25 Apr 07 Your Virtual Clone
Chatterbots from MyCyberTwin can respond to questions about you when you’re not online.
Two yous: MyCyberTwin, an Australian venture led by CEO Liesl Capper and chief innovation officer John Zakos (pictured above), lets users craft sophisticated online agents that can chat with your online friends when you’re not available. Credit: Original Photography by Simon Carroll
Historians of artificial intelligence never talk about AI’s progress in the 1960s without a reference to Eliza, the first virtual personality. Eliza was a text-chat program written in 1966 by MIT AI expert Joseph Weizenbaum to parody a Rogerian psychotherapist, largely by turning every statement by the “patient” back into a question. If you tell Eliza “I am feeling blue today,” it’s apt to respond, “Do you enjoy feeling blue today?” To modern users, the pattern is obvious, and the illusion of talking to a real person drops away almost instantly. (See for yourself here or here.) Yet many people who used Eliza when the program was new were convinced, at least temporarily, that it was a real person.
Now there’s a Web-based service that, in essence, lets you set up your own Eliza and train it to mimic your own personality. No one will be fooled into thinking it’s you, but MyCyberTwin, launched earlier this month, does a decent job of acting as yourstand-in or virtual public-relations agent when you’re not reachable. If you embed your cybertwin in your blog, website, or MySpace profile, visitors can learn about you through an open-ended conversation. You can program your cybertwin with as much factual information and as much of your personality as you like. If you think visitors to your blog
might ask “What are you doing Saturday night?”, you can train it to respond “Going to see Harry Potter with friends. Why don’t you join us?”
(see more Technology Review)
23 Apr 07 Combinatorial Information Theory: I. Philosophical Basis of Cross-Entropy and Entropy
Robert K. Niven
Abstract: This study critically analyses the information-theoretic, axiomatic and combinatorial philosophical bases of the entropy and cross-entropy concepts. The combinatorial basis is shown to be the most fundamental (most primitive) of these three bases, since it gives (i) a derivation for the Kullback-Leibler cross-entropy and Shannon entropy functions, as simplified forms of the multinomial distribution subject to the Stirling approximation; (ii) an explanation for the need to maximize entropy (or minimize cross-entropy) to find the most probable realization; and (iii) new, generalized definitions of entropy and cross-entropy - supersets of the Boltzmann principle - applicable to non-multinomial systems. The combinatorial basis is therefore of much broader scope, with far greater power of application, than the information-theoretic and axiomatic bases. The generalized definitions underpin a new discipline of it combinatorial information theory, for the analysis of probabilistic systems of any type. Jaynes’ generic formulation of statistical mechanics for multinomial systems is re-examined in light of the combinatorial approach.
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