Why Machines Learn: The Math Behind AI
07/16/24 • 40 min
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In this episode Autumn and Anil Ananthaswamy discuss the inspiration behind his book “Why Machines Learn” and the importance of understanding the math behind machine learning. He explains that the book aims to convey the beauty and essential concepts of machine learning through storytelling, history, sociology, and mathematics. Anil emphasizes the need for society to become gatekeepers of AI by understanding the mathematical basis of machine learning. He also explores the history of machine learning, including the development of neural networks, support vector machines, and kernel methods. Anil highlights the significance of the backpropagation algorithm and the universal approximation theorem in the resurgence of neural networks.
Keywords: machine learning, math, inspiration, storytelling, history, sociology, gatekeepers, neural networks, support vector machines, kernel methods, backpropagation algorithm, universal approximation theorem, AI, ML, physics, mathematics, science
You can find Anil Ananthaswamy on Twitter @anilananth and his new book “Why Machines Learn”
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In this episode Autumn and Anil Ananthaswamy discuss the inspiration behind his book “Why Machines Learn” and the importance of understanding the math behind machine learning. He explains that the book aims to convey the beauty and essential concepts of machine learning through storytelling, history, sociology, and mathematics. Anil emphasizes the need for society to become gatekeepers of AI by understanding the mathematical basis of machine learning. He also explores the history of machine learning, including the development of neural networks, support vector machines, and kernel methods. Anil highlights the significance of the backpropagation algorithm and the universal approximation theorem in the resurgence of neural networks.
Keywords: machine learning, math, inspiration, storytelling, history, sociology, gatekeepers, neural networks, support vector machines, kernel methods, backpropagation algorithm, universal approximation theorem, AI, ML, physics, mathematics, science
You can find Anil Ananthaswamy on Twitter @anilananth and his new book “Why Machines Learn”
Subscribe to Breaking Math wherever you get your podcasts.
Become a patron of Breaking Math for as little as a buck a month
Follow Breaking Math on Twitter, Instagram, LinkedIn, Website, YouTube, TikTok
Follow Autumn on Twitter and Instagram
Follow Gabe on Twitter.
Become a guest here
email: [email protected]
Previous Episode
The Intersection of Mathematics and Democracy
This discussion Autumn and Gabe delves into Ismar Volic's personal background and inspiration for writing the book, “Making Democracy Count” as well as the practical and theoretical aspects of voting systems. Additionally, the conversation explores the application of voting systems to everyday decision-making and the use of topological data analysis in understanding societal polarization. The conversation covers a wide range of topics, including data visualization, gerrymandering, electoral systems, and the intersection of mathematics and democracy. Volic, shares insights on the practical implications of implementing mathematical improvements in electoral systems and the legal and constitutional hurdles that may arise. He also discusses the importance of educating oneself about the quantitative underpinnings of democracy and the need for interdisciplinary discussions that bridge mathematics and politics.
Keywords: math podcast, creativity, mascot, background, Matlab, ranked choice voting, elections, author's background, inspiration, voting systems, topological data analysis, societal polarization, mathematics, democracy, data visualization, gerrymandering, electoral systems, interdisciplinary discussions, practical implications, legal hurdles, constitutional considerations
You can find Ismar Volic on Twitter and LinkedIn @ismarvolic. Please go check out the Institute for Mathematics and Democracy and Volic’s new book “Making Democracy Count”
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email: [email protected]
Next Episode
What are Journal Rankings? The basics: a minisode.
In this minisode, Autumn explores the basics in the world of journal rankings and metrics. She discusses the importance of journal rankings and how they are determined, focusing on metrics like impact factor, mathematical citation quotient (MCQ), and publication power approach (PPA). She explains how these metrics provide insights into a journal's influence and performance, but also emphasizes the need for a comprehensive evaluation of research beyond just metrics.
Keywords: journal rankings, journal metrics, impact factor, mathematical citation quotient, publication power approach, research evaluation, math, physics, ai, machine learning, education, publishing, academic journals
Subscribe to Breaking Math wherever you get your podcasts.
Become a patron of Breaking Math for as little as a buck a month
Follow Breaking Math on Twitter, Instagram, LinkedIn, Website, YouTube, TikTok
Follow Autumn on Twitter and Instagram
Follow Gabe on Twitter.
Become a guest here
email: [email protected]
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