On Thu, 2002-10-31 at 11:24, Ben Goertzel wrote: > > But, a connection topology that tends to have a lot of local connections is > not the same as one that really maps a 2D or 3D space, with the precision > desired for processing spatial input data...
For us, even though the accumulated data structure hangs together really nicely in 3D (though not in 2D), the geometry is definitely not cubic and any effective coordinate system would reflect this. However, I would make the point that the "comfortable" geometry of the network is sort of immaterial to how well it handles 2D and 3D spatial processing. Mapping n-dimensional spaces onto a one-dimensional space is what computers do now, and effortlessly at that. But that ignores another point worth mentioning, which is most sensory data the human brain works with is one-dimensional even though we don't think of it that way. Audio, for example, is perceived as a one-dimensional signal that is analyzed for spatial cues at a higher level. You don't need a 3D model of an audio space if you get the exact same value by working with simple vectors discovered from a 1D data stream. I think there is something wrong with trying to expand a bit of information far beyond its actual content in this context. In fact, if you look at the format of the audio data that the brain actually receives, "dimensionality" is a trivial piece of pseudo-meta-data extracted from the stream. What you end up with is multiple layers of one-dimensional pattern data that effectively lets you exist in a 3D space that isn't really implied by the data stream. A machine can behave as though it is actually aware aurally in 3D even though its perception of the world is strictly along a single axis with data structures to match. Or at least it will as long as there is value in learning behaviors that treat certain vector patterns in certain ways. Vision is more complicated, but even that is more like a 1.5-dimensional data stream when you get down to it. Definitely more difficult to analyze though, and not my area of expertise. The lack of true 3-dimensionality in any of our senses is the primary reason it is so easy to fool those senses. I think it is unnecessary to fully map low-dimensionality data into a sophisticated and resource consuming 3D space to be able to effectively behave as though you are fully aware of your 3D surroundings, particularly since humans don't do this in the sense I think is commonly believed. The space is inferred from a relatively small collection of vectors automatically being stripped from a low-dimensionality data stream and simple processing that happens on those vectors. Hearing, which I actually know quite a bit about, does work like this pretty much in its entirety. "Dimensionality" is a behavior learned as relatively simple vector patterns. Naturally, this gets more interesting when you throw in multiple senses working together and add feedback. It is also worth noting that the fact that dimensionality is learned is also the reason it is hard to fake virtual 3D environments well for the general population but easy to fake virtual 3D environments very convincingly for a specific person; the vector patterns each person learns are slightly different. Once your particular dimensionality perception profile is constructed, the software can make fake environments that are indistinguishable from the real thing for that person. If you A/B the standard profile against your personalized profile, the difference in perceived dimensionality is startling, even though they are both nominally the same source material and processed to provide 3D perception. Cheers, -James Rogers [EMAIL PROTECTED] ------- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/
