Randomness conservation inequalities; information and independence in mathematical theories
Information and Control
Minds and Machines
Two Dogmas of Computationalism
Minds and Machines
Effective Computation by Humans and Machines
Minds and Machines
Alan Turing and the Mathematical Objection
Minds and Machines
Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability
Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability
Minds and Machines
Evolved Computing Devices and the Implementation Problem
Minds and Machines
Universal Intelligence: A Definition of Machine Intelligence
Minds and Machines
Turing's Responses to Two Objections
Minds and Machines
An Introduction to Kolmogorov Complexity and Its Applications
An Introduction to Kolmogorov Complexity and Its Applications
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This paper revisits the often debated question Can machines think? It is argued that the usual identification of machines with the notion of algorithm has been both counter-intuitive and counter-productive. This is based on the fact that the notion of algorithm just requires an algorithm to contain a finite but arbitrary number of rules. It is argued that intuitively people tend to think of an algorithm to have a rather limited number of rules. The paper will further propose a modification of the above mentioned explication of the notion of machines by quantifying the length of an algorithm. Based on that it appears possible to reconcile the opposing views on the topic, which people have been arguing about for more than half a century.