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An invited presentation at the Conclave on “Complexity in Physical Interacting Systems, Computation and Thermodynamics”, July 10-13, 2023, Santa Fe, NM, USA: https://sites.google.com/view/comconc….

“The emergence of global order in complex systems with locally interacting components is most striking at criticality, where small changes in control parameters result in a sudden global reorganization. We study the thermodynamic efficiency of interactions in self-organizing systems, which quantifies the change in the system’s order per unit of work carried out on (or extracted from) the system.” [4].

“Importantly, the reduction of entropy achieved through expenditure of work is shown to peak at criticality.” [1]

Watch at: www.youtube.com

Consciousness, our experience of being in the world, is one of the mind’s greatest mysteries, but as the neuroscientist Anil Seth explains to Steven Strogatz, research is making progress in understanding this elusive phenomenon.

Listen at: www.quantamagazine.org

See also: https://perceptioncensus.dreamachine.world 

In most of our episodes so far, we’ve taken a single concept and looked at it through the context of a single example. But in this episode and the next, we’re going to pull back the camera to get a bird’s-eye view of complexity science, by exploring the features common to all complex systems.

We’re joined again by Karoline Wiesner, Professor of Complexity Science in the Department of Physics and Astronomy at the University of Potsdam in Germany. In this episode, Karoline is going to explain four conditions that we see in complexity science: numerosity, disorder and diversity, feedback, and non-equilibrium. At the end of the episode, she’s going to bring them all together to explain a central concept of complex systems: emergence.

Listen at: omny.fm

This workshop, held at SFI April 24-26, 2023, focused on several questions related to the ability of current-day AI systems to “understanding” or “extract meaning” in a humanlike way.

Watch at: www.youtube.com

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Abstract

As the use of machine learning (ML) algorithms in network science increases, so do the problems related to explainability, transparency, fairness, privacy, and robustness, to name a few. In this talk, I will give a brief overview of the field and present recent work from my lab on the (in)stability and explainability of node embeddings, attacks on ML algorithms for graphs, and equality in complex networks.

Bio
Tina Eliassi-Rad is a professor of computer science at Northeastern University and an external faculty member at Santa Fe Institute. She works at the intersection of AI and Network Science and cares about the impact of science and technology on the disadvantaged members of society.

Watch at: www.youtube.com