21.5180, Calls: Cognitive Science, Comp Ling, Philosophy of Lang/Australia

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LINGUIST List: Vol-21-5180. Mon Dec 20 2010. ISSN: 1068 - 4875.

Subject: 21.5180, Calls: Cognitive Science, Comp Ling, Philosophy of Lang/Australia

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1)
Date: 18-Dec-2010
From: David Dowe [David.Dowe at monash.edu]
Subject: Solomonoff 85th Memorial Conference
 

	
-------------------------Message 1 ---------------------------------- 
Date: Mon, 20 Dec 2010 15:04:06
From: David Dowe [David.Dowe at monash.edu]
Subject: Solomonoff 85th Memorial Conference

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Full Title: Solomonoff 85th Memorial Conference 

Date: 30-Nov-2011 - 02-Dec-2011
Location: Melbourne, Victoria, Australia 
Contact Person: David Dowe
Meeting Email: Dianne.Nguyen at monash.edu

Linguistic Field(s): Cognitive Science; Computational Linguistics; Philosophy of Language 

Call Deadline: 23-May-2011 

Meeting Description:

The Solomonoff 85th memorial conference is to celebrate the work of Ray Solomonoff (1926-2009), whose algorithmic probability (ALP) and algorithmic information theory (AIT) work from the early 1960s is gaining increasing use in a variety of fields within linguistics, philosophy of science/language and also cognitive science - as also are the related field of Minimum Message Length (MML) from the 1960s and the related Minimum Description Length (MDL) from the late 1970s. 

Call for Papers:

Papers are sought building upon these notions in linguistics, cognitive science and other areas.

Ray Solomonoff (1926-2009) 85th Memorial Conference

Melbourne, Australia
Wednesday 30/Nov/2011 - Friday 2/Dec/2011

Submission deadline: 20 May 2011

http://www.solomonoff85thmemorial.monash.edu/flyer.pdf

Ray Solomonoff (1926-2009) was the originator (in 1964) of algorithmic information theory.  Solomonoff's (1964) work preceded the slightly later independent work of Kolmogorov (1965) [from whom we have the term Kolmogorov complexity], shortly before the not unrelated work of the then teenage G. J. Chaitin (1966).  But, unlike the slightly later Kolmogorov and Chaitin, Solomonoff (1964) also saw the relevance of this new area to statistics, machine learning, artificial intelligence and prediction - and coined the term algorithmic probability (ALP). Given a body of data, the algorithmic probability distribution behind Solomonoff prediction is obtained by doing a posterior-weighted averaging of the outputs of all available computable theories - with the prior probabilities of theories depending (monotonically decreasingly) upon the lengths of their encodings on the chosen Universal Turing Machine (UTM).

Independently of and shortly after the above was the Minimum Message Length (MML) work of Wallace and Boulton (1968), based on very similar Bayesian information-theoretic principles but instead focusing on the one single best model for statistical and inductive inference (and whose relationship with algorithmic information theory was formalized in the 1990s).  The related Minimum Description Length (MDL) principle followed a decade later in Rissanen (1978), co-incidentally taking the same form as Schwarz's (1978) Bayesian Information Criterion (BIC) of the same year - and with some approaches [such as the still popular but largely unrelated Akaike's Information Criterion (AIC)] formed after MML but before MDL.  The (algorithmic) information theory behind both Solomonoff prediction and (the two-part form of) MML inference (or model selection and point estimation) leads to a variety of statistical consistency (or convergence) results - apparently more general than for other approaches - and likewise makes the results of both approaches statistically invariant to re-parameterisation.

These approaches - both the MML inductive or inferential approach to choosing the single 'best' model and the Solomonoff predictive approach of weighting over the posterior to form a predictive distribution - are two of at least as many approaches from (Kolmogorov complexity or) algorithmic information theory which have been applied to a range of areas.  Such areas include (e.g.) philosophy of science, the problem of induction, linguistics, philosophy of language, etc., statistical inference (and model selection and point estimation) and prediction, machine learning, econometrics (including time series and panel data), in principle proofs of financial market inefficiency, theories of (quantifying) intelligence and new forms of (universal) intelligence test (for robotic, terrestrial and extra-terrestrial life), knowledge discovery and 'data mining', bioinformatics, evolutionary (tree) models in biology and linguistics, geography, climate modeling and bush-fire detection, environmental science, image processing, spectral analysis, engineering, arguments that entropy is not the arrow of time, etc. Of course, this list will continue to grow and is not exhaustive.

Perhaps Solomonoff's next main contribution was the notion of 'infinity point' (Solomonoff, 1985), later referred to as the 'singularity', where machine intelligence catches up to and overtakes human intelligence - an increasingly discussed scenario which forms the basis of many science fiction films.

Solomonoff's obituary from the New York Times (January 2010) is at www.nytimes.com/2010/01/10/science/10solomonoff.html, duplicated at www.csse.monash.edu.au/~dld/MML.html#rjs .

In the year in which Ray Solomonoff would have had his 85th birthday and some weeks before the year in which Alan Turing (upon whose Universal Turing Machines much of Solomonoff's work is based) would have turned 100, this multi-disclipinary conference is timed for late 2011.  It also follows on 15 years after the Information, Statistics and Induction in Science (ISIS) conference in 1996 and also held in Melbourne, Australia - whose invited speakers included Ray Solomonoff, (Turing Award winner and fellow artificial intelligence pioneer) Marvin Minsky, Jorma Rissanen (of Minimum Description Length [MDL]) and (prominent machine learning researcher) J. Ross Quinlan.

The contributions sought for this Solomonoff 85th memorial conference are the abovementioned themes and/or anything (else) directly or at least indirectly comparing with or building upon Solomonoff's work.

This inter-disciplinary conference will be held in Melbourne, Australia. The conference will run for three days, from Wedn 30 November 2011 to Friday 2 December 2011, but might possibly be preceded by a day or half-day of workshops and/or tutorials on Tues 29 November 2011.

Conference proceedings will be fully-refereed and published with a suitable prestigious publisher.  Selected papers on suitable topics might be chosen to be expanded upon for journal special issues.

Program Committee:

Andrew Barron, Statistics, Yale Univ, U.S.A.
Greg Chaitin, IBM T.J. Watson Research, U.S.A.
Fouad Chedid, Notre Dame Univ, Lebanon
Bertrand Clarke, Medical Statistics, Univ Miami, U.S.A.
A. Phil Dawid, Statistics, Cambridge University, U.K.
David Dowe (Conference and Program chair), Monash Univ
Peter Gacs, Boston University, U.S.A.
Alex Gammerman, Royal Holloway Univ London, England
John Goldsmith, Linguistics, Univ. Chicago, U.S.A.
Marcus Hutter, Australian National Univ (ANU)
Leonid Levin, Boston University, U.S.A.
Ming Li, Mathematics, U Waterloo, Canada
Kee Siong Ng, ANU (Australia) & EMC Corp
Juergen Schmidhuber, IDSIA, Switzerland
Farshid Vahid, Econometrics, Monash Univ, Australia
Paul Vitanyi, CWI, Amsterdam, Holland
Vladimir Vovk, Royal Holloway Univ London, England




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