Stable Diffusion is open in all senses of the word.
GPT-4 is closed but the techniques it enables are reproducible by anyone with a credit card.
GPT-like things like LLaMA are, well, available. Direct, individually reproducible, these tools and their related academic publications qualify as computer science.
Nullius in verba certainly applies.
Requiring a credit card to verify scientific claims is a large deterrent. And in addition one needs to provide them with a phone number, which many people will not be comfortable with when considering their CEO’s history.¹
Even ignoring the above, using the output of ChatGPT is definitely not the same as having “access to any of the data or code used in the research”, which is the author’s complaint and what the original commenter quoted.
As for the other cases, we could only properly assess them if we discussed specific studies which I don’t think we’re going to do in the limited context of HN comments. Thank you for taking the time to explain your view.
¹ https://www.buzzfeednews.com/article/richardnieva/worldcoin-...
I would prefer more open advances, but hard not to feel gate keeper for a term to claim they aren't advancing the science.
The former is science. The latter is a commercial product.
Stretching their business model to its most charitable interpretation of "open", OpenAI is essentially offering "Here's the cost for the lab and labor for the work you want to do. We'll do it and send you the results, but won't give you any of our lab procedures or intermediate data."
"Science" is a method of determining objective truth. "Engineering" is taking the truths science uncovers and using them to make things.
Commercial enterprises, including OpenAI, are about engineering first and foremost. They may also engage in science, but that is only to serve the engineering. (Saying that is not saying the there's something wrong with the science they engage in).
The point is that the scientist is primarily intending to learn something solid about reality, and the engineer is primarily intending to create something that is practically useful.
Maybe we have had some solid "pure scientists." Largely we would label them closer to philosophers? That said, the vast majority that you know the name of, were probably employed closer to what we think of as engineers in today's parlance.
Or am I off here and the likes of Feynman, Faraday, Fourier, Maxwell, etc. would not have been said to be working on engineering problems, by modern eyes?
My gut is that it gets difficult as looking at things with our modern lens of corporations today makes it hard to really consider institutions of the past?
But you can reproduce the work with a shitton of compute and a massive dataset. They aren’t sharing their exact strategy but the technique is fairly well known.
IMHO it really doesn’t matter, advancing science comes from trying different things and not from reproducing someone else’s work to prove it works.
I disagree with this. If scientists aren't reproducing the work of other scientists, then science cannot advance. Reproduction of results is one of the main mechanisms by which science separates reality from fantasy.
Of course, if reproducing other's results is the only thing scientists do, that also does not advance understanding. But that doesn't take away the extreme importance of reproducing the experiments of others.
Are you or have you been a professional scientist?
Because while that's how it should work, it generally doesn't anymore as it's really, really difficult to get straight replications (which are desperately needed) published.
Not directly, but I've worked as a research assistant in a number of labs for real, professional scientists and I'm a coauthor on a few papers.
> it's really, really difficult to get straight replications (which are desperately needed) published.
This is a fact. But nonetheless, such replications do get done. Not enough, certainly. But pointing out that the way institutional science is done is flawed doesn't invalidate my point at all.
I'd say yes/no.
Efficiently advancing science definitely leverages well-reported and -reproduced results to skip to building on top of them.
The US military has pushed state of the art in many fields, but I don't think anyone would say it pushed stealth composite science forward with the B-2/F-22/F-35 programs, because all the details are classified.
The risk is that non-shared research is eventually lost.
I think the catch is Open AI is risking turning "open" into a weasel word? That is, still science, but hardly open. Which is fine, but then don't call yourself open?
The methods for reproducing the LLMs in question have been published. Much like with the LHC, one hindrance to reproducing claims is the cost of building the machine. Unlike the LHC, mere mortals can interact with the machine and draw their own conclusions of the claims published by the academic journals.
This the the foundation of the issues you raise with regards to science in the 21st century: the cost of reproducing a claim is at odds with “taking no one’s word”.
The leak of the LLaMA models changes this dynamic. It is now possible to run the models locally and with the same datasets published in the journals. This is hardly egalitarian but science has always had a material cost. Competition from both public and private sources almost guarantees that these models will be cheap and abundant in not too long of a time.
That’s a wild extrapolation and an absolute mischaracterisation of what I wrote. Please respond to what people write, not what you think they believe.
I only claimed many people are wary of giving OpenAI their phone number due to their CEO. I did not say AI papers are snake oil. On the contrary, I explicitly said I don’t know and have respectfully asked for clarifications.
Also, Worldcoin is neither a previous venture nor unrelated. They just pivoted to collect your eyeballs to prove you’re not an AI, “solving” the problem they created.
https://worldcoin.org/blog/engineering/humanness-in-the-age-...
e: and I'll never forgive them for it
In fact there's generally a reproducibility crisis in machine learning, since things like models, datasets, training techniques, hyper-parameters are often only documented in the vaguest of terms.
Reproducibility doesn't mean I have a black box and when I push the button (after swiping my credit card) the light comes on as expected. Reproducibility in science means I can reproduce the experiment from scratch and get the same results.
Practically nothing is known about GPT-4. OpenAI's "GPT-4 Technical Report" doesn't say much more than the fact that it's "Transformer-based".
No, you really can't reproduce it this way. Science is not a carnival show. This is why the term snake oil was appropriate.