supervised 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 decision affect how and how well the machine will learn to play? Plus, an intro to reinforcement learning, the approach Yannick will be taking.

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

You can reach out to the hosts on Twitter: @dweinberger and @tafsiri. 

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 that play two versions of tic-tac-toe. Building a machine learning app requires humans making a lot of decisions. We start by agreeing that David will use a “supervised learning” approach while Yannick will go with “reinforcement learning.”

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

You can reach out to the hosts on Twitter: @dweinberger and @tafsiri. 

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.

More at: pair.withgoogle.com/thehardway

In this episode I talk to Katie King, well known AI author and CEO of AI in business. I had the pleasure to catch up with her an hour to discuss her journey in the AI space, tips and perspectives on how to think about AI, common misconceptions, her work with the All-Party Parliament, and the impacts of COVID-19 on the AI industry. 

This episode, Triveni and Will tackle the value, ethics, and methods for good labeled data, while also weighing the need for model interpretability and the possibility of an impending AI winter.  Triveni will also take us through a step-by-step of the decisions made by a Random Forest algorith

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  Be sure to check out the articles we mentioned this week:

 The Side of Machine Learning You’re Undervaluing and How to Fix it by Matt Wilder (LabelBox)

 The Hidden Costs of Automated Thinking by Jonathan Zittrain (The New Yorker)

 Another AI Winter Could Usher in a Dark Period for Artificial Intelligence by Eleanor Cummins (PopSci)