Brain Inspired

Paul Middlebrooks
  • 0.0 (0)
  • NATURAL SCIENCES
  • EVERY TWO WEEKS
Brain Inspired Podcast

Neuroscience and artificial intelligence work better together. Brain inspired is a celebration and exploration of the ideas driving our progress to understand intelligence. I interview experts about their work at the interface of neuroscience, artificial intelligence, cognitive science, philosophy, psychology, and more: the symbiosis of these overlapping fields, how they inform each other, where they differ, what the past brought us, and what the future brings. Topics include computational neuroscience, supervised machine learning, unsupervised learning, reinforcement learning, deep learning, convolutional and recurrent neural networks, decision-making science, AI agents, backpropagation, credit assignment, neuroengineering, neuromorphics, emergence, philosophy of mind, consciousness, general AI, spiking neural networks, data science, and a lot more. The podcast is not produced for a general audience. Instead, it aims to educate, challenge, inspire, and hopefully entertain those interested in learning more about neuroscience and AI.

  1. 11 SEPT

    BI 193 Kim Stachenfeld: Enhancing Neuroscience and AI

    Support the show to get full episodes and join the Discord community. The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists.  Read more about our partnership. Check out this story:  Monkeys build mental maps to navigate new tasks Sign up for “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released. To explore more neuroscience news and perspectives, visit thetransmitter.org. Kim Stachenfeld embodies the original core focus of this podcast, the exploration of the intersection between neuroscience and AI, now commonly known as Neuro-AI. That's because she walks both lines. Kim is a Senior Research Scientist at Google DeepMind, the AI company that sprang from neuroscience principles, and also does research at the Center for Theoretical Neuroscience at Columbia University. She's been using her expertise in modeling, and reinforcement learning, and cognitive maps, for example, to help understand brains and to help improve AI. I've been wanting to have her on for a long time to get her broad perspective on AI and neuroscience. We discuss the relative roles of industry and academia in pursuing various objectives related to understanding and building cognitive entities She's studied the hippocampus in her research on reinforcement learning and cognitive maps, so we discuss what the heck the hippocampus does since it seems to implicated in so many functions, and how she thinks of reinforcement learning these days. Most recently Kim at Deepmind has focused on more practical engineering questions, using deep learning models to predict things like chaotic turbulent flows, and even to help design things like bridges and airplanes. And we don't get into the specifics of that work, but, given that I just spoke with Damian Kelty-Stephen, who thinks of brains partially as turbulent cascades, Kim and I discuss how her work on modeling turbulence has shaped her thoughts about brains. Kim's website. Twitter: @neuro_kim. Related papers Scaling Laws for Neural Language Models. Emergent Abilities of Large Language Models. Learned simulators: Learned coarse models for efficient turbulence simulation. Physical design using differentiable learned simulators. Check out the transcript, provided by The Transmitter. 0:00 - Intro 4:31 - Deepmind's original and current vision 9:53 - AI as tools and models 12:53 - Has AI hindered neuroscience? 17:05 - Deepmind vs academic work balance 20:47 - Is industry better suited to understand brains? 24?42 - Trajectory of Deepmind 27:41 - Kim's trajectory 33:35 - Is the brain a ML entity? 36:12 - Hippocampus 44:12 - Reinforcement learning 51:32 - What does neuroscience need more and less of? 1:02:53 - Neuroscience in a weird place? 1:06:41 - How Kim's questions have changed 1:16:31 - Intelligence and LLMs 1:25:34 - Challenges

    1h 33m

  2. 28 AUG

    BI 192 Àlex Gómez-Marín: The Edges of Consciousness

    Support the show to get full episodes and join the Discord community. Àlex Gómez-Marín heads The Behavior of Organisms Laboratory at the Institute of Neuroscience in Alicante, Spain. He's one of those theoretical physicist turned neuroscientist, and he has studied a wide range of topics over his career. Most recently, he has become interested in what he calls the "edges of consciousness", which encompasses the many trying to explain what may be happening when we have experiences outside our normal everyday experiences. For example, when we are under the influence of hallucinogens, when have near-death experiences (as Alex has), paranormal experiences, and so on. So we discuss what led up to his interests in these edges of consciousness, how he now thinks about consciousness and doing science in general, how important it is to make room for all possible explanations of phenomena, and to leave our metaphysics open all the while. Alex's website: The Behavior of Organisms Laboratory. Twitter: @behaviOrganisms. Previous episodes: BI 168 Frauke Sandig and Eric Black w Alex Gomez-Marin: AWARE: Glimpses of Consciousness. BI 136 Michel Bitbol and Alex Gomez-Marin: Phenomenology. Related: The Consciousness of Neuroscience. Seeing the consciousness forest for the trees. The stairway to transhumanist heaven. 0:00 - Intro 4:13 - Evolving viewpoints 10:05 - Near-death experience 18:30 - Mechanistic neuroscience vs. the rest 22:46 - Are you doing science? 33:46 - Where is my. mind? 44:55 - Productive vs. permissive brain 59:30 - Panpsychism 1:07:58 - Materialism 1:10:38 - How to choose what to do 1:16:54 - Fruit flies 1:19:52 - AI and the Singularity

    1h 31m

  3. 15 AUG

    BI 191 Damian Kelty-Stephen: Fractal Turbulent Cascading Intelligence

    Support the show to get full episodes and join the Discord community. Damian Kelty-Stephen is an experimental psychologist at State University of New York at New Paltz. Last episode with Luis Favela, we discussed many of the ideas from ecological psychology, and how Louie is trying to reconcile those principles with those of neuroscience. In this episode, Damian and I in some ways continue that discussion, because Damian is also interested in unifying principles of ecological psychology and neuroscience. However, he is approaching it from a different perspective that Louie. What drew me originally to Damian was a paper he put together with a bunch of authors offering their own alternatives to the computer metaphor of the brain, which has come to dominate neuroscience. And we discuss that some, and I'll link to the paper in the show notes. But mostly we discuss Damian's work studying the fractal structure of our behaviors, connecting that structure across scales, and linking it to how our brains and bodies interact to produce our behaviors. Along the way, we talk about his interests in cascades dynamics and turbulence to also explain our intelligence and behaviors. So, I hope you enjoy this alternative slice into thinking about how we think and move in our bodies and in the world. Damian's website. Related papers In search for an alternative to the computer metaphor of the mind and brain. Multifractal emergent processes: Multiplicative interactions override nonlinear component properties. 0:00 - Intro 2:34 - Damian's background 9:02 - Brains 12:56 - Do neuroscientists have it all wrong? 16:56 - Fractals everywhere 28:01 - Fractality, causality, and cascades 32:01 - Cascade instability as a metaphor for the brain 40:43 - Damian's worldview 46:09 - What is AI missing? 54:26 - Turbulence 1:01:02 - Intelligence without fractals? Multifractality 1:10:28 - Ergodicity 1:19:16 - Fractality, intelligence, life 1:23:24 - What's exciting, changing viewpoints

    1h 28m

  4. 31 JUL

    BI 190 Luis Favela: The Ecological Brain

    Support the show to get full episodes and join the Discord community. Luis Favela is an Associate Professor at Indiana University Bloomington. He is part philosopher, part cognitive scientist, part many things, and on this episode we discuss his new book, The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment. In the book, Louie presents his NeuroEcological Nexus Theory, or NExT, which, as the subtitle says, proposes a way forward to tie together our brains, our bodies, and the environment; namely it has a lot to do with the complexity sciences and manifolds, which we discuss. But the book doesn't just present his theory. Among other things, it presents a rich historical look into why ecological psychology and neuroscience haven't been exactly friendly over the years, in terms of how to explain our behaviors, the role of brains in those explanations, how to think about what minds are, and so on. And it suggests how the two fields can get over their differences and be friends moving forward. And I'll just say, it's written in a very accessible manner, gently guiding the reader through many of the core concepts and science that have shaped ecological psychology and neuroscience, and for that reason alone I highly it. Ok, so we discuss a bunch of topics in the book, how Louie thinks, and Louie gives us some great background and historical lessons along the way. Luis' website. Book: The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment 0:00 - Intro 7:05 - Louie's target with NEXT 20:37 - Ecological psychology and grid cells 22:06 - Why irreconcilable? 28:59 - Why hasn't ecological psychology evolved more? 47:13 - NExT 49:10 - Hypothesis 1 55:45 - Hypothesis 2 1:02:55 - Artificial intelligence and ecological psychology 1:16:33 - Manifolds 1:31:20 - Hypothesis 4: Body, low-D, Synergies 1:35:53 - Hypothesis 5: Mind emerges 1:36:23 - Hypothesis 6:

    1h 41m

  5. 29 JUN

    BI 189 Joshua Vogelstein: Connectomes and Prospective Learning

    Support the show to get full episodes and join the Discord community. Jovo, as you'll learn, is theoretically oriented, and enjoys the formalism of mathematics to approach questions that begin with a sense of wonder. So after I learn more about his overall approach, the first topic we discuss is the world's currently largest map of an entire brain... the connectome of an insect, the fruit fly. We talk about his role in this collaborative effort, what the heck a connectome is, why it's useful and what to do with it, and so on. The second main topic we discuss is his theoretical work on what his team has called prospective learning. Prospective learning differs in a fundamental way from the vast majority of AI these days, which they call retrospective learning. So we discuss what prospective learning is, and how it may improve AI moving forward. At some point there's a little audio/video sync issues crop up, so we switched to another recording method and fixed it... so just hang tight if you're viewing the podcast... it'll get better soon. 0:00 - Intro 05:25 - Jovo's approach 13:10 - Connectome of a fruit fly 26:39 - What to do with a connectome 37:04 - How important is a connectome? 51:48 - Prospective learning 1:15:20 - Efficiency 1:17:38 - AI doomerism

    1h 27m

  6. 27 MAY

    BI 188 Jolande Fooken: Coordinating Action and Perception

    Support the show to get full episodes and join the Discord community. Jolande Fooken is a post-postdoctoral researcher interested in how we move our eyes and move our hands together to accomplish naturalistic tasks. Hand-eye coordination is one of those things that sounds simple and we do it all the time to make meals for our children day in, and day out, and day in, and day out. But it becomes way less seemingly simple as soon as you learn how we make various kinds of eye movements, and how we make various kinds of hand movements, and use various strategies to do repeated tasks. And like everything in the brain sciences, it's something we don't have a perfect story for yet. So, Jolande and I discuss her work, and thoughts, and ideas around those and related topics. Jolande's website. Twitter: @ookenfooken. Related papers I am a parent. I am a scientist. Eye movement accuracy determines natural interception strategies. Perceptual-cognitive integration for goal-directed action in naturalistic environments. 0:00 - Intro 3:27 - Eye movements 8:53 - Hand-eye coordination 9:30 - Hand-eye coordination and naturalistic tasks 26:45 - Levels of expertise 34:02 - Yarbus and eye movements 42:13 - Varieties of experimental paradigms, varieties of viewing the brain 52:46 - Career vision 1:04:07 - Evolving view about the brain 1:10:49 - Coordination, robots, and AI

    1h 28m

  7. 20 APR

    BI 187: COSYNE 2024 Neuro-AI Panel

    Support the show to get full episodes and join the Discord community. Recently I was invited to moderate a panel at the annual Computational and Systems Neuroscience, or COSYNE, conference. This year was the 20th anniversary of COSYNE, and we were in Lisbon Porturgal. The panel goal was to discuss the relationship between neuroscience and AI. The panelists were Tony Zador, Alex Pouget, Blaise Aguera y Arcas, Kim Stachenfeld, Jonathan Pillow, and Eva Dyer. And I'll let them introduce themselves soon. Two of the panelists, Tony and Alex, co-founded COSYNE those 20 years ago, and they continue to have different views about the neuro-AI relationship. Tony has been on the podcast before and will return soon, and I'll also have Kim Stachenfeld on in a couple episodes. I think this was a fun discussion, and I hope you enjoy it. There's plenty of back and forth, a wide range of opinions, and some criticism from one of the audience questioners. This is an edited audio version, to remove long dead space and such. There's about 30 minutes of just panel, then the panel starts fielding questions from the audience. COSYNE.

    1h 4m

  8. 25 MAR

    BI 186 Mazviita Chirimuuta: The Brain Abstracted

    Support the show to get full episodes and join the Discord community. Mazviita Chirimuuta is a philosopher at the University of Edinburgh. Today we discuss topics from her new book, The Brain Abstracted: Simplification in the History and Philosophy of Neuroscience. She largely argues that when we try to understand something complex, like the brain, using models, and math, and analogies, for example - we should keep in mind these are all ways of simplifying and abstracting away details to give us something we actually can understand. And, when we do science, every tool we use and perspective we bring, every way we try to attack a problem, these are all both necessary to do the science and limit the interpretation we can claim from our results. She does all this and more by exploring many topics in neuroscience and philosophy throughout the book, many of which we discuss today. Mazviita's University of Edinburgh page. The Brain Abstracted: Simplification in the History and Philosophy of Neuroscience. Previous Brain Inspired episodes: BI 072 Mazviita Chirimuuta: Understanding, Prediction, and Reality BI 114 Mark Sprevak and Mazviita Chirimuuta: Computation and the Mind 0:00 - Intro 5:28 - Neuroscience to philosophy 13:39 - Big themes of the book 27:44 - Simplifying by mathematics 32:19 - Simplifying by reduction 42:55 - Simplification by analogy 46:33 - Technology precedes science 55:04 - Theory, technology, and understanding 58:04 - Cross-disciplinary progress 58:45 - Complex vs. simple(r) systems 1:08:07 - Is science bound to study stability? 1:13:20 - 4E for philosophy but not neuroscience? 1:28:50 - ANNs as models 1:38:38 - Study of mind

    1h 44m
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About

Neuroscience and artificial intelligence work better together. Brain inspired is a celebration and exploration of the ideas driving our progress to understand intelligence. I interview experts about their work at the interface of neuroscience, artificial intelligence, cognitive science, philosophy, psychology, and more: the symbiosis of these overlapping fields, how they inform each other, where they differ, what the past brought us, and what the future brings. Topics include computational neuroscience, supervised machine learning, unsupervised learning, reinforcement learning, deep learning, convolutional and recurrent neural networks, decision-making science, AI agents, backpropagation, credit assignment, neuroengineering, neuromorphics, emergence, philosophy of mind, consciousness, general AI, spiking neural networks, data science, and a lot more. The podcast is not produced for a general audience. Instead, it aims to educate, challenge, inspire, and hopefully entertain those interested in learning more about neuroscience and AI.

Information

  • Creator
    Paul Middlebrooks
  • Years Active
    2018 - 2024
  • Episodes
    157
  • Rating
    Clean
  • Copyright
    © 2019 Brain-Inspired
  • Show Website
    Brain Inspired

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