AI at 50

From Programs to Solvers, Models and Techniques for General Intelligence

4pm, Wednesday, 5 December 2007
Psychology Lecture Theatre F21,
7 George Square, Edinburgh

Over the past 20 years, a significant change has occurred in AI research. Many researchers have moved from the early AI paradigm of writing programs for ill-defined problems to writing solvers for well-defined mathematical models such as Constraint Satisfaction Problems, Strips Planning, SAT, Bayesian Networks and Partially Observable Markov Decision Processes.

Solvers are programs that take a compact description of a particular model instance (a planning problem, a CSP instance, and so on) and automatically compute its solution. Unlike the early AI programs, these are general-purpose in the sense that they are not designed to deal with a particular problem but with a large, in fact, infinite collection of problems.

This presents a crisp computational challenge: how to make these solvers scale-up to large and interesting problems given that all these models are intractable in the worst case. Work in these areas has uncovered techniques that accomplish this by automatically recognizing and exploiting the structure of the problem at hand.

My goal in this talk is to articulate this research agenda, to go over some of ideas that underlie these techniques, and to show the relevance of these models and techniques to those interested in models of general intelligence and human cognition.

Hector Geffner is a Distinguished Visiting Researcher in the School of Informatics, and a recently elected Fellow of AAAI. He was a student of Judea Pearl, and won the ACM Distinguished Dissertation Award in 1990. Hector has worked at IBM (Yorktown Heights, NY, USA) and the Universidad Simon Bolivar (Caracas, Venezuela). He is currently a researcher at the Institucion Catalana de Recerca i Estudis Avançats (ICREA) and a professor at the Departamento de Tecnologia, Universitat Pompeu Fabra, where he heads the Artificial Intelligence Group.

People, Computation, and Intelligence

Eric Horvitz, Adaptive Systems and Interaction Group,
Microsoft Research, Redmond

1600, Wednesday, 7 November 2007
Lecture Theatre 2, Appleton Tower,
11 Crichton Street, Edinburgh

Abstract Technical and infrastructural developments are coming together to provide a nurturing environment for creating, studying, and fielding valuable machine learning and reasoning systems. Numerous efforts have been stimulated by the increasing availability of data for studies in learning and adaptation. The data-rich environment poses interesting new challenges and opportunities, and frames new theoretical and practical work. I will present several illustrative research efforts that highlight challenges and directions with the streaming of machine intelligence into the daily lives of people. I will focus thematically on opportunities for harnessing machine learning and reasoning to better understand and support people, and the critical role of methods for representing and reasoning about human intentions, preferences, and initiative.

Biography Eric is a Principal Researcher and Research Area Manager at Microsoft Research. His interests span core challenges in machine reasoning and learning, search and information retrieval, and human-computer interaction. He has served as Associate Editor of the Journal of the ACM, as Chair of the Association for Uncertainty and Artificial Intelligence (AUAI), and on the DARPA Information Science and Technology Study Group (ISAT). He has been elected Councilor and Fellow of the American Association for Artificial Intelligence (AAAI) and is currently serving as President of the organization. He received his PhD and MD degrees at Stanford University.

Distinguished Lecture: Donald MacKenzie

Models and Markets:
Option Theory and the Construction of Derivatives Markets

4pm Wednesday
7 March 2007
Lecture Theatre 1
Appleton Tower
11 Crichton Street

The talk will be followed by a reception.

What difference does it make to a market for there to be a well-regarded mathematical model of the market, especially one that is not just an external analysis by academics but is used by market practitioners?

This talk will ask this question mainly in regard to the most famous model in modern financial economics, the Nobel-Prize winning Black-Scholes-Merton model of option pricing, which is the core mathematical foundation of the global market in ‘financial derivatives’. (At the end of June 2006, derivatives contracts outstanding worldwide totaled $454 trillion, the equivalent of nearly $70,000 for every human being on Earth.)

The talk will describe how the practical uses of the model initially had the effect of making markets more like the postulates of the model, but will discuss how this effect reversed in direction in the 1987 stock market crash, with near-disastrous consequences for the global financial system.

No previous knowledge of economics will be necessary to understand the talk: what an ‘option’ and a ‘derivative’ are, and what the Black-Scholes-Merton model consists in, will be explained in simple terms.