Love Triangle and Other Maths (with Matt Parker)

In this episode, Gabriel and Autumn interview mathematician, comedian, and author Matt Parker about his latest book, "Love Triangle." They discuss the unique page numbering system in the book, which is based on the sine function, and how it adds an extra layer of discovery for readers. They also explore the use of triangles and quads in 3D modeling, the concept of Perlin noise, and the perception of randomness. The conversation touches on the intersection of mathematics and creativity, as well as the practical applications of mathematical concepts in various fields. The conversation explores various topics related to mathematics, including the analysis of the Mona Lisa, the use of math in playing pool, the discovery of new shapes, and the application of math in various fields. The speakers discuss the motivation behind exploring these topics and the interplay between math and art. They also provide advice for science and math content creators on YouTube.

About Our Guest

Matt Parker

Matt Parker is a stand-up comedian and a YouTuber with over one hundred million views. He is the author of the international bestseller Humble Pi and Things to Make and Do in the Fourth Dimension. He writes about math for The Guardian, hosts the Science Channel’s Outrageous Acts of Science, and appears regularly on various BBC shows including More or LessThe Infinite Monkey Cage, and QI. Originally a math teacher from Australia, Matt now lives in the UK.

How to Support Us

Stay Connected: You can find Matt Parker on Twitter @standupmaths, his website https://standupmaths.com/, his YouTube, and his new book “Love Triangle” on Amazon and everywhere books are sold! :)

Support Breaking Math Podcast: Join our Patreon at patreon.com/breakingmath, follow Breaking Math @breakingmathpod on Twitter, and @breakingmathmedia on Instagram. If you also want to follow our hosts on Twitter you can find Gabe at @techpodgabe and Autumn @1autumn_leaf. Don’t forget to subscribe and rate us five stars!


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Episode 103: Why Machines Learn: The Math Behind AI