Baley shrugged. He would never teach himself to avoid asking useless questions. The robots knew. Period. It occurred to him that, to handle robots with true efficiency, one must needs be expert, a sort of roboticist. How well did the average Solarian do, he wondered?
You come off as snarky, but I kind of agree. We tried this first.
It turns out digital collaborative calendars are pretty great for us in general, there is no chance in hell I could keep the analog one up to date, so it was definitely worth having a screen on the wall.
I ended up building a nice charging station right near the entrance. It has storage for keys, wallet, and other things to grab when heading out. It has an abundance of wired and wireless chargers for all devices.
Then I got a dumb (but nice) alarm clock for the bedroom.
Then I noticed that a common reason to pick up the phone is to check the calendar. I ended up hanging a monitor on the wall, displaying the family month/agenda calendars. It’s read only, but it prevents a lot of device checking.
Cannot recommend enough restructuring physical reality to not have device on your person at home. It also helps the kids to put theirs away and learn good habits.
Are there good ways for US companies to host international grad students in their internship programs? How hard is it for them?
Specifically I had a conversation with a few administrators at universities in Sweden and this question came up on behalf of their students.
Thanks for regularly showing up here. I went EB2NIW some years ago and still get ptsd reading these threads. Hang in there everyone struggling with immigration.
I’d be interested in your thoughts on the case where the f_i are optimizable: f_i(t) = K(t, z_i), i=1..m << N. Like the representer thm but much fewer terms than you have data points to fit. The points z are usually called inducing points and may be optimized by gradient descent.
There is literature on approximating exact GP inference with (something like) these objects when m << N (variational inference).
However, I’m not aware of anyone drawing a clear picture of the other direction, starting from the optimization picture and explaining it in terms of inference, similar to what TFA does.
In TFA the number of functions is large, so the system is underdetermined. In the variational inference the system is overdetermined and I wonder what inference, if any, gradient descent does..
Caveat: 1am and a few drinks deep so if I’m not making sense that’s ok
This seems like a young talent that we’ll see more of. I like your to the point writing style and obvious passion for mathematical clarity. Keep it up and best wishes for your phd studies.
“in order to settle any lingering unease about using such tools in physics” spoken like a true mathematician :D
I enjoyed the post a lot (at least the parts that didn’t pass right over my head). But I never met a physicist with lingering unease about dimensional analysis. We get that beaten into us until it’s as natural as breathing.
Baley shrugged. He would never teach himself to avoid asking useless questions. The robots knew. Period. It occurred to him that, to handle robots with true efficiency, one must needs be expert, a sort of roboticist. How well did the average Solarian do, he wondered?