artificial general intelligence

From Constructionist to Constructivist A.I.

AAAI Conferences

The development of artificial intelligence systems has to date been largely one of manual labor. This Constructionist approach to A.I. has resulted in a diverse set of isolated solutions to relatively small problems. Small success stories of putting these pieces together in robotics, for example, has made people optimistic that continuing on this path would lead to artificial general intelligence. This is unlikely. "The A.I. problem" has been divided up without much guidance from science or theory, resulting in a fragmentation of the research community and a set of grossly incompatible approaches. Standard software development methods come with serious limitations in scaling; in A.I. the Constructionist approach results in systems with limited domain application and severe performance brittleness. Genuine integration, as required for general intelligence, is therefore practically and theoretically precluded. Yet going beyond current A.I. systems requires significantly more complex integration than attempted to date, especially regarding transversal functions such as attention and learning. The only way to address the challenge is replacing top-down architectural design as a major development methodology with methods focusing on self-generated code and self-organizing architectures. I call this Constructivist A.I., in reference to the self-constructive principles on which it must be based. Methodologies employed for Constructivist A.I. will be very different from today's software development methods. In this paper I describe the argument in detail and examine some of the implications of this impending paradigm shift.

Report on the First Conference on Artificial General Intelligence (AGI-08)

AI Magazine

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.

Mixing Cognitive Science Concepts with Computer Science Algorithms and Data Structures: An Integrative Approach to Strong AI

AAAI Conferences

We posit that, given the current state of development of cognitive science, the greatest synergies between this field and artificial intelligence arise when one adopts a high level of abstraction. On the one hand, we suggest, cognitive science embodies some interesting, potentially general principles regarding cognition under limited resources, and AI systems that violate these principles should be treated with skepticism. But on the other hand, attempts to precisely emulate human cognition in silicon are hampered by both their ineffectiveness at exploiting the power of digital computers, and the current paucity of algorithm-level knowledge as to how human cognition takes place. We advocate a focus on artificial general intelligence design. This means building systems capturing the salient high-level features of human intelligence (e.g., goal-oriented behavior, sophisticated learning, self-reflection, etc...), yet with software architectures and algorithms specifically designed for effective performance on modern computing hardware. We give several illustrations of this broad principle drawn from our work, including the adaptation of estimation of distribution algorithms in evolutionary programming for complex procedure learning.