How not to start with machine learning

I’m a technical and scientific person. I’ve done some online courses on machine learning, read enough articles about different machine learning projects, I go through the discussions of those projects on Hacker News, and kept a bunch of ideas what would be cool for the machines to actually learn. I’m in the right place to actually do some project, right? Right? 🚨 Wrong, the Universe says no…

This is the story of how I’ve tried one particular project that seemed easy enough, but leading me to go back a few (a bunch of) steps, and rethink my whole approach.

I bet almost everyone in tech (and a lot of people beyond) heard of AlphaGo, Deepmind’s program to play the game of Go beyond what humans can do. That has evolved, and the current state of the art is Alpha Zero, which takes the approach of starting from scratch, just the rules of the game, and applying self-play, can master games like Go to an even higher level than the previous programmatic champion after relatively brief training (and beating AlphaGo and it’s successor AlphaGo Zero), but also apply to other games (such as chess and shogi). AlphaZero’s self-learning (and unsupervised learning in general) fascinates me, and I was excited to see that someone published their open source AlphaZero implementation: alpha-zero-general. That project applies a smaller version of AlphaZero to a number of games, such as Othello, Tic-tac-toe, Connect4, Gobang. My plan was to learn by adding some features and training some models for some of the games (learn by doing). That sounds much easier to say than to do, and unravelled pretty quickly (but probably not as quickly as it should have been).