In our last episode we talked all about intelligence, specifically about what made us intelligent. In this episode we jump into artificial intelligence, and we're joined again by David Krakauer, President and William H. Miller Professor of Complex Systems at the Santa Fe Institute.  This episode was recorded before the release of GPT-4, so David doesn't mention it specifically, but he does take us through the history of artificial intelligence, from Alan Turing, all the way to machine learning and neural networks. And he's going to ask the question: Are we really building something that's intelligent, or are we just building mimics and parrots?    Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.

With the recent release of GPT-4, now seemed like a good time for our episodes on intelligence. And not just artificial intelligence, but intelligence in general. To help us on this journey, we're joined again by David Krakauer, President and William H. Miller Professor of Complex Systems at the Santa Fe Institute. This episode is part one of our two-part conversation with David about intelligence. In part 2, David is going to cover artificial intelligence. But in this episode, we're going back to basics and David asks, what is it that makes us intelligent?   Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.

If there's one type of music that goes particularly well with complexity science, it's free jazz. The sort of jazz that you get when you put a group of musicians together without a conductor or any written music. But despite this, they still produce incredible music. So how does this group of musicians play so tightly together, whilst creating such dramatic changes to their music? In this episode, we're joined again by Tyler Marghetis, Assistant Professor of Cognitive and Information Sciences at the University of California, Merced. Tyler is going to return to the concept of tipping points, but this time, he's going to explore tipping points through the context of jazz music. To understand how they occur, he's going to go to one of the most unlikely places for help: the study of ecologies.   Resources and links: 'Zadok the Priest' by Handel - Classical (composed) - This piece tips at 01:22 Listen on Spotify Listen on YouTube 'Implosion' by Alex Levine Quartet - Free jazz (improvised) - This piece tips between 00:10 to 00:30, and again between 02:20 to 02:40. Listen on Spotify Listen on YouTube   Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.

Imagine you were going to Mars with a swarm of robots, and you needed to send those robots out foraging. How would you program them? A traditional top-down approach to programming would mean programming what every single robot is going to do, and that's going to get complicated fast.  So in this episode, we're joined by Melanie Moses, Professor of Computer Science at the University of New Mexico, and External Faculty at the Santa Fe Institute. Melanie is going to explain how you can take lessons from complexity science, and utilise a bottom-up approach to programming a swarm. In other words, she's going to explain how you can program the robots to interact with one another. And if you thought you'd heard the end of scaling or power laws, then you're in for a surprise, because Melanie is going to share how scaling fits in with her work on getting robots to work as a team.    Resources and links: Simplifying Complexity - How do bees self organise? Simplifying Complexity - Scaling 1: Why do we live longer than mice? Simplifying Complexity - Scaling 2: You and I are fractals Simplifying Complexity - Scaling 3: Why companies die, but cities don’t Complexity - Melanie Moses on Metabolic Scaling in Biology & Computation   Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.

In the last few episodes, we learnt all about scaling laws or power laws and how they apply to mammals. In this episode, the final part of our discussion of scaling and complex systems, for now, we're looking even bigger.  We're joined again by Geoffrey West, Shannan Distinguished Professor and Former President of the Santa Fe Institute, who in this episode will be leaving mammals behind to look at other complex systems. In particular, Geoffrey is going to explain how scaling laws apply to cities or companies.    Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.

In our last episode, you heard all about the relationship between a mammal's weight and its metabolic rate, and how this holds true regardless of the size of the mammal. You heard other examples of so-called scaling laws, and how these laws seem to be guided by the number four. In this episode, we're joined again by Geoffrey West, Shannan Distinguished Professor and Former President of the Santa Fe Institute. Geoffrey is going to explain why when we double the size of an animal, we only increase its metabolic rate by three quarters. He's going to explain why the number four is behind these curious laws, and he's going to reveal how you and I are fractals.   Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.

Have you ever thought about why the average human lifespan is 80 years? Or why smaller animals, like mice, live for much shorter periods compared to large animals like blue whales?  To help answer these questions, we're joined by Geoffrey West, Shannan Distinguished Professor and Former President of the Santa Fe Institute. Geoffrey will introduce us to the concept of scaling in complex systems, and how it helps explain not just lifespan, but a whole range of physiological characteristics in mammals. In Parts 2 and 3, he will explain what creates these laws, and how they apply not just to mammals, but to companies and cities as well.   Resources and links: Scaling Graphs   Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.

One of the things that make complexity science so fascinating is the diversity of the systems that it applies to. In this series so far, you've learnt about everything from ecologies to economies, tipping points in ecologies and economies, to power and influence in the 1400s, and even the spread of coronavirus in the lungs and the thing that brings all of these different topics together is complexity. This means that we can study one system to help us understand other systems — including bees. In today's episode, Orit Peleg, Faculty at the University of Colorado, Boulder, and External Faculty at the Santa Fe Institute, explains how bees self-organise and produce sophisticated behaviour. In this case, you'll hear how thousands of bees can work out where their queen is at any given point.   Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.

In our last episode, we heard from W. Brian Arthur, who shared his journey in economics as he studied increasing returns. Now, Brian's going to take us to 1987, to a small meeting in the Rockies in Santa Fe. At this time, he was struggling to gain recognition for his work within the economics community, but it was when Brian went to what would become the Santa Fe Institute that things really kicked off. In this episode, you're going to hear again from W. Brain Arthur, External Professor at the Santa Fe Institute, and Researcher at Palo Alto Research Center, as he remembers the early days of the Santa Fe Institute. From the early meetings of economists, physicists, and a biologist that started it all, to an early model Brian built of a stock market that was unique to any models before it — because this model included booms and busts.   Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.

Mitchell Waldrop's 'Complexity' brought complexity science into the limelight with an account of the early days of the Santa Fe Institute. One of the people who appear in this book is W. Brian Arthur, the engineer turned economist who found economics unsatisfactory — because it treated the economy purely as a system in equilibrium when he knew it very obviously wasn't. In this episode, you'll hear from W. Brian Arthur, External Professor at the Santa Fe Institute, and Researcher at Palo Alto Research Center, as he explains his journey to understanding the economy as a non-equilibrium system, and his work on increasing returns. But what are increasing returns? Well in complexity terms, it's how positive feedback affects the economy.   Connect: Simplifying Complexity on Twitter Sean Brady on Twitter Sean Brady on LinkedIn Brady Heywood website This show is produced in collaboration with Wavelength Creative. Visit wavelengthcreative.com for more information.