I don’t know. Pets.com failed because they weren’t adequately prepared for their success. AI seems to fail because they overprepared for success that was never coming.

Yes. We could still try blindly throwing money at the problem. /s

Thanks for the laugh.

I've spent the last month or so using nothing but a local 27B Qwen model as my coding assistant and it's working fine. The big American AI companies could all collapse in ruins tomorrow and it's not going to slow things down all that much, anyone who wants to use AI will be able to continue using it. Might even be better in the long run if that happens - the people who are scared of ASI will get their much-demanded temporary slowdown at the frontier while everyone else catches up with open-weight models that everyone can use without central control.

Pity about the economy in the meantime, I guess.

The truly silly (and simultaneously neat and fascinating, and to a large extent, very logical and obvious) part is that US attempts to cripple Chinese ML/LLM development by putting most advanced GPU tech behind ITAR has more or less made the Chinese ML industry just write straight up better and more optimized code for the domain.

US and western companies are currently relying on throwing bleeding edge hardware at the problem (read: very obviously brute-forcing the problem), whereas Chinese companies and engineers, having to rely on comparatively less advanced (though still highly capable) hardware has meant they’re staying competitive by optimizing the software layer more effectively.

This has very interesting and likely negative long-term implications for western engineering practices in a lot of ways. And it’s also very much not a new problem - simply throwing compute at things is a mindset that became highly prevalent in the west around when The Cloud became a thing. Previously, you couldn’t just spawn another mainframe in your company’s datacenter, but when it’s as simple as aws ec2 start-instances ..., the barrier to entry for getting more compute is so low that most people were just going “hey, it’s faster”. I feel like the engineering expertise around embedded/baremetal optimization has just been largely evaporating over the last couple decades; in the vast majority of professional software domains, it’s simply not a valued skill anymore (or, more correctly, at the moment). It’s sad for me in several ways - not least of which because I did my degree in EECS, and hardware/software interplay and optimization is an area of engineering that I have always found very interesting.

Is anyone trying to make LLMs with analog computing + memristors? That would make them absurdly efficient

If you’re talking about the company I think you are, that acquisition by AMD is interesting news.

The company was the one with a demo AI in their page that was super quick??

I always figured that was the future of AI when I saw it. Where you’d buy processors with n number of “AI cores” that’d have work/prompts scheduled as needed in the same way we now CPUs and RAM.

lol, I wrote a reply about a company etching onto silicon and then go down and saw yours. Oops.

I hadn't heard AMD bought them though. Looks like they're working on a 20b model as well for this year.

Cryptocoins deliberately burn more energy doing useless calculations to prevent inflation. That's a little bit of an oversimplification, but I don't feel like explaining the full thing and the vibes are there.

That'd be fine. Cryptocurrency as a whole is still going strong (total market cap is around 3 trillion dollars) and most of the major ones have switched over to energy-free proof-of-stake validation.

Depends. Is it solar energy that started production to power these models? Great! If not, then you're still consuming power and the difference will have to be made somewhere, likely by burning more fossil fuels.

Well that is an alternative. But I bet each query to a local 27b model takes less energy anyway than the same query to Claude Opus or Fable in some data centre. So it is also a case of improving energy efficiency by running smaller models.

I read it needs 75gb at the minimum quantization? It doesn't seem "typical gaming machine"