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PaperclipPerfector  6mo ago (text post)   26769 thread views

Culture War Roundup for the week of November 20, 2023

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Dear reader, please don't let Hlynka distract you from the fact that a humble "Stochastic Parrot" did a better job of both understanding a complicated physics question from implied context and answering it correctly than he did.

The most utterly glaring error here is that you're flat out wrong about LLMs being a subset of regression-based ML algorithms. I will risk wasting the time of @curious_straight_ca and @DaseindustriesLtd here to back me up on that, even if a cursory search reveals that they're completely different things.

But Hlynka is of the opinion that Chihuahuas are good hunting dogs, so who's surprised at yet more abuse of truth or the meaning of language?

At any rate, such a combination of such utter confidence while being "not even wrong" levels of confused about things is unique, if not particularly charming.

Besides, maybe the error is on my part, translations to and from "Indian" can be fraught, am I right? It's entirely possible I've mistaken a very subtle and important argument for gish-galloping.

While this sort of statistical processes can excel at associative tasks where the bounds of likely inputs and outputs are known in advance such as linguistic translation and ranking search results, it ends up being worse than useless for other more agentic tasks like pathfinding, and is only capable of "finding useful information" in so far as what is "useful" and what is "statistically probable" based on its training data are in alignment.

@HlynkaCG Actually, the techniques used in language modeling are great at "pathfinding" and other "agentic" tasks, too. See Decision Transformers and similar work. One of the most central, and at-the-time most surprising to many, results of ML is that the same techniques work for a wide variety of tasks. Neural nets "want to work".

You say NNs / language models are regression based. This is vacuously true. Wiki says:

In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome' or 'response' variable, or a 'label' in machine learning parlance) and one or more independent variables (often called 'predictors', 'covariates', 'explanatory variables' or 'features').

So, language models are regression based in the sense that they predict things based on other things. Every possible technique for doing what language models do, or indeed any method of machine learning or AI, or indeed humans behavior itself, could be cast as a "regression", in that sense. In the sense you mean, though, of randomness or simplicity, they aren't - the models that are trained are horrendously complex, and capable of representing very complicated computations. As opposed to "regressions" in the colloquial sense, which are relatively simple statistical models.

What's happening mechanically when you "train" a regression engine is that you are populating that table and assigning different statistical weights to the various outputs within it based on the prompt provided

You're, presumably, familiar with physics and causality, right? Any discrete theory of physics (and modern physics is strongly suspected to be discrete, as involving real numbers anywhere leads to all sorts of paradoxes) can, necessarily, be modeled as a (very large) "table", or matrix, with a row/column for each world-state, and various transition "statistical weights" / probabilities from each state to each state. This is certainly an incredibly coarse-grained representation, especially given you need a state for each large-scale quantum state (distribution-across-universe-branches), but it's doable. So, given your "regression engine" can, in theory, run the entire universe, I think it's premature to say it can't run an AI.

Now, obviously there are chinese room-level scale issues with the comparison, and physics has mathematical patterns that lead to a description much simpler, and smaller, than a transition matrix of size 2^2^(number of atoms in the universe). Fortunately, neural networks have those too! They're not huge transition matrices either, but very complicated functions with a lot of internal regularity.

So, HlynkaCG, I don't get why we keep having these discussions, you just assert a bunch of things that are patently false, and then repeat them a few months later after they're corrected.

If it's a transformer, its not regression-based. Yes transformers are often used in the training of regression engines (parallel processing is a hell of a drug) but they are not the same thing, they have different use cases.

All of the big LLMs are based on transformers. You said "Ultimately what a regression-based machine learning algorithm (of which LLMs are a subset)".

Like I said "Yes transformers are often used in the training of regression engines" but that doesn't make them the same thing, the underlying principles of operation are different.

So, HlynkaCG, I don't get why we keep having these discussions, you just assert a bunch of things that are patently false, and then repeat them a few months later after they're corrected.

At this point I'm just tempted to make a FAQ-style compilation to save my breath later.

@HlynkaCG may be stretching the definition of «regression» past the breaking point in my view. But if one wants to argue that attention over 80 layers is «regression» over a trained collection of regressors, then fine, I won't stop it – categories were made for man, not… and all that. I think at this point it's a fool's errand to fight over such stuff manually instead of…

Well, typing some prompt like «How do large language models (transformers) correspond to regression-based ML algorithms? Answer at the level of PhD CS adjunct professor level. Focus on mechanistic details, not use cases» into a frontier model of your fancy. I quite like Claude 2's style but GPT-4 is still king.

Of course, that reference to regression is just a more specific way to diss the «complex statistical model», and a complex enough transformer model can approximate most anything in a compact domain (with some sane constraints, but as much can be said of the brain with its finite expressivity and learning capacity). Maybe we could talk about actual expressivity limits of some architectures, and orthodox Transformers can't learn to solve PARITY problem in the general case, but Universal Transformers do better, and path independent equilibrium models must do better still; at some point human+tool generalization will be comprehensively surpassed, and we'll be able to confidently say that an AI of such and such design and hyperparameters can learn everything a human mind can learn and more, and even does that in practice, and the question will be moot. Or is the question about the possibility to establish the correspondence between some types of data and some types of things, like, symbol sequences and thoughts?

I am not aware of some strong information-theoretical or broadly mathematical reason, which Hlynka and some other guy (@IGI-111 maybe?) alluded to, for believing this won't be done with known ML primitives in a few years. It looks to be about the «just» fallacy: some people think that if they understand the primitives (like regression, or gradient descent, or matmul – whatever abstraction layer they want to squint at), the full thing is «just» the interaction of those primitives and thus… something something… cannot be intelligent/conscious/superhuman/your option. I can't understand this way of thinking, it seems mainly ego-driven to me but that's a hypothesis, I literally cannot comprehend it, it does not compute.

This is all progressively far from the high-level generator of disagreement, which is… what is it again? And how many are there?

That said, I also do not share your theory of consciousness/personal identity, my views are closer to Christof Koch's. I think a high-quality computable upload of myself would be able to output thoughts in the distribution of my own (hell, one can finetune an LLM and see the resemblance already, it would even fool some); but it would be, for most intents and purposes, a p-zombie, even if you throw an «agentic» for loop on top. I do not subscribe to the Lesswrongian purely computational doctrine; I am a specific subject, not information about an object. For the same reason I would not use destructive teleportation nor advocate it to anyone, I think humans are causal entanglements, not blueprints for those.

Well, typing some prompt like «How do large language models (transformers) correspond to regression-based ML algorithms? Answer at the level of PhD CS adjunct professor level. Focus on mechanistic details, not use cases» into a frontier model of your fancy.

This is a nice theory, but the problem with regression-based algorithms in practice is that to receive a "correct" response to such a query you not only need to have an example of the correct response in your training data, you need to have enough such responses (or a robust enough statistical model) to ensure that it becomes the most probable output.

You're a uniquely fake person, Hlynka. It's incredible how you falsify your down-to-earth practically-thinking red-triber creds, but actually hinge your beliefs on a half-understood galaxy brained theory and despise evidence or actual learning. You're much more similar to Eliezer Yudkowsky than to an average Joe in this, but at least Yud is sincere.

Ive been posting in rat-adjacent spaces under this pseudonym since the fall semester of 2012. I don't think that I've ever misrepresented my background or positions here. At least not intentionally. When you call me a "uniquely fake person" i can't help but wonder what exactly it is you think I've been insincere about or otherwise "faking".

I am exceedingly clear in my accusation above, I believe.

Even so, please explain your chain of reasoning.

You can talk all the shit you want, but it will still just be talk.

You can yell at clouds all you like, but much like Shamans and their hexes, RCTs have shown that doesn't do much to help with rain ;)

talk talk talk

@HlynkaCG and @self_made_human: knock it off.

Or echoing @ArjinFerman: get a room.

For whatever reason, the two of you have gotten to the point where every argument becomes a long thread that gradually unwinds into this sort of low-effort asinine bickering. It's annoying, it's not entertaining, it's not proving anything (except that you can't quit each other), and if I have to give you timeouts for this petty bullshit, I will not be happy about it, but I will do it.

I suppose "he started it" isn't a valid defense is it? Leaving aside that that's true, I admit I do know better.

I commit to ignoring Hlynka in the future, since I cannot believably claim to extend him the same degree of charity or patience as I usually provide and as dictated by the norms of the Motte. If I find a polite and acceptable way of drawing attention to his consistent pattern of ad hominem attacks, outright racism, refusal to remember rebuttals to the claims he's made before and everything else, I might reconsider, but none come to mind.

I can't tell if you guys need to get a room, or rent a boxing ring at the local gym, but something has to be done. It's depressing to watch interesting conversations between the two of you devolve into this. Maybe we can set up an online Mortal Kombat match?

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