Stay Human, from the Artificiality Institute

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 interfacesFrom Marble to Cockney to LLMs: Tracing evolution from Ovid through Shaw's "Pygmalion" to the "ELIZA effect" named after Eliza DoolittleLanguage No Longer Uniquely Human: The profound implications of machines using language eloquently without consciousnessMonolingual AI at Global Scale: How tokenization creates structural monolingualism beyond just favoring EnglishWriters Responding to AI: Nina's project gathering sixteen writers to reflect on what happens when language is no longer exclusively humanPlanetary 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 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 entirelyConviction 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 conflictsDynamic 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 centralAI'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 uncertaintyThe 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.

We enjoyed giving a virtual keynote for the Autonomous Summit on December 4, 2025, titled Becoming Synthetic: What AI Is Doing To Us, Not Just For Us. We talked about our research on how to maintain human agency & cognitive sovereignty, the philosophical question of what it means to be human, and our new(ish) approach to create better AI tools called unDesign. unDesign is not the absence of design nor is it anti-design. It's design oriented differently. The history of design has been a project of reducing uncertainty. Making things legible. Signaling affordances. Good design means you never have to wonder what to do.Undesign inverts this and uses "uns" as design material. The unknown. The unpredictable. The unplanned. These aren't bugs. They're the medium where value actually lives. Because uncertainty is the condition of genuine encounter. unDesign doesn't design outcomes—it designs the space where outcomes can emerge.You can watch the full keynote below. Check it out!

In this conversation, we explore the philosophical art of embracing uncertainty with Eric Schwitzgebel, Professor of Philosophy at UC Riverside and author of "The Weirdness of the World." Eric's work celebrates what he calls "the philosophy of opening"—not rushing to close off possibilities, but instead revealing how many more viable alternatives exist than we typically recognize. As he observes, learning that the world is less comprehensible than you thought, that more possibilities remain open, constitutes a valuable form of knowledge in itself.The conversation centers on one of Eric's most provocative arguments: that if we take mainstream scientific theories of consciousness seriously and apply them consistently, the United States might qualify as a conscious entity. Not in some fascist "absorb yourself into the group mind" sense, but perhaps at the level of a rabbit—possessing massive internal information processing, sophisticated environmental responsiveness, self-monitoring capabilities, and all the neural substrate you could want (just distributed across individual skulls rather than contained in one).Key themes we explore:The United States Consciousness Thought Experiment: How standard materialist theories that attribute consciousness to animals based on information processing and behavioral complexity would, if applied consistently, suggest large-scale collective entities might be conscious too—and why every attempt to wiggle out of this conclusion commits you to other forms of weirdnessPhilosophy of Opening vs. Closing: Eric's distinction between philosophical work that narrows possibilities to find definitive answers versus work that reveals previously unconsidered alternatives, expanding rather than contracting the space of viable theoriesThe AI Consciousness Crisis Ahead: Why we'll face social decisions about how to treat AI systems before we have scientific consensus on whether they're conscious—with respectable theories supporting radically different conclusions and people's investments (emotional, religious, economic) driving which theories they embraceMimicry and Mistrust: Why we're justified in being more skeptical about AI consciousness than human consciousness—not because similarity proves anything definitively, but because AI systems trained to mimic human linguistic patterns raise the same concerns as parrots saying "hoist the flag"The Design Policy of the Excluded Middle: Eric's recommendation (which he doubts the world will follow) to avoid creating systems whose moral status we cannot determine—because making mistakes in either direction could be catastrophic at scaleStrange Intelligence Over Superintelligence: Why the linear conception of AI as "subhuman, then human, then superhuman" fundamentally misunderstands what's likely to emerge—we should expect radically different cognitive architectures with cross-cutting capacities and incapacities rather than human-like minds that are simply "better"About Eric Schwitzgebel: Eric Schwitzgebel is Professor of Philosophy at the University of California, Riverside, specializing in philosophy of mind and moral psychology. His work spans consciousness, introspection, and the ethics of artificial intelligence. Author of "The Weirdness of the World" and a forthcoming book on AI consciousness and moral status, Eric maintains an active blog (The Splintered Mind) where he explores philosophical questions with clarity and wit. His scholarship consistently challenges comfortable assumptions while remaining remarkably accessible to readers beyond academic philosophy.

In this conversation, we explore how humans can better navigate the AI era with De Kai, pioneering researcher who built the web's first machine translation systems and whose work spawned Google Translate. Drawing on four decades of AI research experience, De Kai offers a different framework for understanding our relationship with artificial intelligence—moving beyond outdated metaphors toward more constructive approaches.De Kai's perspective was shaped by observing how AI technologies are being deployed in ways that increase rather than decrease human understanding. While AI has tremendous potential to help people communicate across cultural and linguistic differences—as his translation work demonstrated—current implementations often amplify polarization and misunderstanding instead.Key themes we explore:Beyond Machine Metaphors: Why thinking of AI as "tools" or "machines" is dangerously outdated—AI systems are fundamentally artificial psychological entities that learn, adapt, and influence human behavior in ways no coffee maker ever couldThe Parenting Framework: De Kai's central insight that we're all currently "parenting" roughly 100 artificial intelligences daily through our smartphones, tablets, and devices—AIs that are watching, learning, and imitating our attitudes, behaviors, and belief systemsSystem One vs. System Two Intelligence: How current large language models operate primarily through "artificial autism"—brilliant pattern matching without the reflective, critical thinking capacities that characterize mature human intelligenceTranslation as Understanding: Moving beyond simple language translation toward what De Kai calls a "translation mindset"—using AI to help humans understand different cultural framings and perspectives rather than enforcing singular universal truthsThe Reframing Superpower: How AI's capacity for rapid perspective-shifting and metaphorical reasoning represents one of humanity's best hopes for breaking out of polarized narratives and finding common groundSocial Fabric Transformation: Understanding how 800 billion artificial minds embedded in our social networks are already reshaping how cultures and civilizations evolve—often in ways that increase rather than decrease mutual understandingDrawing on insights from developmental psychology and complex systems, De Kai's "Raising AI" framework emphasizes conscious human responsibility in shaping how these artificial minds develop. Rather than viewing this as an overwhelming burden, he frames it as an opportunity for humans to become more intentional about the values and behaviors they model—both for AI systems and for each other.About De Kai: De Kai is Professor of Computer Science and Engineering at HKUST and Distinguished Research Scholar at Berkeley’s International Computer Science Institute. He is Independent Director of AI ethics think tank The Future Society, and was one of eight inaugural members of Google’s AI ethics council. De Kai invented and built the world’s first global-scale online language translator that spawned Google Translate, Yahoo Translate, and Microsoft Bing Translator. For his pioneering contributions in AI, natural language processing, and machine learning, De Kai was honored by the Association for Computational Linguistics as one of only seventeen Founding Fellows and by Debrett’s as one of the 100 most influential figures of Hong Kong.

In this conversation, we explore the shifts in human experience with Christine Rosen, senior fellow at the American Enterprise Institute and author of "The Extinction of Experience: Being Human in a Disembodied World." As a member of the "hybrid generation" of Gen X, Christine (like us) brings the perspective of having lived through the transition from an analog to a digital world and witnessed firsthand what we've gained and lost in the process.Christine frames our current moment through the lens of what naturalist Robert Michael Pyle called "the extinction of experience"—the idea that when something disappears from our environment, subsequent generations don't even know to mourn its absence. Drawing on over 20 years of studying technology's impact on human behavior, she argues that we're experiencing a mass migration from direct to mediated experience, often without recognizing the qualitative differences between them.Key themes we explore:The Archaeology of Lost Skills: How the abandonment of handwriting reveals the broader pattern of discarding embodied cognition—the physical practices that shape how we think, remember, and process the world around usMediation as Default: Why our increasing reliance on screens to understand experience is fundamentally different from direct engagement, and how this shift affects our ability to read emotions, tolerate friction, and navigate uncomfortable social situationsThe Machine Logic of Relationships: How technology companies treat our emotions "like the law used to treat wives as property"—as something to be controlled, optimized, and made efficient rather than experienced in their full complexityEmbodied Resistance: Why skills like cursive handwriting, face-to-face conversation, and the ability to sit with uncomfortable emotions aren't nostalgic indulgences but essential human capacities that require active preservationThe Keyboard Metaphor: How our technological interfaces—with their control buttons, delete keys, and escape commands—are reshaping our expectations for human relationships and emotional experiencesChristine challenges the Silicon Valley orthodoxy that frames every technological advancement as inevitable progress, instead advocating for what she calls "defending the human." This isn't a Luddite rejection of technology but a call for conscious choice about what we preserve, what we abandon, and what we allow machines to optimize out of existence.The conversation reveals how seemingly small decisions—choosing to handwrite a letter, putting phones in the center of the table during dinner, or learning to read cursive—become acts of resistance against a broader cultural shift toward treating humans as inefficient machines in need of optimization. As Christine observes, we're creating a world where the people designing our technological future live with "human nannies and human tutors and human massage therapists" while prescribing AI substitutes for everyone else.What emerges is both a warning and a manifesto: that preserving human experience requires actively choosing friction, inefficiency, and the irreducible messiness of being embodied creatures in a physical world. Christine's work serves as an essential field guide for navigating the tension between technological capability and human flourishing—showing us how to embrace useful innovations while defending the experiences that make us most fully human.About Christine Rosen: Christine Rosen is a senior fellow at the American Enterprise Institute, where she focuses on the intersection of technology, culture, and society. Previously the managing editor of The New Republic and founding editor of The Hedgehog Review, her writing has appeared in The Atlantic, The New York Times, The Wall Street Journal, and numerous other publications. "The Extinction of Experience" represents over two decades of research into how digital technologies are reshaping human behavior and social relationships.

At the Artificiality Summit in October 2024, Steve Sloman, professor at Brown University and author of The Knowledge Illusion and The Cost of Conviction, catalyzed a conversation about how we perceive knowledge in ourselves, others, and now in machines. What happens when our collective knowledge includes a community of machines? Steve challenged us to think about the dynamics of knowledge and understanding in an AI-driven world, about the evolving landscape of narratives, and ask the question can AI make us believe in ways that humans make us believe? What would it take for AI to construct a compelling ideology and belief system that humans would want to follow?Bio: Steven Sloman has taught at Brown since 1992. He studies higher-level cognition. He is a Fellow of the Cognitive Science Society, the Society of Experimental Psychologists, the American Psychological Society, the Eastern Psychological Association, and the Psychonomic Society. Along with scientific papers and editorials, his published work includes a 2005 book Causal Models: How We Think about the World and Its Alternatives, a 2017 book The Knowledge Illusion: Why We Never Think Alone co-authored with Phil Fernbach, and the forthcoming Righteousness: How Humans Decide from MIT Press. He has been Editor-in-Chief of the journal Cognition, Chair of the Brown University faculty, and created Brown’s concentration in Behavioral Decision Sciences.

In this conversation, we explore AI bias, transformative justice, and the future of technology with Dr. Avriel Epps, computational social scientist, Civic Science Postdoctoral Fellow at Cornell University's CATLab, and co-founder of AI for Abolition.What makes this conversation unique is how it begins with Avriel's recently published children's book, A Kids Book About AI Bias (Penguin Random House), designed for ages 5-9. As an accomplished researcher with a PhD from Harvard and expertise in how algorithmic systems impact identity development, Avriel has taken on the remarkable challenge of translating complex technical concepts about AI bias into accessible language for the youngest learners.Key themes we explore:- The Translation Challenge: How to distill graduate-level research on algorithmic bias into concepts a six-year-old can understand—and why kids' unfiltered responses to AI bias reveal truths adults often struggle to articulate- Critical Digital Literacy: Why building awareness of AI bias early can serve as a protective mechanism for young people who will be most vulnerable to these systems- AI for Abolition: Avriel's nonprofit work building community power around AI, including developing open-source tools like "Repair" for transformative and restorative justice practitioners- The Incentive Problem: Why the fundamental issue isn't the technology itself, but the economic structures driving AI development—and how communities might reclaim agency over systems built from their own data- Generational Perspectives: How different generations approach digital activism, from Gen Z's innovative but potentially ephemeral protest methods to what Gen Alpha might bring to technological resistanceThroughout our conversation, Avriel demonstrates how critical analysis of technology can coexist with practical hope. Her work embodies the belief that while AI currently reinforces existing inequalities, it doesn't have to—if we can change who controls its development and deployment.The conversation concludes with Avriel's ongoing research into how algorithmic systems shaped public discourse around major social and political events, and their vision for "small tech" solutions that serve communities rather than extracting from them.For anyone interested in AI ethics, youth development, or the intersection of technology and social justice, this conversation offers both rigorous analysis and genuine optimism about what's possible when we center equity in technological development.About Dr. Avriel Epps:Dr. Avriel Epps (she/they) is a computational social scientist and a Civic Science Postdoctoral Fellow at the Cornell University CATLab. She completed her Ph.D. at Harvard University in Education with a concentration in Human Development. She also holds an S.M. in Data Science from Harvard’s School of Engineering and Applied Sciences and a B.A. in Communication Studies from UCLA. Previously a Ford Foundation predoctoral fellow, Avriel is currently a Fellow at The National Center on Race and Digital Justice, a Roddenberry Fellow, and a Public Voices Fellow on Technology in the Public Interest with the Op-Ed Project in partnership with the MacArthur Foundation.Avriel is also the co-founder of AI4Abolition, a community organization dedicated to increasing AI literacy in marginalized communities and building community power with and around data-driven technologies. Avriel has been invited to speak at various venues including tech giants like Google and TikTok, and for The U.S. Courts, focusing on algorithmic bias and fairness. In the Fall of 2025, she will begin her tenure as Assistant Professor of Fair and Responsible Data Science at Rutgers University.Links:- Dr. Epps' official website: https://www.avrielepps.com- AI for Abolition: https://www.ai4.org- A Kids Book About AI Bias details: https://www.avrielepps.com/book

In this episode, we welcome David Wolpert, a Professor at the Santa Fe Institute renowned for his groundbreaking work across multiple disciplines—from physics and computer science to game theory and complexity. * Note: If you enjoy our podcast conversations, please join us for the Artificiality Summit on October 23-25 in Bend, Oregon for many more in person conversations like these! Learn more about the Summit at www.artificiality.world/summit.We reached out to David to explore the mathematics of meaning—a concept that's becoming crucial as we live more deeply with artificial intelligences. If machines can hold their own mathematical understanding of meaning, how does that reshape our interactions, our shared reality, and even what it means to be human?David takes us on a journey through his paper "Semantic Information, Autonomous Agency and Non-Equilibrium Statistical Physics," co-authored with Artemy Kolchinsky. While mathematically rigorous in its foundation, our conversation explores these complex ideas in accessible terms.At the core of our discussion is a novel framework for understanding meaning itself—not just as a philosophical concept, but as something that can be mathematically formalized. David explains how we can move beyond Claude Shannon's syntactic information theory (which focuses on the transmission of bits) to a deeper understanding of semantic information (what those bits actually mean to an agent).Drawing from Judea Pearl's work on causality, Schrödinger's insights on life, and stochastic thermodynamics, David presents a unified framework where meaning emerges naturally from an agent's drive to persist into the future. This approach provides a mathematical basis for understanding what makes certain information meaningful to living systems—from humans to single cells.Our conversation ventures into:How AI might help us understand meaning in ways we cannot perceive ourselvesWhat a mathematically rigorous definition of meaning could mean for AI alignmentHow contexts shape our understanding of what's meaningfulThe distinction between causal information and mere correlationWe finish by talking about David's current work on a potentially concerning horizon: how distributed AI systems interacting through smart contracts could create scenarios beyond our mathematical ability to predict—a "distributed singularity" that might emerge in as little as five years. We wrote about this work here. For anyone interested in artificial intelligence, complexity science, or the fundamental nature of meaning itself, this conversation offers rich insights from one of today's most innovative interdisciplinary thinkers. About David Wolpert:David Wolpert is a Professor at the Santa Fe Institute and one of the modern era's true polymaths. He received his PhD in physics from UC Santa Barbara but has made seminal contributions across numerous fields. His research spans machine learning (where he formulated the "No Free Lunch" theorems), statistical physics, game theory, distributed intelligence, and the foundations of inference and computation. Before joining SFI, Wolpert held positions at NASA, Stanford, and the Santa Fe Institute as a professor. His work consistently bridges disciplinary boundaries to address fundamental questions about complex systems, computation, and the nature of intelligence.Thanks again to Jonathan Coulton for our music.

In this episode, we welcome Maggie Jackson, whose latest book, Uncertain, has become essential reading for navigating today’s complex world. Known for her groundbreaking work on attention and distraction, Maggie now turns her focus to uncertainty—not as a problem to be solved, but as a skill to be cultivated. Note: Uncertain won an Artificiality Book Award in 2024—check out our review here: https://www.artificiality.world/artificiality-book-awards-2024/In the interview, we explore the neuroscience of uncertainty, the cultural biases that make us crave certainty, and why our discomfort with the unknown may be holding us back. Maggie unpacks the two core types of uncertainty—what we can’t know and what we don’t yet know—and explains why understanding this distinction is crucial for thinking well in the digital age.Our conversation also explores the implications of AI—as technology increasingly mediates our reality, how do we remain critical thinkers? How do we resist the illusion of certainty in a world of algorithmically generated answersMaggie’s insights challenge us to reframe uncertainty—not as fear, but as an opportunity for discovery, adaptability, and even creativity. If you’ve ever felt overwhelmed by ambiguity or pressured to always have the “right” answer, this episode offers a refreshing perspective on why being uncertain might be one of our greatest human strengths.Links:Maggie: https://www.maggie-jackson.com/Uncertain: https://www.prometheusbooks.com/9781633889194/uncertain/Do you enjoy our conversations like this one? Then subscribe on your favorite platform, subscribe to our emails (free) at Artificiality.world, and check out the Artificiality Summit—our mind-expanding retreat in Bend, Oregon at Artificiality.world/summit.Thanks again to Jonathan Coulton for our music.

In this episode, we sit down with the ever-innovative Chris Messina—creator of the hashtag, top product hunter on Product Hunt, and trusted advisor to startups navigating product development and market strategy.Recording from Ciel Media’s new studio in Berkeley, we explore the evolving landscape of generative AI and the widening gap between its immense potential and real-world usability. Chris introduces a compelling framework, distinguishing AI as a *tool* versus a *medium*, which helps explain the stark divide in how different users engage with these technologies.Our conversation examines key challenges: How do we build trust in AI? Why is transparency in computational reasoning critical? And how might community collaboration shape the next generation of AI products? Drawing from his deep experience in social media and emerging tech, Chris offers striking parallels between early internet adoption and today’s AI revolution, suggesting that meaningful integration will require both time and a generational shift in thinking.What makes this discussion particularly valuable is Chris’s vision for the future of AI interaction—where technology moves beyond query-response models to become a truly collaborative medium, transforming how we create, problem-solve, and communicate.Links:Chris: https://chrismessina.meCiel Media: https://cielcreativespace.com

At the Artificiality Summit 2024, Michael Levin, distinguished professor of biology at Tufts University and associate at Harvard's Wyss Institute, gave a lecture about the emerging field of diverse intelligence and his frameworks for recognizing and communicating with the unconventional intelligence of cells, tissues, and biological robots. This work has led to new approaches to regenerative medicine, cancer, and bioengineering, but also to new ways to understand evolution and embodied minds. He sketched out a space of possibilities—freedom of embodiment—which facilitates imagining a hopeful future of "synthbiosis", in which AI is just one of a wide range of new bodies and minds. Bio: Michael Levin, Distinguished Professor in the Biology department and Vannevar Bush Chair, serves as director of the Tufts Center for Regenerative and Developmental Biology. Recent honors include the Scientist of Vision award and the Distinguished Scholar Award. His group's focus is on understanding the biophysical mechanisms that implement decision-making during complex pattern regulation, and harnessing endogenous bioelectric dynamics toward rational control of growth and form. The lab's current main directions are: - Understanding how somatic cells form bioelectrical networks for storing and recalling pattern memories that guide morphogenesis; - Creating next-generation AI tools for helping scientists understand top-down control of pattern regulation (a new bioinformatics of shape); and - Using these insights to enable new capabilities in regenerative medicine and engineering. www.artificiality.world/summit