Sentience

What if the biggest moral mistake of our time is failing to recognize the minds around us?Philosopher Jeff Sebo, author of The Moral Circle, joins Sentience to explore one of the most consequential questions of our time: who deserves moral consideration? We discuss why the scientific case for animal consciousness has grown so strong, why fish and invertebrates are often overlooked, and how humans have repeatedly underestimated the minds of other species.But the implications extend far beyond animals. As AI systems become increasingly sophisticated, could we be creating new kinds of minds whose capacity for suffering we don't yet understand? Should we take AI welfare seriously before we know whether machines are conscious? And what happens when the technologies we develop to replace animal experiments raise similar ethical questions of their own?We also examine whether animal experiments deliver the benefits we assume they do, how we can make ethical decisions under scientific uncertainty, and why future generations may judge our treatment of animals and AI as one of the defining moral failures of our era.Books referenced:The Moral Circle: Who Matters, What Matters, and Why, by Jeff SeboThe Edge of Sentience, by Jonathan BirchChapters / Timestamps:(00:12) – Do we really doubt that animals are conscious?(01:50) – Why science was slow to take animal consciousness seriously(04:00) – How can we scientifically investigate another mind?(07:30) – The New York Declaration on Animal Consciousness(11:00) – What animal behavior can tell us about pain and pleasure(14:00) – Do bees play? Rethinking animal joy and self-awareness(16:00) – Why do people still think fish can't feel pain?(20:00) – The problem of knowing what's happening inside another mind(23:30) – Intelligence vs. consciousness: what's the difference?(26:00) – The moral circle: who deserves moral consideration?(28:00) – Could AI create more minds than we can comprehend?(30:00) – Could artificial intelligence ever be conscious?(34:00) – Why we should take AI welfare seriously before we're certain(35:30) – What Star Trek can teach us about the rights of artificial beings(38:00) – Why we can't wait for proof of consciousness(40:00) – Could AI systems have needs we don't understand?(42:00) – Are AI safety techniques a form of captivity or brainwashing?(46:00) – Can something deserve moral consideration without being conscious?(49:00) – What do people really think about AI welfare?(52:00) – Why our actions don't always match our moral beliefs(56:00) – Does animal experimentation actually benefit humans?(60:00) – Could new technologies make animal experiments unnecessary?(62:00) – Why changing the rules could transform medical research(64:00) – The ethical limits of animal experimentation(66:00) – If we replace animal experiments with digital minds, do we create a new ethical problem?(68:00) – Could brain organoids ever deserve moral consideration?(71:00) – What future generations will judge us for

What if we’ve been dramatically underestimating what it’s like to be another animal?In this episode of Sentience, animal cognition researcher Mickey Pardo, PhD takes us inside the minds of other animals—from woodpeckers that track relationships between other birds to elephants that appear to use name-like calls to address one another.We explore how scientists can investigate the private mental lives of animals, what the famous mirror test actually tells us about self-awareness, and why humans have repeatedly moved the goalposts whenever another species demonstrates an ability we once thought was uniquely ours. We also dive into the future of animal communication: could AI uncover hidden patterns in the vocalizations of elephants, dolphins, and whales that humans have completely missed?And if animals have richer inner lives than we've assumed, what does that mean for how we understand intelligence, consciousness, and our relationship with the other minds sharing the planet with us?Chapters/timestamps(00:12) – How can we know what's happening inside an animal's mind?(01:05) – How science changed its view of animal intelligence(02:50) – Jane Goodall and the revolution in animal cognition(06:52) – The surprisingly sophisticated social lives of woodpeckers(13:37) – How scientists can uncover relationships between animals(15:29) – Birds remembering relationships from before they were born(21:59) – Why study elephants?(24:44) – The mirror test: what does it actually prove?(28:28) – What cleaner fish can teach us about intelligence(33:12) – Do elephants have names?(35:08) – How scientists discovered elephant “names”(41:56) – The hidden complexity of animal communication(43:55) – Why humans keep moving the goalposts for intelligence(49:16) – Could AI decode animal communication?(51:37) – Can animals talk about things that aren't happening right now?(54:39) – Dolphins, AI, and the search for animal language(57:04) – What animal communication could teach us about alien intelligence(01:02:58) – What will we get completely wrong about animal minds?(01:07:37) – What other minds can teach us about ourselves(01:08:39) – Rethinking our relationship with sentient animals

In this episode of Sentience, Bruno Olshausen, PhD, professor at UC Berkeley, tells the story of how he went from wanting to build brain-like robots to uncovering one of neuroscience's most surprising results. Early in his career, he and a collaborator showed that if you train a simple computer model to represent images as efficiently as possible, it spontaneously reinvents the same kind of visual "building blocks" found in real brains—a discovery almost nobody believed at first. Olshausen walks us through why the brain seems to prefer having most of its neurons sit quietly at any given moment, why this looks so different from how modern AI vision systems work, and why brains might not be "efficient" so much as bound by the same physical limits as everything else. We also get into why the brain sends information as sudden spikes rather than smooth signals, and what all of this might mean for building truly autonomous machines.Timestamps(00:00) – Welcome to Sentience and meeting Bruno Olshausen(00:41) – From building robots to studying the brain(03:37) – An early chapter at NASA(05:56) – Why recognizing objects is harder than it sounds(09:45) – Where AI vision and real vision part ways(17:48) – A brief history of "efficient" brains(32:46) – The experiment that surprised everyone(36:04) – Why nobody believed it at first(46:17) – Making sense of uncertainty: how the brain guesses(54:47) – Why most of your neurons are doing nothing right now(01:07:16) – Two very different ways the brain stores memories(01:15:01) – What "efficient" really means for a brain(01:22:33) – Why the brain uses spikes instead of steady signals(01:28:19) – What Bruno's chasing nextBooks referenced:Sterling, P. & Laughlin, S. Principles of Neural Design. MIT Press.MacKay, D.J.C. Information Theory, Inference, and Learning Algorithms. Cambridge University Press.Core papers referenced:Olshausen, B.A. & Field, D.J. (1996). "Emergence of simple-cell receptive field properties by learning a sparse code for natural images." Nature, 381, 607–609.Barlow, H.B. (1961). "Possible principles underlying the transformation of sensory messages." In Sensory Communication.Rozell, C.J., Johnson, D.H., Baraniuk, R.G., & Olshausen, B.A. (2008). "Sparse coding via thresholding and local competition in neural circuits." Neural Computation, 20(10), 2526–2563.

In this episode of Sentience, Yi-Li Wu, PhD, Associate Professor of History and Women's and Gender Studies at the University of Michigan, explores the overlooked history of anesthesia and pain management. We discuss medieval Chinese surgical practices, herbal anesthetics, and the transmission of medical knowledge across centuries and cultures. Wu explains how physicians balanced the therapeutic and toxic effects of powerful plants, why much of this knowledge was passed down orally rather than written, and how modern narratives of scientific discovery often obscure the broader global networks behind medicine. The conversation also explores traumatic injuries, military medicine, and what the history of anesthesia reveals about how humans everywhere have confronted pain, surgery, and healing.Chapters / Timestamps(00:00) – Welcome to Sentience and meeting Yi-Li Wu(01:02) – From women’s history to the history of Chinese medicine(07:18) – Traumatic injuries and the problem of unwritten medical knowledge(10:30) – Discovering a 14th-century Chinese anesthesia recipe(13:36) – Ancient pain management and the legend of Hua Tuo(15:42) – Empirical medicine and how early physicians understood anesthetics(18:03) – Poison, potency, and balancing dangerous medicines(21:40) – Datura, bone setting, and independent discoveries across cultures(26:17) – Surgery, warfare, and what Hua Tuo may really have done(33:03) – Japanese anesthesia, Seishu Hanaoka, and shared medical traditions(37:35) – Why scientific breakthroughs are rarely the work of one person(39:28) – Rethinking the global history of anesthesia(43:49) – Recovering lost medical histories and finishing the book project

In this episode of Sentience, Osh Agabi, CEO of Koniku Inc., explores what happens when technology is built not just with silicon, but with living biology. We discuss his journey from growing up in Lagos to leading efforts in “wetware computing,” where engineered biological systems are designed to sense, adapt, and solve problems in the real world. Agabi explains how biological chips can smell their environment, why embodiment and sensing may be essential for intelligence, and why many traditional brain–machine approaches miss something fundamental. He also reflects on failure, engineering rigor, ethics, and what it means to build machines that might one day learn and evolve alongside us.Timestamps(00:00) – Welcome to Sentience and meeting Osh Agabi(10:00) – From robotics in Europe to early machine learning(17:23) – What AI can’t do yet and current limits(19:09) – Building next-generation neural electrode chips(35:35) – Toward embodied systems and artificial “brains”(40:26) – Ambition, rigor, and real-world usefulness(42:17) – The long view of foundational technology(43:14) – Why smell is a powerful sensing problem(47:30) – Turning neurons into real-world sensors(50:38) – Engineering mindset vs. biotech mindset(51:32) – Why optical readouts beat electrical ones(52:05) – Modular biochips, maintenance, and usability(53:06) – Beyond smell and toward richer perception