[climatologist]

I get what you're saying but people don't really care. The typical/average person does not know anything about derivatives, backpropagation, or probabilities so to them it all seems like magic and they anthropomorphize what they're seeing as something intelligent.

Some folks that know how this stuff works and do a good job of explaining the limitations are Melanie Mitchel and Francois Chollet. Both have extensive experience in the field and have also written books on AI.

You can spend your time trying to explain to every random person that computers can't think but they're not gonna understand what you're saying because to them it seems like a large enough Markov chain is actually thinking.

[selalipop]

What about those of us who do understand them and just don't agree?

After all you could simplify it to a layperson as: 'the LLM is just doing fancy autocomplete based on how stuff appeared in the training data so that means they're not creative'

The first part is not really up for debate, but it's the second part is where some of us disagree. Creativity doesn't mean novel in existence, it means novel within some context: https://www.researchgate.net/publication/254301596_The_Stand...

—

At some point, the push back against these models being creative starts to feel like it's just as emotion driven as the people who are over-anthropomorphizing the models: "If I accept something I know is just a ball of linear algebra is creative, then it's cheapening the definition of creativity."

People bring up the stochastic parrot argument forgetting that the original paper was predicated on the dangers of not considering the power that lies in something that's "just" a stochastic parrot.

[climatologist]

Ask it to solve sudoku and report back. Not generate code but actually solve a puzzle in the prompt.

[selalipop]

This is what I mean when I say "inverse-anthropomorphization" crowd is increasingly emotion over facts.

My reply to you was predicated on a compilation of centuries of scientific study on the subject of creativity. Your knee-jerk reply is to proclaim it's bad at sudoku while going out of your way to place artificial constraints on it.

Touting its inability to solve sudoku in-context feels like a slightly hamfisted way of saying it's a probability based model operating on tokens but like I said before, there are plenty of us who already understand that.

We also realize that you can find arbitrary gaps in any sufficiently complex system. You didn't even need to rely on such a specific example, you could have touted any number of variations on common logic puzzles that they just fall completely on their faces for.

Gaps aren't damning until you tie them to what you want out of the system. The LLM can be bad at Sudoku and capable of creativity in some domain. It's more useful to explore unexpected properties of a complex system than it is to parade things that the system is already expected to be bad at.

[climatologist]

The fact is that no neural network can solve sudoku puzzles. I think it's hilarious that AI proponents/detractors keep worrying about existential risk when not a single one of these systems can solve logic puzzles.

[selalipop]

I didn't say anything about existential risk, and I'm going to assume you meant LLM since training a NN to solve sudoku puzzles has been something you could do as an into to ML project going years back: https://arxiv.org/abs/1711.08028

To me the existential risks are pretty boring and current LLMs are already capable of them: train on some biased data, people embed LLMs in a ton of places, the result is spreading bias in a black box where introspection is significantly harder.

In some ways it mirrors the original stochastic parrot warning, except "parrot" is a much significantly less loaded term in this context.

[climatologist]

Then I don't know what you're arguing about. If you think LLMs are useful continue using them.

[CamperBob2]

Ah, the God of the Gaps argument. What's your next move when somebody implements a plugin that has the effect of being able to solve Sudoku puzzles?

A good friend swore that ML was 50 years away from being able to beat a Go grandmaster. To his credit, he stopped making such sweeping predictions after that happened. He didn't fall back to, "Well, I don't know, let's see how it does at Risk."