contubernio

As a professional mathematician it seems that AI is already at the point where, when well guided, it both does better research and writes better exposition than run if the mill mathematicians.

Well guided is a qualifier that matters.

What is the problem if it is AI written? Properly guided AI writes better than most human expositors.

Many many gaps had been found and patched (by humans) already over the last forty years.

It's not AI that has created those open problems. It has simply shown that some conjecture is false and that leads naturally to problems in a sense already posed.

A very different thing is proposing a framework and in it identifying interesting questions. So far AI has only worked within existing frameworks addressing problems already posed. That may change in the future, but it would be a genuine and qualitative evolution.

He doesn't pay TAs. The university does. They work for the university, not for individual professors. If you have no idea how teaching works in US universities, probably better not to comment on it.

Ways will be found to get it to write propaganda instead.

We research mathematicians are still learning how to use AI in research and how to deal with the output. I drafted eight papers last weekend. This week I advanced on none. AI still gets stuck at the key point on a problem where there isn't already a lot of work done. What falls is where techniques not known to the researcher are available and profitably employed - ai brings them into the fray fast - and someone with experience immediately sees where to go with it - and the dominos fall.

So far AI still doesn't make definitions. It tries but it doesn't really see structure - only patterns on which it can impose (by brute force) known structures.

What's not clear is how the professional infrastructure will survive. Journals seem likely to die. The arxiv is swamped. A human can't assess the volume of the output. Serious conjectures have fallen and serious people have checked them.

We are looking for constructive and productive and socially useful ways forward. One reflection is that a lot of what we have been doing was maybe not so interesting as we thought.

AI would have gotten to the point more quickly.

My first job programming was in a geophysics lab. I had to read fortran 77 codes and write in some slightly more modern variant (I can't remember now which). They were simulating the Earth's mantle and used it because it was fast.

Engineers often forget about thermodynamics. Computer scientists apparently never even study it.

I did best by acting like a real world developer - low-ball everything, everything non-union, ignore community activists at all costs, pay for access, sell for a profit, etc.

Fear-mongering clickbait title signals complete nonsense.

Since I started using AI seriously in my (mathematics) research about 6 weeks ago I've advanced more and been more productive than in years. I'm more motivated and persistent than before because I have at my disposal a tool that helps me get past technical obstacles that previously took up a lot of my time.

Most of the problems that have been solved are problems on which a great deal of progress had already been made. Those who work on well known problems posed by famous people are those who suffer the most from this. Those who do their own thing and pose new problems, on the contrary, benefit from it. Suddenly raw technical power and great memory are not so valuable as a broad perspective, structural insight, and wild ideas. Who can be successful in this new ecosystem is different. Some of the elites are (correctly) more threatened by it than some "mid tier" mathematicians. I see lots of opportunities to overcome obstacles in my research program some of which had confounded me for years.

On the other hand, it puts a premium on resources. AI is not cheap for mathematicians. Folks are fancy universities in rich countries with forward thinking ministries of science will have an advantage over the rest.

What is clearly in immediate crisis is the traditional model of doctoral education. Most of the problems that were "given" to ordinary doctoral students are solvable (quickly) even by something like Claude pro. Mathematicians need to adopt training models more like what is done in experimental and laboratory sciences - collaborative and structured.

Where Tao is wrong is in regards to exposition. AI already writes better lecture notes, problems, and exercises for mid level undergrad math classes than do most of my colleagues. It's exposition is generally well structured and clear and it can adjust level on request quite well. It writes research better than most professional mathematicians too.

Article is not useful because it does not bother to motivate why the scans were requested or why anyone other than a legalistic, formalistic person would consider them administrative documents. The author assumes all this is obvious and it isn't.

Arxiv is the principal forum for publishing mathematics research of quality. Journals serve a purely archival and bureaucratic purpose; almost no value is added by the refereeing process (probably more is lost) and journal publishing neither improves accessibility nor diffusion of results.