I've been interested in AI since undergrad.  Hofstatder's GED was one of my favorites.  In the 90s though, AI felt like it was still purely academic.  Around 2015 I was lucky enough to work at a start up whose value proposition was focused on displacing an industry standard with AI. I was impressed by how far things had come and decided to try to get back up to speed.  I took it upon myself to learn the math necessary for understanding modern Machine Learning and finished several math courses at a local college.  The canonical coursework calls for Calculus (through multivariable), Linear Algebra, and Statistics/Probability Theory.  I went a bit further and got into some math ended up being surprisingly helpful to everyday Software Engineering. My goal here is to share some of them in case they help anyone else. Information Theory  There are so many immediately, practically useful ideas here for a computer scientist.  The fundament...

English is efficient for human communication because of the shared context we have.  We can mostly understand each other without having to spell out every detail.  Step into a foreign culture and even if you're fluent in the language, you'll find you still struggle with communication until you've built up the shared cultural experience. This is true even for regional moves within a given macro-culture like the USA.  I grew up moving frequently with a military family.  As a boy, I'd dedicate time after each move to learn all the local sports teams and their players' names.  This was the language the local boys spoke.  It was the minimal necessary to follow conversations at school.  Code has to be unambiguous because the computer requires precise instruction.  However, there's necessarily ambiguity in the spoken word.  This is the result of cultural evolution.  A culture that spelled everything out verbally the way code does w...