Polarizable One of the core concerns of computer science is how the resources needed to perform a given computation depend on that computation. Understanding the minimal resources required to perform a given computation has also been a longstanding focus of research in the physics community. Modern work on this issue can be traced back to the work of Landauer in which he concluded that thermodynamic resources of at least $kT \ln[2]$ were needed to erase a bit on any physical system.
However no work has been done before on the thermodynamic resources needed to perform more complicated computations than bit erasure. In this talk I will introduce some preliminary work on this issue, focusing specifically on how the thermodynamic resources needed to implement a desired inputoutput function with a digital circuit depend on the topology of the circuit. Specifically, I will show how an analysis of the thermodynamics of digital circuits:
 uncovers novel connections between nonequilibrium statistical physics and information theory;
 reveals new, challenging engineering problems for how to design a circuit to have minimal thermodynamic costs;
 most importantly, allows us to extend computer science theory (specifically circuit complexity theory) to include thermodynamic costs.

David Wolpert is a professor at the Santa Fe Institute, visiting professor at MIT, and adjunct professor at ASU. He is the author of three books, over 200 papers, has three patents, is an associate editor at over half a dozen journals, has received numerous awards, and is a fellow of the IEEE.
He has over 19,000 citations, in fields including thermodynamics of computation, molecular biology, foundations of physics, machine learning, dynamics of languages, game theory and distributed optimization. In particular his machine learning technique of stacking was instrumental in both winning entries for the Netflix competiton, and his papers on the no free lunch theorems have over 7,000 citations. (Details at http://davidwolpert.weebly.com).
Most of his current research involves two topics:
 Combining recent breakthroughs in nonequilibrium statistical physics with computer science theory to lay the foundations of a complete theory of the thermodynamics of computation.
 Modeling social organization (command and information networks within social groups) using information theory.
Before his current position he was the Ulam scholar at the Center for Nonlinear Studies, and before that he was at NASA Ames Research Center and a consulting professor at Stanford University, where he formed the Collective Intelligence group. He has worked at IBM and a data mining startup, and is external faculty at numerous international institutions.
His degrees in Physics are from Princeton and the University of California.
Coffee at 15:45 PM.
