Can 'Big Math' Solve for the Future? (with Terence Tao and Dawn Nakagawa)

As AI floods the world with answers that merely sound right, math  tethers them to the need to be actually right.  New machine learning tools and collaboration platforms are pushing theoretical mathematics toward something bigger: large, open projects where progress is shared early; rabbit holes are avoided; and more people can contribute. In this episode, Terence Tao, a Fields Medal-winning mathematician at UCLA, lays out his case for “big math.” He explains what AI can do well — and where it still fails. The question isn’t whether machines can produce answers. It’s whether we can build systems, human and technical, that keep those answers tethered to truth. Resources Mentioned in this Episode: Green-Tao Theorem The Primes Contain Arbitrarily Long Arithmetic Progressions — Ben Green & Terence Tao (Paper, 2004) Observation of a New Boson at a Mass of 125 GeV With the CMS Experiment at the LHC — The CMS Collaboration (Paper, 2012) Where to find Terence Tao:  Mastodon: mathstodon.xyz/@tao  Blog: terrytao.wordpress.com Home Page: www.math.ucla.edu/~tao/ Bluesky: https://bsky.app/profile/teorth.bsky.socialShow ideas and feedback? Email: [email protected] Learn more about the Berggruen Institute  https://www.berggruen.org Follow Futurology! Instagram:   /futurologypod Twitter/X:   / futurologypod Facebook:   / berggrueninst   LinkedIn:   / berggrueninst   Bluesky: / futurologypod Credits  Executive Producers: Nicolas Berggruen, Nathan Gardels, Nils Gilman, Dawn Nakagawa, & Jason Hoch Producers: Grant Slater, Alex Gardels, & Nathalia Ramos Associate Producer: Elissa Mardiney Theme Music: Marcus Bagala Audio Engineer: Aaron Bastinelli & Kyle Scott Wilson Futurology is a production of Studio B and Wavland for the Berggruen Institute in Los Angeles, California.