Contemporary Approaches to Artificial General Intelligence

1 A Brief History of AGI The vast bulk of the AI field today is concerned with what might be called " narrow AI " – creating programs that demonstrate intelligence in one or another specialized area, such as chess-playing, medical diagnosis, automobile-driving, algebraic calculation or mathematical theorem-proving. Some of these narrow AI programs are extremely successful at what they do. The AI projects discussed in this book, however, are quite different: they are explicitly aimed at artificial general intelligence, at the construction of a software program that can solve a variety of complex problems in a variety of different domains, and that controls itself autonomously, with its own thoughts, worries, feelings, strengths, weaknesses and predispositions. Artificial General Intelligence (AGI) was the original focus of the AI field, but due to the demonstrated difficulty of the problem, not many AI researchers are directly concerned with it anymore. Work on AGI has gotten a bit of a bad reputation, as if creating digital general intelligence were analogous to building a perpetual motion machine. Yet, while the latter is strongly implied to be impossible by well-established physical laws, AGI appears by all known science to be quite possible. Like nanotechnology, it is " merely an engineering problem " , though certainly a very difficult one. The presupposition of much of the contemporary work on " narrow AI " is that solving narrowly defined subproblems, in isolation, contributes significantly toward solving the overall problem of creating real AI. While this is of course true to a certain extent, both cognitive theory and practical experience suggest that it is not so true as is commonly believed. In many cases, the best approach to implementing an aspect of mind in isolation is very different from the best way to implement this same aspect of mind in the framework of an integrated AGI-oriented software system. The chapters of this book present a series of approaches to AGI. None of these approaches has been terribly successful yet, in AGI terms, although several of them have demonstrated practical value in various specialized domains (narrow-AI style). Most of the projects described are at an early stage of engineering development, and some are still in the design phase. Our aim is not to present AGI as a mature field of computer science – that would be