Report on the Third Conference on Artificial General Intelligence Abstract During March 5-8, 2010, around 75 researchers from various disciplines converged at the University of Lugano for the Third Conference on Artificial General Intelligence (AGI-10). During March 5-8, 2010, around 75 researchers from various disciplines converged at the University of Lugano for the Third Conference on Artificial General Intelligence (AGI-10).
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The First Conference on Artificial General Intelligence (AGI-08) was held on March 1-3, 2008, at the University of Memphis. The overall goal of the conference was to work toward a common understanding of the most promising paths toward creating AI systems with general intelligence at the human level and beyond, and to share interim results and ideas achieved by researchers actively working toward powerful artificial general intelligence.
We address the question of how to build AI agents that behave ethically by appealing to a computational creativity framework in which output artifacts are agent behaviors and candidate behaviors are evaluated using a normative ethics as the aesthetic measure. We then appeal again to computational creativity to address the meta-level question of which normative ethics the system should employ as its aesthetic, where now output meta-artifacts are normative ethics and candidate ethics are evaluated using a meta-ethics-based aesthetic. We consider briefly some of the issues raised by such a proposal as well as how the hybrid base-meta-level system might be evaluated from three different perspectives: creative, behavioral and ethical.
A good number of media pundits never point at the difference between narrow AI and general artificial intelligence. It is a huge and very impactful difference. Narrow AI, by definition, is capable of performing certain tasks or solving certain problems within a narrow field of expertise such as assembling cars in a car factory. This type of robot can and will replace humans in many areas where high level of accuracy is required and where monotonous work results in human errors.