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In this episode, I sit down with renowned Oxford mathematician, philosopher of science, and public intellectual John C. Lennox to explore the urgent questions surrounding artificial intelligence and humanity’s future.

We dive into the biggest questions about AI, consciousness, truth, faith, and what it means to be human in an age of accelerating technology. If you’re curious about whether AI is humanity’s greatest achievement, its greatest temptation, or something even more profound, watch the full conversation and join us as we explore the future of intelligence—and our place within it.

Key Takeaways:

01:05 - Who Decides What’s True in the Age of AI?

03:14 - Can AI Ever Be Truly Creative? The ‘Einstein Test’

04:10 - Consciousness: The Uncrossable Barrier

07:20 - The QWERTY Keyboard Problem: Are We Stuck with LLMs?

19:10 - The AI Hall of Mirrors: Training on Itself

27:02 - Information Isn’t Just Bits and Atoms

32:05 - Are We Trying to Build God?

36:48 - The Deepest Question—What Does It Mean to Be Human?

47:50 - The Temptation to Play God: From Babel to the Singularity

50:14 - AI’s Relationship to Power and Surveillance

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In this conversation, I break down a provocative idea: science doesn’t “prove” ultimate truths — it eliminates what’s false. And if that’s how physics works, then the search for God (or the disproof of God) has to play by the same rules: hypotheses, predictions, and falsifiability.

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My tell-all cosmic memoir Losing the Nobel Prize: http://amzn.to/2sa5UpA

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In this episode, I revisit my podcast with Sam Harris. At the end of our discussion, Harris abruptly cut things short after we debated the meaning of slavery in the Bible and Talmud. Was this an honest philosophical disagreement, or an example of intellectual overstatement and rhetorical sleight-of-hand?

Sam Harris is widely regarded as a leading public intellectual, yet during our 3.5-hour dialogue, I encountered a curious blind spot he consistently sidesteps.

We cover:

Sam Harris fleeing the 2025 LA Fires with a 'jar of MDMA and a gun" (02:00)

Harris’s controversial claim that slavery disproves a benevolent God (09:12)

The Hebrew word *“eved”* and its deeper meaning (09:30–11:51)

Why Biblical servitude is not the same as American chattel slavery

Sam’s dismissal of centuries of rabbinic scholarship (15:39–16:17)

His argument about the optic blind spot as a “design flaw” (12:59–14:36)

The role of psychedelics in Harris’s worldview (18:45–20:43)

How confidence without expertise can turn into intellectual bad faith

This isn’t about attacking Sam Harris — it’s about testing the limits of rationalism, confronting blind spots, and asking whether even the most brilliant thinkers can fall into ideological echo chambers.

Watch the full original conversation here: • Sam Harris: The TRUTH About Consciousness ...

👉 Subscribe for more debates with leading scientists, philosophers, and iconoclasts.

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Sir Roger Penrose: https://youtu.be/AMuqyAvX7Wo

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What can we truly learn about the brain from a kidney cell? And what do aliens and alien-like limbs have to teach us? And if language is our escape velocity moment, what does that mean for the future of AI?

Nikolay Kukushkin is a scientist who believes that memory, intelligence, and even the roots of awareness may exist in places we never thought to look in. The timing of molecules in the learning of single cells, in the slow abstractions of evolution. He takes us through all of this in this wonderful new book, One Hand Clapping. Now let's go into the impossible.

KEY TAKEAWAYS

00:00 "Sea Slugs and Abstract Thought"

09:55 "Cell Intelligence and Sound"

10:47 Cellular Intelligence and Memory

19:22 "One Hand Clapping: Zen Riddle"

23:18 "Evolution of Inner Worlds"

31:05 "Einstein, Thought Experiments, and LLMs"

32:50 "Can LLMs Achieve Life?"

38:01 Alien Encounters: Unlikely Similarity

45:39 Unanticipated Findings on Cellular Memory

53:33 "Interdisciplinary Creativity and Slow Multitasking"

55:58 Evolution of the Gut

01:04:57 "Interconnection and Existential Koan"

01:08:19 "Reconstructing Nature's Patterns"

01:13:55 "Evolution of Brain's Role"

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The first-ever audiobook from Galileo: Dialogue Concerning the Two Chief World Systems: Ptolemaic and Copernican https://a.co/d/iZPi9Un

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Neil Turok https://www.youtube.com/watch?v=Dt5cFLN65fI

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Eric Weinstein vs. Stephen Wolfram https://www.youtube.com/watch?v=OI0AZ4Y4Ip4?sub_confirmation=1

Sir Roger Penrose: https://youtu.be/AMuqyAvX7Wo

Sabine Hossenfelder: https://youtu.be/g00ilS6tBvs

Avi Loeb: https://youtu.be/N9lUceHsLRw

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Code. It's the closest thing humans have ever invented to magic. Write symbols on a screen, and reality emerges. Money moves, doors unlock, planes land, diseases get diagnosed, and most of us have no idea how it actually works. Today's conversation is about why code feels magical and why that feeling is both powerful and also dangerous. In a moment, you'll hear why the real risk of code is artificial intelligence. It's scale. Tiny ideas multiplied across billions of lives.

My guest is Sam Arbesman, complexity scientist and author of The Magic of Code, a book about how software quietly became the most influential force shaping modern civilization. magic and sorcery. Often in our stories, they require a great deal of effort, like training. And so, like, you have to go to Hogwarts for seven years to really know magic and wizardry very well. The same kind of thing is true with code like so let's go cast some spells and learn the magic of code. Let's go.

KEY TAKEAWAYS

00:00 — Code feels magical because text can change reality.

00:25 — The real risk of code is scale, not intelligence.

01:55 — Software shapes modern civilization more than we realize.

02:40 — Coding is both technical and a humanistic liberal art.

03:05 — Code fulfills an ancient desire to control the world with language.

04:15 — Most “new” tech ideas have deep historical roots.

07:00 — Universal computation emerges from conditional logic and loops.

09:10 — Biological computation works very differently from digital computers.

10:30 — Biology expands the definition of computation itself.

12:00 — Software is powerful but inherently temporary.

16:00 — Code resembles magic because it requires long training to master.

17:45 — Software creation is becoming accessible to everyone.

19:40 — Not all software must scale; personal tools matter.

24:10 — Computers are tools, not ends in themselves.

26:35 — Bugs are inevitable and often educational.

56:00 -- The Half Life Of Facts

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Additional resources:

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The first-ever audiobook from Galileo: Dialogue Concerning the Two Chief World Systems: Ptolemaic and Copernican https://a.co/d/iZPi9Un

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Neil Turok https://www.youtube.com/watch?v=Dt5cFLN65fI

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Sir Roger Penrose: https://youtu.be/AMuqyAvX7Wo

Sabine Hossenfelder: https://youtu.be/g00ilS6tBvs

Avi Loeb: https://youtu.be/N9lUceHsLRw

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One of the most powerful AI systems we’ve ever built is succeeding for reasons we still don’t understand. And worse, they may succeed for reasons that might lock us into the wrong future for humanity.

Today’s guest is Anil Ananthaswamy, an award-winning science writer and one of the clearest thinkers on the mathematical foundations of machine learning.

In this conversation, we’re not just talking about new demos, incremental improvements, or updates on new models being released. We’re asking even harder questions: Why does the mathematics of machine learning work at all?

How do these models succeed when they suffer from problems like overparameterization and lack of training data?

And are large language models revealing deep structure, or are they just producing very convincing illusions and causing us to face an increasingly AI-slop-driven future?

KEY TAKEAWAYS

00:00 — Book explores why ML works through math

02:47 — Perceptron proof shows simple math guarantees learning

05:11 — Early AI failed due to single-layer limits

07:12 — Nonlinear limits caused the first AI winter

09:04 — Backpropagation revived neural networks

10:59 — GPUs + big data enabled deep learning

15:25 — AI success risks technological lock-in

17:30 — LLMs lack human-like learning and embodiment

22:57 — High-dimensional spaces power ML behavior

27:36 — Data saturation may slow future gains

31:11 — Continual learning is still missing in AI

33:46 — Neuromorphic chips promise energy efficiency

41:49 — Overparameterized models still generalize well

45:05 — SGD succeeds via randomness in complex landscapes

48:27 — Perceptrons remain the core of modern neural net

-

Additional resources:

Anil's NEW Book "Why Machines Learn: The Elegant Math Behind Modern AI": https://www.amazon.com/Why-Machines-Learn-Elegant-Behind/dp/0593185749

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The first-ever audiobook from Galileo: Dialogue Concerning the Two Chief World Systems: Ptolemaic and Copernican https://a.co/d/iZPi9Un

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Sir Roger Penrose: https://youtu.be/AMuqyAvX7Wo

Sabine Hossenfelder: https://youtu.be/g00ilS6tBvs

Avi Loeb: https://youtu.be/N9lUceHsLRw

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Andrew Jaffe Book: The Random Universe: https://www.amazon.com/Random-Universe-Models-Probability-Cosmos/dp/0300250509

Is the universe intrinsically random? In this conversation, we dive deep into why the universe may be fundamentally, intrinsically random. Whether inflation on life support, the truth behind the Hubble tension, and whether cosmology is approaching the event horizon, limits beyond which humans can never know. Today we're joined by one of the architects of modern cosmological inference, Professor Andrew Jaffee, author of a new book called The Random Universe that argues that every observation in science is shaped by the models we bring to it, biases and all.

KEY TAKEAWAYS

00:00–01:13 — Science and life rely on building models.

01:13–03:35 — Models of people and reality are often wrong and revised.

04:04–06:01 — Observation depends on prior theories.

06:01–07:32 — Models can’t be escaped, only improved.

07:32–08:57 — No single scientific method exists.

08:57–11:25 — Science uses induction, not pure proof.

11:25–13:22 — Induction isn’t certain, only probabilistic.

13:22–15:36 — Induction works because nature is regular.

17:44–19:08 — Big Bang emerges from well-tested models.

19:08–21:15 — Current cosmology is stressed, not broken.

29:19–30:36 — Probability gives meaning to models.

39:45–41:11 — Randomness often reflects limited knowledge.

43:46–45:00 — Quantum physics is fundamentally probabilistic.

49:09–50:04 — Inflation awaits decisive observational tests.

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The first-ever audiobook from Galileo: Dialogue Concerning the Two Chief World Systems: Ptolemaic and Copernican https://a.co/d/iZPi9Un

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Neil Turok https://www.youtube.com/watch?v=Dt5cFLN65fI

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Eric Weinstein vs. Stephen Wolfram https://www.youtube.com/watch?v=OI0AZ4Y4Ip4?sub_confirmation=1

Sir Roger Penrose: https://youtu.be/AMuqyAvX7Wo

Sabine Hossenfelder: https://youtu.be/g00ilS6tBvs

Avi Loeb: https://youtu.be/N9lUceHsLRw

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Enjoy this classic episode from the vault: Max Tegmark & Eric Weinstein, New Years Eve 2020!

Brian Keating brings together two thought leaders at the edge of physics and philosophy: Max Tegmark, physicist, cosmologist, and pioneer at MIT, and Eric Weinstein, mathematician, economist, and creator of Geometric Unity.

Timestamps:

00:00 "Ambitions in AI, Physics, News"

16:58 "Emergent Reality from Proto-Spacetime"

22:50 "Rethinking Unification in Physics"

36:09 "Value of Disagreeable Individualism"

45:32 "Optimism for Academia Careers"

01:04:08 "Dangers of Oversimplifying Physics"

01:07:34 "On Success, Science, and Wonder"

01:18:47 "Funding and Advancing Physics Research"

01:30:28 "Perspective on Science and Society"

01:43:13 "Three Types of Scientific Experiments"

01:52:05 "Collaboration and Discovery in Science"

02:02:20 "The Messiness of Scientific Truth"

02:13:08 "Simulation, AI, and Ethics"

02:20:55 "Limits of Technology in Cosmos"

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My tell-all cosmic memoir Losing the Nobel Prize: http://amzn.to/2sa5UpA

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Will AI solve future math proofs?

Every time you type a password, buy something online, or send an encrypted message, you’re trusting an assumption about prime numbers: that they don’t hide an exploitable pattern. Modern cryptography depends on primes behaving “randomly enough,” yet many fundamental questions about primes remain unproven.

In this episode, I’m joined by Fields Medalist Terence Tao to explore what mathematicians can prove, what they strongly suspect, and what could change if unexpected structure appeared in the primes. We discuss pseudorandomness and why it matters for encryption, the twin prime conjecture, and how quantum computing reshapes what is feasible in computation and security.

We also get into AI and mathematics: why large language models can sound convincing even when unreliable, how AI can help with idea generation and literature recall, and why verification and proof assistants will matter if AI is to contribute to real mathematical progress. Along the way, Tao explains proof techniques like proof by contradiction, why complex numbers and the square root of minus one are so central, and how high-dimensional geometry breaks low-dimensional intuition.

Finally, Tao shares a real-world example of how math breakthroughs translate into technology: compressed sensing, which has enabled much faster MRI scans by reconstructing images from far less data.

Timestamps:

00:00 Discrepancy Theory Explained

09:35 Induction: Science and Mathematics

14:38 "Proof Concept Through Play"

18:43 "Complex Numbers and Completeness"

25:36 Prime Numbers and Cryptography

29:51 "Computability and Complexity in Mathematics"

35:04 AI Discovers New Knot Theory Insights

42:02 "Elegance in Nature's Laws"

47:12 "AI as Complementary Research Tools"

51:27 "Humility in Pursuit of Proofs"

54:46 "Multiple Approaches to Mathematics"

01:03:04 Rethinking Reality and Physics

01:07:14 "Origins of Compressed Sensing"

01:10:17 "Shannon Bound and Information Limits"

Follow Terry's Blog: https://terrytao.wordpress.com/

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From the electrifying environment of high-speed particle collisions to the challenge of sifting signals from heaps of experimental noise, you'll hear how Prof Whiteson and his team are pushing boundaries. They discuss bold new algorithms capable of spotting non-standard tracks—think wild trajectories that defy classical expectations and could reveal surprises nature has kept hidden. Practical questions about detector design, efficiency, and even the mathematics of “smooth” particle paths make for a rich, dynamic dialogue.

If you’re curious about how physicists ask the universe its most challenging questions, the frustrations and breakthroughs of innovation, and the fascinating interplay between theory and experiment, this episode will take you to the front lines of discovery. Plus, hear how machine learning might help us find not just the next weird particle, but perhaps the next Nobel-worthy revelation. Get ready for a fascinating journey into the impossible!

Daniel Whiteson is a physicist whose research spans a wide range of topics at UC Irvine. By day, he works on the ATLAS experiment, one of the major physics collaborations at the Large Hadron Collider, where he contributes to Higgs boson precision measurements and develops advanced techniques in machine learning, data acquisition, and trigger systems. His research group is known for applying machine learning innovations to physics problems, including projects beyond ATLAS—like using approximate symmetries or jet pattern matching. Recently, his team has been focused on machine learning projects to identify unusual particle tracks, always pushing the frontier between physics and data science.

Timestamps:

00:00 Revisiting Discovery with New Tools

04:43 Particle Tracking Constraints Explained

06:56 Challenges in Non-Helical Track Detection

10:29 Non-Helical Tracks and Dark QCD

14:38 "Track Reconstruction and Efficiency"

18:43 Quirk Detection and Reconstruction"

23:27 Testing Generalization Beyond Memorization

25:23 Quirk Tracks and Overlap Analysis

30:36 "Smooth Paths and Signal Control"

31:17 "Training Pipeline on Weird Tracks"

35:55 Filtering Standard Model Tracks

38:24 "Challenges in Parameter Optimization"

43:15 "Neural Networks Learn Complex Mappings"

44:38 "Machine Learning for Track Detection"

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