AI is becoming heavily integrated into everyday society, however, learning about AI can initially be difficult or confusing. Here are some helpful podcasts that you don’t need a PhD to listen to:
Linear Digressions
Hosted By: Katie Malone and Ben Jaffe
Why We Recommend It: This Podcast is extremely listener-friendly. Episodes average about 20 minutes a-piece and make advanced concepts easy to understand.
Click HERE to listen
Talking Machines
Hosted By: Neil Lawrence and Katherine Gorman
Why We Recommend It: This Podcast’s episode lengths vary and is still understandable for those not well-versed in the AI language. Talking Machines also features interviews with AI professionals, and takes questions from their viewers.
Click HERE to listen
Eye On A.I.
Hosted By: Craig S. Smith
Why We Recommend It: Episodes are on average between 20-30 minutes and feature interviews with fascinating AI innovators. These interviews help listeners to understand how AI advancements affect the world on a global scale.
Click HERE to listen
This Week in Machine Learning & AI
Hosted By: Sam Charrington
Why We Recommend It: While this Podcast’s episodes average about 50 minutes, it provides frequent updates on the newest innovations of the AI field. TWiML&AI also features exclusive and thought-provoking interviews with experts in the AI and Machine Learning community.
Click HERE to listen
Data Skeptic
Hosted By: Kyle Polich (mini-episodes are hosted by Linh Da Tran)
Why We Recommend It: This Podcast’s episodes range from about 20-45 minutes. Data Skeptic covers basic AI and Data Science concepts and how they relate to the world. Polich starts out with basic explanations and goes more in-depth as the Podcast continues.
Click HERE to listen
Those are just a few of our favorite podcasts about machine learning, big data, and artificial intelligence. If you have a favorite AI podcast not featured on this list let us know!
*Disclaimer: None of the above podcasts are affiliated with PolyChord and the views expressed by the hosts and guests are entirely their own*