Tic-Tac-Toe the Hard Way People + AI Research
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- Technology
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A writer and a software engineer from Google's People + AI Research team explore the human choices that shape machine learning systems by building competing tic-tac-toe agents.
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Howdy, and the myth of “pouring in data”
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.
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What does a tic-tac-toe board look like to machine learning?
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.
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From tic-tac-toe moves to ML model
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.
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Beating random: What it means to have trained a model
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?
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Give that model a treat! : Reinforcement learning explained
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.
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Head to Head: the Big ML Smackdown!
David and Yannick’s tic-tac-toe ML agents face-off against each other in tic-tac-toe!