2018-05-22 21:18 GMT+03:00 Linas Vepstas <linasveps...@gmail.com>: > > > On Sun, May 20, 2018 at 3:26 AM, Alexey Potapov <pota...@aideus.com> > wrote: > >> >> >> For me, observational data is sensory data. It doesn't contain concepts, >> predicates, etc. . If we have an observation that a particular crow is >> black, ... But there are no purely black crows. It's just an abstraction, >> which itself should be somehow generalized from raw data. >> How can we calculate P(crow,black|image)? >> > > Do not assume that a probability is what you actually want. Let me give > three examples. > > In real life, when you see a crow, and it is dark, and you want to talk > about it, you just say "black crow" as an identifier of the object in the > scene. You don't pull out your photometer and measure it's darkness at > 87.68% and a blueish hue of 77%. Why? Because you don't need to do that to > have a conversation about it's presence, location, movement, etc. You only > need to evaluate crow-ness and blackness sufficiently to distinguish it > from all other elements of the scene, and then you can assign P==100% for > most practical purposes. > > In neural nets, the sigma-function is a non-linear component, used to > boost results towards extremes. whatever sum of weights or evidence or > whatever it is that you have, as inputs feeding the neural net, you apply > the non-linear sigma, to try to sharpen everything closer to either 0% or > 100% -- to discriminate. To increase contrast. This is kind-of the > "secret" as to why neural nets work, and probabilities don't. > > In "integrated information" theory, you work with a large complex network > of things that are all inter-related, all interconnected. The goal of > applying the theory is to find those extensions of the net that are highly > interlinked, interconnected, and then to draw an accurate boundary around > them. If and when you can perceive that boundary, you can give everything > inside one name, and everything outside a different name. The names > assigned are unambiguous, unique, even if the actual boundary is perhaps > uncertain, even if there is a gradation, a smooth-ish transition from the > highly-interconnected thing, to the mostly disconnected parts. The act of > name-tagging is what gives a handle on being able to think about the object > in symbolic terms. > > Well, I cannot agree with you. I can object to the first two paragraphs at least. However, I'm not sure if it will be productive, since I have a feeling that the source of discrepancy between our views is mostly in different definitions... But if you wish, we can continue...
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