By David L. Dowe (auth.), David L. Dowe (eds.)
Algorithmic likelihood and pals: lawsuits of the Ray Solomonoff eighty fifth memorial convention is a suite of unique paintings and surveys. The Solomonoff eighty fifth memorial convention used to be held at Monash University's Clayton campus in Melbourne, Australia as a tribute to pioneer, Ray Solomonoff (1926-2009), honouring his a number of pioneering works - so much relatively, his innovative perception within the early Nineteen Sixties that the universality of common Turing Machines (UTMs) can be used for common Bayesian prediction and synthetic intelligence (machine learning). This paintings keeps to more and more impact and under-pin information, econometrics, laptop studying, info mining, inductive inference, seek algorithms, information compression, theories of (general) intelligence and philosophy of technology - and functions of those components. Ray not just anticipated this because the route to real man made intelligence, but additionally, nonetheless within the Nineteen Sixties, expected levels of growth in desktop intelligence which might finally bring about machines surpassing human intelligence. Ray warned of the necessity to count on and speak about the capability results - and hazards - quicker instead of later. potentially foremostly, Ray Solomonoff was once an outstanding, chuffed, frugal and adventurous individual of mild get to the bottom of who controlled to fund himself whereas electing to behavior rather a lot of his paradigm-changing study outdoor of the college process. the quantity comprises 35 papers bearing on the abovementioned themes in tribute to Ray Solomonoff and his legacy.
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Additional resources for Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence: Papers from the Ray Solomonoff 85th Memorial Conference, Melbourne, VIC, Australia, November 30 – December 2, 2011
The completeness and statistical consistency (and other merits) of approaches to inference and prediction based on algorithmic information theory have been discussed here a few times, but “There is, however another aspect of algorithmic probability that people ﬁnd disturbing - it would seem to take too much time and memory to ﬁnd a good prediction. There is a technique for implementing ALP that seems to take as little time as possible to ﬁnd regularities in data” [163, sec. 2], and this is then discussed in [163, sec.
AGI 2011. LNCS, vol. 6830, pp. 122–132. L. Dowe 70. : Evaluating a reinforcement learning algorithm with a general intelligence test. A. ) CAEPIA 2011. LNCS, vol. 7023, pp. 1–11. Springer, Heidelberg (2011) 71. : Complexity measures for meta-learning and their optimality. L. ) Solomonoﬀ Festschrift. LNCS (LNAI), vol. 7070, pp. 198–210. Springer, Heidelberg (2013) 72. : An invariant form for the prior probability in estimation problems. Proc. of the Royal Soc. of London A 186, 453–454 (1946) 73.
Dowe abovementioned log-loss scoring system since 1995 [32, sec. 5, p541, col. 2] for Australian AFL football (with a Gaussian competition based on the margin starting in 1996 [34, sec. 5]). Second, one can introduce a Bayesian prior for the log-loss scoring system, as originally tried somewhat unsuccessfully (with ratios of logarithms) in . The correction (stated verbally in 2002) from [171, sec. 2][32, footnote 176][34, sec. 4] takes logarithms of ratios of probabilities (equivalent to diﬀerences in logarithms of probabilities when at least one of these is ﬁnite).
Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence: Papers from the Ray Solomonoff 85th Memorial Conference, Melbourne, VIC, Australia, November 30 – December 2, 2011 by David L. Dowe (auth.), David L. Dowe (eds.)