Thx for your response, Ben (and for the many other contributions on the list!)
Re Hebbian neural – I assume you could calculate an eigenvalue matrix or some other heuristic approximation (to matrix**n) to speed up calculations. However, the matrix changes dynamically each time your AGI learns. Also, the evidence is that the mind switches dynamically quite easily between different ‘islands of stability’ so small changes in weights or inputs are likely to produce quite different eigenvalue values – if indeed it converges at all. Hence I’d venture to guess that it may be computationally less expensive to iterate than to calculate a reduced matrix each time. Despite this, personally I’d still prefer an activation (not necessarily Hebbian) spreading network (tho you have some of that in your Novamente architecture as well – for your patterns) especially for the ‘middle level’ (for my top-level I also favour a purely symbolic though much less formal one than Novamente/NARS/Cyc approach, mainly because I’m not smart and mathematically skilled enough :) Also I think it’s better for different people to try out different approaches so as to explore the AGI solution space a bit wider. PS Current theorem-proving approaches I always considered to be narrow-AI alternatively one of many specialized modules in an AGI tho obviously a computer-AGI c/would be much more efficient at theorem-proving than humans. Tho maths, being abstract, would indeed be one of the areas in which any computer AGI should excel (it should be one of her main hobbies:) >>> "Benjamin Goertzel" <[EMAIL PROTECTED]> 06/03/07 3:02 PM >>> > of the 3 different AGI approaches you entertained, you went > with Novamente instead of the Hebbian neural net (and the theorem proving > one)... us scruffies would like to know... is it just your mathematical > bias/background or something more fundamental? The Hebbian neural net approach seemed like it would be dramatically more computationally expensive, requiring a whole bundle of synapses to do what we can do with a single Novamente link. I.e., it's less natural for the von Neumann infrastructure we are stuck with at the moment. And, once you get beyond simple stuff, we don't know how the brain works so we need to invent stuff anyway, even in that plan (e.g. I have a scheme for doing higher-order logic in neural nets that involves feeding a dimensionally-reduced version of a neural net's connection matrix to the same network as an input vector ... but tuning that would take a lot of work, and there is no neuroscience to guide such work, at this point...) ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=231415&user_secret=e9e40a7e
