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.
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.
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.
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.
https://www.microsoft.com/en-us/research/blog/zero-deepspeed...
The communications overhead of doing this over the internet might be unworkable though.
All of this makes it very poorly suited to a collection of heterogeneous compute connected via the internet, which wants a large chunk of mostly independent tasks which have a high compute cost but relatively low bandwidth requirements.
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...)
Yes, but.
The human genome isn't that big (3.1 gigabases), and most of that is shared with other species that aren't anything like as intelligent — it's full of stuff that keeps us physically alive, lets us digest milk as adults, darkens our skin when exposed to too much UV so we don't get cancer, gives us (usually) four limbs with (usually) five digits that have keratin plates on their tips, etc.
That pre-training likely gives us innate knowledge of smiles and laughter, of the value judgment that pain is bad and that friendship is good, and (I suspect from my armchair) enough* of a concept of gender that when we hit puberty we're not all bisexual by default.
Also, there's nothing stopping someone from donating their genome to be used as a pre-training system, if we could decode the genome well enough to map out pre-training like that.
* which may be some proxy for it, e.g. "arousal = ((smell exogenous sex hormone) and (exogenous hormone xor endogenous hormone))", which then gets used to train the rest of our brains for specific interests — evolution is full of hack jobs like that
To make this data 'actionable' for a synthetic intelligence you'd need to functionally replicate the contributions of the intrauterine environment to development, and lastly simulate the social and physical environment. This can't be 'decoded' in the way you implicitly suggest - since it's decompression is computationally irreducible. These are dynamic processes that need to be undergone in order to create the fully developed individual.
[1] https://www.bbc.com/future/article/20230210-the-man-whose-ge...
Knowing the weights without knowing the full graph of the model they're used in, just the endpoints.
There's a lot of valid stuff in what you say, I am aware I'm glossing over a lot of challenges to get a copy of a human — to what extent is e.g. the microbiome even contributing to our intelligence, vs. being several hundred different parasites that share a lot of DNA with each other and which happen to accidentally also sometimes give us useful extras? It's hard work telling which is which — but my claim is that the nature and scope of such work itself still allows us to say, as per one of the parent comments:
> 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.
If this were a 100m sprint, then I would agree with you essentially saying that we don't even know which country the starting blocks are in, but I am still saying that despite that we know the destination can be reached from the starting blocks in 10 seconds.
Yes. But that is not part of the training cost; this is basically the equivalent to figuring out a suitable artificial neural net architecture and hyperparameter tuning in general. That is not energy cost that you pay per training run, but fixed cost overhead instead.
You raise a good point that when doing artificial training, the "environment" has to be provisioned as well (i.e. feeding audio/visual/text input in some way to do the training), but here I would argue that in energy terms, that is a rather small overhead (less than an order of magnitude) because our digital information storage/transmission capabilities are frankly insane compared to a human already (and reasonably efficient as well).
You can study and reproduce with your own training data right?
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.
Copyright is intended to reward investment in creative works by giving sole license to distribute. It is not intended to create a monopoly on knowledge about the work.
If I can ask an LLM (or person!) “what’s the first sentence in Harry Potter?” And then “what’s the second sentence?” and so on, that does not mean they are distributing the work in competition with the rights holders.
We have gone way overboard with IP protections. The purpose of copyright is served when Rowling buys her 10th mansion. We do not need to further expand copyright to make it illegal to learn from a work or to remember it after reading.
Perhaps, but it's more an example of the problem: something can be fine at small scale, but cause issues when everyone does it. Tragedy of the commons, but with words.
(From an even more extreme point of view, consider that an image generating AI trained on nothing but photographs taken from drones flying and androids walking all over the place would be able to create photo-realistic images of anything, irregardless of if even one single human artist's works end up in the training set, which in turn means the current concerns about "did the human artists agree to this use" will be quickly made irrelevant because there were none in the training set in the first place).
"Quantity has a quality all its own", whoever really said it first.
> Copyright is intended to reward investment in creative works by giving sole license to distribute. It is not intended to create a monopoly on knowledge about the work.
Sure, but laws change depending on economics. I can easily believe AI will lead to either much stronger or much weaker copyright laws.
Depends who is wielding the power when the change comes.
> If I can ask an LLM (or person!) “what’s the first sentence in Harry Potter?” And then “what’s the second sentence?” and so on, that does not mean they are distributing the work in competition with the rights holders.
Isn't that a description of how BitTorrent works? And The Pirate Bay is kinda infamous for "distributing the work in competition with the rights holders".
> We have gone way overboard with IP protections. The purpose of copyright is served when Rowling buys her 10th mansion. We do not need to further expand copyright to make it illegal to learn from a work or to remember it after reading.
I agree, and was already in favour of radical changes to copyright rules well before LLMs.
(That said, it's more complex because of how hit-driven lots of things are, which means that while nobody needs to defend Rowling's second billion, having looked at the distribution of book sales in the best-seller lists… most of them will/would have need/ed a second source of income to keep publishing).