Hi all, This paper indicates Jeff Hawkins' neuroscience theory gradually converging on ideas more similar to those in Novamente, via the use of the common language of probability theory.
http://www.stanford.edu/~dil/RNI/DilJeffTechReport.pdf Of course there are some oversimplifications that need to be relaxed in further research (Markovicity, for one; and the absence of heterarchical connections, for another). But, the basic approach seems to make sense to me. As I've said before (see my essay on "Hebbian Logic"), I believe that conditional probability based inference on the neural-cluster level results in a pretty direct way from Hebbian learning on the neuronal level --- and there is a long, mostly not yet understood story in the way neural cluster properties tune the parameters of neuron-level Hebbian learning to make this happen. But I agree with Jeff and Dileep that one can study the conditional probability dynamics in a neural context without getting down to the Hebbian-learning level of granularity. Where things will get interesting is when they try to extend the framework you describe in your paper to model a system that: * perceives visually in the manner you describe in your paper * responds via actuators to the visual stimuli it perceives, in a way that requires it to do some object recognition in the visual stimuli This requires learning of what Gerald Edelman calls "neural maps." It seems to me that learning nontrivial maps of this sort requires, to use mathematical vocabulary, the construction of moderately complex predicates involving both perception and action variables. It is for the formation of these predicates that Edelman proposed the "neural darwinist" quasi-evolutionary-programming neural learning mechanism. These are probabilistic predicates that involve (among others) the same probabilistic variables that are isolated in their papern. But I'll be curious what learning mechanism they will propose when your research gets to the learning of nontrivial perception-action maps (let alone cognition!). Simple manipulations of conditional probabilities won't do the trick anymore. There seems to be nothing in Jeff Hawkins' recent book addressing this problem. Perhaps they'll rediscover your own version of Edelmanian evolutionary learning, or invent something else analogous.... Anyway, it is nice to see some convergence btw neuroscience ideas and AI ideas, with probablity theory as the unifying language ;-) -- Ben G ------- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
