pks016

With all the talk about great level of problem solving. I wonder if their LLM could come with unique research problems to solve?

Not from math. But I know people who did math PhD. Like you said, competition is already high in all research fields. People looking for positions are way more than Universities can hire or number of profs retiring.

I'm with you, doing a PhD was already a risky choice. Now it's even more risky.

Well, if they can afford in first place

That's true. OpenAI has some of the best talents. In my experience, domain experts get the most benefits from the models. They can work much faster, catch false positives, and stir the model in right direction.

I wish they take a bit of more time to communicate the findings effectively.

> But it also sucks when mathematicians, who are considered experts on a particular problem, refuse to engage with breakthrough results about that problem.

It might be a shock for you but they are very few in numbers. Most of researchers I know are always busy with something. They cannot just drop other responsibilities for something like this. They will take their own time getting through the proofs (if they want to).

> That seems like a much healthier attitude towards these new results.

Another thing to consider is not all mathematicians are from US or with good funding. The PI or graduate students cannot afford to pay 200/month.

With the models and scientists they have, I didn't expect this level of mistakes.

I'm up for all the exciting results. But, can't they check once properly before publishing?

You don't seem to understand how the company evaluation and perception works. I would suggest reading on that.

At least check them properly. They have already withdrawn some of them.

My grunt work is different than doing grunt work for a company to fix their problems so that they can make more money.

I tried precise editing with a photo of horse with fence in front of it. One shot didn't work. Tried with multiple smaller regions. Still didn't work

Tried adjusting exposure; Didn't work as well.

I don't like it. Now, the TAs have high cognitive load. Earlier, you only need to know the material of the course. But now, you're forced to navigate AI output. And you got to keep with all new model developments.

One of my colleagues tried it for a few terms. Unfortunately, it didn't work well. Most students don't care about learning, they are here for grades :(

It would be a waste of time. LLMs have become better but not perfect. Personal experience; it's easier/faster to grade for me than have the LLMs grade and cross-check each one.

> Put a mixture of questions in that are impossible to answer

Students will complain to the admins and waste more of your time.

Please read the post. It's not written by Tao.

I'm low-key interested in reading the pre-print. I'll have to take some time this week to read thoroughly. I'm not from the field, so I can't judge the specifics.

On the surface, the preprint looks good. I glanced through the Methods and couldn't figure out if Claude wrote the preprint in Claude Science session or authors wrote it.

I was curious about the exact prompts they gave. If they share it, we could see how much domain specific knowledge was required and if we can replicate similar research with other models.

AI made a discovery. AI is doing it.

AI hacked a system. Humans did it.

We don't know for this particular case with certainty.

But, here many people are okay with the concept of stealing other ideas, plagiarism, unethical aspects of collaboration in research. At least for me and many people I know, this is no okay. This is the part for morality and ethics. I see that no everyone agrees with this.

The issue is, these AI ecosystems don't effect everyone equally. Not everyone knows how to judge AI model output accurately. There is already a large divide on AI usage in research. The fear comes from this. If we wait longer, it might be alright too late. I could be also wrong with these speculations but better to cautious.

That's the thing. It might be. We won't know the impact until the next few years. The things that are changing now: culture of research, aspects of collaboration and sharing of knowledge.

Academia is toxic for sure. But, also there are genuinely good people who work for greater good and whose training and teaching transcend across domains.

I mean it is. Most of the time in graduate school, you're getting trained and learning to do reasearch. People rarely produce great work during graduate school. Only towards the end of their degree or start of new position (post doc, independent researcher, or assistant professor), you start seeing good work. And for a professor, most of their research is done by graduate students.

Academia has it's own sets of big problems. Before this AI boom also, most people graduating never stayed in academia. The way graduate schools are structured, I would be happy if few people joined. That would also mean we lose good researchers in a long run.

Well, who watches the watcher?

Sort of, yeah. We are seeing more people commenting who have anti-AI sentiments or in the fence on the topic of AI usage.

On why some people are making emotionally charged claims, my guess: This affects the core belief of what is right or wrong, Impressions based on past doings of OpenAI, losing trust for OpenAI based on sequence of events.

I don't think we will get to see any clear evidence. I'm not even sure what would be the evidence. I would be surprised if OpenAI comes out clean if they have made a mistake. They move on to the next shiny thing.

I agree. I was wrong and I had a naive world view of morality and ethics in research and academia.

I now realize many people have different tolerance level for this.

That's the crux of it. In the current ecosystem of AI model usage, it's very hard to figure this out for research.

If you set a wrong foot and start trusting the model outputs, you can waste years searching for nothing.

How can someone realize this? By getting proper research training, failing, and learning from mistakes. For people beginning their research, it would be really hard to make decisions to move forward.

I never expected this many people (on this thread) arguing semantics and what not. I know that not everyone has morality and ethics, but I didn't realize it was this bad.

I'm afraid of the ripple effect of the agenda pushed by AI companies will have. In future and even now, they say AI has significantly progressed math and scientific research in general. There is truth to this, but the narrative has done more damage (so far) to the students, researchers, and the culture of knowledge transfer in academia. Many graduate students (I know) are having a crisis if any of their research worth it? If AI can (or will) do everything, what's the point of doing experiments and all? This will eventually deter a whole generation of curious minded students from research.

I guess, only time will whether this is for the good or bad. And how good AI models get without new data from research and experiments.

> Ph.D. graduate in every field

I have yet to see this in my field. Maybe like a PhD student who bullshits their way through. LLMs still can't make correct decisions, only as useful as the person who uses them. To me, LLMs are only useful for making some mundane tasks faster.

Would I read it? No

But, It would be helpful for beginners. I work with students and I know a lot (like a lot) of students (not math or stats major, other disciplines) are scared of statistics. They are always looking for resources to learn.

For Biology; the paper was retracted due to ethical issues.