Assume all the big AI firms die: Anthropic, OpenAI, Microsoft, Google, and Meta. Poof! They’re gone!
Here would be my reaction: “So anyway… have you tried GLM-7? It’s amazing! Also, there’s a new workflow in ComfyUI I’ve been using that works great to generate…”
Generative AI is here to stay. You don’t need a trillion dollars worth of data centers for progress to continue. That’s just billionaires living in an AGI fantasy land.
Same places as usual: Academia and open source foundations.
That’s where 99% of all advancements in AI come from. You don’t actually think Big AI is paying as many people to do computer science and mathematics research as all the universities in the world (with computer science programs)?
It’s the same shit as always: Big companies commercialize advancements and discoveries made by scientist and researchers from academia (mostly) and give almost nothing back.
Big AI has partnerships with tons of schools and if it weren’t for that, they wouldn’t be advancing the technology as fast as they are. In fact, the only reason why many of these discoveries are made public at all is because of the agreements with the schools that require the discoveries/papers be published (so their school, professors, researchers, and students can get credit).
Like I was saying before: You don’t need a trillion dollars in data centers to do this stuff. Almost all the GPUs and special chips being used (and preordered, sigh) by Big AI are being used to serve their customers (at great expense). Not for training.
Training used to be expensive but so many advancements have been made this is no longer the case. Instead, most of the resources being used in “AI data centers” (and research) is all about making inference more efficient. That’s the step that comes after you give an AI a prompt.
Training a super modern AI model can be done with a university’s data center or a few hundred thousand to a few million dollars of rented GPUs/compute. It doesn’t even take that long!
Generative AI improves at a ridiculously fast rate. In nearly all the ways you could think of: Training, inference (e.g. figuring out user intent), knowledge, understanding, and weirder, fluffier stuff like “creativity” (the benchmarks of which are dubious, BTW).
What inclined you to @ me into this? As far as I can see, I haven’t even replied in this thread, and you just seem like you’re on the warpath with anyone that wants to defend using LLMs. If Greg KH thinks it’s coming into it’s own, you might want to heed him.
Assume all the big AI firms die: Anthropic, OpenAI, Microsoft, Google, and Meta. Poof! They’re gone!
Here would be my reaction: “So anyway… have you tried GLM-7? It’s amazing! Also, there’s a new workflow in ComfyUI I’ve been using that works great to generate…”
Generative AI is here to stay. You don’t need a trillion dollars worth of data centers for progress to continue. That’s just billionaires living in an AGI fantasy land.
I’m sick and tired of AI fans making statements like
without evidence.
Citation needed.
Um… Where would it go? I’ve got about 30 models on my machine right now and I download new ones to try out all the time.
Are you suggesting that they’d all just magically disappear one day‽
Where do you think the “new ones” are coming from?
Same places as usual: Academia and open source foundations.
That’s where 99% of all advancements in AI come from. You don’t actually think Big AI is paying as many people to do computer science and mathematics research as all the universities in the world (with computer science programs)?
It’s the same shit as always: Big companies commercialize advancements and discoveries made by scientist and researchers from academia (mostly) and give almost nothing back.
Big AI has partnerships with tons of schools and if it weren’t for that, they wouldn’t be advancing the technology as fast as they are. In fact, the only reason why many of these discoveries are made public at all is because of the agreements with the schools that require the discoveries/papers be published (so their school, professors, researchers, and students can get credit).
Like I was saying before: You don’t need a trillion dollars in data centers to do this stuff. Almost all the GPUs and special chips being used (and preordered, sigh) by Big AI are being used to serve their customers (at great expense). Not for training.
Training used to be expensive but so many advancements have been made this is no longer the case. Instead, most of the resources being used in “AI data centers” (and research) is all about making inference more efficient. That’s the step that comes after you give an AI a prompt.
Training a super modern AI model can be done with a university’s data center or a few hundred thousand to a few million dollars of rented GPUs/compute. It doesn’t even take that long!
Generative AI improves at a ridiculously fast rate. In nearly all the ways you could think of: Training, inference (e.g. figuring out user intent), knowledge, understanding, and weirder, fluffier stuff like “creativity” (the benchmarks of which are dubious, BTW).
Before we spin into a tangent about theory and “what ifs” etc, care to link me to all these great models from academics and open-source institutions?
Because right now, the only companies I see making advancements in “AI” are burning through obscene amounts of cash, with no end in sight.
And there is no evidence the cost of inference is going down, and even Anthropic admits training will continue burning resources.
@ikidd@lemmy.world @ingeanus@ttrpg.network do you two have a source for these supposed great models?
What inclined you to @ me into this? As far as I can see, I haven’t even replied in this thread, and you just seem like you’re on the warpath with anyone that wants to defend using LLMs. If Greg KH thinks it’s coming into it’s own, you might want to heed him.
https://mastodon.social/@nixCraft/111695037458159431
Oh wow, comparing a thing to a completely different thing without demonstrating the comparison is valid.
Exactly the non-evidence I expected.