Algorithmic Randomness as Foundation of Inductive Reasoning and Artificial Intelligence

This article is a brief personal account of the past, present, and future of algorithmic randomness, emphasizing its role in inductive inference and artificial intelligence. It is written for a general audience interested in science and philosophy. Intuitively, randomness is a lack of order or predictability. If randomness is the opposite of determinism, then algorithmic randomness is the opposite of computability. Besides many other things, these concepts have been used to quantify Ockham's razor, solve the induction problem, and define intelligence.Comment: 9 LaTeX page

Heasuring (machine) intelligence universally: An interdisciplinary challenge

Artificial intelligence (Al) is having a deep impact on the way humans work, communicate and enjoy their leisure time. Al systems have been traditionally devised to solve specific tasks, such as playing chess, diagnosing a disease or driving a car. However, more and more Al systems are now being devised to be generally adaptable, and learn to solve a variety of tasks or to assist humans and organisations in their everyday tasks. As a result, an increasing number of robots, bots, avatars and 'smart' devices are enhancing our capabilities as individuals, collectives and humanity as a whole. What are these systems capable of doing? What is their global intelligence? How to tell whether they are meeting their specifications?Are the organisations including Al systems being less predictable and difficult to govern? The truth is that we lack proper measurement tools to evaluate the cognitive abilities and expected behaviour of this variety of systems. includino hybrid [e.g. machine-enhanced humans] and collectives. Once realised the relevance of Al evaluation and its difficulty, we will survey what has been done in the past twenty years in this area, focussing on approaches based on algorithmic information theory and Kolmogorov complexity, and its relation to other disciplines that are concerned with intelligence evaluation in humans and animals, such as psychometrics and comparative cognition. This will lead us to the notion of universal intelligence test and the new endeavour of universal psychometrics

Ultimate Intelligence Part I: Physical Completeness and Objectivity of Induction

We propose that Solomonoff induction is complete in the physical sense via several strong physical arguments. We also argue that Solomonoff induction is fully applicable to quantum mechanics. We show how to choose an objective reference machine for universal induction by defining a physical message complexity and physical message probability, and argue that this choice dissolves some well-known objections to universal induction. We also introduce many more variants of physical message complexity based on energy and action, and discuss the ramifications of our proposals.Comment: Under review at AGI-2015 conference. An early draft was submitted to ALT-2014. This paper is now being split into two papers, one philosophical, and one more technical. We intend that all installments of the paper series will be on the arxi