I have not been well for a while; my brain has turned to gamgee, and I find it difficult to concentrate on hard mathematics. So let me try to get things working by an easier warm-up exercise.
There has been a lot of change recently in the mathematical capabilities of AI systems. I understand that it was not so long ago when they didn’t know what 2+2 was, and had to look it up. Now eminent mathematicians and computer scientists such as Don Knuth and Tim Gowers are professing themselves impressed by the capabilities of these systems.
Can they be useful to the working mathematician? I can see three possible ways this could happen, all with significant risks. At present, AI systems are like gifted but extremely careless students; their outputs have to be checked very carefully!
Proof checking
This idea is from Kevin Buzzard, and I was reminded of it in his beautiful talk to the London Mathematical Society earlier this month (wHich has now appeared on the YouTube channel; do take a look!). If you use a LaTeX editor such as Overleaf, you will know that it flags your LaTeX errors as you type. Kevin envisages a system which will check your proofs and flag your mathematical errors as you type.
The basic idea is simple. AI is traditionally not bad at translation; so use it to translate a human proof into a formal proof checking system such as Lean (of which Kevin is a great supporter). Then feed the result into the Lean compiler, and see whether it flags an error; if not, you are in good shape.
This would be great for someone like me, who doesn’t need yet another journal publication. If the system found that my proofs were correct, I could put the paper on the arXiv with a note to this effect, and readers could safely use the result.
There are two dangers. The most significant is that the translation needs to be checked, since AI translations are notoriously unreliable; this is a lot of work and requires a lot of expertise in Lean, so if you have this expertise you could simply do the translation yourself.
The second, which Kevin mentioned, is that this system might be developed by a large corporation which then charges us to use it, and of course I wouldn’t be able to pay. All the current facilities most useful to me (TeX, GAP, R, the arXiv, Zentralblatt für Mathematik, diamond OA journals) are free. But maybe, once the genie is out of the bottle, someone will produce a free version, as happened with R.
Proof assistant
If AI can solve mathematical problems, can it help me prove my big theorem by knocking off some of the difficulties I meet along the way? Probably yes, and almost certainly it will be used for this purpose. I have already had to review one paper in which it had apparently been used like this. (Fortunately it lay outside my competence, so I didn’t have to face the issue head-on.)
All I can say is that, while I am happy to attribute some of the best ideas in my papers to my co-authors, I would be less happy acknowledging a computer; but this is my problem, not AI’s.
The risk here is: once the computer can solve non-trivial problems arising in a project, how do we assign topics to PhD students? They need something which requires some creativity to solve, and gives some sense of achievement; a problem which can be solved by pressing a few keys will not do the job.
We have faced something similar before. In the 1960s and 1970s, a lot of PhD student time was spent computing character tables of the recently discovered sporadic simple groups. Now this can be done in computer algebra systems like GAP very simply. I invite you to compare and contrast the two situations. I think the one we face now is much more serious.
Autonomous mathematician
How long until we see the robots doing mathematics independently of human intervention?
Yang-Hui He, of the London Institute for Mathematical Sciences, and his colleagues devised what they called the Birch test, for whether a computer is capable of independent mathematical discovery. This is inspired by the Turing test, and commemorates the work of Bryan Birch and Peter Swinnerton-Dyer who made an early use of the computer as an experimental tool in coming up with their celebrated conjecture. You can red about it here.
To pass the Birch test, the computer must meet three requirements (I quote Yang’s description):
- Automaticity: it is completely made by AI from pattern-spotting, without any human intervention;
- Interpretability: any statement – conjecture or conclusion – must be precise to a human mathematician, who cannot distinguish it from one given by a human colleague;
- Non-triviality: it is non-trivial enough that the community of human experts will work on it.
(I think that the last condition says a little more; it must be interesting enough to catch the attention of mathematicians. A many-page identity would probably not count.)
Yang feels that computers have achieved two of these but not yet all three. His own work on “murmurations” derived from the classification of elliptic curves satisfies the second and third.
In my more dystopian moments, I can imagine AI of the future coming up with a similar test for human mathematicians, where those who fail it are to be terminated. Maybe my time isn’t long …