Into the Impossible With Brian Keating

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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
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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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Think Like a Nobel Prize Winner, with life changing interviews with 9 Nobel Prizewinners: https://a.co/d/03ezQFu

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

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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Get My NEW Book: Focus Like a Nobel Prize Winner: https://www.amazon.com/dp/B0FN8DH6SX

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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Additional resources:

Get My NEW Book: Focus Like a Nobel Prize Winner: https://www.amazon.com/dp/B0FN8DH6SX?ref_=pe_93986420_775043100


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-

Join this channel to get access to perks like monthly Office Hours:

https://www.youtube.com/channel/UCmXH_moPhfkqCk6S3b9RWuw/join

📚 Get a copy of my books:

Think Like a Nobel Prize Winner, with life changing interviews with 9 Nobel Prizewinners: https://a.co/d/03ezQFu

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

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

📺 Watch my most popular videos:📺

Neil Turok https://www.youtube.com/watch?v=Dt5cFLN65fI
Frank Wilczek https://youtu.be/3z8RqKMQHe0?sub_confirmation=1
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

Follow me to ask questions of my guests:

🏄‍♂️ Twitter: https://twitter.com/DrBrianKeating
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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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Think Like a Nobel Prize Winner, with productivity tips from 9 Nobel Prize winners: https://a.co/d/03ezQFu

Focus Like a Nobel Prize Winner, with life-changing interviews with 9 Nobel Prizewinners: https://a.co/d/hi50U9U

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

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

Follow me to ask questions of my guests:

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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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Join this channel to get access to perks like monthly Office Hours:

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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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Join this channel to get access to perks like monthly Office Hours:

https://www.youtube.com/channel/UCmXH_moPhfkqCk6S3b9RWuw/join

📚 Get my books:

Think Like a Nobel Prize Winner, with productivity tips from 9 Nobel Prize winners: https://a.co/d/03ezQFu

Focus Like a Nobel Prize Winner, with life-changing interviews with 9 Nobel Prizewinners: https://a.co/d/hi50U9U

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

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

Follow me to ask questions of my guests:

🏄‍♂️ Twitter: https://twitter.com/DrBrianKeating
🔔 Subscribe https://www.youtube.com/DrBrianKeating?sub_confirmation=1
📝 Join my mailing list; just click here http://briankeating.com/list
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🎙️ Listen on audio-only platforms: https://briankeating.com/podcast

#universe #podcast #briankeating #intotheimpossible #science #astronomy #cosmology #cosmicmicrowavebackground #intotheimpossible #briankeating
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