I broadly agree with this take, though my guess is the tractability is quite low for most people, even for top 25 CS schools like Yale/Penn*/UMich as opposed to the best 4-5 schools. For example, it’s probably not the case that the average Berkeley or MIT CS PhD can become a professor at a top 25 school, and that’s already a very selected cohort. There are a lot of grad students (Berkeley has ~50 AI PhDs and ~100 CS PhDs in my cohort, for example!), of which maybe half would want to be professors if they thought it was tractable. Schools just don’t hire that many professors every year, even in booming fields like CS!
That being said, if the actual advice is: if you’re doing an ML PhD, you should seriously consider academia, I do fully agree with this.
It might be relatively tractable and high-value to be a CS professor somewhere with a CS department that underperforms but has a lot of potential. An ideal university like this would be wealthy, have a lot of smart people, and have a lot of math talent yet underperforms in CS and is willing to spend a lot of money to get better at it soon.
Another large advantage you get being at a top research university in the US (even one that’s mediocre at CS) is you end up with significantly more talented undergrads for research assistants (as most undergrads don’t pick schools based on their major, or switch majors midway). I think the main disadvantage to going to a lower-tier CS program is that it become significantly harder to recruit good grad students.
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*That being said, Penn is great and Philadelphia is wonderful; would recommend even though the number of undergrads who want to do research is quite low! (Disclaimer: I went to Penn for undergrad).
Thanks! One thing I’ll add is that there’s a chance that someone at a school without normally a ton of great grad students might be able to get some good applicants to apply via the AI safety community.
I broadly agree with this take, though my guess is the tractability is quite low for most people, even for top 25 CS schools like Yale/Penn*/UMich as opposed to the best 4-5 schools. For example, it’s probably not the case that the average Berkeley or MIT CS PhD can become a professor at a top 25 school, and that’s already a very selected cohort. There are a lot of grad students (Berkeley has ~50 AI PhDs and ~100 CS PhDs in my cohort, for example!), of which maybe half would want to be professors if they thought it was tractable. Schools just don’t hire that many professors every year, even in booming fields like CS!
That being said, if the actual advice is: if you’re doing an ML PhD, you should seriously consider academia, I do fully agree with this.
Another large advantage you get being at a top research university in the US (even one that’s mediocre at CS) is you end up with significantly more talented undergrads for research assistants (as most undergrads don’t pick schools based on their major, or switch majors midway). I think the main disadvantage to going to a lower-tier CS program is that it become significantly harder to recruit good grad students.
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*That being said, Penn is great and Philadelphia is wonderful; would recommend even though the number of undergrads who want to do research is quite low! (Disclaimer: I went to Penn for undergrad).
Thanks! One thing I’ll add is that there’s a chance that someone at a school without normally a ton of great grad students might be able to get some good applicants to apply via the AI safety community.