COMPLEXITY – Détails, épisodes et analyse

 

Guests: 

• Alison Gopnik, SFI External Faculty; Professor of Psychology and Affiliate Professor of Philosophy at University of California, Berkeley; Member of Berkeley AI Research Group
• John Krakauer, SFI External Faculty; John C. Malone Professor of Neurology, Neuroscience, and Physical Medicine & Rehabilitation, Johns Hopkins University

Hosts: Abha Eli Phoboo & Melanie Mitchell

Producer: Katherine Moncure

Podcast theme music by: Mitch Mignano

Podcast logo by Nicholas Graham

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More info:

Complexity Explorer: 

Tutorial: Fundamentals of Machine Learning

Lecture: Artificial Intelligence

SFI programs: Education

Books: 

• Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell
• Words, Thoughts and Theories by Alison Gopnik and Andrew N. Meltzoff
• The Scientist in the Crib: Minds, Brains, and How Children Learn by Alison Gopnik, Andrew N. Meltzoff, and Patricia K. Kuhl
• The Philosophical Baby: What Children's Minds Tell Us About Truth, Love, and the Meaning of Life by Alison Gopnik
• The Gardener and the Carpenter: What the New Science of Child Development Tells Us About the Relationship Between Parents and Children by Alison Gopnik

Talks: 

• The Future of Artificial Intelligence by Melanie Mitchell
• Imitation Versus Innovation: What Children Can Do That Large Langauge Models’ Can’t by Alison Gopnik
• The Minds of Children by Alison Gopnik
• What Understanding Adds to Cambrian Intelligence: A Taxonomy by John Krakauer

Papers & Articles:

• “Why you can’t make a computer that feels pain,” by Daniel C. Dennett
• “Transmission versus truth, imitation versus innovation: What children can do that Large Language and Language-and-Vision models cannot (yet),” in Perspectives on Psychological Science  (October 26, 2023), doi.org/10.1177/17456916231201401
• “Empowerment as Causal Learning, Causal Learning as Empowerment: A bridge between Bayesian causal hypothesis testing and reinforcement learning,” by Alison Gopnik
• “What can AI Learn from Human Exploration? Intrinsically-Motivated Humans and Agents in Open-World Exploration” by Yuqing Du et al, for Workshop: Agent Learning in Open-Endedness Workshop, NeurIPS 2024 conference
• “Two views on the cognitive brain,” by David L. Barack & John W. Krakauer, Perspectives in Nature Reviews Neuroscience Vol 22 (April 15, 2021)
• “The intelligent reflex,” by John W. Krakauer, Philosophical Psychology (May 23, 2019), doi.org/10.1080/09515089.2019.1607281
• “Representation in Cognitive Science by Nicholas Shea: But Is It Thinking? The Philosophy of Representation Meets Systems Neuroscience” by John W. Krakauer

Right now, AI is having a moment — and it’s not the first time grand predictions about the potential of machines are being made. But, what does it really mean to say something like ChatGPT is “intelligent”? What exactly is intelligence? In this season of the Complexity podcast, The Nature of Intelligence, we'll explore this question through conversations with cognitive and neuroscientists, animal cognition researchers, and AI experts in six episodes. Together, we'll investigate the complexities of human intelligence, how it compares to that of other species, and where AI fits in. We'll dive into the relationship between language and thought, examine AI's limitations, and ask: Could machines ever truly be like us?

One way of looking at the world reveals it as an interference pattern of dynamic, ever-changing links — relationships that grow and break in nested groups of multilayer networks. Identity can be defined by informational exchange between one cluster of relationships and any other. A kind of music starts to make itself apparent in the avalanche of data and new analytical approaches that a century of innovation has availed us. But just as with new music genres, it requires a trained ear to attune to unfamiliar order…what can we learn from network science and related general, abstract mathematical approaches to discovering this order in a flood of numbers?

Welcome to COMPLEXITY, the official podcast of the Santa Fe Institute. I’m your host, Michael Garfield, and in every episode we bring you with us for far-ranging conversations with our worldwide network of rigorous researchers developing new frameworks to explain the deepest mysteries of the universe.

This week we speak with SFI External Professor, UCLA mathematician Mason Porter (UCLA Website, Twitter, Google Scholar, Wikipedia), about his research on community detection in networks and the topology of data — going deep into a varied toolkit of approaches that help scientists disclose deep structures in the massive data-sets produced by modern life.

If you value our research and communication efforts, please subscribe, rate and review us at Apple Podcasts or Spotify, and consider making a donation — or finding other ways to engage with us — at santafe.edu/engage.

I know it comes as a surprise, but this is our penultimate episode.  Please stay tuned for one more show in May when SFI President David Krakauer and I will reflect on major themes and highlights from the last three-and-a-half years, and look forward to what I’ll be doing next! It’s been an honor and a pleasure to bring complex systems science to you in this way, and hope we stay in touch. I won’t be hard to find.

Thank you for listening.

Podcast theme music by Mitch Mignano.

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Mentioned & Related Media:

Bounded Confidence Models of Opinion Dynamics on Networks
SFI Seminar by Mason Porter (live Twitter coverage & YouTube stream recording)

Communities in Networks
by Mason Porter, Jukka-Pekka Onnela, & Peter Mucha

Social Structure of Facebook Networks
by Amanda Traud, Peter Mucha, & Mason Porter

Critical Truths About Power Laws
by Michael Stumpf & Mason Porter

The topology of data
by Mason Porter, Michelle Feng, & Eleni Katifori

Complex networks with complex weights
by Lucas Böttcher & Mason A. Porter

A Bounded-Confidence Model of Opinion Dynamics on Hypergraphs
by Abigail Hicock, Yacoub Kureh, Heather Z. Brooks, Michelle Feng, & Mason Porter

A multilayer network model of the coevolution of the spread of a disease and competing opinions
by Kaiyan Peng, Zheng Lu, Vanessa Lin, Michael Lindstrom, Christian Parkinson, Chuntian Wang, Andrea Bertozzi, & Mason Porter

Social network analysis for social neuroscientists
Elisa C Baek, Mason A Porter, & Carolyn Parkinson

Community structure in social and biological networks
by Michelle Girvan & Mark Newman

The information theory of individuality
by David Krakauer, Nils Bertschinger, Eckehard Olbrich, Jessica C Flack, Nihat Ay

Social capital I: measurement and associations with economic mobility
by Raj Chetty, Matthew O. Jackson, Theresa Kuchler, Johannes Stroebel, Nathaniel Hendren, Robert B. Fluegge, Sara Gong, Federico Gonzalez, Armelle Grondin, Matthew Jacob, Drew Johnston, Martin Koenen, Eduardo Laguna-Muggenburg, Florian Mudekereza, Tom Rutter, Nicolaj Thor, Wilbur Townsend, Ruby Zhang, Mike Bailey, Pablo Barberá, Monica Bhole & Nils Wernerfelt 

Hierarchical structure and the prediction of missing links in networks
by Aaron Clauset, Cristopher Moore, M.E.J. Newman

Gregory Bateson (Wikipedia)

Complexity Ep. 99 - Alison Gopnik on Child Development, Elderhood, Caregiving, and A.I.

“Why Do We Sleep?”
by Van Savage & Geoffrey West at Aeon Magazine

Complexity Ep. 4 - Luis Bettencourt on The Science of Cities

Complexity Ep. 12 - Matthew Jackson on Social & Economic Networks

Complexity Ep. 68 - W. Brian Arthur on Economics in Nouns and Verbs (Part 1)

Complexity Ep. 100 - Dani Bassett & Perry Zurn on The Neuroscience & Philosophy of Curious Minds

 

If the economy is better understood as an evolving system, an out-of-equilibrium ecology composed of agents that adapt to one another’s strategies, how does this change the way we think about our future? By drawing new analogies between technology and life, and studying how tools evolve by building on and recombining what has come before, what does this tell us about economics as a sub-process of our self-organizing biosphere? Over the last forty years, previously siloed scientific disciplines have come together with new data-driven methods to trace the outlines of a unifying economic theory, and allow us to design new human systems that anticipate the planet-wide disruptions of our rapidly accelerating age. New stories need to be articulated, ones that start earlier than human history, and in which societies work better when engineered in service to the laws of physics and biology they ultimately follow…

This week’s guest is W. Brian Arthur, External Professor at the Santa Fe Institute, Fellow at the Center for Advanced Study in the Behavioral Sciences at Stanford, and Visiting Researcher at Xerox PARC.  In this second part of our two-episode conversation, we discuss technology as seen through the lens of evolutionary biology, and how he foresees the future of the economy as our labor market and financial systems are increasingly devoured by artificial intelligence.

If you enjoy this podcast, please help us reach a wider audience by leaving a review at Apple Podcasts, or by sharing the show on social media. Thank you for listening!

Visit our website for more information or to support our science and communication efforts.

Join our Facebook discussion group to meet like minds and talk about each episode.

Podcast Theme Music by Mitch Mignano.

Follow us on social media:
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Brian’s Website.

Brian’s Google Scholar page.

“Where is technology taking the economy?” in McKinsey, 2017.

The Nature of Technology: What It Is and How It Evolves.

“Punctuated equilibria: the tempo and mode of evolution reconsidered” by Gould & Eldredge.

"A natural bias for simplicity" by Mark Buchanan in Nature Physics.

"Economic Possibilities for our Grandchildren" by John Maynard Keynes.

From its beginnings as a discipline nearly 150 years ago, economics rested on assumptions that don’t hold up when studied in the present day. The notion that our economic systems are in equilibrium, that they’re made of actors making simple rational and self-interested decisions with perfect knowledge of society— these ideas prove about as useful in the Information Age as Newton’s laws of motion are to quantum physicists. A novel paradigm for economics, borrowing insights from ecology and evolutionary biology, started to emerge at SFI in the late 1980s — one that treats our markets and technologies as systems out of balance, serving metabolic forces, made of agents with imperfect information and acting on fundamental uncertainty. This new complexity economics uses new tools and data sets to shed light on puzzles standard economics couldn’t answer — like why the economy grows, how sudden and cascading crashes happen, why some companies and cities lock in permanent competitive advantages, and how technology evolves. And complexity economics offers insights back to biology, providing a new lens through which to understand the vastly intricate exchanges on which human life depends.

This week’s guest is W. Brian Arthur, External Professor at the Santa Fe Institute, Fellow at the Center for Advanced Study in the Behavioral Sciences at Stanford, and Visiting Researcher at Xerox PARC.  In this first part of a two-episode conversation, we discuss the heady early days when complex systems science took on economics, and how biology provided a new paradigm for understanding our financial and technological systems.  Tune in next week for part two...

If you enjoy this podcast, please help us reach a wider audience by leaving a five-star review at Apple Podcasts, or by sharing the show on social media. Thank you for listening!

Visit our website for more information or to support our science and communication efforts.

Join our Facebook discussion group to meet like minds and talk about each episode.

Podcast Theme Music by Mitch Mignano.

Follow us on social media:
Twitter • YouTube • Facebook • Instagram • LinkedIn

For more information:

Brian’s Website.

Brian’s Google Scholar page.

“Where is technology taking the economy?” in McKinsey, 2017.

The Nature of Technology: What It Is and How It Evolves.

“Punctuated equilibria: the tempo and mode of evolution reconsidered” by Gould & Eldredge.

It may be a cliché, but it’s a timeless truth regardless: who you know matters. The connectedness of actors in a network tells us not just who wields the power in societies and markets, but also how new information spreads through a community and how resilient economic systems are to major shocks. One of the pillars of a complex systems understanding is the network science that reveals how structural differences lead to (or help counter) inequality and why a good idea alone can’t change the world. As human beings, who we are is shaped by those around us — not just our relationships to them but their relationships to one another. And the topology of human networks governs everything from the diffusion of fake news to cascading bank failures to the popularity of social influencers and their habits to the potency of economic interventions. To learn about your place amidst the networks of your life is to awaken to the hidden seams of human culture and the flows of energy that organize our world.

This week’s guest is SFI External Professor Matthew O. Jackson, William D. Eberle Professor of Economics at Stanford University and senior fellow of CIFAR, also a Member of the National Academy of Sciences, and a Fellow of the American Academy of Arts and Sciences. In this episode, we discuss key insights from his book, The Human Network: How Your Social Position Determines Your Power, Beliefs, and Behaviors.

For transcripts, show notes, research links, and more, please visit complexity.simplecast.com.

And note that we’re taking a short break over the winter holiday. COMPLEXITY will be back with new episodes in January 2020.

If you enjoy this show, please help us reach a wider audience by leaving a review at Apple Podcasts, or by telling your friends on social media…after this episode’s discussion, we know you’ll understand how crucial this can be. Thank you for listening!

Visit our website for more information or to support our science and communication efforts.

Join our Facebook discussion group to meet like minds and talk about each episode.

Matthew Jackson’s Stanford Homepage.

WSJ reviews The Human Network.

Jackson’s Coursera MOOCs on Game Theory I, Game Theory II, and Social & Economic Networks.

Podcast Theme Music by Mitch Mignano.

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In this show’s first episode, David Krakauer explained how art and science live along an axis of explanatory depth: science strives to find the simplest adequate abstractions to explain the world we observe, where art’s devotion is to the incompressible — the one-offs that resist abstraction and attempts to write a unifying framework. Between the random and the regular, amidst the ligaments that bind our scientific and artistic inquiries, we find a huge swath of the world that we struggle to articulate in formal quantitative terms, but that rewards our curiosity and offers us profound insights regardless. Here lives the open question of what we can learn from history — specifically, the histories of other people’s lives.  Why do we love biographies?  What can the stories of the lives of others teach us about both situational and common truths of being?  This is a different kind of episode and conversation, one living at the intersection of philosophy and history and science…

This week’s episode features guest interviewer, SFI President David Krakauer, in conversation with philosopher and biographer Ray Monk.  Monk teaches at the University of Southhampton and was SFI’s 2017 Miller Scholar, a position that he earned for his biographies of Ludwig Wittgenstein, Bertrand Russell, and J. Robert Oppenheimer — three mavericks whose legacies are lessons for contemporary leaders.

If you enjoy this podcast, please help us reach a wider audience by leaving a five-star review at Apple Podcasts, or by sharing the show on social media. Thank you for listening!

Visit our website for more information or to support our science and communication efforts.

Join our Facebook discussion group to meet like minds and talk about each episode.

Ray Monk on Twitter.

Ray Monk’s SFI Miller Scholar Profile Page.

Ray Monk on Hidden Forces Podcast.

Podcast Theme Music by Mitch Mignano.

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What is the difference between 100 kilograms of human being and 100 kilograms of algae? One answer to this question is the veins and arteries that carry nutrients throughout the human body, allowing for the intricate coordination needed in a complex organism. Energy requirements determine how the evolutionary process settles on the body plans appropriate to an environment — one way to tell the story of life’s major innovations is in terms of how a living system solves the problems of increasing body size with internal transport networks and more extensive regulation. And the same is true in our invented information systems, every bit as subject to the laws of physics as we are. Computers, just like living tissue, seek effective tradeoffs between their scale and energy efficiency. A physics of metabolic scaling — one that finds deep commonalities and crucial differences between ant hives and robot swarms, between the physiology of elephants and server farms — can help explain some of the biggest puzzles of the fossil record and sketch out the likely future evolution of technology. It is already revolutionizing how we understand search algorithms and the genius of eusocial organisms. And just maybe, it can also help us solve the challenge of sustainability for planetary culture.

This week’s guest is Melanie Moses, External Professor at the Santa Fe Institute, Professor of Computer Science and Biology at the University of New Mexico, and Principal Investigator for the NASA Swarmathon. In this episode, we talk about her highly interdisciplinary work on metabolic scaling in biology and computer information-processing, and how complex systems made and born alike have found ingenious ways to balance the demands of growth and maintenance — with implications for space exploration, economics, computer chip design, and more.

If you enjoy this podcast, please help us reach a wider audience by leaving a five-star review at Apple Podcasts, or by sharing the show on social media. Thank you for listening!

Visit our website for more information or to support our science and communication efforts.

Join our Facebook discussion group to meet like minds and talk about each episode.

Melanie’s UNM Webpage & full list of publications.

“Beyond pheromones: evolving error-tolerant, flexible, and scalable ant-inspired robot swarms” by Joshua Hecker & Melanie Moses.

“Energy and time determine scaling in biological and computer designs” by Moses, et al.

“Shifts in metabolic scaling, production, and efficiency across major evolutionary transitions of life” by DeLong, Moses, et al.

“Distributed adaptive search in T cells: lessons from ants” by Melanie Moses, et al.

“Curvature in metabolic scaling” by Kolokotrones, et al.

The NASA Swarmathon.

Podcast Theme Music by Mitch Mignano.

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We live in a world so complicated and immense it challenges our comparably simple minds to even know which information we should use to make decisions. The human brain seems tuned to follow simple rules, and those rules change depending on the people we can turn to for support: when we decide to follow the majority or place our trust in experts, for example, depends on the networks in which we’re embedded. Consequently, much of learning and decision-making has as much or more to do with social implications as it has to do with an objective world of fact…and this has major consequences for the ways in which we come together to solve complex problems. Whether in governance, science, or private life, the strategies we lean on — mostly unconsciously — determine whether we form wise, effective groups, or whether our collective process gets jammed up with autocrats or bureaucrats. Sometimes the crowd is smarter than the individual, and sometimes not, and figuring out which strategies are better requires a nuanced look at how we make decisions with each other, and how information flows through human networks. Given the scale and intensity of modern life, the science of our social lives takes on profound importance.

This week’s guest is SFI Professor & Cowan Chair in Human Social Dynamics Mirta Galesic, External Faculty at the Complexity Science Hub in Vienna, and Associate Researcher at the Harding Center for Risk Literacy at the Max Planck Institute for Human Development in Berlin. In this episode we talk about her research into how simple cognitive mechanisms interact with social and physical environments to produce complex social phenomena…and how we can understand and cope with the uncertainty and complexity inherent in many everyday decisions.

If you enjoy this podcast, please help us reach a wider audience by leaving a five-star review at Apple Podcasts. Thanks for listening!

Visit our website for more information or to support our science and communication efforts.

Join our Facebook discussion group to meet like minds and talk about each episode.

Mirta’s Website.

Visit Mirta’s Google Scholar Page for links to all the papers we discuss.

Mirta’s 2015 talk at SFI: “How interaction of mind and environment shapes social judgments.”

Digital Transformation documentary about Mirta and her work.

Michelle Girvan’s SFI Community Lecture on reservoir computing.

Podcast Theme Music by Mitch Mignano.

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It’s easy to take modern Earth for granted — our breathable atmosphere, the delicately balanced ecosystems we depend on — but this world is nothing like the planet on which life first found its foothold. In fact it may be more appropriate to think of life in terms of verbs than nouns, of processes instead of finished products. This is the evolutionary turn that science started taking in the 19th Century…but only in the last few decades has biology begun to see this planet’s soil, air, and oceans as the work-in-progress of our biosphere. The story of our planet can’t be adequately told without some understanding of how life itself depends on opportunities that life creates, based on the energy and mineral resources made as byproducts of our metabolisms. A new, revelatory narrative of the last 3.8 billion years refigures living systems in terms of thermodynamic flows and the ever-growing range of possibilities created by our ever-more-complex ecologies. And in the telling, this new history sheds light on some of the biggest puzzles of the fossil record: why complex animals took so long to appear, why humans are the way we are, and maybe even why the sky is blue.

This week’s guest is evolutionary biologist and science journalist Olivia Judson, an honorary research fellow at The Imperial College of London who received her PhD from the University of Oxford and whose writing has appeared in The Economist, The New York Times, The Guardian, and National Geographic. She is also the author of the internationally best-selling popular science book, Dr. Tatiana’s Sex Advice to All Creation. In this episode, we discuss her work on major energy transitions in evolution (the subject of her next book), and what we can learn by studying the intimate dance of biology and geology over the last 4 billion years.

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Olivia’s Website.

“The energy expansions of evolution” in Nature.

The Atlantic on Olivia’s essay.

Music by Mitch Mignano.

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