Painting Roses, Eating Marshmallows And Network Protocols

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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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Painting Roses, Eating Marshmallows and Network Protocols - JSJ 662

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MP3•Beranda episode

Welcome to another insightful episode of the JavaScript Jabber podcast, hosted by Charles alongside our expert panelists Dan and AJ. Today, they are thrilled to be joined by Avishai Ish-Shalom a seasoned technologist with an eye for challenging conventions in the tech world. In this episode, Avishai takes us through fascinating discussions comparing industrial food products to technological abstractions, including his unique perspective on the “marshmallow effect” and the evolving complexities of virtualization.
They delve into the challenges of backward compatibility in modern tech, using real-world parallels like AWS virtual storage options, and discuss the impact of technologies like Docker and cloud services on our understanding of underlying infrastructures. Charles shares his upgrade journey from an aging Mac laptop to a powerful System76 desktop for AI tasks, reflecting the changing demands on development environments.
Listen in as they explore the nuances of binary vs. textual protocols, the importance of future-proofing legacy systems, and Avishai's compelling arguments in his articles "Don't Paint the Roses" and "The Marshmallow Effect." Plus, they discuss Avishai's career evolution and the intellectual challenges faced by today’s engineers in the rapidly advancing tech landscape. Whether you’re a casual listener or a tech enthusiast, this episode promises to offer valuable insights and thought-provoking discussions. Stay tuned!
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LinkedIn: Avishai Ish-Shalom

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AJ - Deku Deals
Avishai - marimo | a next-generation Python notebook
Charles - Challengers! Beach Cup | Board Game
Dan - The Penguin (TV Mini Series 2024)
Dan - Bluesky

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