Tic-Tac-Toe the Hard Way

What have we learned about machine learning and the human decisions that shape it? And is machine learning perhaps changing our minds about how the world outside of machine learning — also known as the world — works?

Yannick and David’s systems play against each other in 500 games. Who’s going to win? And what can we learn about how the ML may be working by thinking about the results?

David’s variant of tic-tac-toe that we’re calling tic-tac-two is only slightly different but turns out to be far more complex. This requires rethinking what the ML system will need in order to learn how to play, and how to represent that data.

David and Yannick’s tic-tac-toe ML agents face-off against each other in tic-tac-toe!

Switching gears, we focus on how Yannick’s been training his model using reinforcement learning. He explains the differences from David’s supervised learning approach. We find out how his system performs against a player that makes random tic-tac-toe moves.

David did it! He trained a machine learning model to play tic-tac-toe! How did his model do against a player that makes random tic-tac-toe moves?

Once we have the data we need—thousands of sample games—how do we turn it into something the ML can train itself on? That means understanding how training works, and what a model is.

David delves into questions around data and training for his model including: What does a tic-tac-toe board “look” like to ML? Plus, an intro to reinforcement learning, the approach Yannick will be taking.

David and Yannick get started on their project to build competing machine learning systems that play tic-tac-toe. They discuss the human choices that will shape their systems along the way.

Introducing the podcast where a writer and a software engineer explore the human choices that shape machine learning systems by building competing tic-tac-toe agents. Brought to you by Google's People + AI Research team.