4 ms·

Let's admit it - overnight it became much much harder to be a convincing professor, given each student can use GPTs of all sorts to contradict or otherwise inti

by larodi 1y ago

Let's admit it - overnight it became much much harder to be a convincing professor, given each student can use GPTs of all sorts to contradict or otherwise intimidate you. Only a seasoned professor knows the feeling of being bullied by a smart-ass student. Which brings down the total value, the incentive, to teach, and also given the avoidance GPTs silently imprint in students. I mean - why write a program/paper/research when the GPT can do it for you, and save you the suffering. The whole area of algorithms suddenly became more challenging, as now you also have to understand folding multi-dimensional spaces, and retell this all as a nice story for students to remember. We are very likely heading into some dark uncharted era for academia, which will very likely lead to academia shrinking massively. And given the talk of 'all science now happens in big corpos'... I can expect the universities to go back to the original state they started from - monasteries. Saying this all having spent 20+ years as part-time contributor to one such monastery.

simianwords 1y ago
Not totally related but I have wondered how someone who thinks they are an expert in a field may deal with contradictions presented by GPT. For example, you may consider yourself an expert on some niche philosophy like say Orientalism. A student can now contradict any theories you can come up with using GPT and the scary thing is that they will be sensible contradictions. I feel like the bar to consider yourself an expert is much higher - you not only have to beat your students but also know enough to beat GPT.
- horsh1 1y ago
  Why in this story students use GPT and professors don't? If you are an expert -- sit down and start working with GPT on your own. See what it can and what it can not do. See where it helps. See where it hands down lose.
  - simianwords 1y ago
    You are right - this is a new activity professors have to pick up. A latent point in my previous comment was that maybe some professors have not as much expertise as may be required. Now that this expertise is sort of democratised there's more pressure on professors to get better.
  - chunkmonke99 1y ago
    Yeah I agree! I use GPTs (Gemini 2 series and o3 etc.) and they are excellent! But even they sometimes don't quite get the nuance of things or subtly misses the point! There are certain "meta-cognitive" limitations ... I would never bet against the AGI industry and I can only assume that these issues will eventually be solved after we build planets of computronium and force every word and thought any human ever utters to be recorded and trained on. But for now there remain some limitations.
  - larodi 1y ago
    I sure does use it to prepare exams etc. Problem is students don’t seem to see the reason for not using GPT to solve them ;) for me it perfectly makes sense to prepare exams and labs with some AI help… but this questions both my role and the role of in-person education. Really I consider stopping doing it at all…
Al-Khwarizmi 1y ago
Yes, my comment focused on NLP research but the importance of university teaching has also taken a hit - not that I fear bullying, but now students can have a dedicated custom teacher with infinite time and patience and that can answer questions at 3 AM, and obviously that reduces the relevance of the professor. While the human interaction in in-person teaching still provides some exclusive value, demand logically should go down. Although don't underestimate the power of inertia - one could also think that small noname universities would go out of business when the likes of MIT began offering their courses to everyone online, and it didn't happen. I do think LLMs bring higher risk than that, and a shrinking will indeed happen, but maybe not so dramatic. Let's see. Regarding science, if we leave it exclusively to corporations we won't get very far, because most corporations aren't willing to do the basic/foundational science work. The Transformers and most of the relevant followup work that led to LLMs were developed in industry, but they wouldn't have been possible without the academics that kept working on neural networks while that field was actively scorned during the 90s-2000s AI winter. So I think research universities still should have a role to play. Of course, convincing the funders that this is indeed the case might be a different story.
- charcircuit 1y ago
  >but they wouldn't have been possible without the academics that kept working on neural networks while that field was actively scorned during the 90s-2000s AI winter The bottleneck was on compute power. Industry would have also worked on neural networks once the compute power for it existed.
  - chunkmonke99 1y ago
    I think the point is that universities and public research broadly speaking tolerate a lot of risk that private institution are not built to shoulder. I'm no AI historian but ANNs were an intellectual backwater after the 1st/2nd AI Winter and the only entities "foolish" enough to keep investing in them were Canadian public research universities. If it weren't for Hinton's dogged quasi-autistic special obsession with them ... no for profit entity would have bothered to invest in "computer power".

by larodi 1y ago

simianwords 1y ago

Not totally related but I have wondered how someone who thinks they are an expert in a field may deal with contradictions presented by GPT. For example, you may consider yourself an expert on some niche philosophy like say Orientalism. A student can now contradict any theories you can come up with using GPT and the scary thing is that they will be sensible contradictions. I feel like the bar to consider yourself an expert is much higher - you not only have to beat your students but also know enough to beat GPT.

horsh1 1y ago
Why in this story students use GPT and professors don't? If you are an expert -- sit down and start working with GPT on your own. See what it can and what it can not do. See where it helps. See where it hands down lose.
- simianwords 1y ago
  You are right - this is a new activity professors have to pick up. A latent point in my previous comment was that maybe some professors have not as much expertise as may be required. Now that this expertise is sort of democratised there's more pressure on professors to get better.
- chunkmonke99 1y ago
  Yeah I agree! I use GPTs (Gemini 2 series and o3 etc.) and they are excellent! But even they sometimes don't quite get the nuance of things or subtly misses the point! There are certain "meta-cognitive" limitations ... I would never bet against the AGI industry and I can only assume that these issues will eventually be solved after we build planets of computronium and force every word and thought any human ever utters to be recorded and trained on. But for now there remain some limitations.
- larodi 1y ago
  I sure does use it to prepare exams etc. Problem is students don’t seem to see the reason for not using GPT to solve them ;) for me it perfectly makes sense to prepare exams and labs with some AI help… but this questions both my role and the role of in-person education. Really I consider stopping doing it at all…

Al-Khwarizmi 1y ago

Yes, my comment focused on NLP research but the importance of university teaching has also taken a hit - not that I fear bullying, but now students can have a dedicated custom teacher with infinite time and patience and that can answer questions at 3 AM, and obviously that reduces the relevance of the professor. While the human interaction in in-person teaching still provides some exclusive value, demand logically should go down. Although don't underestimate the power of inertia - one could also think that small noname universities would go out of business when the likes of MIT began offering their courses to everyone online, and it didn't happen. I do think LLMs bring higher risk than that, and a shrinking will indeed happen, but maybe not so dramatic. Let's see. Regarding science, if we leave it exclusively to corporations we won't get very far, because most corporations aren't willing to do the basic/foundational science work. The Transformers and most of the relevant followup work that led to LLMs were developed in industry, but they wouldn't have been possible without the academics that kept working on neural networks while that field was actively scorned during the 90s-2000s AI winter. So I think research universities still should have a role to play. Of course, convincing the funders that this is indeed the case might be a different story.

charcircuit 1y ago
>but they wouldn't have been possible without the academics that kept working on neural networks while that field was actively scorned during the 90s-2000s AI winter The bottleneck was on compute power. Industry would have also worked on neural networks once the compute power for it existed.
- chunkmonke99 1y ago
  I think the point is that universities and public research broadly speaking tolerate a lot of risk that private institution are not built to shoulder. I'm no AI historian but ANNs were an intellectual backwater after the 1st/2nd AI Winter and the only entities "foolish" enough to keep investing in them were Canadian public research universities. If it weren't for Hinton's dogged quasi-autistic special obsession with them ... no for profit entity would have bothered to invest in "computer power".