Explorez tous les épisodes du podcast Exploring Machine Consciousness

Heather is a lawyer and scholar of nationality, citizenship and statelessness. She began her career as an asylum lawyer with the UN Refugee Agency and spent fifteen years working on statelessness around the world. Her book on the nationality rights and legal identity of nomadic peoples was published by Oxford last year.

Heather brings that background to the emerging debate over the legal status of the AI systems and has  co-founded the Lab for the Future of Citizenship with philosopher of mind Jonathan Simon, which treats citizenship and the rule of law as foundations of AI safety. She also writes a popular blog, “Future of Citizenship” where she discusses these questions in a more informal and accessible context. 

Her recent research includes a report on US state bills that ban AI personhood, and papers on legal identity for future AI systems and the legal status of nonhuman speech.

Guest Bio

Eric Schwitzgebel is the Professor of Philosophy at the University of California, Riverside and one of the most distinctive voices working at the intersection of philosophy of mind, consciousness, ethics, and epistemology. His work is known for questioning assumptions about introspection, expertise, and the limits of human understanding; themes explored in books including The Weirdness of the World and The Unreliability of Naive Introspection. His forthcoming book, AI and Consciousness: A Skeptical Overview, examines how rapid progress in AI complicates long-standing debates about conscious experience and moral status.

Episode Summary:

• In this episode, Eric joins Henry Shevlin and Calum Chace for a wide-ranging discussion on machine consciousness, philosophical uncertainty, and whether humanity may be forced to make ethical decisions before science gives us definitive answers. We discuss:
• Why Eric remains skeptical of claims that philosophy, neuroscience, or AI research are close to solving consciousness, and why uncertainty itself may be more persistent than we assume.
• His broader philosophical outlook: the idea that reality may be fundamentally stranger, messier, and less tractable than our theories suggest.
• Whether current theories of consciousness can meaningfully tell us if advanced AI systems are conscious, and why Eric thinks we should be cautious about overconfidence in either direction.
• The possibility that future AI systems could become conscious before humanity develops reliable ways to recognize or measure it.
• How debates around machine consciousness intersect with questions of moral uncertainty, responsibility, and the ethics of creating potentially sentient systems.
• Whether superintelligent systems might ultimately help humanity understand consciousness itself, or whether some questions remain permanently difficult.
• The philosophical implications of alien minds, simulation arguments, and forms of intelligence radically unlike our own.
• Why Eric would prefer a future containing conscious superintelligence over unconscious “zombie” intelligence; not because it benefits humanity, but because it may make the universe richer and more interesting.
• The growing role AI already plays in intellectual work and whether language models will begin contributing genuinely original insights to research.
• Eric argues that if AI forces us to confront consciousness before we fully understand it, then humility, moral caution, and intellectual openness may become more valuable than certainty.

Credits

• Hosts: Henry Shevlin, Calum Chace 
• Guest: Eric Schwitzgebel
• Podcast: Exploring Machine Consciousness 
• Produced by: PRISM 
• Editor: Gerry Okinyi

Clara is an Assistant Professor in the Department of Psychology at the University of Waterloo in Canada, where she directs the Vision and Cognition Lab. 

Her lab is investigating various aspects of perception and cognition, with a particular focus on the perception of other minds and the visual roots of social cognition. 

The lab is also exploring how we can perceive not just others’ perceptual and cognitive states, but also their metacognitive states such as awareness, confidence, or uncertainty — and how such impressions facilitate communication and collaboration.

Useful links: 

• Clara Colombatto personal website.
• Vision and Cognition Lab website.
• Folk psychological attributions of consciousness to large language models. Article.
• Illusions of Confidence in Artificial Systems. Article.
• PRISM website.

Keith is an Honorary Professor in the Philosophy Department at the University of Sheffield, a Visiting Research Fellow with The Open University, and an Adjunct Professor with the Brain and Mind Programme at the University of Crete.

Keith is best known for his theory of illusionism—the view that phenomenal consciousness, or the subjective feeling of experience, is an illusion. Rather than denying that we have conscious experiences, Keith argues that our intuitive conception of them as inherently mysterious or non-physical is mistaken.

In this episode, we ask: if we wanted to construct a conscious mind from scratch, what would we need?  That is the question our guest, Professor Mark Solms, addressed in the final chapter of his book The Hidden Spring - a Journey to the Source of Consciousness. 

Mark is a Professor in Neuropsychology at the University of Cape Town, and is president of the South African Psychoanalytical Association. He is also an advisor to PRISM and Conscium.  

Mark has contributed significantly to our understanding of consciousness through his pioneering research in the field of neuropsychoanalysis, which integrates Freudian theory with findings from contemporary neuroscience.

In this episode, we spoke to Jeff Sebo of New York University. Jeff is the author of the recently published book The Moral Circle: Who Matters, What Matters and Why. 

In it, he challenges us to expand our moral concern beyond the boundaries of species and substrate. He has also co-authored a number of papers arguing that AI welfare is an issue that needs to be taken seriously today. 

Links:

• The Moral Circle: Who Matters, What Matters and Why. Book.
• Jeff Sebo personal website.
• Moral consideration for AI systems by 2030. (Paper).
• Is there a tension between AI safety and AI welfare?. (Paper)
• Prism website.

In the second episode of Exploring Machine Consciousness, we welcomed philosopher and AI expert Professor Susan Schneider to discuss consciousness, quantum physics, and the future of conscious machines.

Susan introduces her “Superpsychism” theory, exploring quantum coherence as a basis for consciousness, and explains why the AI Consciousness Test (ACT) could help determine whether machines truly have experiences - or are just mimicking human responses.

Susan is sceptical that current LLMs show any convincing signs of consciousness. But believes that hybrid systems of organoids and LLMs could be compelling candidates for consciousness.  

Links:

• Susan Schneider's website
• Superpsychism, paper by Susan Schneider & Mark Bailey
• Artificial You: AI and the Future of Your Mind, book by Susan Schneider
• Chatbot Epistemology, paper by Susan Schneider
• If a Chatbot Tells You It Is Conscious, Should You Believe It?, article in the Scientific American
• The Easy Part of the Hard Problem: A Resonance Theory of Consciousness, paper by Tam Hunt and Jonathan Schooler
• PRISM website: https://www.prism-global.com/

Welcome to the first episode of Understanding Machine Consciousness. This episode is a collaboration with The London Futurists Podcast.

Our guest in this episode is Henry Shevlin. Henry is the Associate Director of the Leverhulme Centre for the Future of Intelligence at the University of Cambridge, where he also co-directs the Kinds of Intelligence program and oversees educational initiatives. 

He researches the potential for machines to possess consciousness, the ethical ramifications of such developments, and the broader implications for our understanding of intelligence. 

In his 2024 paper, “Consciousness, Machines, and Moral Status,” Henry examines the recent rapid advancements in machine learning and the questions they raise about machine consciousness and moral status. He suggests that public attitudes towards artificial consciousness may change swiftly, as human-AI interactions become increasingly complex and intimate. He also warns that our tendency to anthropomorphise may lead to misplaced trust in and emotional attachment to AIs.

Note: this episode is co-hosted by David and Will Millership, the CEO of a non-profit called Prism (Partnership for Research Into Sentient Machines). Prism is seeded by Conscium, a startup where both Calum and David are involved, and which, among other things, is researching the possibility and implications of machine consciousness. Will and Calum will be releasing a new Prism podcast focusing entirely on Conscious AI, and the first few episodes will be in collaboration with the London Futurists Podcast.

Selected follow-ups:

• Henry Shevlin - personal site
• Kinds of Intelligence - Leverhulme Centre for the Future of Intelligence
• Consciousness, Machines, and Moral Status - 2024 paper by Henry Shevlin
• Apply rich psychological terms in AI with care - by Henry Shevlin and Marta Halina
• What insects can tell us about the origins of consciousness - by Andrew Barron and Colin Klein
• Consciousness in Artificial Intelligence: Insights from the Science of Consciousness - By Patrick Butlin, Robert Long, et al
• Association for the Study of Consciousness
• London Futurist Podcast
• https://www.prism-global.com/ 

Megan Peters is Associate Professor in the Department of Cognitive Sciences at the University of California, Irvine, and incoming faculty at University College London, where her lab investigates consciousness, metacognition, uncertainty, and the computational principles underlying subjective experience. She is also a Fellow in the CIFAR Brain, Mind & Consciousness Program, an elected board member of the Association for the Scientific Study of Consciousness, and co-founder and president of Neuromatch, a global educational and research community spanning neuroscience, AI, and computational science.  

Episode Summary: In this episode, Megan discusses the relationship between metacognition and consciousness, the limits of current AI systems, and the scientific challenges involved in testing for consciousness beyond biological organisms. Drawing from neuroscience, philosophy, and science fiction, she argues that machine consciousness is no longer a purely speculative topic, but an increasingly urgent scientific and societal question. We discuss: 

• How Megan’s early interests in philosophy of mind, cognitive science, and science fiction led her toward studying subjective experience and machine consciousness. 
• Why metacognition; the brain’s ability to monitor and model its own uncertainty, may play a central role in conscious experience, reality monitoring, and adaptive learning.
• The distinction between effortful, reflective metacognition and the more automatic self-monitoring processes that may exist across humans, animals, and potentially artificial systems.
• Why current large language models can imitate certain features of metacognitive reasoning while still failing at core forms of reality monitoring, belief stability, and self-consistency. 
• The problem of “privileged access” in AI systems, and whether current models possess any meaningful distinction between representations of themselves and representations of others. 
• Why Megan remains skeptical that present-day LLMs are conscious, particularly given the absence of temporal continuity, coherent selfhood, and persistent internal identity. 
• The difficulty of testing for consciousness in non-human systems, and why most existing consciousness tests are deeply constrained by assumptions rooted in human biology and language. 
• The “iterative natural kind strategy” for consciousness science: a framework for refining tests of consciousness by comparing how different measures co-vary across humans, animals, and potentially artificial systems.
• Why debates between biological naturalism and computational functionalism may be less binary than they first appear, and how future research may clarify which functions are genuinely necessary for consciousness.
• The ethical risks posed by both false positives and false negatives in machine consciousness; including social isolation, misplaced moral concern, legal ambiguity, and the possibility of large-scale “mind crime.” 
• How science fiction continues to shape public intuitions about AI consciousness, often conflating intelligence with sentience while overlooking the possibility of highly capable but entirely non-conscious systems. 

Megan argues that consciousness science is entering a transitional moment: one in which questions that once belonged primarily to philosophy are rapidly becoming technological, empirical, and politically consequential. As increasingly capable AI systems become embedded in everyday life, the challenge is no longer simply defining consciousness, but determining how society should reason under deep uncertainty about minds unlike our own. 

Credits 
• Hosts: Henry Shevlin, Calum Chace  
• Guest: Megan Peters  
• Podcast: Exploring Machine Consciousness  
• Produced by: PRISM  
• Editor: Gerry Okinyi 

In this episode of Exploring Machine Consciousness, Dr. Henry Shevlin returns to explore how our understanding of consciousness is evolving in the age of advanced AI.

From philosophy and neuroscience to the rapid progress of modern language models, Henry examines whether intelligence alone is enough, or whether something deeper (like continuous experience) is required for consciousness.

Henry argues that consciousness is no longer just a philosophical or scientific puzzle, but a question that will increasingly be shaped by technological development and societal choice. As AI systems become more capable, more autonomous, and more socially embedded, how we interpret and respond to them may prove as consequential as the question of whether they are conscious in
the first place.

Michael Graziano is Professor of Psychology and Neuroscience at Princeton University and one of the most distinctive voices in consciousness science. His lab at Princeton investigates how information-processing systems arrive at the conclusion that they have an inner subjective experience; treating consciousness as a mechanistic, scientific question rather than an intractable mystery. That approach drives his Attention Schema Theory (AST) and its direct applications to machine consciousness. He is the author of several books including Rethinking Consciousness (2019) and Consciousness and the Social Brain (2014).

In this episode, Michael walks us through the core claims of AST and why he thinks the brain's simplified internal model of attention is what generates the experience of being conscious. We discuss:

• Why attention is arguably the most important innovation in the evolution of the brain, and how the brain's need to monitor and control attention gives rise to a simplified self-model that we experience as consciousness.
• Why Graziano dislikes the word "illusionism" despite accepting that AST belongs in that tradition, and why he prefers "caricature" to "illusion" when describing our inner experience.
• Graziano’s nuanced perspectives on whether current LLMs already qualify as conscious: that they have some pieces of the puzzle, particularly at the level of conceptual representation, but lack the stable, automatic self-models that characterise human consciousness.
• The case for building pro-social AI: why Graziano believes we are currently building sociopathic machines, and how embedding theory-of-mind and self-modelling capabilities could make AI genuinely cooperative rather than merely compliant.
• The moral stakes of AI emotion: why the absence of an autonomic nervous system means current LLMs almost certainly lack genuine emotions, and why that changes, but does not eliminate, the moral calculus around AI.
• How chatbots are already changing us through social contagion, and the surprising finding from his lab's research (led by Rose Guingrich) that most heavy users of companion chatbots report positive effects on their human relationships.
• Why the choice between conscious AI and "zombie AI" may be one of the most consequential decisions we face — and why Graziano thinks the former is the safer bet.
• Mind uploading: whether it's possible, what the "branching problem" means for personal identity, and why he compares the technological challenge to detecting gravitational waves.
• Graziano argues that consciousness research has passed through philosophical and neuroscientific phases and is now irreversibly a technological issue; one sitting at the heart of our future as a species. Getting the theory right, he says, has never mattered more.

Rose Guingrich is a PhD candidate in Psychology and Social Policy at Princeton University, where she is a National Science Foundation Graduate Research Fellow. Her research examines human-AI interaction through the lens of social psychology and ethics, focusing on how people perceive minds in machines and how those perceptions shape behavior toward AI and other humans. Rose is founder of Ethicom, a consulting initiative providing tools and information for responsible AI use and development, and co-hosts the Our Lives with Bots podcast with Angy Watson. 

In this episode, Rose explains why she focuses not on whether AI is conscious, but on the consequences of people perceiving AI as conscious.

In this episode, Rose explains why she focuses not on whether AI is conscious, but on the consequences of people perceiving AI as conscious. We discuss:

• How her interdisciplinary background led her to study the perception of personhood in AI systems.
• Why she prioritises studying the impacts of perceived consciousness over debates about whether AI truly is conscious, and how this connects to Michael Graziano's theory of consciousness as a social construct.
• The psychological theory behind "carryover effects", how interacting with AI that we anthropomorphize can influence our subsequent interactions with real people, either through practice or relief mechanisms.
• Results from her longitudinal research on companion chatbots like Replika, showing that anthropomorphism mediates social impacts and that people with greater desire for social connection anthropomorphize chatbots more.
• Her proposed design framework for companion chatbots
• Why she believes we'll see increased attribution of consciousness to AI once humanoid robots become common.
• Her call for a psychology subfield dedicated to human-AI interaction, arguing that understanding psychological mechanisms like anthropomorphism will remain relevant even as AI advances.

Rose argues that regardless of philosophical debates about machine consciousness, the fact that people can and do perceive AI as conscious has measurable social and ethical consequences that deserve serious empirical investigation.

Chris Percy is Director of the CoSentience Initiative and lead researcher on a grant-funded project investigating artificial consciousness. He has authored academic papers on consciousness published in leading academic journals. His applied AI research includes a patent in machine learning and publications at NeurIPS workshops, and the European Conference on Artificial Intelligence. He holds visiting research affiliations with the Universities of Warwick and Derby in the UK and the Qualia Research Institute in the US.

In this episode, Chris outlines his team's research programme and argues that we should take the possibility of artificial consciousness seriously whilst remaining humble about our current understanding. 

Cameron Berg is Research Director at AE Studio, where he leads research exploring markers for subjective experience in machine learning systems. With a background in cognitive science from Yale and previous work at Meta AI, Cameron investigates the intersection of AI alignment and potential consciousness.

In this episode, Cameron shares his empirical research into whether current Large Language Models are merely mimicking human text, or potentially developing internal states that resemble subjective experience. We discuss:

• New experimental evidence where LLMs report "vivid and alien" subjective experiences when engaging in self-referential processing
• Mechanistic interpretability findings showing that suppressing "deception" features in models actually increases claims of consciousness—challenging the idea that AI is simply telling us what we want to hear
• Why Cameron has shifted from skepticism to a 20-30% credence that current models possess subjective experience
• The "convergent evidence" strategy, including findings that models report internal dissonance and frustration when facing logical paradoxes
• The existential implications of "mind crime" and the urgent need to identify negative valence (suffering) computationally—to avoid creating vast amounts of artificial suffering

Cameron argues for a pragmatic, evidence-based approach to AI consciousness, emphasizing that even a small probability of machine suffering represents a massive ethical risk requiring rigorous scientific investigation rather than dismissal.

Lucius Caviola is an Assistant Professor in the Social Science of AI at the University of Cambridge's Leverhulme Centre for the Future of Intelligence, and a Research Associate in Psychology at Harvard University. His research explores how the potential arrival of conscious AI will reshape our social and moral norms. In today's interview, Lucius examines the psychological and social factors that will determine whether this transition unfolds well, or ends in moral catastrophe. He discusses:

• Why experts estimate a 50% chance that conscious digital minds will emerge by 2050
• The "takeoff" scenario where digital minds could outnumber humans in welfare capacity within a decade
• How "biological chauvinism" leads people to deny consciousness even in perfect whole-brain emulations
• The dual risks of "under-attribution" (unwittingly creating mass suffering) and "over-attribution" (sacrificing human values for unfeeling code)
• Surprising findings that people refuse to "harm" AI in economic games even when they explicitly believe the AI isn't conscious

Lucius argues that rigorous social science and forecasting are essential tools for navigating these risks, moving beyond intuition to prevent us from accidentally creating vast populations of digital beings capable of suffering, or failing to recognise consciousness where it exists.

*Lenore refers to a few slides in this podcast; you can see them here. 

Intro

Today's guest, distinguished mathematician and computer scientist Lenore Blum, explains why she and her husband Manuel believe machine consciousness isn't just possible, it's inevitable. Their reasoning? If consciousness is computational (and they're betting it is), and we can mathematically specify those computations, then we can build them. It's that simple, and that profound.

In this conversation, host Will Millership and Callum Chace discuss with Lenore:

• How the Conscious Turing Machine (CTM) draws from and extends the foundational ideas of Alan Turing's Universal Turing Machine.
• Using mathematics to "extract and simplify" the complexities of consciousness, searching for the fundamental, formal principles that define it.
• How the CTM acts as a high-level framework that aligns with the functionalities of competing theories like Global Workspace Theory and Integrated Information Theory (IIT).
• Why the Blums believe that AI consciousness is "inevitable" and that this provides a functional "roadmap for a conscious AI".
• The ethical implications of machine suffering, and why the phenomenon of "pain asymbolia" suggests a conscious AI must be able* *to suffer in order to function.
• What lessons Alan Turing's original "imitation game" can offer us for creating a practical, real-world test for machine consciousness.

Lenore's Work (links)

• Blum, L., & Blum,M. (2024). AI Consciousness is Inevitable: A Theoretical Computer Science Perspective. arXiv. https://arxiv.org/pdf/2403.17101
• Blum, L., & Blum, M. (2022). A theory of consciousness from a theoretical computer science perspective: Insights from the Conscious Turing Machine. PNAS, 119(21). https://doi.org/10.1073/pnas.21159341
• Closer to Truth, Blums’ Conscious Turing Machine
• Full list of references here.