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Training AI models might not need enormous data centres

economist.com

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Re: Training AI models might not need enormous data centres

#21

Post by myrmidon »

grumbelbart2 wrote: post #7
openrisk wrote: post #6

Open source public models trained on kosher data are substantially derisking the AI hype. It makes a lot of sense to push this approach as far as it can get. Its similar to SETI at home etc. but potentially with far more impact.

They could, would and should. But: Training a state of the art LLM costs millions in GPU, electricity alone. There is no "open" organization at this point that can cover this. Current "open source public models" are shared by big players like Meta to undermine the competition. And they only publish their weights, not the training data, training protocols, training code; meaning it's not reproducible, and questionable…

I think it's important to remember that we know neural networks can be trained to a very useful state from scratch for 24 GJ: This is 25 W for 30 years (or 7000 kWh, or a good half ton of diesel fuel), which is what a human brain consumes until adulthood.

Even though our artificial training efficiency is worse now, likely to stay worse because we want to trade efficiency for faster training, and because we want to cram more knowledge into our training data than a human would be exposed to, it still seems likely to me that we'll get within orders of magnitude of this sooner or later.

Even if our training efficiency topped out at a hundred times worse than a biological system, that would be the energy equivalent of <100 tons of diesel fuel. Compared to raising and educating a human (and also considering this training can the be utilized for billions of queries before it becomes obsolete) that strikes me as a very reasonable cost (especially compared to the amounts of energy we wasted on cryptocurrency mining without blinking an eye...)

Re: Training AI models might not need enormous data centres

#22

Post by dbspin »

myrmidon wrote: post #21
grumbelbart2 wrote: post #7

Earlier quoted context omitted.

They could, would and should. But: Training a state of the art LLM costs millions in GPU, electricity alone. There is no "open" organization at this point that can cover this. Current "open source public models" are shared by big players like Meta to undermine the competition. And they only publish their weights, not the training data, training protocols, training code; meaning it's not reproducible, and questionable…

I think it's important to remember that we know neural networks can be trained to a very useful state from scratch for 24 GJ: This is 25 W for 30 years (or 7000 kWh, or a good half ton of diesel fuel), which is what a human brain consumes until adulthood. Even though our artificial training efficiency is worse now, likely to stay worse because we want to trade efficiency for faster training, and because we want to cr…

This misses that evolution has been pre-training the human cognitive architecture - brain, limbic system, sympathetic and parasympathetic nervous systems, coevolved viral and bacterial ecosystems - for millions of years. We're not a tabula rasa training at birth to perfectly fit whatever set of training data we're presented. Far from it. Human learning is more akin to RAG, or test time training - specialising a heavily pre-trained model. It's not that we're born with very much knowledge, it's more that we're heavily specialised to acquire and retain certain kinds of knowledge and behaviour that are adaptive in the EEA (environment of evolutionary adaptedness). If the environment then doesn't provide the correct triggers at the correct times for activation of various learning mechanisms - best known being the critical period for language acquisition, we don't unfold into fully trained creatures. Bear in mind also that the social environment is vital both for human learning and functioning - we learn in the emotional, cognitive and resource provision context of other humans. And what we learn are behaviours that are effective in that context. Even in adulthood, the quickest way to make our cognitive architecture break down is to deny us social contact (hence the high rates of 'mental illness' in solitary confinement).

Re: Training AI models might not need enormous data centres

#23

Post by godelski »

whazor wrote: post #17

You could consider a LLM as a very lossy compression artifact. Where they took terabytes of input data, and ended up with model under the 100 gigabytes. It is quite remarkable what such a model can do, even fabricating new output that was not in the input data. However, in my naïvety, I wonder whether vastly simpler algorithms could be used to end up with similar results. Regular compression techniques work with spee…

  > However, in my naïvety, I wonder whether vastly simpler algorithms could be used to end up with similar results.
Almost certainly. Distillation demonstrates this. The difficulty is training. It's harder to train a smaller network and harder to train with less data. But look at humans, they ingest far less data and certainly less diverse data. We are extremely computationally efficient. I guess you have to be when you run on meat

Re: Training AI models might not need enormous data centres

#24

Post by ghxst »

whazor wrote: post #17

You could consider a LLM as a very lossy compression artifact. Where they took terabytes of input data, and ended up with model under the 100 gigabytes. It is quite remarkable what such a model can do, even fabricating new output that was not in the input data. However, in my naïvety, I wonder whether vastly simpler algorithms could be used to end up with similar results. Regular compression techniques work with spee…

I'm not sure how accurate it is but my gut feeling is that the level of meaningful compression is somehow correlated to the level of intelligence behind a model, I wouldn't be surprised if it ends up being a major focus in general intelligence.

Re: Training AI models might not need enormous data centres

#25

Post by ghxst »

HPsquared wrote: post #15
lz400 wrote: post #12

Earlier quoted context omitted.

I understood SETI style meaning crowdsourced. Instead of mining bitcoin you mine LLMs. It's a nice idea I think. Not sure about technical details, bandwidth limitations, performance, etc.

Unfortunately, LLM training is not as computationally easy (embarrassingly parallel) as mining bitcoins.

If that were to be solved (if at all possible, and feasible / competitive) I can definitely see "LLM mining" be a historic milestone. Also much closer to the spirit of F@H in some sense, depending how you look at it. Would there be a financial incentive? And how would it be distributed? Could you receive a stake in the LLM proportional to the contribution you did? Would that be similar in some sense to purchasing stock in an AI company, or mining tokens for a crypto currency? Potentially a lot of opportunity here.

Re: Training AI models might not need enormous data centres

#26

Post by CaptainFever »

Aerroon wrote: post #14
Mountain_Skies wrote: post #13

Earlier quoted context omitted.

SETI had a clear purpose that donors of computer resources could get behind. The LLM corps early on decided to drink the steering poison that will keep there from ever being a united community for making open LLMs. At best you'll get a fractured world of different projects, each with its own steering directives.

The internet is for ____. That could be a factor that unites enough people to donate their compute time to build diffusion models. At least if it was easy enough to set up.

Related: people donating computing power to run diffusion and text models, which is definitely largely used for porn.

https://stablehorde.net/

Or the large amounts of community efforts (not exactly crowd sourced though) for diffusion fine-tunes and tools! Pony XL, and other uncensored models, for example. I haven't kept up with the rest, because there's just too much.

Re: Training AI models might not need enormous data centres

#27

Post by HPsquared »

ghxst wrote: post #25
HPsquared wrote: post #15

Earlier quoted context omitted.

Unfortunately, LLM training is not as computationally easy (embarrassingly parallel) as mining bitcoins.

If that were to be solved (if at all possible, and feasible / competitive) I can definitely see "LLM mining" be a historic milestone. Also much closer to the spirit of F@H in some sense, depending how you look at it. Would there be a financial incentive? And how would it be distributed? Could you receive a stake in the LLM proportional to the contribution you did? Would that be similar in some sense to purchasing sto…

The models are too large to fit on a desktop GPU's VRAM. Progress would either require smaller models (MoE might help here? not sure) or bigger VRAM. For example training a 70 billion parameter model would require at least 140GB of VRAM in each system, whereas a large desktop GPU (4090) has only 24GB.

You need enough memory to run the unquantized model for training, then stream the training data through - that part is what is done in parallel, farming out different bits of training data to each machine.

Re: Training AI models might not need enormous data centres

#28

Post by ben_w »

openrisk wrote: post #6

Open source public models trained on kosher data are substantially derisking the AI hype. It makes a lot of sense to push this approach as far as it can get. Its similar to SETI at home etc. but potentially with far more impact.

I don't see how it really helps?

We could pass laws requiring models to demonstrate their training sets irregardless of how the training is distributed; and conversely if this is a community-led project, those also have copyright issues to deal with (wikipedia for example).

I suspect there's also a problem in that, e.g. ten million student essays about different pages of Harry Potter can each in isolation be justified by the right to quote small fragments for critical purposes, but the collection together isn't because it quotes an entire book series.

Re: Training AI models might not need enormous data centres

#29

Post by TZubiri »

grumbelbart2 wrote: post #7
openrisk wrote: post #6

Open source public models trained on kosher data are substantially derisking the AI hype. It makes a lot of sense to push this approach as far as it can get. Its similar to SETI at home etc. but potentially with far more impact.

They could, would and should. But: Training a state of the art LLM costs millions in GPU, electricity alone. There is no "open" organization at this point that can cover this. Current "open source public models" are shared by big players like Meta to undermine the competition. And they only publish their weights, not the training data, training protocols, training code; meaning it's not reproducible, and questionable…

Asking to share the training data is a bit too much, it's petabytes of data, probably has privacy implications.

You can study and reproduce with your own training data right?

Re: Training AI models might not need enormous data centres

#30

Post by netdevphoenix »

openrisk wrote: post #6

Open source public models trained on kosher data are substantially derisking the AI hype. It makes a lot of sense to push this approach as far as it can get. Its similar to SETI at home etc. but potentially with far more impact.

what's "kosher data"? Never heard of that before
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