3 ms·

While I can't speak for everyone in academia, I personally don't feel comfortable in putting my research questions and outputs to a private website, before the

While I can't speak for everyone in academia, I personally don't feel comfortable in putting my research questions and outputs to a private website, before the idea is at least arxived. Especially as all the Fable/Mythos prompts are said to be human reviewed. So I believe that, at least in the short run, we might be seeing breakthroughs in hard open problems or in low hanging problems which are not that interesting to spend time on. I may be wrong, if some research labs have private contracted access to the models

eamag 1mo ago
Isn't it showing a problem with an academia? "I don't want to live in a world where someone else makes the world a better place than we do."
- aners_xyz 1mo ago
  This feels like an unwarranted strawman. There are plenty of reasons for researchers to share openly at times and plenty of times it makes sense to wait until the meal is ready to serve before publishing.
- jazzyjackson 1mo ago
  grants are competitive
- cube00 1mo ago
  Academics have to eat and they're judged on the quality of the research they produce. They're more likely to share their research then big tech once it's ready and they can get the credit they deserve. This can then be used to succeed in future grants or if your institution is particularly strict, meet your publish quota to keep your position.
- jltsiren 1mo ago
  The problem is a lack of funding, which leads to excessive competition and ties continued employment to sustained contributions. Many results are obvious in retrospect, and such results are often the best ones. The difficult part with such results is framing the problem in the right way and asking the right questions. If you manage to do that, the result simply follows. You may still need funding and hard work to confirm your finding, in which case someone with more resources can claim your result, if they are aware of the idea.
- deleted 1mo ago
  [deleted]
- plaidfuji 1mo ago
  I think the sentiment is misplaced here (there is a legitimate concern for IP protection), but this is my absolute favorite line from Silicon Valley - small correction though: “… makes the world a better place better than we do”
- dguest 1mo ago
  I think the GP was suggesting that their reluctance was more about someone taking their idea. I still think it might suggest a problem with academia, but the summary would be closer to "I don't want to live in a world where someone else follows through with my ideas without giving me credit" It's still a problem because a lot of academics aren't especially well equipped to follow through with their ideas, which can create information silos that lead to ideas never being implemented. Still, I don't know if this is the biggest fish to fry: you have other silos like IP law and NDAs etc.
kccqzy 1mo ago
That’s actually common. Not in academia but a lot of enterprises are specifically not using Fable because Anthropic doesn’t provide a Zero Data Retention mode like they do for Opus. Even at my employer when Fable is available, some employees just aren’t comfortable using it when they perceive that they are working on extremely sensitive research.
- nightpool 1mo ago
  Did you read TFA? They're adding zero data retention back for 5.1
  - kccqzy 1mo ago
    I did. It’s only for approved users and only until EFS is available. And given no human at Anthropic reviews messages, I fail to see how EFS will be extended to a large enough audience to be worthwhile. As such my comment did not want to extrapolate what will happen.
timster6442 1mo ago
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.
- nixlaz 1mo ago
  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!
  - desterothx 1mo ago
    The compatibility with the lab hardware part is going to take a little while, for now running experiments is problematic for LLMs
  - timster6442 29d ago
    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.

eamag 1mo ago

Isn't it showing a problem with an academia? "I don't want to live in a world where someone else makes the world a better place than we do."

aners_xyz 1mo ago
This feels like an unwarranted strawman. There are plenty of reasons for researchers to share openly at times and plenty of times it makes sense to wait until the meal is ready to serve before publishing.
jazzyjackson 1mo ago
grants are competitive
cube00 1mo ago
Academics have to eat and they're judged on the quality of the research they produce. They're more likely to share their research then big tech once it's ready and they can get the credit they deserve. This can then be used to succeed in future grants or if your institution is particularly strict, meet your publish quota to keep your position.
jltsiren 1mo ago
The problem is a lack of funding, which leads to excessive competition and ties continued employment to sustained contributions. Many results are obvious in retrospect, and such results are often the best ones. The difficult part with such results is framing the problem in the right way and asking the right questions. If you manage to do that, the result simply follows. You may still need funding and hard work to confirm your finding, in which case someone with more resources can claim your result, if they are aware of the idea.
deleted 1mo ago
[deleted]
plaidfuji 1mo ago
I think the sentiment is misplaced here (there is a legitimate concern for IP protection), but this is my absolute favorite line from Silicon Valley - small correction though: “… makes the world a better place better than we do”
dguest 1mo ago
I think the GP was suggesting that their reluctance was more about someone taking their idea. I still think it might suggest a problem with academia, but the summary would be closer to "I don't want to live in a world where someone else follows through with my ideas without giving me credit" It's still a problem because a lot of academics aren't especially well equipped to follow through with their ideas, which can create information silos that lead to ideas never being implemented. Still, I don't know if this is the biggest fish to fry: you have other silos like IP law and NDAs etc.

kccqzy 1mo ago

That’s actually common. Not in academia but a lot of enterprises are specifically not using Fable because Anthropic doesn’t provide a Zero Data Retention mode like they do for Opus. Even at my employer when Fable is available, some employees just aren’t comfortable using it when they perceive that they are working on extremely sensitive research.

nightpool 1mo ago
Did you read TFA? They're adding zero data retention back for 5.1
- kccqzy 1mo ago
  I did. It’s only for approved users and only until EFS is available. And given no human at Anthropic reviews messages, I fail to see how EFS will be extended to a large enough audience to be worthwhile. As such my comment did not want to extrapolate what will happen.

timster6442 1mo ago

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.

nixlaz 1mo ago
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!
- desterothx 1mo ago
  The compatibility with the lab hardware part is going to take a little while, for now running experiments is problematic for LLMs
- timster6442 29d ago
  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.