[chaxor]

"Theoretically everything should run in ai"

Odd statement. I don't really know what you mean by that. Perhaps 'math _works_, code should too' ?

I would definitely agree that it _should_ work.

I'm of the belief that no one should _have to_ publish (e.g. to graduate, get promotions, etc) in academia, and that publications should only occur if they're believed to be near Novel prize worthy, and fully reproducible by code with packaging that should last and work in 10 years, from data archives that will exist in 10 years.

But it seems I have been outvoted by the administration in academia.

Hence, we get this "ai that doesn't run" phenomenon

[KeplerBoy]

What's the point of academia if not to publish?

Do you want to publicly fund researchers only for the industrial research partner's benefit?

[chaxor]

It already is effectively just for industry benefit. It's been like that since the start. Work that is too expensive for industry to do (research and discovery) was put into the public sphere such that the role of industry was to take that innovation and optimize it. That's at least how it is intentionally constructed.

My main point was that there is a lot of noise in scientific journals that are caused from pressures in academia that are requirements if publishing. If these are removed, then the quality of work published increases and quantity decreased.

There are other places to post work that is derivative and non-novel like blogs. The field of biology has an immense amount of work that is mostly observational without strong conclusions or predictivity. A tabulation of observation should definitely be put out by a lab, and it should be much sooner with far less pressures than today, such as the typical dance of putting the data in during publication. The SRA is one example of a place to share data. If the typical way to work was put all data immediately onto a public repo, sometimes comment on it in ways that have been seen before on blogs and other classes below scientific journals, and then if something truly substantial comes out of it (a novel model that is analytical and highly predictive of cell behavior in all situations for example) then publish.

It could alleviate the noise from the signal. LLMs is one case where the noise is very strong in that many papers are simply 'we fine tuned an llm'.

[michaelmior]

So how should knowledge be shared in academia without publishing? Any work worthy of a Nobel Prize (or more likely, a Turing Award) is built on top of significant amounts of other research that itself wasn't so groundbreaking.

That said, I certainly think that researchers can do more to make their code and data more accessible. We have the tools to do so already but the incentives are often misaligned.

[ganeshkrishnan]

>"Theoretically everything should run in ai"

> Odd statement. I don't really know what you mean by that. Perhaps 'math _works_, code should too' ?

It was a typo. I mean "Everything should run in it" as in most LLM should be able to run at least quantized in 4090