Breaking Math Podcast | Podcast Guru

Details

Breaking Math is a deep-dive science, technology, engineering, AI, and mathematics podcast that explores the world through the lens of logic, patterns, and critical thinking. Hosted by Autumn Phaneuf, an expert in industrial engineering, operations research, and applied mathematics, and Noah Giansiracusa, a mathematician and leading voice in algorithmic literacy and technology ethics, the show is dedicated to uncovering the mathematical structures behind science, technology, and the systems shaping our future. What began as a conversation about math as a pure and elegant discipline has evolved into a platform for bold, interdisciplinary dialogue. Each episode of Breaking Math takes listeners on an intellectual journey—into the strange beauty of chaos theory, the ethical dilemmas of AI and algorithms, the hidden math of biology and evolution, or the physics governing black holes and the cosmos. Along the way, Autumn and Noah speak with working scientists, researchers, and thinkers across fields: computer scientists, physicists, chemists, engineers, economists, philosophers, and more. But this isn’t just a podcast about equations. It’s a show about how mathematics shapes the way we think, decide, build, and understand the world. Breaking Math pushes back against the idea that STEM belongs behind a paywall or an academic podium. It’s for the curious, the critical, and the creative—for anyone who believes that ideas should be rigorous, accessible, and infused with wonder. If you’ve ever wondered: What’s the math behind machine learning and modern algorithms? How do we quantify uncertainty in climate and economic models? Can intelligence or consciousness be meaningfully described in AI? Why does beauty matter in an equation? You’re in the right place. At its heart, Breaking Math is about building bridges—between disciplines, between experts and the public, and between abstract mathematics and the messy, magnificent reality we live in. With humor, clarity, and deep respect for complexity, Autumn and Noah invite you to rethink what math can be—and how it can help us shape a better future. Listen wherever you get your podcasts. Website: https://breakingmath.io Linktree: https://linktr.ee/breakingmathmedia Email: [email protected]

Recent Episodes

Forecasting Explained: How Prediction Markets Beat Experts

Professional forecaster Molly Hickman breaks down what it really means to assign a probability to the future — and why she believes generalists often out-forecast subject-matter experts. This episode explores the art and science of forecasting, from techniques to ethical considerations, and how AI and prediction markets are shaping our understanding of the future. Key Topics The definition of forecasting and its importance Techniques for starting in forecasting The role of AI and large language models in forecasting How to interpret probabilities and conditional forecasts Forecasting in complex systems like climate and geopolitics Ethical boundaries and red lines in prediction markets The impact of AI bots on forecasting accuracy and decision making Chapters 03:06 Getting Started with Forecasting: Tools and Techniques 06:15 Beginning Forecasting as a Beginner 07:31 Gut Feelings vs Market Wisdom 08:33 The Delphi Loop and Group Forecasting 09:39 Measuring Forecast Accuracy and Skill 11:17 Forecasting Long-Term and Uncertain Events 12:40 Extrapolating Trends and Model Limitations 14:19 AI Bots in Forecasting and Their Performance 18:16 Prediction Markets as Collective Wisdom 19:19 The Future of Prediction Markets and Society 24:06 The Meaning of Probabilities and Risk Assessment 27:20 Dealing with Chaos and Unpredictability 32:52 Combining Models and Expert Opinions 36:31 Forecasting and Expertise in Science and Policy 39:01 Forecasting AI Risks and Ethical Boundaries Follow Molly Hickman on X (https://x.com/celloMolly) Follow Breaking Math on Substack (https://breakingmath.substack.com/) X (https://x.com/breakingmathpod) Instagram (https://www.instagram.com/breakingmathmedia/) Website (https://www.breakingmath.io/) YouTube (https://www.youtube.com/@BreakingMathPod) Follow Noah on Instagram (https://www.instagram.com/profnoahgian/) X (https://x.com/ProfNoahGian) Bluesky (https://bsky.app/profile/profnoahgian.bsky.social) Follow Autumn on X (https://x.com/1autumn_leaf) Bluesky (https://bsky.app/profile/1autumnleaf.bsky.social) Instagram (https://www.instagram.com/1autumnleaf/) Substack (https://substack.com/@1autumnleaf) email: [email protected]

43 MIN

What Actually Makes Something Alive? with Melanie Challenger

What does it mean to be alive? In this episode of Breaking Math, Autumn and Noah speak with Melanie Challenger, author of Alive, about one of the most profound questions in science and philosophy: how do we define life? Challenger argues that life is not simply a machine-like process or a bundle of genetic instructions. Living beings are embodied, purposeful agents. From single-celled organisms to sequoia seeds, from animals to human beings, life is marked by an astonishing capacity to work to keep itself alive. Chapters 08:12 The concept of purpose in living beings 09:14 The scientific view of purpose and agency 11:52 The importance of purpose and meaning in life 13:19 The danger of ignoring organism agency in science 14:34 Living beings as purposeful agents 15:35 Comparing purpose in a Roomba and a single-celled organism 18:03 Autopoetic vs allopoetic systems 20:03 Free will, agency, and the universe 23:24 The physical basis of life and energy 28:38 Aristotle's concept of psyche and purpose 33:46 The importance of understanding what life truly is 37:56 Material integration and the difference between machines and living beings 38:15 The concept of self and embodiment in life 41:09 The whole body as the agent, not just the brain Follow Melanie Challenger on her website: (https://www.melaniechallenger.com/) Subscribe for more on math, AI, technology, and the systems running the world. Follow Breaking Math on Substack (https://breakingmath.substack.com/) X (https://x.com/breakingmathpod) Instagram (https://www.instagram.com/breakingmathmedia/) Bluesky (https://bsky.app/profile/breakingmath.bsky.social) Website (https://www.breakingmath.io/) YouTube (https://www.youtube.com/@BreakingMathPod) Follow Noah on Instagram (https://www.instagram.com/profnoahgian/) X (https://x.com/ProfNoahGian) Bluesky (https://bsky.app/profile/profnoahgian.bsky.social) Follow Autumn on X (https://x.com/1autumn_leaf) Bluesky (https://bsky.app/profile/1autumnleaf.bsky.social) Instagram (https://www.instagram.com/1autumnleaf/) Substack (https://substack.com/@1autumnleaf) email: [email protected]

48 MIN

Why Uncertainty Is Science's Greatest Strength with Stuart Firestein

Neuroscientist Stuart Firestein (Columbia University) joins Breaking Math to make an extravagant claim: uncertainty isn't a weakness in science — it's the defining feature that makes progress possible. In this episode, we break down why the "one right answer" myth is one of the most damaging ideas in science, why real experts are often the most uncertain people in the room, and why authority and expertise pull in opposite directions, covering two fundamentally different kinds of probability, why Darwin never erased a 300-year-old classification system built on an assumption he disproved, why AI is exceptional at prediction but not built for causation, and why pseudoscience always has a confident answer while real science rarely does — plus the philosophical difference between hope and optimism, and why Voltaire had to invent the word "optimism" in 1759 to describe it. Chapters 03:00 Predictability and the sea of uncertainties 04:08 Science as a search for probabilities and multiple solutions 06:16 Biological classification and the dynamic nature of species 09:10 The optimistic view of a branching universe 12:41 Probability as the language of optimism 16:48 Two types of probability and their roles 17:50 AI, probabilistic models, and the future of certainty 21:40 Science and the creation of better ignorance 23:21 The importance of asking questions over giving answers 27:21 Authority versus knowledge in science 30:04 Pluralism and multiple solutions in science 32:46 Science in the gray area of uncertainty 35:39 The brain and randomness in thought 39:44 Science as a source of hope and optimism Follow Breaking Math on Substack (https://breakingmath.substack.com/) X (https://x.com/breakingmathpod) Instagram (https://www.instagram.com/breakingmathmedia/) Bluesky (https://bsky.app/profile/breakingmath.bsky.social) Website (https://www.breakingmath.io/) YouTube (https://www.youtube.com/@BreakingMathPod) Follow Noah on Instagram (https://www.instagram.com/profnoahgian/) X (https://x.com/ProfNoahGian) Bluesky (https://bsky.app/profile/profnoahgian.bsky.social) Follow Autumn on X (https://x.com/1autumn_leaf) Bluesky (https://bsky.app/profile/1autumnleaf.bsky.social) Instagram (https://www.instagram.com/1autumnleaf/) Substack (https://substack.com/@1autumnleaf) email: [email protected]

43 MIN

Robot Proof: Why Better AI Starts With Better People with Vivienne Ming

Neuroscientist, entrepreneur, and author Dr. Vivienne Ming joins Autumn and Noah to make the case that if we want better AI, we need to build better people first. We get into why AI tutors that hand students answers make learning worse, not better; what her research on "hybrid intelligence" reveals about the human traits — not the AI model — that predict elite human-AI collaboration; a wild experiment running Dungeons & Dragons with Claude and Gemini as dungeon masters to expose the gap between knowing and understanding; her case for "fiduciary AI," legal duty-of-care standards for tutors, hiring tools, and diagnostic models; and the real story of a hiring algorithm that learned to discriminate against women after every explicit gender marker was stripped out. Chapters 02:20 Why build this book now? The importance of human qualities 04:16 AI in education and the concept of robot-proofing 06:37 The median student and AI personalization 09:31 The limitations of AI understanding and theory of mind 11:30 Building better people with AI and human interaction 14:23 Hybrid intelligence and the role of human-AI collaboration 23:56 Case study: AI in Dungeons & Dragons 30:42 AI's strengths and limitations in understanding and cognition 37:34 The science of purpose and its impact on life and society 44:44 The collective intelligence of humans versus AI 46:54 Key takeaway: Build better people for better Follow Vivienne Ming on X (https://x.com/neuraltheory) Get Vivienne's book, Robot Proof: (https://amzn.to/3Tz21aP) Follow Breaking Math on Substack (https://breakingmath.substack.com/) X (https://x.com/breakingmathpod) Instagram (https://www.instagram.com/breakingmathmedia/) Website (https://www.breakingmath.io/) YouTube (https://www.youtube.com/@BreakingMathPod) Follow Noah on Instagram (https://www.instagram.com/profnoahgian/) X (https://x.com/ProfNoahGian) Bluesky (https://bsky.app/profile/profnoahgian.bsky.social) Follow Autumn on X (https://x.com/1autumn_leaf) Bluesky (https://bsky.app/profile/1autumnleaf.bsky.social) Instagram (https://www.instagram.com/1autumnleaf/) Substack (https://substack.com/@1autumnleaf) email: [email protected]

59 MIN

Why Nothing Works: Robber Barons, Algorithms & Governing AI

In this episode, Historian and author Marc Dunkelman to explain why the 19th-century fight over railroad power is the exact fight we're about to have over algorithms and AI. Drawing on his acclaimed book Why Nothing Works: Who Killed Progress — and How to Bring It Back (a Best Book of the Year in the Financial Times and The Economist), Marc unpacks the two competing tools America has always used against concentrated power — antitrust vs. regulation — and why our government's "endemic diffusion of authority" now means nobody can decide anything, from congestion pricing to clean-energy transmission lines to AI safety. CHAPTERS 04:52 — When private projects come back to the public: Warp Speed, DARPA, CHIPS 08:55 — Two ways to fight concentrated power: break them up vs. regulate 10:52 — Railroads, island communities & the birth of regulation 12:29 — The railroad = algorithm parallel 20:33 — Why nothing gets built: the diffusion of authority 27:30 — "A voice without a veto" and the AI moment 32:53 — Where should government draw the line on new tech? 37:20 — Dunkelman the pragmatist: there is no simple answer 38:21 — Where math and AI can genuinely help public policy 40:46 — The lesson we keep overlooking Follow Marc on X [https://x.com/MarcDunkelman] Get Marc's book, Why Nothing Works: https://amzn.to/4pbFvAB] Substack (https://breakingmath.substack.com/) X (https://x.com/breakingmathpod) Instagram (https://www.instagram.com/breakingmathmedia/) Bluesky (https://bsky.app/profile/breakingmath.bsky.social) Website (https://www.breakingmath.io/) YouTube (https://www.youtube.com/@BreakingMathPod) Follow Noah on Instagram (https://www.instagram.com/profnoahgian/) X (https://x.com/ProfNoahGian) Bluesky (https://bsky.app/profile/profnoahgian.bsky.social) Follow Autumn on X (https://x.com/1autumn_leaf) Bluesky (https://bsky.app/profile/1autumnleaf.bsky.social) Instagram (https://www.instagram.com/1autumnleaf/) Substack (https://substack.com/@1autumnleaf) email: [email protected]

44 MIN