Head to Head: the Big ML Smackdown!

Tic-Tac-Toe the Hard Way

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تمت الإضافة منذ قبل five أعوام

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Understanding the Elegant Math Behind Modern Machine Learning 1:14:43

منذ 1 day1:14:43

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1:14:43

Artificial intelligence is evolving at an unprecedented pace—what does that mean for the future of technology, venture capital, business, and even our understanding of ourselves? Award-winning journalist and writer Anil Ananthaswamy joins us for our latest episode to discuss his latest book Why Machines Learn: The Elegant Math Behind Modern AI . Anil helps us explore the journey and many breakthroughs that have propelled machine learning from simple perceptrons to the sophisticated algorithms shaping today’s AI revolution, powering GPT and other models. The discussion aims to demystify some of the underlying math that powers modern machine learning to help everyone grasp this technology impacting our lives, even if your last math class was in high school. Anil walks us through the power of scaling laws, the shift from training to inference optimization, and the debate among AI’s pioneers about the road to AGI—should we be concerned, or are we still missing key pieces of the puzzle? The conversation also delves into AI’s philosophical implications—could understanding how machines learn help us better understand ourselves? And what challenges remain before AI systems can truly operate with agency? If you enjoy this episode, please subscribe and leave us a review on your favorite podcast platform. Sign up for our newsletter at techsurgepodcast.com for exclusive insights and updates on upcoming TechSurge Live Summits. Links: Read Why Machines Learn, Anil’s latest book on the math behind AI https://www.amazon.com/Why-Machines-Learn-Elegant-Behind/dp/0593185749 Learn more about Anil Ananthaswamy’s work and writing https://anilananthaswamy.com/ Watch Anil Ananthaswamy’s TED Talk on AI and intelligence https://www.ted.com/speakers/anil_ananthaswamy Discover the MIT Knight Science Journalism Fellowship that shaped Anil’s AI research https://ksj.mit.edu/ Understand the Perceptron, the foundation of neural networks https://en.wikipedia.org/wiki/Perceptron Read about the Perceptron Convergence Theorem and its significance https://www.nature.com/articles/323533a0…

منذ 5 أعوام 25:19

MP3•منزل الحلقة

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

10 حلقات

#Tech #Podcasting Education #Rebecca Salois #People AI Research #Machine Learning #Human Centered #Reinforcement Learning #Supervised Learning #Tic-tac-toe #Games #Google

Head to Head: the Big ML Smackdown!

Tic-Tac-Toe the Hard Way

published منذ 5 أعوام

MP3•منزل الحلقة

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

10 حلقات

#Tech #Podcasting Education #Rebecca Salois #People AI Research #Machine Learning #Human Centered #Reinforcement Learning #Supervised Learning #Tic-tac-toe #Games #Google

كل الحلقات

1
Lessons learned 33:01

منذ 5 years33:01

33:01

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? For more information about the show, check out pair.withgoogle.com/thehardway/ . You can reach out to the hosts on Twitter: @dweinberger and @tafsiri .…

1
Head to Head: The Even Bigger ML Smackdown! 24:26

منذ 5 years24:26

24:26

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 other in Tic-Tac-Two ! For more information about the show, check out pair.withgoogle.com/thehardway/ . You can reach out to the hosts on Twitter: @dweinberger and @tafsiri .…

1
Enter tic-tac-two 21:20

منذ 5 years21:20

21:20

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. For more information about the show, check out pair.withgoogle.com/thehardway/ . You can reach out to the hosts on Twitter: @dweinberger and @tafsiri .…

1
Head to Head: the Big ML Smackdown! 25:19

منذ 5 years25:19

25:19

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

1
Give that model a treat! : Reinforcement learning explained 26:04

منذ 5 years26:04

26:04

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. Resources: Deep Learning for JavaScript book Playing Atari with Deep Reinforcement Learning Two Minute Papers episode on Atari DQN For more information about the show, check out pair.withgoogle.com/thehardway/ . You can reach out to the hosts on Twitter: @dweinberger and @tafsiri .…

1
Beating random: What it means to have trained a model 17:14

منذ 5 years17:14

17:14

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 model do against a player that makes random tic-tac-toe moves? For more information about the show, check out pair.withgoogle.com/thehardway/ . You can reach out to the hosts on Twitter: @dweinberger and @tafsiri .…

1
From tic-tac-toe moves to ML model 21:37

منذ 5 years21:37

21:37

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 definition of one-hot encoding For more information about the show, check out pair.withgoogle.com/thehardway . You can reach out to the hosts on Twitter: @dweinberger and @tafsiri .…

1
What does a tic-tac-toe board look like to machine learning? 23:26

منذ 5 years23:26

23:26

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 .…

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Howdy, and the myth of “pouring in data” 22:01

منذ 5 years22:01

22:01

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 .…

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Introducing Tic-Tac-Toe the Hard Way 2:09

منذ 5 years2:09

2:09

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

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