From: Emil Gilliam (email@example.com)
Date: Tue Jan 04 2005 - 12:52:12 MST
This month longtime member and general inspiration Ray Solomonoff will
be delivering three lectures on
Algorithmic Probability and Artificial Intelligence
When: 12, 19 and 26 January 2005 -- 7 to 10 pm
Where: Bldg 32, room 144
No enrollment limit, no advance signup.
Participants are welcome at individual sections.
The lectures will be about an hour followed by
questions and discussion. See below for information
on individual sessions.
Lecture notes will appear at the URL
http://world.std.com/~rjs/ These notes will be
periodically updated and will eventually
include text of the entire set of lectures
Contact: G.J.Sussman, firstname.lastname@example.org
Sponsor: Electrical Engineering and Computer Science
Cosponsor: Engineering Systems Division
Lecture 1: Algorithmic Probability
Definitions of induction, prediction.
Motivations for redefinition of probability: Heavy and
light models -- over and under fitting.
Algorithmic Probability and the Universal distribution.
Three kinds of probabilistic prediction: How
Algorithmic Probability gives very small expected error
in all of them.
How algorithmic probability differs from minimum description length
Rissanen's Stochastic complexity, Kolmogorov complexity.
Incomputability of Algorithmic Probability -- How it enables good
estimates despite its incomputability.
Subjectivity in algorithmic probability, in other Bayesian methods,
and in non-Bayesian methods.
Basis of subjective a priori. The Anthropic principle.
Explanation based learning. Placebo explanations.
Is an Anthropic principle necessary?
Lecture 2: Applications of Algorithmic Probability
Linear and nonlinear prediction.
Neural Nets and Genetic Programming.
Lecture 3: Systems for Strong Artificial Intelligence.
A definition of Strong Artificial Intelligence. Training
sequences. The role of Levin's Search Algorithm and Enhanced
Genetic Programming in preliminary and advanced Artificial
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