Hi, I pretty much always think of a NN as a physical device.
I think the first binary computer was dreamt up with balls going through the system with ball representing 1's and 0's. The idea was written down but never built. Jamming balls that give way at a certain point is the same as using ">". ie When more than 6 balls jam up, the pressure is released, sending a 1 or a value > 6 balls. Addition can be a little different in such systems. ie a value > 6 + a value > 3 = a value > 9. On Sun, 2010-07-11 at 23:02 -0700, Steve Richfield wrote: > 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
