291–300 of 301 posts
This article uses the computational complexity hammer way too hard, discounts huge progress in every field of AI outside of the hot trend of transformers and LLMs. Nobody is saying the future of AI is autoregressive and this article pretty much ignores any of the research that has been posted here around diffusion based text generation or how it can be combined with autoregressive methods… discounts multi-modal model…
Earlier quoted context omitted.
I think you're missing the point of "AI winter". It's not about how good the products are now. It's about how quickly the products are improving and creating the potential for profit. That's what drives investment. 3 things we know about the AI revolution in 2025: - LLMs are amazing, but they have reached a plateau. AGI is not within reach. - LLM investment has sacrificed many hundreds of billions of dollars, much of…
> 3 things we know about the AI revolution in 2025: > - LLMs are amazing, but they have reached a plateau. AGI is not within reach. Source/citation?
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Replace "hallucination" with "oversight" or "ignorance" and you have the same issue when a human writes the code. A lot of that will come to the prompter's own foresight much like the vigilance of a beginner developer where they know they are working on a part of the system that is particularly sensitive to get right. That said, only a subset of software needs an authentication solution or has zero tolerance to some…
> A lot of that will come to the prompter's own foresight And, on the current trend, how on earth are prompters supposed to develop this foresight, this expertise, this knowledge? Sure, fine, we have them now , in the form of experienced devs, but these people will eventually be lost via attrition, last even faster if companies actually do make good on their threat to replace a team of 10 devs with a team of three pr…
I suppose curiosity. The same way anyone develops expertise in the abstractions below after getting excited about the higher layer.
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I would conjecture that this is another manifestation of the
NP-completeness wall that slammed symbolic AI, causing the
first AI winter. It's always possible to turn an NP-complete
algorithm into one that runs quickly, if you don't mind that it
fails to generate any output if you hit a timeout. The
transformer equivalent of this is generating plausible, wrong,
hallucinated output in cases where it can't pattern match a
good result based on its training. The problem, though, is that
with traditional AI algorithms you typically know if you've hit
a timeout, or if none of your knowledge rules match. With
transformers, generating wrong output looks exactly like
generating correct output, and there is no way to know which is
which.
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Many technologies plateau, but we don't say they all have winters. Terrestrial radio winter? Television winter? Automobile winter? Air travel winter? Nuclear power comes close in terms of its tarnished image and reluctance to reinvest. I personally believe contemporary AI is over-hyped, but I cannot say with confidence that it is going to lead to a similar winter as the last time. It seems like today's products satis…
I think it's easy to talk past one another on this subject. To be sure, I love Claude code. I wish I could pay for several years in advance at today's prices. But the consumer's is not the important perspective for the AI hype cycle. It's the investors' perspective. I'm going to guess that well over a trillion dollars has gone into "AI", including the major labs and all the little "agents for XYZ" startups popping up…
I am simply stunned at the negativity. Yes, there is hype. But if you actually filter it out, instead of (over) reacting to it in either direction, progress has been phenomenal and the fact there is visible progress in many areas, including LLMs, in the order of months demonstrates no walls. Visible progress doesn’t mean astounding progress. But any tech that is improving year to year is moving at a good speed. Huge…
If you like the whole corpus of the internet being turned into 98% filler words, it must be pretty awesome for you.
Mixing them up doesn’t help with understanding or fixing anything.
Spontaneously projecting that confusion onto others, who agree with you on the latter problem, is even less helpful.
This article uses the computational complexity hammer way too hard, discounts huge progress in every field of AI outside of the hot trend of transformers and LLMs. Nobody is saying the future of AI is autoregressive and this article pretty much ignores any of the research that has been posted here around diffusion based text generation or how it can be combined with autoregressive methods… discounts multi-modal model…
I agree with everything you wrote, the technology is unbelievable and 6 years ago, maybe even 3.1 years would have been considered magic. A steel man argument for why winter might be coming is all the dumb stuff companies are pushing AI for. On one hand (and I believe this) we argue it’s the most consequential technology in generations. On the other, everybody is using it for nonsense like helping you write an email…
Yep, it's like if we invented the combustion engine today and suddenly EVERYONE was putting them EVERYWHERE.
Your fridge? Combustion powered! Your toilet will now flush faster with INTERNAL COMBUSTION ENGINES! Who needs a normal fireplace, you can have a COMBUSTIONATOR FIREPLACE!
But for bikes and cars... hmm, maybe this engine is actually a good idea.
Some shitty AI ideas will die eventually as they're just stupid from the start or untenable. But some will stay and change the way we work.
LLMs are an amazing advancement. The tech side of things is very impressive. Credit where credit is due. Where the current wave all falls apart is on the financials. None of that makes any sense and there’s no obvious path forward. Folks say handwavy things like “oh they’ll just sell ads” but even a cursory analysis shows that math doesn’t ad up relative to the sums of money being invested at the moment. Tech wise I’…
Hard disagree, I'm in the process of deploying several AI solutions in Healthcare. We have a process a nurse usually spends about an hour on, and costs $40-$70 depending on if they are offshore and a few other factors. Our AI can match it at a few dollars often less. A nuse still reviews the output, but its way less time. The economics of those tokens is great. We have another solution that just finds money, $10-$30…