1. EachPod

Tic-Tac-Toe the Hard Way - Podcast

Tic-Tac-Toe the Hard Way

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.

Education Games Technology Ai
Update frequency
every day
Average duration
21 minutes
Episodes
10
Years Active
2020
Share to:
Lessons learned

Lessons learned

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 t…

00:33:01  |   Wed 22 Jul 2020
Head to Head: The Even Bigger ML Smackdown!

Head to Head: The Even Bigger ML Smackdown!

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?

See the agents play each othe…

00:24:26  |   Wed 22 Jul 2020
Enter tic-tac-two

Enter tic-tac-two

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 …

00:21:20  |   Wed 22 Jul 2020
Head to Head: the Big ML Smackdown!

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!

See the agents play each other!


For more information about the show, check out pair.withgoogle.com/thehardway/.


You …

00:25:19  |   Wed 22 Jul 2020
Give that model a treat! : Reinforcement learning explained

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 per…

00:26:04  |   Wed 22 Jul 2020
Beating random: What it means to have trained a model

Beating random: What it means to have trained a model

David did it! He trained a machine learning model to play tic-tac-toe! (Well, with lots of help from Yannick.) How did the whole training experience go? How do you tell how training went? How did his…

00:17:14  |   Wed 22 Jul 2020
From tic-tac-toe moves to ML model

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.

Resources:
See a…

00:21:37  |   Wed 22 Jul 2020
What does a tic-tac-toe board look like to machine learning?

What does a tic-tac-toe board look like to machine learning?

How should David represent the data needed to train his machine learning system? What does a tic-tac-toe board “look” like to ML? Should he train it on games or on individual boards? How does this de…

00:23:26  |   Wed 22 Jul 2020
Howdy, and the myth of “pouring in data”

Howdy, and the myth of “pouring in data”

Welcome to the podcast! We’re Yannick and David, a software engineer and a non-technical writer. Over the next 9 episodes we’re going to use two different approaches to build machine learning systems…

00:22:01  |   Tue 21 Jul 2020
Introducing Tic-Tac-Toe the Hard Way

Introducing Tic-Tac-Toe the Hard 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 Peopl…

00:02:09  |   Tue 21 Jul 2020
Disclaimer: The podcast and artwork embedded on this page are the property of People + AI Research. This content is not affiliated with or endorsed by eachpod.com.