[Alchemista]

LLMs aren't designed to emulate human cognition, they are a statistical model designed to predict the next word in a sentence. It happens that they seem to exhibit some similarities to human cognition as a side effect, but that does not mean they are on some developmental path to a "full human" like a child. Again it is silly to try and compare the two.

[nomel]

> LLMs aren't designed to emulate human cognition

I'm not sure that's fair, or correct. The behavior of an LLM is set by the design of the combination of loss function and training data. Achieving it is the desired result of the selection of those.

> It happens that they seem to exhibit some similarities to human cognition as a side effect

Yes, the desired behavior of nearly all LLM projects is to emulate the capabilities of human cognition, and that stated goal is the justification that organizations are using for spending millions to train them.

> but that does not mean they are on some developmental path to a "full human" like a child.

You are the first to suggest any such thing, in this comment chain. But, the field of AI is objectively on that development path, since that is the stated goal of many of the orgs, with LLM existing on that path. If that path actually leads anywhere is anyone's guess.

[Alchemista]

> You are the first to suggest any such thing, in this comment chain.

I'm sorry, did you miss the original post: "Because they are at a child level of development. Give it a few years."

I extrapolated a little bit, but not much. They were clearly implying that it is on a similar developmental path as a child.

[nomel]

It's not clear to me, since they used "at a" not "on a", as you've reworded it.

> Because they are at a child level of development.

I read this as, "Because the capabilities of LLM are at the child level of development."

[Alchemista]

I feel like you are being deliberately obtuse here. They said "Give it a few years" and linked https://en.wikipedia.org/wiki/Child_development_stages. How is this not strongly implying that in a "few years" LLMs will be at a later "level of development" as described in the article they linked.

[nomel]

I think you're not being charitable in the interpretation.

It, almost certainly, will be in later stages of emulating human cognition. If not, then AI winter is already here.

It's trivial and legitimate to relate capabilities of AI to those in that table. Because, again, emulating human cognition is the stated goal of most AI research happening right now.

An AI's capabilities, progressing in that table, does not mean it's human. It means the stated objectives, and all the hard work, and money spent, is on track. It's not a goal to match that table. It's not a goal to end in a human. But, more and more of the table will turn green, as the goal is completed. I'm not understanding the hesitance of using a human metric for a product whose goal is to match human metrics.

[nomel]

> An AI's capabilities

Meant this as a product (e.g. ChatGPT).

[orbital-decay]

What exactly makes you think those two are different in nature, not just in scale and training data? It seems like a lot of these discussions are walking in circles trying to compare ill-defined things (human cognition) with well-defined ones (prediction).

[Alchemista]

Don't you think the onus should be on the people making the fantastical claims to prove it? If human cognition is ill-defined, then define it before making grand claims like ML models being on some path of childhood development and is a few steps from being an adult.

[orbital-decay]

I think that neither side has a convincing argument in these discussions, actually, I'm pointing out that both extremes - anthropomorphism and stochastic parrot - lack any foundation, so I wouldn't be so confident. The behavior of neither statistical models nor biological systems is well understood. It's entirely possible that every trait you consider human can naturally emerge from a dumb statistical model, and indeed certain processes are remarkably similar as the models get bigger, smarter, and have better training data. It's possible that it can't, however.

[daveguy]

The stochastic parrot is much more grounded in the physical reality of how the models are structured, trained, and produce outputs than the "it's like people" argument. Statistical models are much better understood than biological systems. That said, your penultimate statement is true:

> It's entirely possible that every trait you consider human can naturally emerge from a dumb statistical model, and indeed certain processes are remarkably similar as the models get bigger, smarter, and have better training data.

I just don't think it's likely given the biological complexity we are washing over with the statistical model.