Comment by timster6442 | Hacker News Reader

Comment by timster6442 | Hacker News Reader

Parent Full thread

I'm in academia (biology but highly computational) and I would say opinions on AI are quite polarized. Some professors in the department equate not using AI as lost productivity. Contrarily some professors abhor the idea of even using AI at all. For us (biologists) it's less of an issue because we have no fear of openai or A/ publishing a biology paper. Though even people I known in physics, data science, or computer science still heavily use AI.

Our university has agreements that stipulate that our institutional accounts cannot be used to train AI models and certain research groups have differential model access.

Further from academic journal sense there is mixed feelings. I once was able to meet with a senior journal editor (general non-medical high IF journal > 50) who claimed that if they think something is written by AI they wouldn't consider it. Yet another high IF journal said it was completely fine if something was written by AI. About a month ago I reviewed a paper by yet a different high IF journal and in big bold red letters it said I was not allowed to feed any part of the paper through AI (even if it was locally ran) but you could ask it to rephrase text that you wrote.

Do you mind me asking why you have no fear of OpenAI etc publishing a biology paper? With increasing model capability and compatibility with lab hardware could we not be in a scenario soon(ish) where these agents are able to autonomously complete and publish experimental results?

I was debating this with a friend the other day and the consensus we came to was that a highly trained scientist would (or should) always review output like that described above, but that's starting to feel like a weakening argument!

The compatibility with the lab hardware part is going to take a little while, for now running experiments is problematic for LLMs

Firstly the underlying worry here is about privacy which hinges on the fact that AI companies are stealing ideas in the first place. Stealing from your customers is an incredibly bad business model and I think if they were to steal IP (intellectual property) from researchers mathematicians or computer scientists would be first.

Now why I think biology is safer: 1) Producing novel biology still has to be done in a lab. It requires laboratories, equipment, experimental protocols, trained personnel, regulatory and safety infrastructure, and often substantial institutional organization all of which there is no indication they're heading for. Also I disagree that lab hardware is near a "soon state" where labs can be full autonomous, (liquid handlers are really good at niche tasks but lack any type of experimental general ability [not AI-bounded], especially for in vivo work where its footprint is non-existent). Even the most automated Labs I know where robots do 80% of experimental work, they still have grad students to carry out that last 20% and to oversee.

2) Even if AI could do the pipeline it's not worth it for AI LLM companies to dedicate capital to it currently. A lot of biology research itself doesn't produce a sellable product, in fact most of it never does. It seems currently and for at least the next couple years at least, AI capital is best spent growing compute to research better models, train better models, and sell inference.

Reply on news.ycombinator.com