> Ahead of launch, OpenAI's Sam Altman said he'd felt "useless" compared to the model's intelligence, even drawing parallels with the Manhattan Project. When it arrived, users apparently felt less intimidated.
So Sam Altman admitted that he isn't so smart after all? :-)
Assuming this prediction of a tepid future for AI pans out, where is the accountability for all those CEOs and managers who pushed for deep investment based on FOMO, bolstered by wishful thinking and a willing ignorance of the technical details?
IOW, who will be fired for getting this wrong? Answer: nobody.
Some small AI-based companies will tank, but all the leaders in F500 companies know how to survive. If the emperor has no clothes, they'll all have collective amnesia and say they knew it the whole time. A few quotes to the press here, a few emails there, and they will move on with their BSing and talk about "what we learned."
The title is clickbaity compared to the content of the article, which seems to have more nuance. Its claim is that recent AI advances were both overhyped and still have a lot of utility that hasn't propagated fully yet (a la the Internet). Not terribly revelatory at this point
AI has huge potential. LLMs though? Not so much. And the timeline for AI to transform the the way we live and work should be measured in decades, not months.
It's funny how this "crash" is not being explained by headline examples, but by just stating "it's a crash." Normally, crashes are led by bankruptcies, broken promises and other tangible issues. Right now it's just "everyone says it's crashing" which just feels like Mr Altman wants to buy back shares cheaply.
implying development has levelled off from a functional point of view. Which was kind of true with iPhones - my 13 does basically the same as the 4 did just with better images and speed. And in the future the iphone 29 will probably still do apps, photos and calls, just a bit better.
But I don't think that'll be true with AI - there are huge categories of stuff - being able to do human jobs, being self improving, having robots that can build houses and factories and so on that don't work at the moment but may well in a decade or two. I think it may be more of a Sopwith Camel moment.
to be honest with the level of hype it's been sustaining, it's about damn time people get disappointed with unrealistic expectations they set (or let others set) for themselves. All the while people who merely view it as a fancy tool with all of its shortcomings, and treat it as such in building something with it, will keep finding benefits from it
With this, hopefully we can stop having peope using AI as a term exclusively for llms
We're about to, or have already hit the "data" wall recently as it gets increasingly hard to get more data to train on, exacerbated by more and more content being generated that risks autophagy
it's less and less about gathering more data now and either we're gonna try to engineer more data into existence, or it's back to the deep learning boom where it's more common to resort to optimizing architecture and training methodologies to squeeze out more performance from what data we have
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[ 3.2 ms ] story [ 29.0 ms ] threadSo Sam Altman admitted that he isn't so smart after all? :-)
IOW, who will be fired for getting this wrong? Answer: nobody.
Some small AI-based companies will tank, but all the leaders in F500 companies know how to survive. If the emperor has no clothes, they'll all have collective amnesia and say they knew it the whole time. A few quotes to the press here, a few emails there, and they will move on with their BSing and talk about "what we learned."
That does not seem like a valid conclusion to draw from the observations in the article.
implying development has levelled off from a functional point of view. Which was kind of true with iPhones - my 13 does basically the same as the 4 did just with better images and speed. And in the future the iphone 29 will probably still do apps, photos and calls, just a bit better.
But I don't think that'll be true with AI - there are huge categories of stuff - being able to do human jobs, being self improving, having robots that can build houses and factories and so on that don't work at the moment but may well in a decade or two. I think it may be more of a Sopwith Camel moment.
With this, hopefully we can stop having peope using AI as a term exclusively for llms
We're about to, or have already hit the "data" wall recently as it gets increasingly hard to get more data to train on, exacerbated by more and more content being generated that risks autophagy
it's less and less about gathering more data now and either we're gonna try to engineer more data into existence, or it's back to the deep learning boom where it's more common to resort to optimizing architecture and training methodologies to squeeze out more performance from what data we have