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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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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 mathematical concepts that power 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…
What the heck with Richard Dweck explicit
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Manage series 2122015
richard dweck and Richard dweck에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 richard dweck and Richard dweck 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
Born December 28 1993, I am a self obsessed unintentional/ natural eccentric who is self-aware enough to self-deprecate but never change, somehow very love-able. I sang in high school( decent tenor ), I did improv comedy from 2009 to 2016 and have been doing stand up since July 18 2015. On this podcast I interview mostly comedians but also any creative content making person who piques my interest, on their start in their endeavor and how it is going
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275 에피소드
모두 재생(하지 않음)으로 표시
Manage series 2122015
richard dweck and Richard dweck에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 richard dweck and Richard dweck 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
Born December 28 1993, I am a self obsessed unintentional/ natural eccentric who is self-aware enough to self-deprecate but never change, somehow very love-able. I sang in high school( decent tenor ), I did improv comedy from 2009 to 2016 and have been doing stand up since July 18 2015. On this podcast I interview mostly comedians but also any creative content making person who piques my interest, on their start in their endeavor and how it is going
…
continue reading
275 에피소드
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What the heck with Richard Dweck

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