OP could have been more substantive, but there is no contradiction between "current AI tools are sincerely useful" and "overinflated claims about the supposed intelligence of these tools will lead to an AI winter." I am quite confident both are true about LLMs.
I use Scheme a lot, but the 1970s MIT AI folks' contention that LISPs encapsulate the core of human symbolic reasoning is clearly ridiculous to 2020s readers: LISP is an excellent tool for symbolic manipulation and it has no intelligence whatsoever even compared to a jellyfish[1], since it cannot learn.
GPTs are a bit more complicated: they do learn, and transformer ANNs seem meaningfully more intelligent than jellyfish or C. elegans, which apparently lack "attention mechanisms" and, like word2vec, cannot form bidirectional associations. Yet Claude-3.5 and GPT-4o are still unable to form plans, have no notions of causality, cannot form consistent world models[2] and plainly don't understand what numbers actually mean, despite their (misleading) successes in symbolic mathematics. Mice and pigeons do have these cognitive abilities, and I don't think it's because God seeded their brains with millions of synthetic math problems.
It seems to me that transformer ANNs are, at any reasonable energy scale, much dumber than any bird or mammal, and maybe dumber than all vertebrates. There's a huge chunk we are missing. And I believe what fuels AI boom/bust cycles are claims that certain AI is almost as intelligent as a human and we just need a bit more compute and elbow grease to push us over the edge. If AI investors, researchers, and executives had a better grasp of reality - "LISP is as intelligent as a sponge", "GPT is as intelligent as a web-spinning spider, but dumber than a jumping spider" - then there would be no winter, just a realization that spring might take 100 years. Instead we see CS PhDs deluding themselves with Asimov fairy tales.
[1] Jellyfish don't have brains but their nerve nets are capable of Pavlovian conditioning - i.e., learning.
[2] I know about that Othello study. It is dishonest. Unlike those authors, when I say "world model" I mean "world."
I guess it depends on what we mean by "AI winter". I completely agree that the current insane levels of investment aren't justified by the results, and when the market realises this it will overreact.
But at the same time there is a lot of value to capture here by building solid applications around the capabilities that already exist. It might be a winter more like the "winter" image recognition went through before multimodal LLMs than the previous AI winter
I think the upcoming AI bust will be similar to the 2000s dotcom bust - ecommerce was not a bad idea or a scam! And neither are transformers. But there are cultural similarities:
a) childish motivated reasoning led people to think a fairly simple technology could solve profoundly difficult business problems in the real world
b) a culture of "number goes up, that's just science"
c) uncritical tech journalists who weren't even corrupt, just bedazzled
In particular I don't think generative AI is like cryptocurrency, which was always stupid in theory, and in practice it has become the rat's nest of gangsters and fraudsters which 2009-era theory predicted. After the dust settles people will still be using LLMs and art generators.
I see the same way. My current strategy is what I think I should have done in the dotcom bubble: carefully avoid pigeonholing myself in the hype topics while learning the basics so I can set up well positioned teams after the dust settles.
Let's start with a multimodal[1] LLM that doesn't fail vacuously simple out-of-distribution counting problems.
I need to be convinced that an LLM is smarter than a honeybee before I am willing to even consider that it might be as smart as a human child. Honeybees are smart enough to understand what numbers are. Transformer LLMs are not. In general GPT and Claude are both dramatically dumber than honeybees when it comes to deep and mysterious cognitive abilities like planning and quantitative reasoning, even if they are better than honeybees at human subject knowledge and symbolic mathematics. It is sensible to evaluate Claude compared to other human knowledge tools, like an encyclopedia or Mathematica, based on the LLM benchmarks or "demonstrated LLM abilities." But those do not measure intelligence. To measure intelligence we need make the LLM as ignorant as possible so it relies on its own wits, like cognitive scientists do with bees and rats. (There is a general sickness in computer science where one poorly-reasoned thought experiment from Alan Turing somehow outweighs decades of real experiments from modern scientists.)
[1] People dishonestly claim LLMs fail at counting because of minor tokenization issues, but
a) they can count just fine if your prompt tells them how, so tokenization is obviously not a problem
b) they are even worse at counting if you ask them to count things in images, so I think tokenization is irrelevant!
I use Scheme a lot, but the 1970s MIT AI folks' contention that LISPs encapsulate the core of human symbolic reasoning is clearly ridiculous to 2020s readers: LISP is an excellent tool for symbolic manipulation and it has no intelligence whatsoever even compared to a jellyfish[1], since it cannot learn.
GPTs are a bit more complicated: they do learn, and transformer ANNs seem meaningfully more intelligent than jellyfish or C. elegans, which apparently lack "attention mechanisms" and, like word2vec, cannot form bidirectional associations. Yet Claude-3.5 and GPT-4o are still unable to form plans, have no notions of causality, cannot form consistent world models[2] and plainly don't understand what numbers actually mean, despite their (misleading) successes in symbolic mathematics. Mice and pigeons do have these cognitive abilities, and I don't think it's because God seeded their brains with millions of synthetic math problems.
It seems to me that transformer ANNs are, at any reasonable energy scale, much dumber than any bird or mammal, and maybe dumber than all vertebrates. There's a huge chunk we are missing. And I believe what fuels AI boom/bust cycles are claims that certain AI is almost as intelligent as a human and we just need a bit more compute and elbow grease to push us over the edge. If AI investors, researchers, and executives had a better grasp of reality - "LISP is as intelligent as a sponge", "GPT is as intelligent as a web-spinning spider, but dumber than a jumping spider" - then there would be no winter, just a realization that spring might take 100 years. Instead we see CS PhDs deluding themselves with Asimov fairy tales.
[1] Jellyfish don't have brains but their nerve nets are capable of Pavlovian conditioning - i.e., learning.
[2] I know about that Othello study. It is dishonest. Unlike those authors, when I say "world model" I mean "world."