A Computer Scientist's View of Life, the Universe, and Everything
Foundations of Computer Science: Potential - Theory - Cognition, to Wilfried Brauer on the occasion of his sixtieth birthday
The Speed Prior: A New Simplicity Measure Yielding Near-Optimal Computable Predictions
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability
Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability
Universal Intelligence: A Definition of Machine Intelligence
Minds and Machines
Measuring universal intelligence: Towards an anytime intelligence test
Artificial Intelligence
Completely self-referential optimal reinforcement learners
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
A family of Gödel machine implementations
AGI'11 Proceedings of the 4th international conference on Artificial general intelligence
Real-world limits to algorithmic intelligence
AGI'11 Proceedings of the 4th international conference on Artificial general intelligence
Real-world limits to algorithmic intelligence
AGI'11 Proceedings of the 4th international conference on Artificial general intelligence
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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.