You observe a phenomenon, and come up with an explanation for it. That’s true for scientists, but also for literally every person. (Why won’t my car start? I bet it’s out of gas.) But there are literally an infinite number of possible explanations for every phenomenon we observe. How do we invent ones we think are promising, and then decide between them once invented? Simon DeDeo (in collaboration with Zachary Wojtowicz) has proposed a way to connect explanatory values (“simplicity,” “fitting the data,” etc) to specific mathematical expressions in Bayesian reasoning. We talk about what makes explanations good, and how they can get out of control, leading to conspiracy theories or general crackpottery, from QAnon to flat earthers.
Today on the pod is Marta Sales-Pardo & Roger Guimera. What a great talk. We could have gone on for hours. Peer review, power-laws, becoming scientists, Bayesian statistics, and much, much more. Marta and Roger study fundamental problems in all areas of science including natural, social and economic sciences. They have expertise in a broad set of tools from statistical physics, network science, statistics and computer science. Both were many years at Northwestern before starting a group at URV in Catalonia. They are authors of many classic papers in Network Science, lots of important work, e.g. on community detection. We talk about their paper “A Bayesian machine scientist to aid in the solution of challenging scientific problems”
Since its beginning in the 1950s, the field of artificial intelligence has cycled several times between periods of optimistic predictions and massive investment (“AI Spring”) and periods of disappointment, loss of confidence, and reduced funding (“AI Winter”). Even with today’s seemingly fast pace of AI breakthroughs, the development of long-promised technologies such as self-driving cars and housekeeping robots has turned out to be much harder than we thought.
One reason for these repeating cycles is a lack of understanding of the nature and complexity of intelligence itself. In this talk I will discuss some fallacies in common assumptions made by AI researchers, which can lead to overconfident predictions about the field. I will also speculate on what is needed for the grand challenge of making AI systems more robust, general, and adaptable—in short, more intelligent.
Speaker Bio: Melanie Mitchell is the Davis Professor of Complexity at the Santa Fe Institute, and Professor of Computer Science (currently on leave) at Portland State University. Her current research focuses on conceptual abstraction, analogy-making, and visual recognition in artificial intelligence systems. Melanie is the author or editor of six books and numerous scholarly papers in the fields of artificial intelligence, cognitive science, and complex systems. Her book Complexity: A Guided Tour (Oxford University Press) won the 2010 Phi Beta Kappa Science Book Award and was named by Amazon.com as one of the ten best science books of 2009. Her latest book is Artificial Intelligence: A Guide for Thinking Humans (Farrar, Straus, and Giroux).
Many people might not bother to define complexity, thinking that we know it when we see it. Scientists should not afford such luxury. I will provide a compact but comprehensive overview of the different ways that systems can be complex, offering an aggregate definition. I will discuss the role of complexity measures, and why complexity cannot be captured by a single number. This work was done in collaboration with James Ladyman, published with Yale University Press in 2020. At the other end of the spectrum of complexity science is the application to real-world problems. I will present two examples from recent work. The project ‘Aiding the mitigation of and adaptation to climate change using the tools of complexity science’ was done in collaboration with the Green Climate Fund, founded by the UN members in 2014. Equally, political systems are more and more focus of computational and mathematical investigations. I will present conceptual work on the stability of democracy, a collaboration with an international and interdisciplinary group of scientists.
I’ve got a treat for you today. Today’s author’s are Gourab Ghoshal and Petter Holme, who are here to talk about a classic paper. A paper they co-authored and published in PRL in 2006. The paper has a fantastic title, which is basically also a mini abstract. It is called “Dynamics of Networking Agents Competing for High Centrality and Low Degree” (1). In the podcast we get into it!
Gourab is at at Rochester University, where he is an Associate Professor of Physics and Astronomy with joint appointments at the departments of Computer Science and Mathematics. He works in the field of Complex Systems. His research interests are in the theory and applications of Complex Networks as well as Non-equilibrium Statistical Physics, Game theory, Econophysics, Dynamical Systems and the Origins of Life.
Petter is Swedish scientist living and working in Japan, where he is a Specially Appointed Professor at the Institute of Innovative Research at the Tokyo Institute of Technology. His research focuses on large-scale structures in society, technology and biology; mostly trying to understand them as networks.
