True knowledge about later times doesn't let you generally make arbitrary predictions about intermediate times, given valid knowledge of later times. But true knowledge does usually imply that you can make some theory-specific predictions about intermediate times, given later times.

Thus, vis-a-vis your examples: Predictions about the climate in 2100 don't involve predicting tomorrow's weather. But they do almost always involve predictions about the climate in 2040 and 2070, and they'd be really sus if they didn't.

Similarly:

• If an astronomer thought that an asteroid was going to hit the earth, the astronomer generally could predict points it will be observed at in the future before hitting the earth. This is true even if they couldn't, for instance, predict the color of the asteroid.
• People who predicted that C19 would infect millions by T + 5 months also had predictions about how many people would be infected at T + 2. This is true even if they couldn't predict how hard it would be to make a vaccine.
• (Extending analogy to scale rather than time) The ability to predict that nuclear war would kill billions involves a pretty good explanation for how a single nuke would kill millions.

So I think that -- entirely apart from specific claims about whether MIRI does this -- it's pretty reasonable to expect them to be able to make some theory-specific predictions about the before-end-times, although it's unreasonable to expect them to make arbitrary theory-specific predictions.

I mean, sure, but I've been updating in that direction a weirdly large amount.

For a back and forth on whether the "LLMs are shoggoths" is propaganda, try reading this.

In my opinion if you read the dialogue, you'll see the meaning of "LLMs are shoggoths" shift back and forth -- from "it means LLMs are psychopathic" to "it means LLMs think differently from humans." There isn't a fixed meaning.

I don't think trying to disentangle the "meaning" of shoggoths is going to result in anything; it's a metaphor, some of whose understandings are obviously true ("we don't understand all cognition in LLMs"), some of which are dubiously true ("LLM's 'true goals' exist, and are horrific and alien"). But regardless of the truth of these props, you do better examining them one-by-one than in an emotionally-loaded image.

It's sticky because it's vivid, not because it's clear; it's reached for as a metaphor -- like "this government policy is like 1984" -- because it's a ready-to-hand example with an obvious emotional valence, not for any other reason.

If you were to try to zoom into "this policy is like 1984" you'd find nothing; so also here.

As you said, this seems like a pretty bad argument.

Something is going on between the {user instruction} ..... {instruction to the image model}. But we don't even know if it's in the LLM. It could be there's dumb manual "if" parsing statements that act differently depending on periods, etc, etc. It could be that there are really dumb instructions given to the LLM that creates instructions for the language model, as there were for Gemini. So, yeah.

So Alasdair MacIntyre, says that all enquiry into truth and practical rationality takes place within a tradition, sometimes capital-t Tradition, that provides standards for things like "What is a good argument" and "What things can I take for granted" and so on. You never zoom all the way back to simple self-evident truths or raw-sense data --- it's just too far to go. (I don't know if I'd actually recommend MacIntyre to you, he's probably not sufficiently dense / interesting for your projects, he's like a weird blend of Aquinas and Kuhn and Lakatos, but he is interesting at least, if you have a tolerance for.... the kind of thing he is.)

What struck me with a fair number of reviews, at this point, was that they seemed... kinda resigned to a LW Tradition, if it ever existed, no longer really being a single thing? Like we don't have shared standards any more for what is a good argument or what things can be taken for granted (maybe we never did, and I'm golden-age fallacying). There were some reviews saying "idk if this is true, but it did influence people" and others being like "well I think this is kinda dumb, but seems important" and I know I wrote one being like "well these are at least pretty representative arguments of the kind of things people say to each other in these contexts."

Anyhow what I'm saying is that -- if we operate in a MacIntyrean frame -- it makes sense to be like "this is the best work we have" within a Tradition, but humans start to spit out NaNs / operation not defined if you try to ask them "is this the best work we have" across Traditions. I don't know if this is true of ideal reasoners but it does seem to be true of... um, any reasoners we've ever seen, which is more relevant.

So I agree with some of what you're saying along "There is such a thing as a generally useful algorithm" or "Some skills are more deep than others" but I'm dubious about some of the consequences I think that you think follow from them? Or maybe you don't think these consequences follow, idk, and I'm imagining a person? Let me try to clarify.

There's clusters of habits that seem pretty useful for solving novel problems

My expectation is that there are many skills / mental algorithms along these lines, such that you could truthfully say "Wow, people in diverse domains have found X mental algorithm useful for discovering new knowledge." But also I think it's probably true that the actually shared information between different domain-specific instances of "X mental algorithm" is going to be pretty small.

Like, take the skill of "breaking down skills into subskills, figuring out what subskills can be worked on, etc". I think there's probably some kind of of algorithm you can run cross-domain that does this kind of thing. But without domain-specific pruning heuristics, and like a ton of domain-specific details, I expect that this algorithm basically just spits back "Well, too many options" rather than anything useful.

So: I expect non-domain specific work put into sharpening up this algorithm to run into steeply diminishing returns, even if you can amortize the cost of sharpening up the algorithm across many different domains that would be benefitted. If you could write down a program that can help you find relevant subskills in some domain, about 95% of the program is going to be domain-specific rather than not domain specific, and there are something like only ~logarithmic returns to working on the domain-specific problem. (Not being precise, just an intuition)

Put alternately, I expect you could specify some kind of algorithm like this in a very short mental program, but when you're running the program most mental compute goes into finding domain-specific program details.

---

Let me just describe the way the world looks to me. Maybe we actually think the same thing?

-- If you look throughout the history of science, I think that most discoveries look less like "Discoverer had good meta-level principles that let them situate themselves in the right place to solve the issue" and more like "Discoverer happened to be interested in the right chunk of reality that let them figure out an important problem, but it was mostly luck in situating themselves or their skills in this place." I haven't read a ton of history of science, but yeah.

-- Concretely, my bet is that most (many?) scientific discoverers of important things were extremely wrong on other important things, or found their original discovery through something like luck. (And some very important discoveries (Transformers) weren't really identified as such at the time.)

-- Or, concretely, I think scientific progress overall probably hinges less on individual scientists having good meta-level principles, and more on like...whatever social phenomena is necessary to let individuals or groups of scientists run a distributed brute-force search. Extremely approximately.

-- So my belief is that so far we humans just haven't found any such principles like those you're seeking for. Or that a lack of such principles can screw over your group (if you eschew falsifiability to a certain degree you're fucked; if you ignore math you're fucked) but that you can ultimately mostly raise the floor rather than the ceiling through work on them. Like there is a lot of math out there, and different kinds are very useful for different things!

-- I would be super excited to find such meta-level principles, btw. I feel like I'm being relentlessly negative. So to be clear, it would be awesome to find substantive meta-level principles such that non-domain specific work on the meta-level principles could help people situate themselves and pursue work effectively in confusing domains. Like I'm talking about this because I am very much interested in the project. I just right now... don't think the world looks like they exist? It's just in that in the absence of seeing groups that seem to have such principles, nothing that I know about minds in general makes me think that such principles are likely.

Or maybe I'm just confused about what you're doing. Really uncertain about all the above.

This is less of "a plan" and more of "a model", but, something that's really weirded me out about the literature on IQ, transfer learning, etc, is that... it seems like it's just really hard to transfer learn. We've basically failed to increase g, and the "transfer learning demonstrations" I've heard of seemed pretty weaksauce.

But, all my common sense tells me that "general strategy" and "responding to novel information, and updating quickly" are learnable skills that should apply in a lot of domains.

I'm curious why you think this? Or if you have a place where you've explained why you think this at more length? Like my common sense just doesn't agree with this -- although I'll admit my common sense was probably different 5 years ago.

Overall a lot of the stuff here seems predicated on there being a very thick notion of non-domain specific "rationality" or "general strategy" that can be learned, that then after being learned speed you up in widely disparate domains. As in -- the whole effort is to find such a strategy. But there seems to be some (a lot? a little?) evidence that this just isn't that much of a thing, as you say.

I think current ML evidence backs this up. A Transformer is like a brain: when a Transformer is untrained, nearly literally the same architecture could learn to be a language model; to be an image diffusion model; to play Starcraft; etc etc. But once you've trained it, although it can learn very quickly in contexts to which it is adapted, it basically learns pretty poorly outside of these domains.

Similarly, human brains start of very plastic. You can learn to echolocate, or speak a dozen languages, or to ride a unicycle, or to solve IMO problems. And then brains specialize, and learn a lot of mostly domain-specific heuristics, that let them learn very quickly about the things that they already know. But they also learn to kinda suck elsewhere -- like, learning a dozen computer languages is mostly just going to not transfer to learning Chinese.

Like I don't think the distinction here I'm drawing is even well-articulated. And I could spend more time trying to articulate it -- there's probably some generality, maybe at the level of grit -- but the "learn domain-non-specific skills that will then speed up a particular domain" project seems to take a position that's sufficiently extreme that I'm like... ehhhh seems unlikely to succeed? (I'm in the middle of reading The Secret of Our Success fwiw, although it's my pre-existing slant for this position that has inclined me to read it.)

To the best of my knowledge, the majority of research (all the research?) has found that the changes to a LLM's text-continuation abilities from RLHF (or whatever descendant of RLHF is used) are extremely superficial.

So you have one paper, from the abstract:

Our findings reveal that base LLMs and their alignment-tuned versions perform nearly identically in decoding on the majority of token positions (i.e., they share the top-ranked tokens). Most distribution shifts occur with stylistic tokens (e.g., discourse markers, safety disclaimers). These direct evidence strongly sup- ports the hypothesis that alignment tuning primarily learns to adopt the language style of AI assistants, and that the knowledge required for answering user queries predominantly comes from the base LLMs themselves.

Or, in short, the LLM is still basically doing the same thing, with a handful of additions to keep it on-track in the desired route from the fine-tuning.

(I also think our very strong prior belief should be that LLMs are basically still text-continuation machines, given that 99.9% or so of the compute put into them is training them for this objective, and that neural networks lose plasticity as they learn. Ash and Adams is like a really good intro to this loss of plasticity, although most of the research that cites this is RL-related so people don't realize.)

Similarly, a lot of people have remarked on how the textual quality of the responses from a RLHF'd language model can vary with the textual quality of the question. But of course this makes sense from a text-prediction perspective -- a high-quality answer is more likely to follow a high-quality question in text than a high-quality answer from a low-quality question. This kind of thing -- preceding the model's generation with high-quality text -- was the only way to make it have high quality answers for base models -- but it's still there, hidden.

So yeah, I do think this is a much better model for interacting with these things than asking a shoggoth. It actually gives you handles to interact with them better, while asking a shoggoth gives you no such handles.