Foreword re C. S. Wallace

@article{Dowe2008ForewordRC,
  title={Foreword re C. S. Wallace},
  author={David L. Dowe},
  journal={Comput. J.},
  year={2008},
  volume={51},
  pages={523-560}
}
  • D. Dowe
  • Published 1 September 2008
  • Computer Science
  • Comput. J.
One of the second generation of computer scientists, Chris Wallace completed his tertiary education in 1959 with a Ph.D. in nuclear physics, on cosmic ray showers, under Dr Paul George at Sydney University. Needless to say, computer science was not, at that stage, an established academic discipline. With Max Brennan and John Malos he had designed and built a large automatic data logging system for recording cosmic ray air shower events and with Max Brennan also developed a complex computer… 
Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence
  • D. Dowe
  • Computer Science, Mathematics
    Lecture Notes in Computer Science
  • 2013
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Algorithmic probability and friends: Proceedings of the Ray Solomonoff 85th memorial conference is a collection of original work and surveys that contains 35 papers pertaining to the abovementioned topics in tribute toRay Solomonoff and his legacy.
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Light is shed on information theory, turing machines and algorithmic information theory—and relates all of these to MML, which moves on to Ockham's razor and the distinction between inference (or induction, or explanation) and prediction.
On More Realistic Environment Distributions for Defining, Evaluating and Developing Intelligence
TLDR
This paper agrees with previous criticisms that a universal distribution using a reference universal Turing machine (UTM) over tasks, environments, etc., is perhaps amuch too general and proposes the notion of Darwin-Wallace distribution for environments, which is inspired by biological evolution, artificial life and evolutionary computation.
IQ tests are not for machines, yet
Complex, but specific, tasks —such as chess or Jeopardy!— are popularly seen as milestones for artificial intelligence (AI). However, they are not appropriate for evaluating the intelligence of
Measuring universal intelligence: Towards an anytime intelligence test
TLDR
This paper introduces many new ideas that develop early ''compression tests'' and the more recent definition of '' universal intelligence'' in order to design new ''universal intelligence tests'', one of which is the ''anytime intelligence test'', which adapts to the examinee's level of intelligence within a limited time.
How universal can an intelligence test be?
TLDR
It is argued that such tests must be highly adaptive, i.e. that tasks, resolution, rewards and communication have to be adapted according to how the evaluated agent is reacting and performing, and set the quest for universal tests as a progressive rather than absolute goal.
On environment difficulty and discriminating power
TLDR
A way to estimate the difficulty and discriminating power of any task instance by analysing the performance of a population of agent policies against the task, leading to a distribution that is examined in terms of policy complexity.
Computing and complexity - Networks, nature and virtual worlds
TLDR
This chapter explores this intimate relationship and its applications in complexity theory between computing and complexity theory and how computers have helped to advance complexity theory in several ways.
Inductive Inference and Partition Exchangeability in Classification
TLDR
It is proven that, in contrast to classifiers based on de Finetti type exchangeability which can optimally handle test items independently of each other in the presence of infinite amounts of training data, a classifier based on partition exchangeability still continues to benefit from a joint prediction of labels for the whole population of test items.
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References

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A fairly elementary WWW-based computer program is presented here a fairly elementary I.Q. test which regularly obtains a score close to the purported average human score of 100, and is briefly considered to ascribe intelligence to the program.
Wallace's Approach to Unsupervised Learning: The Snob Program
TLDR
The Snob program for unsupervised learning as it has evolved from its beginning in the 1960s until its present form, with particular attention to the revision of Snob in the 1980s where definite assignment of things to classes was abandoned.
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This is a remarkable book by a remarkable scientist. E. T. Jaynes was a physicist, principally theoretical, who found himself driven to spend much of his life advocating, defending and developing a
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In medieval times the philosopher's stone was supposed to enable its possessors to convert base metals into gold. Its discovery would revolutionize the world, presumably not by undermining the value
Discussion of the Papers by Rissanen, and by Wallace and Dowe
Commenting on the papers by Dr Rissanen and Professors Wallace and Dowe is a daunting sort of pleasure. Superficially, the papers are not closely related: Dr Rissanen has focused on the Shtarkov
A Formal Theory of Inductive Inference. Part II
1. Summary In Part I, four ostensibly different theoretical models of induction are presented, in which the problem dealt with is the extrapolation of a very long sequence of symbols—presumably
MML clustering of multi-state, Poisson, von Mises circular and Gaussian distributions
TLDR
This work outlines how MML is used for statistical parameter estimation, and how the MML mixture modelling program, Snob, uses the message lengths from various parameter estimates to enable it to combine parameter estimation with selection of the number of components and estimation of the relative abundances of the components.
Bayes not Bust! Why Simplicity is no Problem for Bayesians1
TLDR
This work defends a Bayesian alternative: the simplicity of a theory is to be characterised in terms of Wallace's Minimum Message Length (MML), and shows that MML provides answers to many of Forster's objections to Bayesianism.
The discovery of algorithmic probability: A guide for the programming of true creativity
TLDR
This talk will describe a voyage of discovery the discovery of Algorithmic Probability, the result of "goat motivated discovery" like theiscovery of the double helix in biology, but with fewer people involved and relatively little political skullduggery.
Rejoinder
We thank Dr Gilbert and Professor Sinha for their thoughtful and provocative comments. We would like to respond to several points that have emerged from the discussion. Professor Sinha expresses some
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