kzz102

I think this is an important point we need to grapple with more and more in the future. We like to think an act as either lawful or not lawful, either ethical or not ethical. We need to bring in scale as a separate axis. There's a world of difference whether an activity is done at human scale or machine scale. At human scale, it's an act of deviance that may contribute to the diversity of human experience. At machine scale, it's a distortion that threatens the society.

Yeah, while it's sad they are gone, the reality is that they have been zombies for a long time.

The aim is to produce a body of knowledge that will be useful to humanity. So far, the best way to ensure that knowledge is useful is to have a society of experts who understand them well. That may not be necessary any more with AI, but it's a scary thought -- because that logic can be applied to any human knowledge, not just mathematical.

AI tends to take nearly finished research directions and push it to the conclusion in one step. If deployed massively, it will pluck all the low hanging fruits causing a drought of near term promising research project. Because people who start promising research directions do not get to see it finish, over the long term fewer people will start new directions, causing the field to slowly whither.

The difference is the scale. A few incomprehensible long papers per year, sure, we will study it. A flood of AI results closing research directions left and right, that will be a problem.

I think both the competitiveness and and lack of problems will mean fewer people will do mathematics. I think collaboration will always be there, but the community as a whole will be smaller and weaker.

The mathematical community was very competitive in its early years, but in the last 70 to 100 years, it has been generally less competitive and very collegial. The community was in a good place, and progress has been very good. In a few cases when competitiveness was ramped up, it lead to bad behaviour and destructive fights. Few would like to return to those competitive years.

It was copied from a pdf, maybe LaTeX generated. All the ligatures are wrong -- not sure where the problem lies, the pdf itself, pdf reader, or the clipboard on Linux.

Education is a complex topic. I don't claim to understand it, nor do I think we can sort this out in a hacker news thread. What I reject is the simplistic claims like "exams don't mean anything". There are certainly exams that are badly designed, but there are also exams that are well designed. A well designed exam can look very badly to different people, based on what they know and where they come from. It's a bad idea to think exam scores as the single metric of education quality, it is equally bad to reject them completely, because chances are any alternative measurement people come up with are going to be worse.

My apologies for coming off as over-enthusiastic, I am currently obsessed with this study. Here is another quote:

"The negative learning effects are larger for students with higher initial achievement. The differences in the estimated full (6-10 month average) effects are substantial, with a 50% gap between the most negative effect (-24 percent) for the highest tercile and the least negative (-16 percent) for the lowest tercile. "

Not top 10% as you asked, but the closest to what you asked. My working hypothesis is that top performance is highly correlated with willingness to work hard, and AI decreases the motivation to work hard.

The fact that exam scores are correlated with how much you study is not the same as exams only reflect how much you study. Two students who study the same amount could have very different exam scores. The reason that there still is a strong correlation between exam score and time of study is because if all other things being equal, students who studies more have higher exam scores.

It's not so clear that AI is an amplifier.. The paper has some fascinating analysis on this topic:

"At the other end of the distribution, AI students who spend more than 65 minutes on their homework receive homework and exam scores similar to those of non-AI students, suggesting that these students do not use generative AI for homework assignments. However, this group consists entirely of students who adopted generative AI no more than Öve months. Six months after adoption, no AI student spends more than 65 minutes completing their homework (see Figure A5). This is consistent with the gradual process of learning how to use AI tools. It also suggests that AI crowds out the highest level of e§ort."

"Interestingly, in the range of 50-65 minutes, the median and the interquartile range of exam scores of AI and non-AI students are similar. This implies that, in the range where AI students and non-AI students have overlapping homework times, students who spend the same amount of time completing homework on average receive similar exam scores."

"This pattern shows that students who spend the same amount of time on homework learn similarly, with or without generative AI. In other words, generative AI reduces time spent learning for the majority of AI students but not learning efficiency for those who spend the same time studying as the non-AI students."

Even if we grant that education is to prepare for employment, which a lot of people disagree with, it does not follow education should do the same thing as what's done in employment.