As other commenters said, graders are certainly not paid from grant money at US institutions. NSF (which funds most mathematicians in the US) would never allow it, and grants are usually much better spent getting grad students off teaching so they can get research done. Teaching assistant (TA) funding would ultimately come from the university. Sometimes it is optional, i.e, a faculty member can request a TA or grader for certain courses but does not get one automatically. Some courses, like large entry-level calculus, cannot be run without teams of TAs teaching recitations.
I'm on the math faculty at a R1 (public) university, and we've not had a budget for graders for most courses for a very long time, with the exception of e.g., very large courses. For most classes the instructor does all the grading. Grading has been a significant fraction of my time teaching, certainly much more than what I can spend on preparing lectures and other teaching-related work. Moreover, much of the time grading is giving feedback, figuring out partial credit, etc; just checking "correct" or "incorrect" is often relatively quick.
In the last couple years, my personal experience has been that (i) a very small (and possibly vanishing) fraction of students are declining to use AI, or at least use it sparingly, because they want to learn; (ii) a growing percentage are using it as a personal tutor; and (iii) some are outright outsourcing all the work. The last category is small, but if that category grows (anecdotally this seems to be the case -- I've mostly taught grad courses in the last year and don't have first-hand knowledge) then what would be the point of spending all that time grading and giving detailed written feedback?
Incidentally, many students in the second category are the type who in years past would form study groups, come to office hours, etc.; now they seem to prefer AI over humans. Some of them do seem to benefit from it, but not all. How one uses AI matters a lot, and how to help students use the tech to learn is something we have to figure out (regardless of how we feel about AI's impact on research).
Calcium imaging of neuronal activity (see, e.g., https://alleninstitute.org/education-resources/how-scientist...) is a thing, and has been for a while. It does something like what you said: neurons that have been genetically modified glow briefly, which can be picked up by microscopy. It has many advantages (and some drawbacks) compared to more traditional electrical recording techniques.
Happened right here too: thieves opened up man hole covers in city parks and took the wires. The city now has large boulders right over those man hole covers...
Euler was a master manipulator of formal expressions; don't think it bothered him very much whether, e.g., an infinite series converged or not. (Unfortunately don't remember a source for this offhand -- maybe a leftover impression from having read ET Bell's book long ago? I also don't know how reliable Bell is.)
Cauchy is the one who introduced rigor to calculus with the formalization of limits, including epsilons and deltas, a full century after Leibniz and half a century after Euler.
There's understandably been a focus on speed, but what I really like about Julia is being able to write clear code: it has a lot of the flexibility and expressiveness that I like in Lisps, but with better numerical libraries than most Lisps/Schemes, and I really like prototyping algorithms in it. To me (an applied mathematician) it is just a nice tool for thinking in. Not having to compromise too much on speed is a big bonus.
Unfortunately the NS/macOS version still needs work as far as I can tell. Even not fully featured it's still useful: I prefer reading PDF in xwidget-webkit than pdf-tools, and it's great to have Jupyter notebooks running inside a browser inside Emacs.
Where I live we still have a locally owned video rental store, though several years ago they put in a bar and now it's more a bar with a video section than a video store where you can get beer and watch movies. We do still rent when there's something we can't stream on one of the services we pay for; I much rather do that than give more $ to Amazon and the like.
It's weird when shops pivot. There was a record store in a neighboring village that went from vinyl to video game kiosks. Then the record store that became a candy store. Put couches in bookstores and people stop buying books. I guess they figure the rent is paid by benefactors. Then again, Barnes & Noble seems to have a partnership with Starbucks...
I'm on the math faculty at a R1 (public) university, and we've not had a budget for graders for most courses for a very long time, with the exception of e.g., very large courses. For most classes the instructor does all the grading. Grading has been a significant fraction of my time teaching, certainly much more than what I can spend on preparing lectures and other teaching-related work. Moreover, much of the time grading is giving feedback, figuring out partial credit, etc; just checking "correct" or "incorrect" is often relatively quick.
In the last couple years, my personal experience has been that (i) a very small (and possibly vanishing) fraction of students are declining to use AI, or at least use it sparingly, because they want to learn; (ii) a growing percentage are using it as a personal tutor; and (iii) some are outright outsourcing all the work. The last category is small, but if that category grows (anecdotally this seems to be the case -- I've mostly taught grad courses in the last year and don't have first-hand knowledge) then what would be the point of spending all that time grading and giving detailed written feedback?
Incidentally, many students in the second category are the type who in years past would form study groups, come to office hours, etc.; now they seem to prefer AI over humans. Some of them do seem to benefit from it, but not all. How one uses AI matters a lot, and how to help students use the tech to learn is something we have to figure out (regardless of how we feel about AI's impact on research).
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