If this works well, it will be a game changer. Requiring a fleet of $10k+ GPUs will kill any hope of wide spread adoption of open source "competitors" to GPT-3. Stable Diffusion is so popular because it can run on hardware mere mortals can own.
And you could train something like GPT-3 for cheaper than a superbowl commercial. That would get you a lot of publicity.
In other words, I don't care why people do things, only that they do.
I’m not aware of many examples of starry-eyed divas achieving great results. Usually you hear about them but only because they are exceptional cases, not the norm. It’s a matter of practicality and not virtue (to say otherwise is purely a straw man argument).
This is really overconfident.
That publicity isn't causally connected to success is belied by the existence of the advertising industry. While generally refuting across industries, it is worth noting that the most dominant AI company - Google - happens to be in this industry. They are explicitly known - having publicity for - their generous compensation packages. This is because of a causal model of talent attraction.
Success is obviously causally connected to publicity and the idea that it isn't isn't well supported by the evidence. Contrary to your assertion, it was not a safe assumption. Your appeal to Satoshi is an appeal to authority, not a causal model of its shielding off from project impacts.
The argument was about publicity as a reward motivator, not publicity itself, as a causal relation to success.
To phrase it plainly: Which first-time founders do you think Paul Graham or Keith Rabois would more likely fund: Those who aspire to solve a problem with the world that they care passionately about? Or those looking for money or fame? Last time I checked the latter case would be seen as a strong negative. The appeal to authority argument doesn’t apply in this situation because the VC portfolio performance is causally related to how accurately they predict future success of a company.
On the scale of a smaller project like this, a common failure mode is for a maintainer to stop caring about the project and go to the next thing that motivates them. Someone else may attempt to use the code or project without understanding the theory behind it. And even worse: every time this happens is a signal that this is acceptable.
AI is a different beast. Software bugs with big AI systems will become more costly, and eventually deadly. Unfortunately I’m not sure what can be done about it without a global totalitarian regime to ban its use entirely (which is not an idea I support anyway). Eventually the broken clock will be right and some profit-driven AI project will succeed in making the world a not better place, if we are even around to notice :).
I would advise deeper thought into these topics when convenient. Read Nick Bostrom’s Superintelligence book or watch his talks, at least one of which was at Google HQ.
I think someone should train ChatGPT or similar to argue or teach traditional AGI Philosophy/Ethics and hopefully that will move the needle somewhat more than the OpenAI nannyism we have now.
It is worth reflecting on the fact that the founder of OpenAI has had the strongest possible endorsement from Paul Graham. He was claimed to be among the greats before his successes: Paul Graham put him among Steve Jobs and Elon Musk. When Paul Graham stepped down from YCombinator, he was so convinced of Sam's skills that he put Sam in his place. Later Sam started OpenAI.
> I would advise deeper thought into these topics when convenient. Read Nick Bostrom’s Superintelligence book or watch his talks, at least one of which was at Google HQ.
I've read Superintelligence, the Sequences, PAIP, AIMA, Deep Learning, Reinforcement Learning, and Theory of Games and Economic Behavior, taken a course in control theory, and read a book about evolutionary algorithms. I've also built systems after having understood these techniques for literally each of these things I've mentioned with the exception of all of Superintelligence and much of the Sequences with the exception of parts of the sequences which dealt with Bayesian reasoning, which I did implement and like, though I disagree with that community about its optimality because the conditions of ledger arguments aren't true in the real world. In practice, Bayesian approaches are like trying to build a sportscar for a race - you get beaten even though you are doing the fastest thing, because the fastest thing isn't as fast as the slower methods.
Anyway, the combinatorics of multi-step multi-agent decision problems implies a lot of problems for Bostrom and Yudowsky positions on the limits of what intelligence can hope to achieve. I don't find them to be the most formidable thinkers on this subject. In the case of Yudowsky, he admits this, saying that he finds Norvig to be more formidable than he is. And Norvig disagreed with him on AI risk in exactly the context I also disagree and for the same reason I disagree. To ensure you get the point I'll speak in terms of Bostrom's analogies: notice, there is, in fact, a speed limit. The speed of light. Well, what Norvig notices, and what I also notice, and what Bellman noticed when he coined the term combinatorial explosion, is that intractability is an actual issue that you need to confront. It isn't something you can hand wave away with analogy. We don't have enough atoms in our universe.
This is why we get dual mode systems by the way. Not just humans: notice, it happens in chess engines too. The general solvers provides the heuristic which must have error, then the specific solver uses the heuristic to improve, because it is in a more specific situation. Most of the people in the AI risk camp are pretty Yudowskian. They dwell for long periods of time on the overcoming of the biased heuristic. For sure, this makes them more intelligent, but it misinforms them when they try to make inference about general intelligence informed on the tractability in specific situations. It is because, not despite, the intractability that they find such evidence of tractability.
[1]: https://en.wikipedia.org/wiki/Curse_of_dimensionality
[2]: https://en.wikipedia.org/wiki/Combinatorial_explosion
OpenAI has a pretty good introduction to the Bellman equations in their Spinning Up in RL lessons [3]. Sutton's work in Reinforcement Learning also talks about Bellman's work quite a bit. Though Bellman was actually studying what he called dynamic programming problems his work is now considered foundational in reinforcement learning.
[3]: https://spinningup.openai.com/en/latest/
Uh, and for the dual mode observations the person that brought that to my attention was Noam Brown, not Bellman or Norvig. If you haven't already checked out his work, I recommend it above both Norvig and Bellman. He has some great talks on Youtube and I consider it a shame they aren't more widely viewed [4].
That the causal model supports publicity seeking leads us to ensemble models. When models are good for different reason, the ensemble of the models ends up better than any individual model. Reinforcement learning research has shown you can successfully train an agent out of decomposed reward signals by building an ensemble model atop them.
The fact that the causality says publicity matters means that agents which recognize the importance that publicity contribute to the solution actually do have the expectation of being part of the solution.
It is very common to see this talked about in terms of diversity improving solution quality when talking about it in the context of companies and it is generally considered a good idea to have a diverse team as a consequence.
Anyway, I'm mostly responding because I disagree with apriori declaration that all who disagree are attacking a straw man.
I think that was overconfident, because the causal structure of publicity and its relation to outcomes disagrees with that.
I'm pretty sure this is not how an LLM works.
>It isn't a "do it once and forget about it" task that it is being touted as in this thread.
That's neither here, nor there. Training the LLM itself is not a "do it multiple times per day if you want to compete with Google" thing as it has been stated in this subthread.
> I'm pretty sure this is not how an LLM works.
You can say that about any software. "You can use this software perfectly well without ever updating it." Sure, you can do that, but typically people have lots of reasons to update software. LLM isn't magic in this sense. An LLM does not mysteriously update its own code if you just wish hard enough. If you want to continue the development of the LLM then you need to make changes to the code, just like with any other software.
I wrote about one way to implement that pattern here: https://simonwillison.net/2023/Jan/13/semantic-search-answer...
Things happen everyday, but languages and words and their associations don't change in any measurable way every day...
This is not like web crawling...
This is not "software development" in general, this is LLM training.
It's not like you're building some regular app, api, or backend.
I'm not gonna engage in a rhetorical argument about whether this should be called "software development" or "LLM development" or something else. That's unrelated to the question of how much training is required.
No, I'm rather claiming that what you claimed is wrong in the context of LLM training: "Well maybe not every day, but having a short feedback loop and the ability to run your code multiple times with different variations is generally considered to be a prerequisite for software development".
LLM training is not the same as writing a program and "running your code with different variations". For LLM you don't need to quickly rerun everything with some new corpus - it would be nice, but it's neither a prerequisite, not even crucial for any current use.
Hell, it's not even a "prerequisite" in programming, just good to have. Tons of great programs have been written with very slow build times, without quick edit/compile/build/run cycles.
You said "LLM training is not the same as writing a program and running your code with different variations". How do you think these LLMs were made, seriously? Do you think Facebook researchers sat down for 12 months and wrote code non stop without compiling it once, until the program was finished and was used to train the LLM literally only one time?
Okay. But I was saying someone with millions of dollars to spend could do it. And then another poster was arguing that millions of dollars was not enough to be viable because you need lots of repeated runs.
Nobody was saying a student could train one of these models from scratch. The cool potential is for a student to run one, maybe fine tune it.
> Why would you want to retrain it from scratch every day?
I was explaining why someone might want to retrain it more than once (although not literally every day).
That's how much some tech companies pay for catering.
Hell, that's in the order of a single socialite's wedding costs.
I mean, don't get me wrong. It is a very expensive project. It just isn't astronomical. Anyone reading this and thinking - oh I could never do that even in hundreds of millions of years - that would be wrong. If you won the lottery or just made good financial decisions you could do a project comparable to this instead of getting a very nice house in the Bay Area.
My only problem with stable horde is that their anti-cp measure involves checking the prompt for words like small, meaning I can't use a nsfw-capable model with certain prompts (holding a very small bag, etc). That, and seeing great things in the image rating and being unable to reproduce because it doesn't provide the prompt.
And on top of that, there are some questions on the quality of open source data (The Pile) vs OpenAI’s proprietary dataset, which they seem to have spent a lot of effort cleaning. So: open source models are probably data-constrained, in both quantity and quality.
https://bigscience.huggingface.co/blog/bloom was trained with a $7M grant, and that was the first time they'd done it (so there was a lot of failures).
I'm aware of one very credible one that has applied for a TPU grant from Google worth under $200K to train the whole model.
I think laion.ai will probably get their first with their fleet of A100s though.
It sounds like something that could/should/would be crowdfunded?
Large companies can afford Nvidia. Nvidia's software stack is best in class. There's no business need here and the model execution is increasingly becoming possible on single consumer GPUs.
The only place where I see specialized chips excelling is on the edge or if they are truly revolutionary (in which case they're only an acquisition target for Nvidia).
The truth is... The large language models are likely excessively large.
The only exception is running things on mobile. There is demand for porting models to run natively on mobile, and somewhat reasonable support for doing this.
It likely wouldn't have nearly as much general knowledge since the data just wouldn't be there but the behaviour could be similar?
the history of computing clearly indicates that either the requirements for running or the bar for owning this technology was always going to drop