So my first college interview was today.

It went okay, I guess - apart from talking about math, I mostly talked about being grateful and AoPS. The only downside was that since I've been feeling a bit weak the past few weeks on the Keto diet, I decided last minute to load up on carbs the day before. Unexpectedly, my body didn't adjust to well and the result was two trips to the bathroom this morning but hopefully the interviewer mistook me sitting on the edge of my seat as being enthusiastic so hopefully all is good.

Anyways, let's talk about school.

School is mostly dull, as is to be expected. In retrospect, I realize that high-intensity math training has made me more inclined to be interested in studying things that deliver a sense of thrill rather than being a well-rounded academic (the kind who strokes his beard and says hmmmm a lot) person, kind of like how a competitive swimmer may find it hard to swim at leisure. My grades apart from English are all A's (though most are borderline), but nevertheless I don't have much motivation to try to hard (talked about this many times on my blog before). In fact, I think I've reached the part where cynicism has been replaced by nonchalance - where I just don't really care but still do it anyways cause whatever.

Having 3 free periods has been sort of a blessing - I've had the chance to sleep in, manage two classes on AI (Machine Learning & Deep Learning and Neural Networks) on Coursera and my university-level online math course, and watch YouTube hours a day. This, I realize, has been really conducive to my creative energies - most nights I put myself to sleep listening to some talk by Elon Musk or some other tech magnate on space exploration, AI, self-driving cars, startups, bitcoin and blockchain, quantum computing, blah blah, and I feel never have I learned so much with such little effort.

I've also gotten a bit into competitive programming (USACO, CodeForces and TopCoder) for fun. I kind of bombed my debuts on the latter two so let's not about that...

Learning about AI has been so fascinating. Though most people envision a future where robots rule the world, we don't realize how prevalent machine learning has already become in our lives. From ads to recommendation systems (music, YouTube, Amazon), Uber, prices, machine learning is really about optimizing algorithms based on past experiences. A Chess-playing AI, for example, functions by associating a probability of winning with each position of the board, so that whatever state the game is in, it evaluate the moves it can make with the highest associated probability of winning. How it does so is complex, but the concept is pretty amazing - essentially, the output (probability) is a function of many factors, such as the level of threat by the queen, how far the game has been played, possible number of attacks. Each in turn is the composition of another layer of data - number of targets the queen can take out, the number of pieces left on the board, other combinations,etc. Basically, we can organize these data into layers of abstraction and show the interdependence of one layer on the other by a neural network until at the bottommost layer we have the input - the current position of the board. Essentially a neural network is represented by a weighted-edge graph, and the edge shows how heavily one factor is dependent on another, such as how much is the level of threat by the queen dependent on the number of pieces in its line of attack. By using supervised learning, we can train the AI over pre-played game move sequences, or better yet, by making it play against itself to optimize its strategy. With data mining (millions of inputted or self-generated data sets), the weight of the edges (and thus the neural network as a whole) adjusts to become the most accurate by the regression to the mean. This is akin to optimizing multivariable functions with partial derivatives, still mostly basic math, which is what I've been exposed to so far.

What I find more interesting is unsupervised learning - the basis behind some recent tech perks like iPhoneX's face recognition. Essentially, an AI is given some examples, and teaches itself to organize, distinguish, and excavate the data that users can make use of. For example, AI can recognize cats from images based on only the pixels of the image - RGB values, or can separate out the singing and non-singing voice from an audio file, recognize spam e-mails from non-spam. A cool article recently shows how an AI can come up with one-sentence descriptions of any image it sees - like "The bright-colored sunflower plant in a garden." Specifically what features the AI does and looks for with the data its given isn't the result of hard-coded algorithms, but, referring to above, neural networks.

There's really infinite applications one can think of, so learning about it has been really fun.

Since I'll have plenty of time in second semester, perhaps I can design a neural network to make a virtual AI girlfriend texting app to keep me company (I'm only half-joking).

Anyhoo, only 12 more days before finals week (the same week early app comes out) when I'll hopefully be done submitting all my apps, then the gooooood life begins.

Anyhow let's finish with a CodeForces I solved but haven't implemented up yet (the sol is different from the official but I proved it works yay):

Solution