Tuesday, April 14, 2020.
One thing I don't like about mathematics is how "hierarchical" it is. Person A "is better than" person B, is better than person C, and so on. Mathematical ranking isn't a total order, but it is at least a partial order. Letter writers for jobs are even invited to give "comparisons" -- "well, he's better than X and Y, not as good as Z."
There is also a lot of "genius worship" (and in physics, and parts of CS). People are too much in awe of those with a high degree of natural ability, and have a tendency to ascribe moral goodness to them. Person W may be a total asshole, but because they are such superlative mathematicians, all is forgiven -- and, in fact, those passing judgment are just "not very good".
If you mention that, "Well, they are just very smart. Is it any surprise that they produce such good work, and win all the prizes?" -- there may be howls about how everybody can do good work; I.Q. is overrated; privilege exists, yes, but it can be overcome; and so on. I used to think people really believed that innate talent doesn't matter as much, and that we're all closer to equals than what psychometricians and geneticists keep telling us; but I think what their objections are *really* about is that things like I.Q., personality, and privilege undermine the "moral program", that cerifies someone is "good" because they do good work. If it just comes down to things beyond their control, then how can they be certified as "choosing to do good"?
On the other hand, other fields than mathematics, without as much "hierachy" and moralizing, have their own flaws. Take the empirical side of machine learning and aritifical intelligence: I read what these people are up to on the internet, and one thing that strikes me about that field is how much "flatter" the talent landscape is. There are not as many towering genuises that dominate the field -- there are a few, but in some cases they got there mostly through sheer determination and the force of their personality (not because they are super-intelligent). This is one thig I really like about that field. Anyone can "make it" (well, assuming you have a modicum of ability).
On the other hand, there is often a lot more pettiness (I've noticed), signalling, "classism", and so on. For example, I recently saw a pretty young (and naive) A.I. researcher at OpenAI write this:
Tweet
QUOTE: "Controversial opinion: We should stop teaching students any ML methods other than neural networks. There is so much inertia against forgetting things that never panned out. It's hard to let go of ideas, especially one's own, but our objective should be to make real progress in AI"
And he got tons of nasty Tweets from academics. A Caltech professor called him an "idiot", before deleting the Tweet; another descended from on-high to call him out for "brazenly shitposting to the ML community" (which he didn't do); another wrote some condecending and smug Tweets to try to put him in his place, making him feel small; and so on. It was like a feeding-frenzy, of people seeing who could write the snarkiest Tweet, while at the same time signalling they are members of "the club" (of respectable academics).
Now, I can see maybe they were upset by the guy's suggestion that what people who used those other methods were doing was useless; that would really hurt. But most of the nastiness I saw looked more like signalling and arrogance -- all the more reason to stay away from that community.
It's too bad we can't have the best of both worlds: the flatter landscape like you see in some of the sciences, without the moralizing and with much less hierarchy; but also much less cattiness and signalling.