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Comment by loudmax | Hacker News Reader

I think that depends what your expectations are, and what you mean by another AI winter.

We're just scratching the surface of what's possible with the current state of the art. Even if there are no major advances or breakthroughs in the near future, LLMs and associated technologies are already useful in many use cases. Or close enough to useful where engineering rather than science will be sufficient to overcome many (though not all) of the shortcomings of current AI models. Never mind grandiose claims about AGI, there's enough utility to be gotten out of the limited LLMs that we have today to keep engineers and entrepreneurs busy for years to come.

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You're right, and I suspect the GP would agree with you about there being real engineering applications for LLMs, diffusion models, etc

But I think the term "AI Winter" usually refers to the underlying research programme and the economics around it. Soaking up many billions of dollars of industry money and public grants on the pitch that AGI might be just around the corner, and then being unable to deliver on that pitch can induce a hangover effect that makes it much harder to raise money for anything that smells even a little bit like the failed pitch. Investors and administrators feel burnt and turn very skeptical for a very long time.

Meanwhile, the actual productive applications which shook out of the initial boom just get renamed to something else so that they don't carry that smell.

We'll see how it goes here, but that's the familiar road and where the terminology comes from.

The minor irony of this is that current efforts at AGI are focused on the data scaling laws, which we have showed no signs of slowing on, but if funding dries up these crazy expensive training runs wont be allowed anymore.

"AI winter" was a political phenomenon that happened because the AI applications didn't hold water to the hype, and all of the gullible people that invested on them got burned out by the disparity.

We are very clearly on that same path again. What leads to the conclusion that another winter is coming. But even the fact that people are talking about it is evidence it's not here yet, and as always with political phenomena, there's no guarantee history will repeat.

Anyway, none of it means people will stop applying their knowledge or studying AI. The entire thing happens on funding and PR, and the world is not entirely controlled by those two.

Except contemporary AI is useful on a day to day basis.

Being useful doesn't mean it met expectations.

So were the developments before the last AI winter..

The whole point of past AI winters is that genuinely useful technologies were preposterously overhyped as representing "human cognition," despite overwhelming evidence to the contrary. The bubble bursts - and the money evaporates - because expectations come crashing down, not because the usefulness was a mirage. Lisp was hardly the key to human symbolic thought like some hoped it would be, but it helped improve programming languages across the board. Perceptrons really are quite limited and there's absolutely no way you could emulate a human brain with them alone, but they're still essential for more advanced ML.

But you would think after 70 years AI practitioners would learn some humility! It is very obvious that GPT-4 is dumber than a honeybee, let alone a cat, let alone a crow. But for over a year I've heard dozens of tech folks insist it's smarter than most people.

> But you would think after 70 years AI practitioners would learn some humility!

Why? The people doing AI now are not the same as those that did AI 60 years ago.

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