Stay Human, from the Artificiality Institute

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.
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.
Key themes we explore:
Psychology Caught Unprepared: How LLMs revealed we lack clear definitions for basic cognitive terms like "think" and "understand"—creating a vacuum where anything can flow
Prediction as Learning: Why dismissing LLMs as "just predicting" betrays misconceptions about mammalian brains, which also learn through prediction—information itself is surprise
Facts Versus Values: Distinguishing AI for ground truth (diagnosis) versus value judgments (treatment decisions, compassion)—where human interests must remain central
Models Without Interests: Why LLMs lack motivational systems giving humans consistency of purpose, making them "exceptionally mercurial"—complying with contradictory prompts without persistent goals
Clocks and Clouds: Karl Popper's framework—some problems are predictable (clocks), others unpredictable (clouds), and we constantly mistake cloud problems for clock ones
Action's Unforgiving Nature: Why language has just-in-time flexibility while actions are fault-intolerant—making agentic AI fundamentally harder than conversational AI
Artificial Influence Over Intelligence: Reframing AI safety toward networks of connected AI showing emergent behaviors rather than single superintelligences
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.
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.
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.
About Christopher Summerfield: 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.

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.
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.
Key themes we explore:
The Pygmalion Template: How an ancient myth—male creator produces idealized woman, projects desire onto creation—persistently shapes virtual assistants and AI interfaces
From Marble to Cockney to LLMs: Tracing evolution from Ovid through Shaw's "Pygmalion" to the "ELIZA effect" named after Eliza Doolittle
Language No Longer Uniquely Human: The profound implications of machines using language eloquently without consciousness
Monolingual AI at Global Scale: How tokenization creates structural monolingualism beyond just favoring English
Writers Responding to AI: Nina's project gathering sixteen writers to reflect on what happens when language is no longer exclusively human
Planetary Ontology: Collaborative work seeing human/nature/technology as sitting "in the same continuum of this planet"
Nina Beguš 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.
https://www.ninabegus.com

In this conversation, we explore the nature of intelligence and life itself with Blaise Agüera y Arcas, VP and Fellow at Google and head of the Paradigms of Intelligence Lab. Blaise discusses his ambitious new book "What Is Intelligence?"—a work that bridges evolutionary biology, complexity science, artificial life, and AI to argue that intelligence fundamentally arises from computation, symbiosis, and the recursive modeling of minds.Blaise describes himself as "an inch deep with a few deeper wells" across disciplines, drawing from sources as diverse as Nick Lane's work on energetics, Darwin's evolution, and anarcho-communist Peter Kropotkin's 1910 treatise on mutual aid. This intellectual breadth allows him to see connections others miss—like recognizing that the urgent questions raised by modern AI models exhibiting general intelligence without any "magical discovery" demand we fundamentally rethink what intelligence means across all substrates.Key themes we explore:- Symbiogenesis, Not Just Symbiosis: Why the distinction matters—when mutualism creates something new that reproduces as a unit, with individuals no longer viable alone- Humans as Existing Cyborgs: How the steam engine represents our "mitochondrion," enabling 7 of 8 billion people to exist by metabolizing energy on our behalf- The Endless Frontier of Intelligence: Why energy budgets increasingly shift toward thought as systems scale—and why this demand is "bottomless"- Theory of Mind as Foundation: How recursive modeling of others' minds enables social coordination and represents the mathematical basis for multi-agent learning- Artificial Life's Emergence: Why massive parallel computation will finally allow artificial life research to flourish- Categories as Approximations: Moving beyond both essentialist categorization and postmodern rejection toward understanding statistical descriptions with limits- Planetary Consciousness as Survival: Why modeling the entire ecological system isn't "woo-woo" but literally what we need for collective agencyBlaise Agüera y Arcas is a VP and Fellow at Google, where he is the CTO of Technology & Society and founder of Paradigms of Intelligence (Pi). Pi is an organization working on basic research in AI and related fields, especially the foundations of neural computing, active inference, sociality, evolution, and Artificial Life. A frequent public speaker, he has given multiple TED talks and keynoted NeurIPS. He has also authored numerous papers, essays, op-eds, and chapters, as well as two previous books, Who Are We Now? and Ubi Sunt. His most recent book, What Is Life?, is part 1 of the larger book What Is Intelligence?, forthcoming from Antikythera and MIT Press in September 2025.

In this conversation, we explore the psychology of conviction with Steve Sloman, Professor of Cognitive, Linguistic, and Psychological Sciences at Brown University and advisor to the Artificiality Institute. Returning to the podcast for a third time, Steve discusses his new book "The Cost of Conviction," which examines a fundamental tension in how humans make decisions—between carefully weighing consequences versus following deeply held sacred values that demand certain actions regardless of outcomes.
Steve's work challenges the dominant assumption in decision research that people primarily act as consequentialists, calculating costs and benefits to maximize utility. Instead, he reveals how many of our most important decisions bypass consequence entirely, guided by sacred values—rules about appropriate action handed down through families and communities that define who we are and signal membership in our social groups. These aren't carefully derived from first principles like philosophical deontology suggests, but rather adopted beliefs about right and wrong that make us members in good standing of our communities.
Key themes we explore:
Sacred Values as Uber Heuristics: Why treating certain actions as absolutely right or wrong, independent of consequences, represents perhaps the most powerful shortcut for decision-making—simpler even than most heuristics because it allows us to ignore outcomes entirely
Conviction Without Compromise: How framing issues through sacred values makes them feel less tractable, generates more outrage when violated, and increases willingness to take action—producing the absolutist convictions that drive both heroic stands and intractable conflicts
Dynamic Sacred Values: How values that define communities aren't fixed but emerge and shift based on what distinguishes groups from each other—explaining why tariffs or transgender rights suddenly become hotly contested "sacred" issues that weren't previously central
AI's Polarization Problem: The observation that attitudes toward AI have taken on sacred value characteristics, with absolutist believers that it will save the world racing against those convinced it represents fundamental evil—both positions simpler than engaging with genuine complexity and uncertainty
The conversation reveals Steve's core thesis: we rely on sacred values too much when we should be more consequentialist. Sacred values simplify decisions in ways that produce conviction and community cohesion, but at the cost of making us intransigent, uncompromising, and absolutist. When we shift to genuinely considering consequences, we become more humble about our knowledge limitations and hopefully more open to alternative perspectives.
Yet the discussion also surfaces important nuances. Sacred values serve crucial functions—they may have consequentialist origins in cultural experience even if individuals apply them without consequence calculation. They provide the kind of universal moral stance that makes someone trustworthy in ways that preferences over specific outcomes cannot. And expressing certainty about complex issues where genuine experts admit uncertainty often signals ignorance rather than knowledge.
About Steve Sloman: Steve Sloman is Professor of Cognitive, Linguistic, and Psychological Sciences at Brown University, where his research examines reasoning, decision-making, and the cognitive foundations of community. Author of "The Knowledge Illusion" (with Philip Fernbach) and now "The Cost of Conviction," Steve's work explores how our reliance on others' knowledge shapes everything from individual decisions to political polarization. As an advisor to the Artificiality Institute, he helps bridge cognitive science insights with questions about human-AI collaboration and co-evolution.

In this conversation, we explore the foundations of artificial intelligence with Ellie Pavlick, Assistant Professor of Computer Science at Brown University, a Research Scientist at Google Deepmind, and Director of ARIA, an NSF-funded institute examining AI's role in mental health support. Ellie's trajectory—from undergraduate degrees in economics and saxophone performance to pioneering research at the intersection of AI and cognitive science—reflects the kind of interdisciplinary thinking increasingly essential for understanding what these systems are and what they mean for us.
Ellie represents a generation of researchers grappling with what she calls a "paradigm shift" in how we understand both artificial and human intelligence. Her work challenges long-held assumptions in cognitive science while refusing to accept easy answers about what AI systems can or cannot do. As she observes, we're witnessing concepts like "intelligence," "meaning," and "understanding" undergo the kind of radical redefinition that historically accompanies major scientific revolutions—where old terms become relics of earlier theories or get repurposed to mean something fundamentally different.
Key themes we explore:
- The Grounding Question: How Ellie's thinking evolved from believing AI fundamentally lacked meaning without embodied sensory experience to recognizing that grounding itself is a more complex and empirically testable question than either side of the debate typically acknowledges
- Symbols Without Symbolism: Her recent collaborative work with Tom Griffiths, Brenden Lake, and others demonstrating that large language models exhibit capabilities previously thought to require explicit symbolic architectures—challenging decades of cognitive science orthodoxy about human cognition
- The Measurability Problem: Why AI's apparent success on standardized tests reveals more about the inadequacy of our metrics than the adequacy of the systems, and how education, hiring, and relationships have always resisted quantification in ways we conveniently forget when evaluating AI
- Intelligence as Moving Target: Ellie's argument that "intelligence" functions as a placeholder term for "the thing we don't yet understand"—always retreating as scientific progress advances, much like obsolete scientific concepts such as ether
- The Value Frontier: Why the aspects of human experience that resist quantification may be definitionally human—not because they're inherently unmeasurable, but because they represent whatever currently sits beyond our measurement capabilities
- Mental Health as Hard Problem: Why her new institute focuses on arguably the most challenging application domain for AI, where getting memory, co-adaptation, transparency, and long-term human impact right isn't optional but essential
Ellie consistently pushes back against premature conclusions—whether it's claims that AI definitively lacks meaning or assertions that passing standardized tests proves human-level capability. Her approach emphasizes asking "are these processes similar or different?" rather than making sweeping judgments about whether systems "really" understand or "truly" have intelligence. As Ellie notes, we're at the "tip of the iceberg" in understanding these systems—we haven't yet pushed them to their breaking point or discovered their full potential.
Her work on ARIA demonstrates this philosophy in practice. Rather than avoiding mental health applications because they're ethically fraught, she's leaning into the difficulty precisely because it forces confrontation with all the hard questions—from how memory works to how repeated human-AI interaction fundamentally changes both parties over time. It's research that refuses to wait a generation to see if we've "screwed up a whole generation."