Mapping the Landscape of Human-Level Artificial General Intelligence

by the coauthors, preceding and during the AGI Roadmap Workshop held at the University of Tennessee, Knoxville in October 2009, and from many continuing discussions since then. Some of the ideas also trace back to discussions held during two Evaluation and Metrics for Human Level AI workshopa organized by John Laird and Pat Langley (one in Ann Arbor in late 2008, and one in Tempe in early 2009). Some of the conclusions of the Ann Arbor workshop were recorded (Laird et al. 2009). Inspiration was also obtained from discussion at the Future of AGI postconference workshop of the AGI-09 conference, triggered by Itamar Arel’s presentation AGI Roadmap (Arel 2009); and from an earlier article on AGI road-mapping (Arel and Livingston 2009). Of course, this is far from the first attempt to plot a course toward human-level AGI: arguably this was the goal of the founders of the field of artificial intelligence in the 1950s, and has been pursued by a steady stream of AI researchers since, even as the majority of the AI field has focused its attention on more narrow, specific subgoals. The ideas presented here build on the ideas of others in innumerable ways, but to review the history of AI and situate the current effort in the context of its predecessors would require a much longer article than this one. Thus we have chosen to focus on the results of our AGI roadmap discussions, acknowledging in a broad way the many debts owed to many prior researchers. References to the prior literature on evaluation of advanced AI systems are given by Laird (Laird et al. 2009) and Geortzel and Bugaj (2009), which may in a limited sense be considered prequels to this article. We begin by discussing AGI in general and adopt a pragmatic goal for measuring progress toward its attainment. We also Articles