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PaperclipPerfector  1yr ago (text post)   27147 thread views

Culture War Roundup for the week of November 20, 2023

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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.

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