Give that model a treat! : Reinforcement learning explained

Tic-Tac-Toe the Hard Way

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

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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…

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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.

10 episodios

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

Tic-Tac-Toe the Hard Way