I was in a group chat; in it were a bunch of Chinese students planning to pursue college education in the U.S. This morning, someone said that the June SAT score would be out on Naviance today, and the group had an hour-long, grandiose discussion (full of nonsense and useless memes) about it, but nowhere did anyone affirm or deny the myth. So, was the score really there, I asked. The group fell silent; they hadn't checked yet. Fine, I sighed, and checked that the score wasn't there. I announced so in the group, and said " *please* keep going". I could sense the silence deepened, but I couldn't care less - I really disliked pretense and nonsense - such a waste of time (that I could've used to game).
We talked a bit about physics brawl this morning meeting; it was, to put in the simplest terms, a very fun experience. Niels and I talked a bit about math team and Science Olympiad afterwards, and it seemed Mendon kids weren't quite into academic activities (I see; that's why their track team beat ours). Prof. Hornak wanted us to look for Paris Green online, which was a toxic substance (typical Arsenic). I tried to search on Amazon, but (surprisingly?) didn't find anything. In fact, there was nothing on the whole Internet.
The lunch talk was quite interesting. Essentially, machine learning is a type of artificial intelligence (AI) whose output changes as the AI receives further data input. It's quite interesting that by inputting an image, researchers can get words as outputs. A real world application will be automated ships; it will save 44% of the current expenditure. Don't know about you, but I'd say that's a lot.
I was then dragged to the eye tracking test part two. Whatever little talent and taste I have in liberal arts (if I had any) went to classical music, so I really didn't know how to view arts. It's almost like once I see a color, its corresponding wavelength jumps into my head (well, not exactly, but hey, wavelength of orange is 590 nm).
In the afternoon, I talked with an undergrad for a bit. He was trying to project a set of data to another set that had a different range. His idea was very creative - vectors - our model would be the most optimal if the cross product of the two vectors (formed by two sets) was as close to zero as possible. My natural instinct was to write out a 2000 by 3 matrix, but don't worry about me yet, I wasn't *that* crazy as to evaluate the determinant of the matrix. I'm thinking about minimization with regular calculus, but there's no guarantee that the function (if it's even a function) is differentiable, or even continuous.
Anyways, I call it enough nerdiness for the day. Time to be a liberal arts (LOL, liberal arts) major. Yea; no.