28

Ω 10