BI 217 Jennifer Prendki: Consciousness, Life, AI, and Quantum Physics
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Do AI engineers need to emulate some processes and features found only in living organisms at the moment, like how brains are inextricably integrated with bodies? Is consciousness necessary for AI entities if we want them to play nice with us? Is quantum physics part of that story, or a key part, or the key part? Jennifer Prendki believes if we continue to scale AI, it will get us more of the same of what we have today, and that we should look to biology, life, and possibly consciousness to enhance AI. Jennifer is a former particle physicist turned entrepreneur and AI expert, focusing on curating the right kinds and forms of data to train AI, and in that vein she led those efforts at Deepmind on the foundation models ubiquitous in our lives now.
I was curious why someone with that background would come to the conclusion that AI needs inspiration from life, biology, and consciousness to move forward gracefully, and that it would be useful to better understand those processes in ourselves before trying to build what some people call AGI, whatever that is. Her perspective is a rarity among her cohorts, which we also discuss. And get this: she's interested in these topics because she cares about what happens to the planet and to us as a species. Perhaps also a rarity among those charging ahead to dominate profits and win the race
Jennifer's website: Quantum of Data.
The blog posts we discuss:
The Myth of Emergence
Embodiment & Sentience: Why the Body still Matters
The Architecture of Synthetic Consciousness
On Time and Consciousness
Superalignment and the Question of AI Personhood.
Read theĀ transcript.
0:00 - Intro
3:25 - Jennifer's background
13:10 - Consciousness
16:38 - Life and consciousness
23:16 - Superalignment
40:11 - Quantum
1:04:45 - Wetware and biological mimicry
1:15:03 - Neural interfaces
1:16:48 - AI ethics
1:2:35 - AI models are not models
1:27:13 - What scaling will get us
1:39:53 - Current roadblocks
1:43:19 - Philosophy
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BI 216 Woodrow Shew and Keith Hengen: The Nature of Brain Criticality
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A few episodes ago, episode 212, I conversed with John Beggs about how criticality might be an important dynamic regime of brain function to optimize our cognition and behavior. Today we continue and extend that exploration with a few other folks in the criticality world.
Woodrow Shew is a professor and runs the Shew Lab at the University of Arkansas. Keith Hengen is an associate professor and runs the Hengen Lab at Washington University in St. Louis Missouri. Together, they are Hengen and Shew on a recent review paper in Neuron, titled Is criticality a unified setpoint of brain function? In the review they argue that criticality is a kind of homeostatic goal of neural activity, describing multiple properties and signatures of criticality, they discuss multiple testable predictions of their thesis, and they address the historical and current controversies surrounding criticality in the brain, surveying what Woody thinks is all the past studies on criticality, which is over 300. And they offer a account of why many of these past studies did not find criticality, but looking through a modern lens they most likely would. We discuss some of the topics in their paper, but we also dance around their current thoughts about things like the nature and implications of being nearer and farther from critical dynamics, the relation between criticality and neural manifolds, and a lot more. You get to experience Woody and Keith thinking in real time about these things, which I hope you appreciate.
Shew Lab. @ShewLab
Hengen Lab.
Is criticality a unified setpoint of brain function?
Read the transcript.
0:00 - Intro
3:41 - Collaborating
6:22 - Criticality community
14:47 - Tasks vs. Naturalistic
20:50 - Nature of criticality
25:47 - Deviating from criticality
33:45 - Sleep for criticality
38:41 - Neuromodulation for criticality
40:45 - Criticality Definition part 1: scale invariance
43:14 - Criticality Definition part 2: At a boundary
51:56 - New method to assess criticality
56:12 - Types of criticality
1:02:23 - Value of criticality versus other metrics
1:15:21 - Manifolds and criticality
1:26:06 - Current challenges
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BI 215 Xiao-Jing Wang: Theoretical Neuroscience Comes of Age
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Xiao-Jing Wang is a Distinguished Global Professor of Neuroscience at NYU
Xiao-Jing was born and grew up in China, spent 8 years in Belgium studying theoretical physics like nonlinear dynamical systems and deterministic chaos. And as he says it, he arrived from Brussels to California as a postdoc, and in one day switched from French to English, from European to American culture, and physics to neuroscience. I know Xiao-Jing as a legend in non-human primate neurophysiology and modeling, paving the way for the rest of us to study brain activity related cognitive functions like working memory and decision-making.
He has just released his new textbook, Theoretical Neuroscience: Understanding Cognition, which covers the history and current research on modeling cognitive functions from the very simple to the very cognitive. The book is also somewhat philosophical, arguing that we need to update our approach to explaining how brains function, to go beyond Marr's levels and enter a cross-level mechanistic explanatory pursuit, which we discuss. I just learned he even cites my own PhD research, studying metacognition in nonhuman primates - so you know it's a great book. Learn more about Xiao-Jing and the book in the show notes. It was fun having one of my heroes on the podcast, and I hope you enjoy our discussion.
Computational Laboratory of Cortical Dynamics
Book: Theoretical Neuroscience: Understanding Cognition.
Related papers
Division of labor among distinct subtypes of inhibitory neurons in a cortical microcircuit of working memory.
Macroscopic gradients of synaptic excitation and inhibition across the neocortex.
Theory of the multiregional neocortex: large-scale neural dynamics and distributed cognition.
0:00 - Intro
3:08 - Why the book now?
11:00 - Modularity in neuro vs AI
14:01 - Working memory and modularity
22:37 - Canonical cortical microcircuits
25:53 - Gradient of inhibitory neurons
27:47 - Comp neuro then and now
45:35 - Cross-level mechanistic understanding
1:13:38 - Bifurcation
1:24:51 - Bifurcation and degeneracy
1:34:02 - Control theory
1:35:41 - Psychiatric disorders
1:39:14 - Beyond dynamical systems
1:43:447 - Mouse as a model
1:48:11 - AI needs a PFC
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BI 214 Nicole Rust: How To Actually Fix Brains and Minds
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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:
What, if anything, makes mood fundamentally different from memory?
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To explore more neuroscience news and perspectives, visit thetransmitter.org.
Elusive Cures: Why Neuroscience Hasnāt Solved Brain Disordersāand How We Can Change That. Nicole Rust runs the Visual Memory laboratory at the University of Pennsylvania. Her interests have expanded now to include mood and feelings, as you'll hear. And she wrote this book, which contains a plethora of ideas about how we can pave a way forward in neuroscience to help treat mental and brain disorders. We talk about a small plethora of those ideas from her book. which also contains the story partially which will hear of her own journey in thinking about these things from working early on in visual neuroscience to where she is now.
Nicole's website.
Elusive Cures: Why Neuroscience Hasnāt Solved Brain Disordersāand How We Can Change That.
0:00 - Intro
6:12 - Nicole's path
19:25 - The grand plan
25:18 - Robustness and fragility
39:15 - Mood
49:25 - Model everything!
56:26 - Epistemic iteration
1:06:50 - Can we standardize mood?
1:10:36 - Perspective neuroscience
1:20:12 - William Wimsatt
1:25:40 - Consciousness
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BI 213 Representations in Minds and Brains
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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 series of essays about representations:
What are we talking about? Clarifying the fuzzy concept of representation in neuroscience and beyond
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What do neuroscientists mean when they use the term representation? That's part of what Luis Favela and Edouard Machery set out to answer a couple years ago by surveying lots of folks in the cognitive sciences, and they concluded that as a field the term is used in a confused and unclear way. Confused and unclear are technical terms here, and Luis and Edouard explain what they mean in the episode. More recently Luis and Edouard wrote a follow-up piece arguing that maybe it's okay for everyone to use the term in slightly different ways, maybe it helps communication across disciplines, perhaps. My three other guests today, Frances Egan, Rosa Cao, and John Krakauer wrote responses to that argument, and on today's episode all those folks are here to further discuss that issue and why it matters. Luis is a part philosopher, part cognitive scientists at Indiana University Bloomington, Edouard is a philosopher and Director of the Center for Philosophy of Science at the University of Pittsburgh, Frances is a philosopher from Rutgers University, Rosa is a neuroscientist-turned philosopher at Stanford University, and John is a neuroscientist among other things, and co-runs the Brain, Learning, Animation, and Movement Lab at Johns Hopkins.
Luis Favela.
Favela's book: The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment
Edouard Machery.
Machery's book: Doing without Concepts
Frances Egan.
Egan's book: Deflating Mental Representation.
John Krakauer.
Rosa Cao.
Paper mentioned: Putting representations to use.
The exchange, in order, discussed on this episode:
Investigating the concept of representation in the neural and psychological sciences.
The concept of representation in the brain sciences: The current status and ways forward.
Commentaries:
Assessing the landscape of representational concepts: Commentary on Favela and Machery.
Comments on Favela and Machery's The concept of representation in the brain sciences: The current status and ways forward.
Where did real representations go? Commentary on: The concept of representation in the brain sciences: The current status and ways forward by Favela and Machery.
Reply to commentaries:
Contextualizing, eliminating, or glossing: What to do with unclear scientific concepts like representation.
0:00 - Intro
3:55 - What is a representation to a neuroscientist?
14:44 - How to deal with the dilemma
21:20 - Opposing views
31:00 - What's at stake?
51:10 - Neural-only representation
1:01:11 - When "representation" is playing a useful role
1:12:56 - The role of a neuroscientist
1:39:35 - The purpose of "representational talk"
1:53:03 - Non-representational mental phenomenon
1:55:53 - Final thoughts
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.
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