Steve Richfield wrote: > No, I am NOT proposing building mechanical contraptions, just using the > concept >to compute neuronal characteristics (or AGI formulas for learning).
Funny you should mention that. Ross Ashby actually built such a device in 1948 called a homeostat ( http://en.wikipedia.org/wiki/Homeostat ), a fully interconnected neural network with 4 neurons using mechanical components and vacuum tubes. Synaptic weights were implemented by motor driven water filled potentiometers in which electrodes moved through a tank to vary the electrical resistance. It implemented a type of learning algorithm in which weights were varied using a rotating switch wired randomly using the RAND book of a million random digits. He described the device in his 1960 book, Design for a Brain. -- Matt Mahoney, [email protected] ________________________________ From: Steve Richfield <[email protected]> To: agi <[email protected]> Sent: Mon, July 12, 2010 2:02:20 AM Subject: [agi] Mechanical Analogy for Neural Operation! Everyone has heard about the water analogy for electrical operation. I have a mechanical analogy for neural operation that just might be "solid" enough to compute at least some characteristics optimally. No, I am NOT proposing building mechanical contraptions, just using the concept to compute neuronal characteristics (or AGI formulas for learning). Suppose neurons were mechanical contraptions, that receive inputs and communicate outputs via mechanical movements. If one or more of the neurons connected to an output of a neuron, can't make sense of a given input given its other inputs, then its mechanism would physically resist the several inputs that didn't make mutual sense because its mechanism would jam, with the resistance possibly coming from some downstream neuron. This would utilize position to resolve opposing forces, e.g. one "force" being the observed inputs, and the other "force" being that they don't make sense, suggest some painful outcome, etc. In short, this would enforce the sort of equation over the present formulaic view of neurons (and AGI coding) that I have suggested in past postings may be present, and show that the math may not be all that challenging. Uncertainty would be expressed in stiffness/flexibility, computed limitations would be handled with over-running clutches, etc. Propagation of forces would come close (perfect?) to being able to identify just where in a complex network something should change to learn as efficiently as possible. Once the force concentrates at some point, it then "gives", something slips or bends, to unjam the mechanism. Thus, learning is effected. Note that this suggests little difference between forward propagation and backwards propagation, though real-world wet design considerations would clearly prefer fast mechanisms for forward propagation, and compact mechanisms for backwards propagation. Epiphany or mania? Any thoughts? Steve agi | Archives | Modify Your Subscription ------------------------------------------- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244&id_secret=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
