The Perfect Summary Podcast

Neuroscientist Christof Koch explains what consciousness is, how to measure it, and how VR, meditation and psychedelics can widen our view of reality.

Jeremy Howard on fast.ai's mission, transfer learning, training fast on one GPU, programming languages, bootstrapped startups and learning.

Harvard's Paola Arlotta explains how the brain develops, what brain organoids can and cannot show, and the ethics of growing them.

Poet and novelist Ocean Vuong on turning shame into fuel, reclaiming language, and why a meaningful life is found where you already are.

Bret Contreras explains how often to lift, how to keep progressing without injury, how to grow glutes and how to bring up lagging muscles.

Dr. Amy Shah explains 30/30/3: 30 grams of protein at your first meal, 30 grams of fiber a day and three probiotic foods daily.

iRobot's Colin Angle on why Roomba worked, cheap cameras over lasers, privacy in the home, and robots as partners rather than automatons.

Dr. David Fajgenbaum explains how approved drugs can treat other diseases, and how one saved his own life after five near-fatal relapses.

Neuroscientist Poppy Crum explains how technology reshapes the brain, how to use AI to learn instead of offloading, and what digital twins can do.

Andrew Huberman explains the brain's threat reflex and why undoing fear means detailed re-exposure followed by a new, positive association.

Andrew Doyle tells Joe Rogan why he thinks woke is fading, how Britain polices speech, and why distrust is no substitute for evidence.

Keras creator François Chollet on why intelligence won't explode, what deep learning can't do, and the real risks of recommendation algorithms.

Stanford ear surgeon Konstantina Stankovic explains how hearing works, what damages it, how to protect it, and why hearing loss matters for the brain.

Skiing champion Mikaela Shiffrin explains why thinking about winning makes her lose, and how she learned to race through anxiety.

Neuroscientist David Berson tours the brain from retina to cortex: color vision, the body clock, balance, motion sickness and plasticity.

Andrew Huberman explains how the lymphatic system clears fluid and waste, and how movement, breathing, side sleeping and cardio keep it flowing.

Greg Fitzsimmons and Joe Rogan riff on censorship, the moon landing, AI's dark side, an Alaskan prank and what makes a comedy scene thrive.

Bert Kreischer and Joe Rogan on viral breaks in comedy, tuning out critics, a blood clot scare, awards, competitiveness and Bert's Netflix series.

MIT's Regina Barzilay on how cancer changed her research, why medical AI is held back by data and adoption, and machine learning for drugs.

Steven Pressfield explains Resistance, the self-sabotage that guards your most important work, and the professional habits he uses to beat it at 82.

Yann LeCun explains why deep learning works, why human intelligence is not general, and why self-supervised world models are the next step for AI.

Mel Robbins shares five small steps, from a brain dump to claiming one win, for getting back on track within a day.

Peter Norvig on rewriting the standard AI textbook, fairness trade-offs, trust in machines, online learning, Lisp, Python and early Google search.

Neuroscientist Tommy Wood on why the brain needs challenge, how much dementia is preventable, and what Formula One teaches about performance.

Duke neuroscientist Jennifer Groh explains how we locate sound, why eardrums move with the eyes, what a thought may be, and how she focuses.

Leonard Susskind on intuition in physics, what quantum computers are for, what string theory achieved and why time has a direction.

Stanford lecturer Matt Abrahams shares drills and frameworks for clear, confident speaking, from stage fright to small talk.

Screenwriter Roger Avary talks film craft and favorite movies with Joe Rogan, then spars with him over sweeping conspiracy claims.

Andrew Huberman explains the brain's social homeostasis circuit, why loneliness drives us to connect, and how shared experience builds bonds.

Gary Marcus argues deep learning lacks common sense and abstraction, and that trustworthy AI needs a hybrid with symbolic reasoning.