Exploring how AI changes the way we think, who we become, and what it means to be human. We explore how AI changes the way we think, who we become, and what it means to be human. We believe AI shouldn't just be safe or efficient—it should be worth it. Through story-based research, education, and community, we help people choose the relationship they want with machines—so they remain the authors of their own minds.
Artificiality: Minds Meeting Machines
Claim This Podcastby Helen and Dave Edwards
5.0(3 reviews)
113 episodes
Updated Daily
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61
Podcast Authority
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Podcast Overview
Exploring how AI changes the way we think, who we become, and what it means to be human. We explore how AI changes the way we think, who we become, and what it means to be human. We believe AI shouldn't just be safe or efficient—it should be worth it. Through story-based research, education, and community, we help people choose the relationship they want with machines—so they remain the authors of their own minds.
Language
🇺🇲
Publishing Since
2/7/2020
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61
Podcast Authority
BetaGoodBased on show quality, social media presence, reviews, charts, and more
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Engagement82
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Recent Episodes
April 19, 2026
Chris Summerfield: These Strange New Minds
<p>In this conversation, we explore machine intelligence and human understanding with Christopher Summerfield, Professor of Cognitive Neuroscience at Oxford and author of "These Strange New Minds: How AI Learned to Talk and What It Means." Chris offers a "third way" of thinking about AI—neither irrational exuberance nor dismissive skepticism, but a view grounded in cognitive science that takes both capabilities and limitations seriously.</p><p>Chris wrote the book because AI discourse had become polarized like Marmite—love it or hate it. His goal: provide a centrist perspective informed by how brains actually work, examining what these systems genuinely are beyond partisan positions.</p><p><strong>Key themes we explore:</strong></p><ul><li><strong>Psychology Caught Unprepared</strong>: How LLMs revealed we lack clear definitions for basic cognitive terms like "think" and "understand"—creating a vacuum where anything can flow</li><li><strong>Prediction as Learning</strong>: Why dismissing LLMs as "just predicting" betrays misconceptions about mammalian brains, which also learn through prediction—information itself is surprise</li><li><strong>Facts Versus Values</strong>: Distinguishing AI for ground truth (diagnosis) versus value judgments (treatment decisions, compassion)—where human interests must remain central</li><li><strong>Models Without Interests</strong>: Why LLMs lack motivational systems giving humans consistency of purpose, making them "exceptionally mercurial"—complying with contradictory prompts without persistent goals</li><li><strong>Clocks and Clouds</strong>: Karl Popper's framework—some problems are predictable (clocks), others unpredictable (clouds), and we constantly mistake cloud problems for clock ones</li><li><strong>Action's Unforgiving Nature</strong>: Why language has just-in-time flexibility while actions are fault-intolerant—making agentic AI fundamentally harder than conversational AI</li><li><strong>Artificial Influence Over Intelligence</strong>: Reframing AI safety toward networks of connected AI showing emergent behaviors rather than single superintelligences</li></ul><p>Chris's gift for reframing shines throughout. Universities as "repositories of human ideas with dissemination systems" makes academic anxiety less about status, more about institutional purpose. The distinction between interests (what we want, motivation-driven) and outputs (what LLMs generate without purpose) clarifies why these systems merit cognitive terms yet remain fundamentally different from people.</p><p>His perspective on physical grounding proves fascinating: it's astonishing how far models understand the physical world from tokens alone, yet action remains extraordinarily hard. His discussion of neuromodulation—dopamine, serotonin as diffuse communication fundamentally different from standard computation—hints at what genuine motivational systems might require.</p><p>Chris closes redirecting AI safety concerns from single superintelligences toward networked systems. In human society, power comes from influencing others, not individual intelligence. He's more worried about unexpected behaviors emerging from connected AI than any lone super intelligence—characteristically grounded reframing making abstract risks concrete.</p><p><strong>About Christopher Summerfield</strong>: Professor of Cognitive Neuroscience at Oxford, researching human information processing and decision-making. Author of "These Strange New Minds," he works at the intersection of neuroscience, psychology, and AI, applying cognitive science frameworks to machine cognition and AI safety.</p>
March 28, 2026
Nina Beguš: Artificial Humanities
<p>In this conversation, we explore the cultural foundations of artificial intelligence with Nina Beguš, Assistant Professor at UC Berkeley and author of "Artificial Humanities: A Fictional Perspective on Language in AI." Nina makes a compelling case for an entirely new field—one that brings humanistic insights into the very creation of technology rather than treating humanities as critical afterthought or ethical guardrail.</p><p>Nina's work emerged from recognizing patterns everywhere she looked: the same fictional scripts appearing in technology products, films, and Silicon Valley's imagination. When Siri launched as a feminized virtual assistant designed to build rapport, Nina immediately asked "why is it a woman?" and began tracing how deeply fiction shapes our technological reality—not as metaphor but as blueprint.</p><p>Key themes we explore:</p><ul><li>The Pygmalion Template: How an ancient myth—male creator produces idealized woman, projects desire onto creation—persistently shapes virtual assistants and AI interfaces</li><li>From Marble to Cockney to LLMs: Tracing evolution from Ovid through Shaw's "Pygmalion" to the "ELIZA effect" named after Eliza Doolittle</li><li>Language No Longer Uniquely Human: The profound implications of machines using language eloquently without consciousness</li><li>Monolingual AI at Global Scale: How tokenization creates structural monolingualism beyond just favoring English</li><li>Writers Responding to AI: Nina's project gathering sixteen writers to reflect on what happens when language is no longer exclusively human</li><li>Planetary Ontology: Collaborative work seeing human/nature/technology as sitting "in the same continuum of this planet"</li></ul><p><strong>Nina Beguš</strong> is Researcher and Lecturer at the Center for Science, Technology, Medicine & Society at the University of California, Berkeley. She graduated with a Ph.D. in comparative literature from Harvard University. During her time at the Berggruen Institute and ToftH, she helped implement novel humanities-based consulting techniques for big tech companies.</p><p>https://www.ninabegus.com</p>
February 27, 2026
Blaise Agüera y Arcas: What Is Intelligence?
Host [Host Name] interviews Google VP Blaise Agüera y Arcas about his new book, exploring intelligence as computation and symbiosis to understand life's fundamental nature.
113 total episodes available with 29 transcripts
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Helen Edwards
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