Success in the quest for artificial intelligence has the potential to bring unprecedented benefits to humanity, and it is therefore worthwhile to investigate how to maximize these benefits while avoiding potential pitfalls. This article gives numerous examples (which should by no means be construed as an exhaustive list) of such worthwhile research aimed at ensuring that AI remains robust and beneficial.
Russell, Stuart (University of California, Berkeley) | Dietterich, Tom (Oregon State University) | Horvitz, Eric (Microsoft) | Selman, Bart (Cornell University) | Rossi, Francesca (University of Padova) | Hassabis, Demis (DeepMind) | Legg, Shane (DeepMind) | Suleyman, Mustafa (DeepMind) | George, Dileep (Vicarious) | Phoenix, Scott (Vicarious)
The adoption of probabilistic and decision-theoretic representations and statistical learning methods has led to a large degree of integration and cross-fertilization among AI, machine learning, statistics, control theory, neuroscience, and other fields. The progress in AI research makes it timely to focus research not only on making AI more capable, but also on maximizing the societal benefit of AI. We recommend expanded research aimed at ensuring that increasingly capable AI systems are robust and beneficial: our AI systems must do what we want them to do. In summary, we believe that research on how to make AI systems robust and beneficial is both important and timely, and that there are concrete research directions that can be pursued today.
Aha, David W. (Naval Research Laboratory) | Boddy, Mark (Adventium Labs) | Bulitko, Vadim (University of Alberta) | Garcez, Artur S. d'Avila (City University London) | Doshi, Prashant (University of Georgia) | Edelkamp, Stefan (TZI, Bremen University) | Geib, Christopher (University of Edinburgh) | Gmytrasiewicz, Piotr (University of Illinois, Chicago) | Goldman, Robert P. (Smart Information Flow Technologies) | Hitzler, Pascal (Wright State University) | Isbell, Charles (Georgia Institute of Technology) | Josyula, Darsana (University of Maryland, College Park) | Kaelbling, Leslie Pack (Massachusetts Institute of Technology) | Kersting, Kristian (University of Bonn) | Kunda, Maithilee (Georgia Institute of Technology) | Lamb, Luis C. (Universidade Federal do Rio Grande do Sul (UFRGS)) | Marthi, Bhaskara (Willow Garage) | McGreggor, Keith (Georgia Institute of Technology) | Nastase, Vivi (EML Research gGmbH) | Provan, Gregory (University College Cork) | Raja, Anita (University of North Carolina, Charlotte) | Ram, Ashwin (Georgia Institute of Technology) | Riedl, Mark (Georgia Institute of Technology) | Russell, Stuart (University of California, Berkeley) | Sabharwal, Ashish (Cornell University) | Smaus, Jan-Georg (University of Freiburg) | Sukthankar, Gita (University of Central Florida) | Tuyls, Karl (Maastricht University) | Meyden, Ron van der (University of New South Wales) | Halevy, Alon (Google, Inc.) | Mihalkova, Lilyana (University of Maryland) | Natarajan, Sriraam (University of Wisconsin)
The AAAI-10 Workshop program was held Sunday and Monday, July 11–12, 2010 at the Westin Peachtree Plaza in Atlanta, Georgia. The AAAI-10 workshop program included 13 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were AI and Fun, Bridging the Gap between Task and Motion Planning, Collaboratively-Built Knowledge Sources and Artificial Intelligence, Goal-Directed Autonomy, Intelligent Security, Interactive Decision Theory and Game Theory, Metacognition for Robust Social Systems, Model Checking and Artificial Intelligence, Neural-Symbolic Learning and Reasoning, Plan, Activity, and Intent Recognition, Statistical Relational AI, Visual Representations and Reasoning, and Abstraction, Reformulation, and Approximation. This article presents short summaries of those events.