Ben, et al, After ~5 months of delay for theoretical work, here are the basic ideas as to how really fast and efficient automatic learning could be made almost trivial. I decided NOT to post the paper (yet), but rather, to just discuss the some of the underlying ideas in AGI-friendly terms.
Suppose for a moment that a NN or AGI program (they can be easily mapped from one form to the other), instead of operating on "objects" (in an object-oriented sense), instead, operates on the rate-of-changes in the probabilities of "objects", or dp/dt. Presuming sufficient bandwidth to generally avoid superstitious coincidences, fast unsupervised learning then becomes completely trivial, as like objects cause simultaneous like-patterned changes in the inputs WITHOUT the overlapping effects of the many other objects typically present in the input (with numerous minor exceptions). But, what would Bayesian equations or NN neuron functionality look like in dp/dt space? NO DIFFERENCE (math upon request). You could trivially differentiate the inputs to a vast and complex existing AGI or NN, integrate the outputs, and it would perform *identically* (except for some "little" details discussed below). Of course, while the transforms would be identical, unsupervised learning would be quite a different matter, as now the nearly-impossible becomes trivially simple. For some things (like short-term memory) you NEED an integrated object-oriented result. Very simple - just integrate the signal. How about muscle movements? Note that muscle actuation typically causes acceleration, which doubly integrates the driving signal, which would require yet another differentiation of a differentiated signal to, when doubly integrated by the mechanical system, produce movement to the desired location. Note that once input values are stored in a matrix for processing, the baby has already been thrown out with the bathwater. You must START with differentiated input values and NOT static measured values. THIS is what the PCA folks have been missing in their century-long quest for an efficient algorithm to identify principal components, as their arrays had already discarded exactly what they needed. Of course you could simply subtract successive samples from one another - at some considerable risk, since you are now sampling at only half the Nyquist-required speed to make your AGI/NN run at its intended speed. In short, if inputs are not being electronically differentiated, then sampling must proceed at least twice as fast as the NN/AGI cycles. But - how about the countless lost constants of integration? They "all come out in the wash" - except for where actual integration at the outputs is needed. Then, clippers and leaky integrators, techniques common to electrical engineering, will work fine and produce many of the same artifacts (like visual extinction) seen in natural systems. It all sounds SO simple, but I couldn't find any prior work in this direction using Google. However, the collective memory of this group is pretty good, so perhaps someone here knows of some prior effort that did something like this. I would sure like to put SOMETHING in the "References" section of my paper. Loosemore: THIS is what I was talking about when I explained that there is absolutely NO WAY to understand a complex system through direct observation, except by its useless anomalies. By shifting an entire AGI or NN to operate on derivatives instead of object values, it works *almost* (the operative word in this statement) exactly the same as one working in object-oriented space, only learning is transformed from the nearly-impossible to the trivially simple. Do YOU see any observation-based way to tell how we are operating behind our eyeballs, object-oriented or dp/dt? While there are certainly other explanations for visual extinction, this is the only one that I know of that is absolutely impossible to engineer around. No one has (yet) proposed any value to visual extinction, and it is a real problem for hunters, so if it were avoidable, then I suspect that ~200 million years of evolution would have eliminated it long ago. >From this comes numerous interesting corollaries. Once the dp/dt signals are in array form, it would become simple to automatically recognize patterns representing complex phenomena at the level of the neurons/equations in question. Of course, putting it in this array form is effectively a transformation from AGI equations to NN construction, a transformation that has been discussed in prior postings. In short, if you want your AGI to learn at anything approaching biological speeds, it appears that you absolutely MUST transform your AGI structure to a NN-like representation, regardless of the structure of the processor on which it runs. Unless I am missing something really important here, this should COMPLETELY transform the AGI field, regardless of the particular approach taken. Any thoughts? Steve Richfield ------------------------------------------- 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=123753653-47f84b Powered by Listbox: http://www.listbox.com
