I do see this a lot. It's hard to have a reasonable conversation about AI amidst, on the one hand, hype-mongers and boosters talking about how we'll have AGI in 2027 and all jobs are just about to be automated away, and on the other hand, a chorus of people who hate AI so much they have invested their identify in it failing and haven't really updated their priors since ChatGPT came out. Both groups repeat the same set of tired points that haven't really changed much in three years.

But there are plenty of us who try and walk a middle course. A lot of us have changed our opinions over time. ("When the facts change, I change my mind.") I didn't think AI models were much use for coding a year ago. The facts changed. (Claude Code came out.) Now I do. Frankly, I'd be suspicious of anyone who hasn't changed their opinions about AI in the last year.

You can believe all these things at once, and many of us do:

* LLMs are extremely impressive in what they can do. (I didn't believe I'd see something like this in my lifetime.)

* Used judiciously, they are a big productivity boost for software engineers and many other professions.

* They are imperfect and make mistakes, often in weird ways. They hallucinate. There are some trivial problems that they mess up.

* But they're not just "stochastic parrots." They can model the world and reason about it, albeit imperfectly and not like humans do.

* AI will change the world in the next 20 years

* But AI companies are overvalued at the present time and we're mostly likely in a bubble which will burst.

* Being in a bubble doesn't mean the technology is useless. (c.f. the dotcom bubble or the railroad bubble in the 19th century.)

* AGI isn't just around the corner. (There's still no way models can learn from experience.)

* A lot of people making optimistic claims about AI are doing it for self-serving boosterish reasons, because they want to pump up their stock price or sell you something

* AI has many potential negative consequences for society and mental health, and may be at least as nasty as social media in that respect

* AI has the potential to accelerate human progress in ways that really matter, such as medical research

* But anyone who claims to know the future is just guessing

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IX-103 4 days ago | parent | next [-]

> But they're not just "stochastic parrots." They can model the world and reason about it, albeit imperfectly and not like humans do.

I've not seen anything from a model to persuade me they're not just stochastic parrots. Maybe I just have higher expectations of stochastic parrots than you do.

I agree with you that AI will have a big impact. We're talking about somewhere between "invention of the internet" and "invention of language" levels of impact, but it's going to take a couple of decades for this to ripple through the economy.

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libraryofbabel 4 days ago | parent | next [-]

What is your definition of "stochastic parrot"? Mine is something along the lines of "produces probabilistic completions of language/tokens without having any meaningful internal representation of the concepts underlying the language/tokens."

Early LLMs were like that. That's not what they are now. An LLM got Gold on the Mathematical Olympiad - very difficult math problems that it hadn't seen in advance. You don't do that without some kind of working internal model of mathematics. There is just no way you can get to the right answer by spouting out plausible-sounding sentence completions without understanding what they mean. (If you don't believe me, have a look at the questions.)

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pm 4 days ago | parent [-]

Ignoring its negative connotation, it's more likely to be a highly advanced "stochastic parrot".

> "You don't do that without some kind of working internal model of mathematics."

This is speculation at best. Models are black boxes, even to those who make them. We can't discern a "meaningful internal representation" in a model, anymore than a human brain.

> "There is just no way you can get to the right answer by spouting out plausible-sounding sentence completions without understanding what they mean."

You've just anthropomorphised a stochastic machine, and this behaviour is far more concerning, because it implies we're special, and we're not. We're just highly advanced "stochastic parrots" with a game loop.

	▲	int_19h 3 days ago \| parent [-]
		> This is speculation at best. Models are black boxes, even to those who make them. We can't discern a "meaningful internal representation" in a model, anymore than a human brain. They are not pure black boxes. They are too complex to decipher, but it doesn't mean we can't look at activations and get some very high level idea of what is going on. For world models specifically, the paper that first demonstrated that LLM has some kind of a world model corresponding to the task it is trained on came out in 2023: https://www.neelnanda.io/mechanistic-interpretability/othell.... Now you might argue that this doesn't prove anything about generic LLMs, and that is true. But I would argue that, given this result, and given what LLMs are capable of doing, assuming that they have some kind of world model (even if it's drastically simplified and even outright wrong around the edges) should be the default at this point, and people arguing that they definitely don't have anything like that should present concrete evidence ot that effect. > We're just highly advanced "stochastic parrots" with a game loop. If that is your assertion, then what's the point of even talking about "stochastic parrots" at all? By this definition, _everything_ is that, so it ceases to be a meaningful distinction.

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app134 4 days ago | parent | prev | next [-]

In-context learning is proof that LLMs are not stochastic parrots.

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nuancebydefault 4 days ago | parent | prev [-]

Stochastic parrot here (or not?). Can you tell the difference?

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dvfjsdhgfv 4 days ago | parent | prev [-]

> AI will change the world in the next 20 years

Well, it's been changing the world for quite some time, both in good and bad ways. There is no need to add an arbitrary timestamp.