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Open source AI must win

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361–370 of 538 posts

Re: Open source AI must win

#361
post #186
post #100

Earlier quoted context omitted.

> The total power of all GPUs on the planet dwarf their capabilities That just isn't true. It misunderstands exactly how much silicon has gone directly to those companies, and exactly how much more powerful said silicon is compared to consumer grade gear.

If folding@home is a useful yardstick by which we might estimate the amount of GPU-ish capability that civilians might be coaxed into donating to a shared enterprise, yeah, it doesn't look pretty. This is extremely rough napkin math but comparing to xAI's Collosus 2 for example, for training workflows you're probably looking at 4-5 orders of magnitude the capability of all of folding@home combined. That's 100,000 tim…

Plus a scientific project to benefit all of humanity doesn’t have quite the same ring as the thing thats stealing your job, from the volunteer’s perspective

Re: Open source AI must win

#363

I've been contemplating a decentralized model training system for some time using volunteer machines that we all contribute. But, it is astronomically difficult. The communication speeds are untenable. And, there is the issue of data poisoning from untrusted nodes. I've almost cracked that last issue with a self-healing checkpointed rollback system that doesn't have to throw out anything that follows the corrupt datu…

Ya that'd be an awesome project, the only issue is how do you verify it's not being poisoned? To actually validate it would require more analysis than the training took to run. It would require a trusted network, not an open one, unless that can get solved somehow.

Make multiple nodes do the same job, compare results.

Re: Open source AI must win

#364
post #349

I would be totally willing to pay $50 per month to support an open source AI lab, rather to get open source models as byproducts of corporations.

I think OpenAI ruined the trust. How do you know this 'open source AI lab' won't somehow spin off a for-profit?

Coups like that can happen due to organizations having a small number of board members that can decide to do as they like.

Proper mass-membership organizations are possible though. Same rules as a public corporation, but one vote per members, and the yearly meeting decides the board members and approves important decisions or introduce motions that steer the organization.

So the right way to do this would be to create something like the "Public LLM development club", some criteria on membership (after all entryism is a thing), some membership fee sufficient that there is money for a reasonable amount of work to be done and then one has to hope that people join.

Re: Open source AI must win

#366
I would also want all conversation with AI to be public, searchable and indexable.

It is only fair, give that LLMs are enabled by human generated content from the Internet, that they give it back!

Re: Open source AI must win

#367
post #333

A question I've got which I've been wondering about, not sure if anyone else has been thinking about it, what actually made Fable so effective? From what I could tell from the very little time that I had to interact with it, it's instruction following seemed more consistent The other thing that comes to mind is a lot of people commented on how driven it was, so I'm wondering whether figuring out how to keep existing…

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Re: Open source AI must win

#370
post #333

A question I've got which I've been wondering about, not sure if anyone else has been thinking about it, what actually made Fable so effective? From what I could tell from the very little time that I had to interact with it, it's instruction following seemed more consistent The other thing that comes to mind is a lot of people commented on how driven it was, so I'm wondering whether figuring out how to keep existing…

The big AI labs are also accumulating huge datasets of expert work in a wide range of fields, which is very expensive to re-create. It seems pretty plausible that this this gives them a big advantage that is compounded by their larger training runs and larger models.
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