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Reflections on AI at the End of 2025

antirez.com

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Re: Reflections on AI at the End of 2025

#221

Post by roughly »

> A few well known AI scientists believe that what happened with Transformers can happen again, and better, following different paths, and started to create teams, companies to investigate alternatives to Transformers and models with explicit symbolic representations or world models.

I’m actually curious about this and would love pointers to the folks working in this area. My impression from working with LLMs is there’s definitely a “there” there with regards to intelligence - I find the work showing symbolic representation in the structure of the networks compelling - but the overall behavior of the model seems to lack a certain je ne sais quoi that makes me dubious that they can “cross the divide,” as it were. I’d love to hear from more people that, well, sais quoi, or at least have theories.

Re: Reflections on AI at the End of 2025

#222

Post by NitpickLawyer »

Jaxan wrote: post #201
NitpickLawyer wrote: post #172

Earlier quoted context omitted.

> but this won’t work in any non-trivial case Earlier this year google shared that one of their projects (I think it was alphaevolve) found an optimisation in their stack that sped up their real world training runs by 1%. As we're talking about google here, we can be pretty sure it wasn't some trivial python trick that they missed. Anyhow, at ~100M$ / training run, that's a 1M$ save right there. Each and every time t…

> As we're talking about google here, we can be pretty sure it wasn't some trivial python trick that they missed. Strong disagree on the reasoning here. Especially since google is big and have thousands of developers, there could be a lot of code and a lot of low hanging fruit.

> By finding smarter ways to divide a large matrix multiplication operation into more manageable subproblems, it sped up this vital kernel in Gemini’s architecture by 23%, leading to a 1% reduction in Gemini's training time.

The message I replied to said "if I have some toy poorly optimized python example". I think it's safe to say that matmul & kernel optimisation is a bit beyond a small python example.

Re: Reflections on AI at the End of 2025

#223

Post by Xenoamorphous »

candiddevmike wrote: post #158
Aurornis wrote: post #126

Earlier quoted context omitted.

No, that doesn’t make you a skeptic in this context. The LLM skeptics claim LLM usefulness is an illusion. That the LLMs are a fad, and they produced more problems than they solve. They cite cherry picked announcements showing that LLM usage makes development slower or worse. They opened ChatGPT a couple times a few months ago, asked some questions, and then went “Aha! I knew it was bad!” when they encountered their…

Skeptic here: I do think LLMs are a fad for software development . They're an interesting phenomen that people have convinced themselves MUST BE USEFUL in the context of software development, either through ignorance or a sense of desperation. I do not believe LLMs will be used long term for any kind of serious software development use cases, as the maintenance cost of the code they produce will run development teams…

> Skeptic here: I do think LLMs are a fad for software development.

I think that’s where they’re most useful, for multiple reasons:

- programming is very formal. Either the thing compiles, or it doesn’t. It’s straightforward to provide some “reinforcement” learning based on that.

- there’s a shit load of readily available training data

- there’s a big economic incentive; software developers are expensive

Re: Reflections on AI at the End of 2025

#224

Post by emp17344 »

antirez wrote: post #140
pton_xd wrote: post #133

> For years, despite functional evidence and scientific hints accumulating, certain AI researchers continued to claim LLMs were stochastic parrots: probabilistic machines that would: 1. NOT have any representation about the meaning of the prompt. 2. NOT have any representation about what they were going to say. In 2025 finally almost everybody stopped saying so. It's interesting that Terrence Tao just released his ow…

What happened recently is that all the serious AI researches that were in the stochastic parrot side changed point of view but, incredibly, people without a deep understanding on such matters, previously exposed to such arguments, are lagging behind and still repeat arguments that the people who popularized them would not repeat again. Today there is no top AI scientist that will tell you LLMs are just stochastic par…

You seem to think the debate is settled, but that’s far from true. It’s oddly controlling to attempt to discredit any opposition to this viewpoint. There’s plenty of research supporting the stochastic view of these models, such as Apple’s “Illusion” papers. Tao is also a highly respected researcher, and has worked with these models at a very high level - his viewpoint has merit as well.

Re: Reflections on AI at the End of 2025

#225

Post by emp17344 »

grodriguez100 wrote: post #10
fleebee wrote: post #7

> The fundamental challenge in AI for the next 20 years is avoiding extinction. That's a weird thing to end on. Surely it's worth more than one sentence if you're serious about it? As it stands, it feels a bit like the fearmongering Big Tech CEOs use to drive up the AI stocks. If AI is really that powerful and I should care about it, I'd rather hear about it without the scare tactics.

I would say yes, everyone should care about it. There is plenty of material on the topic. See for example https://ai-2027.com/ or https://www.lesswrong.com/posts/uMQ3cqWDPHhjtiesc/agi-ruin-a...

The fact that people here take AI 2027 seriously is embarrassing. The authors are already beginning to walk back these claims: https://x.com/eli_lifland/status/1992004724841906392?s=20

Re: Reflections on AI at the End of 2025

#226

Post by gaigalas »

This post is a bait for enthusiasts. I like it.

> Chain of thought is now a fundamental way to improve LLM output.

That kinda proves _that LLMs back then were pretty much stochastic parrots indeed_, and the skeptics were right at the time. Today, enthusiasts agree with what they previously said: without CoT, the AI feels underwhelming, repetitive and dumb and it's obvious that something more was needed.

Just search past discussions about it, people were saying the problem would be solved with "larger models" (just repeating marketing stuff) and were oblivious to the possibility of other kinds of innovations.

> The fundamental challenge in AI for the next 20 years is avoiding extinction.

That is a low level sick burn on whoever believes AI will be economically viable short-term. And I have to agree.

Re: Reflections on AI at the End of 2025

#227

Post by timmytokyo »

ur-whale wrote: post #3

Not sure I understand the last sentence: > The fundamental challenge in AI for the next 20 years is avoiding extinction.

It's a tell that he's been influenced by rationalist AI doomer gurus. And a good sign that the rest of his AI opinions should be dismissed.

Re: Reflections on AI at the End of 2025

#228

Post by bgwalter »

rckt wrote: post #36

> Even if LLMs make mistakes, the ability of LLMs to deliver useful code and hints improved to the point most skeptics started to use LLMs anyway Here we go again. Statements with the single source in the head of the speaker. And it’s also not true. The llms still produce bad/irrelevant code at such rate that you can spend more time prompting than doing things yourself. I’m tired of this overestimation of llms.

> Here we go again.

Indeed, he said the same as a reflection on 2024 models:

https://news.ycombinator.com/item?id=42561151

It is always the fault of the "luser" who is not using and paying for the latest model.

Re: Reflections on AI at the End of 2025

#229

Post by cauliflower2718 »

etra0 wrote: post #127

LLMs have certainly become extremely useful for Software Engineers, they're very convincing (and pleasers, too) and I'm still unsure about the future of our day-to-day job. But one thing that has scared me the most, is the trust of LLMs output to the general society. I believe that for software engineers it's really easy to see if it's being useful or not -- We can just run the code and see if the output is what we e…

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Re: Reflections on AI at the End of 2025

#230

Post by echelon »

blibble wrote: post #111
djdishsv wrote: post #97

Earlier quoted context omitted.

> smart, intelligent person gives opinion > woah buddy this persons opinion isn’t worth anything more than a random homeless person off the street. they’re not an expert in this field Is there a term for this kind of pedantry? Obviously we can put more weight behind the words a person says if they’ve proven themselves trustworthy in prior areas - and we should! We want all people to speak and let the best idea win. I…

> Obviously we can put more weight behind the words a person says if they’ve proven themselves trustworthy in prior areas - and we should! no, you shouldn't this is how you end up with crap like vaccine denialism going mainstream "but he's a doctor!"

Credentialism isn't a fix for the problem you've outlined. If anything, over-reliance on credentials bolsters and lends credence to crazy claims. The media hyper-fixates on it and amplifies it.

We've got Avi Loeb on mainstream podcasts and TV spouting baseless alien nonsense. He's a preeminent in his field, after all.

Focus on what you understand. If you don't understand, learn more.

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