this post was submitted on 25 Nov 2023
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Machine Learning

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[–] InterstitialLove@alien.top 1 points 10 months ago (3 children)

The fallacy is the part where you imply that humans have magic.

"An LLM is just doing statistics, therefore an LLM can't match human intellect unless you add pixie dust somewhere." Clearly the implication is that human intellect involves pixie dust somehow?

Or maybe, idk, humans are just the result of random evolutionary processes jamming together neurons into a configuration that happens to behave in a way that lets us build steam engines, and there's no fundamental reason that jamming together perceptrons can't accomplish the same thing?

[–] red75prime@alien.top 1 points 10 months ago (1 children)

LLMs might still lack something that the human brain has. Internal monologue, for example, that allows us to allocate more than fixed amount of compute per output token.

[–] InterstitialLove@alien.top 1 points 10 months ago (1 children)

You can just give an LLM an internal monologue. It's called a scratchpad.

I'm not sure how this applies to the broader discussion, like honestly I can't tell if we're off-topic. But once you have LLMs you can implement basically everything humans can do. The only limitations I'm aware of that aren't trivial from an engineering perspective are

  1. current LLMs mostly aren't as smart as humans, like literally they have fewer neurons and can't model systems as complexly
  2. humans have more complex memory, with a mix of short-term and long-term and a fluid process of moving between them
  3. humans can learn on-the-go, this is equivalent to "online training" and is probably related to long-term memory
  4. humans are multimodal, it's unclear to what extent this is a "limitation" vs just a pedantic nit-pick, I'll let you decide how to account for it
[–] red75prime@alien.top 1 points 10 months ago

It's called a scratchpad.

And the network still uses skills that it learned in a fixed-computation-per-token regime.

Sure, future versions will lift many existing limitations, but I was talking about current LLMs.

[–] venustrapsflies@alien.top 1 points 9 months ago (1 children)

Real brains aren't perceptrons. They don't learn by back-propagation or by evaluating performance on a training set. They're not mathematical models, or even mathematical functions in any reasonable sense. This is a "god of the gaps" scenario, wherein there are a lot of things we don't understand about how real brains work, and people jump to fill in the gap with something they do understand (e.g. ML models).

[–] InterstitialLove@alien.top 1 points 9 months ago

Brains are absolutely mathematical functions in a very reasonable sense, and anyone who says otherwise is a crazy person

You think brains aren't turing machines? Like, you really think that? Every physical process ever studied, all of them, are turing machines. Every one. Saying that brains aren't turing machines is no different from saying that humans have souls. You're positing the existence of extra-special magic outside the realm of science just to justify your belief that humans are too special for science to ever comprehend

(By "is a turing machine" I mean that its behavior can be predicted to arbitrary accuracy by a turing machine, and so observing its behavior is mathematically equivalent to running a turing machine)

[–] Basic-Low-323@alien.top 1 points 9 months ago (1 children)

I mean, if your hypothesis is that the human brain is the product of one billion years of evolution 'searching' for a configuration of neurons and synapses that is very efficient at sampling the environment, detect any changes, and act accordingly to increase likelihood of survival, and also communicate with other such configurations in order to devise and execute more complicated plans, then that...doesn't bode very well for current AI architectures, does it? Their training sessions are incredibly weak by comparison, simply learning to predict and interpolate some sparse dataset that some human brains produced.

If by 'there's no fundamental reason we can't jam together perceptrons this way' you mean that we can always throw a bunch of them into an ever-changing virtual world, let them mutate and multiply and after some long time fish out the survivors and have them work for us, sure, but we're talking about A LOT of compute here. Our hope is that we can find some sort of shortcut, because if we truly have to do it like evolution did, it probably won't happen this side of the millenium.

[–] InterstitialLove@alien.top 1 points 9 months ago (1 children)

We don't currently know exactly why gradient descent works to find powerful, generalizing minima

But, like, it does

The minima we can reliably find, in practice, don't just interpolate the training data. I mean, they do that, but they find compressions which seem to actually represent knowledge, in the sense that they can identify true relationships between concepts which reliably hold outside the training distribution.

I want to stress, "predict the next token" is what the models are trained to do, it is not what they learn to do. They learn deep representations and learn to deploy those representations in arbitrary contexts. They learn to predict tokens the same way a high-school student learns to fill in scantrons: the scantron is designed so that filling it out requires other more useful skills.

It's unclear if gradient descent will continue to work so unreasonably well as we try to push it farther and farther, but so long as the current paradigm holds I don't see a huge difference between human inference ability and Transformer inference ability. Number of neurons* and amount of training data seem to be the things holding LLMs back. Humans beat LLMs on both counts, but in some ways LLMs seem to outperform biology in terms of what they can learn with a given quantity of neurons/data. As for the "billions of years" issue, that's why we are using human-generated data, so they can catch up instead of starting from scratch.

  • By "number of neurons" I really mean something like "expressive power in some universally quantified sense." Obviously you can't directly compare perceptrons to biological neurons
[–] Basic-Low-323@alien.top 1 points 9 months ago

I have to say, this is completely the *opposite* of what i have gotten by playing around with those models(GPT4). At no point did I got the impression that I'm dealing with something that, had you taught it all humanity knew in the early 1800s about, say, electricity and magnetism, it would have learned 'deep representations' of those concepts to a degree that it would allow it to synthesize something truly novel, like prediction of electromagnetic waves.

I mean, the model has already digested most of what's written out there, what's the probability that something that has the ability to 'learn deep representations and learn to deploy those representations in arbitrary contexts' would have made zero contributions, drew zero new connections that had escaped humans, in something more serious that 'write an Avengers movie in the style of Shakespeare'? I'm not talking about something as big as electromagnetism but...something? Anything? It has 'grokked', as you say, pretty much the entirety of stack overflow, and yet I know of zero new programming techniques or design patterns or concepts it has come up with?