Tried adjusting exposure; Didn't work as well.

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

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 hacked a system. Humans did it.

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.

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.

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 now realize many people have different tolerance level for this.

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'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.

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.

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.