Real-world limits to algorithmic intelligence

Abstract

Recent theories of universal algorithmic intelligence, combined with the view that the world can be completely specified in mathematical terms, have led to claims about intelligence in any agent, including human beings. We discuss the validity of assumptions and claims made by theories of universally optimal intelligence in relation to their application in actual robots and intelligence tests. Our argument is based on an exposition of the requirements for knowledge of the world through observations. In particular, we will argue that the world can only be known through the application of rules to observations, and that beyond these rules no knowledge can be obtained about the origin of our observations. Furthermore, we expose a contradiction in the assumption that it is possible to fully formalize the world, as for example is done in digital physics, which can therefore not serve as the basis for any argument or proof about algorithmic intelligence that interacts with the world. © 2011 Springer-Verlag Berlin Heidelberg.