Artificial Intelligence

I am fortunate to live my life from a very philosophical angle. Most of my pursuits are solitary, and my days are spent in a mind palace where I am free to muse on different topics free of external distractions. This privilege is partly designed, but mostly a matter of happenstance. I am incredibly lucky to have the time and space to spend my days working on a project in the kitchen, twiddling my thumbs in a small balcony garden, or going for runs along a waterfront all while pondering the meaning of life, the universe, and everything.

As of late, my mind has turned firmly to the topic of artificial intelligence. No surprise as the last year of my Master’s will focus on a subset of the field, machine learning. To prime myself, I’m spending the summer reading a combination of philosophy and engineering books that cover the history, theory, and practice of artificial intelligence. I began with a book that has long been on my reading list, Superintelligence by Niklas Bostrom.

One of my favorite (personal) sayings about computers is that they are highly advanced guessing machines. You can see this play out practically with things like branch prediction, where a processor must guess the path of a logical branch based on the history of that branch. This heuristic is analogous to how many humans guess; we use history as a predictor for future events. While HPCA has many similar techniques, this scenario is even more common in the other Georgia Tech course that I’m taking this semester, Robotics: AI Techniques.

Artificial intelligence is the pinnacle of guessing as it employs practical techniques (like search algorithms) and combines them with statistical tricks based primarily on probability density (usually Gaussian) distributions. The mathematics behind these distributions, in my opinion, can often seek to confuse and distract from what is actually a delightfully simple concept.