Secondaries with Matt Pellini of Hamilton Lane

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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 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…

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Matt Pellini, Managing Director at Hamilton Lane, joins Olga Serhiyevich on this episode. Highlights:
- Matt says the secondaries market has grown over 5x from 15 years ago to now.
- Selling in the secondary market used to be a sign of distress but is no longer. There are many different reasons for doing so and it’s mostly a sign of a manager being more active in the overall management of the fund.
- Secondaries, in contrast to other asset classes in the private markets, are typically lower risk, shorter duration, and are more IRR-focused.
- It’s key for LPs to understand the motivations behind a GP’s intentions to engage in secondaries transactions. There are very good reasons for doing so but transparency is key.
- AI is in vogue but technology is only as good as the data that it’s using. The most sophisticated data analysis is not going to give a firm much of an edge if it's using incorrect data or publicly available data.
Thanks for listening — if you like what you hear, please review us on your favorite podcast platform.
Check us out on the web at villageglobal.vc or get in touch with us on Twitter @villageglobal.

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