"When they [AI companies] do ‘do’ science, they are still using science mostly as an aesthetic. They publish papers with grand claims but provide no access to any of the data or code used in the research. Their approach to science is what “my girlfriend goes to another school; you wouldn’t know her” is to high school cliques."
Functioning code and tools are available for almost all of the relevant AI and ML work in academic publications.
Yes, it took a big leak from Meta for a certain type of model, but the techniques to use LLMs and other transformer based tools are not vaporware. Most have code repositories and functioning open-source implementations.
We can certainly be critical of the datasets used for testing but we can’t accuse people of selling snake oil when so many of us are implementing and gaining value from the techniques described in these academic publications.
The word “relevant” is an important qualifier which may be doing a lot of lifting. Who defines what is “relevant”? How do you interpret it for this particular case?
I haven‘t looked at the matter myself, but the author did provide a source for their claim¹. Do you have any specific information on why it is wrong? It is not my intention to put you on the spot, I’m trying to understand if your point is based on data or gut feeling so I can myself get a better understanding of the validity of each argument.
¹ https://www.baldurbjarnason.com/2023/beware-of-ai-snake-oil/...
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-...
e: and I'll never forgive them for it
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."
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?
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'd say yes/no.
Efficiently advancing science definitely leverages well-reported and -reproduced results to skip to building on top of them.
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.
"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?
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-...
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.
I certainly disagree that it's the case for the whole field of ML. But the AI that's in the news right now is LLMs with OpenAI in the lead who have done their utmost to imped reproducible research and even research built on top of their already released tools.
It's been a trend that started with Google years ago and their increasingly large models which are unreproducible for most of the scientific community and often not released (if I recall correctly).
It's funny the article makes its own overbroad claims without data or references, in the name of asserting that's all that's going on in AI.
Can you clarify what you mean? There are almost two dozen footnotes pointing to references and papers.
pick any other area of research, and I will happily find you some footnotes citations that support exactly these claims.
I haven’t read every linked reference, but for a relatively short article there are more than a few. It's certainly more annotated than the average article I find on HN. Shouldn’t the quality of the sources matter most? And if what matters is the number, how many would be enough in this case? What would prove the point?
> or how open other areas of research are.
Other areas of research are irrelevant to the point. Even if other areas suck, that doesn’t excuse this one sucking too.
> pick any other area of research, and I will happily find you some footnotes citations that support exactly these claims.
Even if that is true, it doesn’t make the argument automatically false or irrelevant for any particular case.
First you objected the author had no data or references. Now you’re saying that for any area of research you can find references to prove the point they’re making. Which is it? Is the problem that they didn’t provide enough sources, or that the sources aren’t believable?
I meant, the author cites anecdotes (“here’s a paper that’s not open!”) rather than data (“of the 500 most-cited papers in the AI space, 82% were not open, compared to only 36% of papers in anthropology being non-open”).
And, I meant if you pick any other discipline that produces thousands of academic papers a year, I will be able to find 20 that don’t count as open.
Anger often makes it hard to understand things.
The same applies to training data - it should be disclosed.
TriviaQA: https://huggingface.co/datasets/trivia_qa
HotPotQA: https://huggingface.co/datasets/hotpot_qa
Those two are used in most of the question-and-answer based metrics for LLMs in academic publications.
The same applies for other datasets used for other metrics. HuggingFace has most of if not everything of any relevance.
I suspect we’ll be seeing some highly entertaining stuff, coming down the pipes.
Might not be as bad as the crypto snake oil bazaar, but it should have some interesting hucksters.
I think it will be as bad as with cyrptocurrency, if only because so many of the people involved come from the cryptocurrency crowd.
A common pattern around ai companies. To me it appears that a genuinely interesting technology has been hijacked by marketing and fud.
On the plus side, doing research does advance the state of the art, so it's one of the most productive marketing activities.