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TechSurge: Deep Tech VC Podcast
1 Understanding the Elegant Math Behind Modern Machine Learning 1:14:43
1:14:43 
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DisukaiArtificial intelligence is evolving at an unprecedented paceâwhat 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 todayâs AI revolution, powering GPT and other models. The discussion aims to demystify some of the underlying mathematical concepts that power modern machine learning, to help everyone grasp this technology impacting our livesâeven 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 AIâs pioneers about the road to AGIâshould we be concerned, or are we still missing key pieces of the puzzle? The conversation also delves into AIâs philosophical implicationsâcould 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, Anilâs latest book on the math behind AI https://www.amazon.com/Why-Machines-Learn-Elegant-Behind/dp/0593185749 Learn more about Anil Ananthaswamyâs work and writing https://anilananthaswamy.com/ Watch Anil Ananthaswamyâs TED Talk on AI and intelligence https://www.ted.com/speakers/anil_ananthaswamy Discover the MIT Knight Science Journalism Fellowship that shaped Anilâs 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/323533a0âŚ
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