Sergio, > All our sensory organs receive patchworks, not patterns.
I used the term 'pattern' in a broader sense. For me, the input for a retinal array is a (vector) pattern. Many of unsupervised learning algorithms (including neural ones) aim (& do) the compression of patterns/vectors. Can't we say those algorithms (or systems implementing them) generate 'patterns' in your sense? On 2012/07/21, at 23:41, Sergio Pissanetzky wrote: > Naoya, > > before a neural system learns patterns, it has to make them. All our sensory > organs receive patchworks, not patterns. Hofstadter said it: "The central > problem of AI is how to start from 100 million dots of light on your retina > and end with 'Hi, mom' in 0.5 sec?" The brain generates a pattern, and keeps > it in store for further use. The pattern is an invariant representation. But > how does it generate that pattern in the first place? The assumption that a > computer program will do what a physical system does is not correct. You > have to simulate it first. And to do that, you need to know how the > invariant representations come into existence. From what you say, you don't. > > > I have devoted a considerable amount of work to precisely that problem, > generating invariant trepresentations. It is very recent, so it is not > widely yet. > > Sergio > > > -----Original Message----- > From: ARAKAWA Naoya [mailto:[email protected]] > Sent: Friday, July 20, 2012 9:10 PM > To: AGI > Subject: Re: [agi] Re: How the Brain Works -- new H+ magazine article, by me > > On 2012/07/21, at 4:59, Mike Tintner wrote: > >> Sergio: I noticed that Jeff Hawkins in On Intelligence writes about >> "invariant representations," which are hierarchies, but never explains >> how they come into existence. I am just a little confused. > >> I wonder whether you have an outstanding point there. Everyone >> *talks* about "invariant representations". Does anyone anywhere have >> any AI-worthy explanation of their nature/origin whatsoever? >> >> (Of course, invariant representations overlap with concepts. There are >> psych/phil. explanatory theories of concepts, but that's why I put in >> "AI-worthy". I suspect they are all v. vague). > > I interpreted "invariant representations" in the writing of Hawkins as > learned patterns. > When a neural system learns some pattern, say that of a line segment, it > recognizes line segments regardless of their orientation or length (hence > 'invariant"). > "Invariant representations" in a neural network would be distributed so > that one cannot point out saying, for example, *this* is the representation > of a line segment... > > * The Gibsonian invariance might be a different notion while he may > have made the term popular among cognitive scientists (?). > -- > Naoya ARAKAWA > ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-c97d2393 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-2484a968 Powered by Listbox: http://www.listbox.com
