The universe as quantum computer

This article reviews the history of digital computation, and investigates just how far the concept of computation can be taken. In particular, I address the question of whether the universe itself is in fact a giant computer, and if so, just what kind of computer it is. I will show that the universe can be regarded as a giant quantum computer. The quantum computational model of the universe explains a variety of observed phenomena not encompassed by the ordinary laws of physics. In particular, the model shows that the the quantum computational universe automatically gives rise to a mix of randomness and order, and to both simple and complex systems.Comment: 16 pages, LaTe

Computer theorem proving in math

We give an overview of issues surrounding computer-verified theorem proving in the standard pure-mathematical context. This is based on my talk at the PQR conference (Brussels, June 2003)

Will machines ever think

Artificial Intelligence research has come under fire for failing to fulfill its promises. A growing number of AI researchers are reexamining the bases of AI research and are challenging the assumption that intelligent behavior can be fully explained as manipulation of symbols by algorithms. Three recent books -- Mind over Machine (H. Dreyfus and S. Dreyfus), Understanding Computers and Cognition (T. Winograd and F. Flores), and Brains, Behavior, and Robots (J. Albus) -- explore alternatives and open the door to new architectures that may be able to learn skills

A Project Based Approach to Statistics and Data Science

In an increasingly data-driven world, facility with statistics is more important than ever for our students. At institutions without a statistician, it often falls to the mathematics faculty to teach statistics courses. This paper presents a model that a mathematician asked to teach statistics can follow. This model entails connecting with faculty from numerous departments on campus to develop a list of topics, building a repository of real-world datasets from these faculty, and creating projects where students interface with these datasets to write lab reports aimed at consumers of statistics in other disciplines. The end result is students who are well prepared for interdisciplinary research, who are accustomed to coping with the idiosyncrasies of real data, and who have sharpened their technical writing and speaking skills