I'm done trying to explain to him. Explaining to people who don't want to understand is a waste of my time. The only positive side is that I have come to a slightly better explanation in lay terms, but I could've done that under less frustrating circumstances anyway.
On Thu, Oct 25, 2012 at 8:11 AM, Jim Bromer <[email protected]> wrote: > On Thu, Oct 25, 2012 at 5:07 AM, Mike Tintner <[email protected]>wrote: > >> Aaron, you haven’t described anything to me. I asked you/all to analyse >> graphically how you would recognize a chair – you gave me zip, just general >> airy stuff. >> >> > > Aaron, > This is a perfect example of how your examples and explanations weren't > interpreted as examples and explanations. The reason is partly > psychological but it is also because you cannot talk about the key issue in > a way that is subtle enough to get him to think about what you are saying. > For instance, you noted that the combinatorial explosion was a problem > and yet his response was, > "...you gave me zip, just general airy stuff... Language is an > unbelievably complicated thing – and nets don’t even scrape the surface." > The mention of combinatorial explosion (or complexity) does not affect > his thinking. It never has and until you have a sign that it does, you > won't be able to use it. The more intuitive idea that computer programming > is only able to use natural language in a simple way might be the basis of > some future conversation but again, you won't know until you try it. > Jim Bromer > On Thu, Oct 25, 2012 at 5:07 AM, Mike Tintner <[email protected] > > wrote: > >> Aaron, you haven’t described anything to me. I asked you/all to analyse >> graphically how you would recognize a chair – you gave me zip, just general >> airy stuff. >> As for uniform blocks, yes your nets are totally general and uniform. For >> example, how would your system understand: >> I WOULDN’T CALL THAT A TREE, WOULD YOU? >> IT’S A CROSS BETWEEN A TREE AND A POLE. >> THE SCIENCES AND ARTS FORM A TREE. >> NO, IT’S NOT TREE, IT’S FOUR. >> Your semantic nets, which seem fairly standard & similar to what others >> are doing, are toy stuff with toy results. They are not for the real world >> of language and language users. Language is an unbelievably complicated >> thing – and nets don’t even scrape the surface. >> *From:* Aaron Hosford <[email protected]> >> *Sent:* Thursday, October 25, 2012 12:46 AM >> *To:* AGI <[email protected]> >> *Subject:* Re: [agi] The Fundamental Misunderstanding in AGI [was >> Superficiality] >> Why do you insist that the "blocks" have to be uniform for semantic >> nets to work? I already described to you how non-uniform building blocks >> (outlines, connected blobs of color, etc.) could be built up through a >> hierarchical semantic net to represent the contents of an arbitrary image, >> including parts of objects, objects, and entire scenes at different levels >> in the hierarchy. There was no assumption of uniformity there, other than >> that the bits & pieces being put together are at most 2D, since all that a >> 2D image can provide. >> The "3D" that we think we perceive is really just 2 1/2 D, where a flat >> image has been labeled with depth information, but we can't see the >> backsides of objects. Including that sort of information doesn't >> fundamentally change anything, although the extra info should make it even >> easier to carve the image up into outlines & blobs & subsequently determine >> how to connect them together appropriately. >> Besides, the process can generalize from 2D images to full 3D (or >> higher) just as easily as it generalizes from 1D sentences, where it's >> already working to parse sentences, up to 2D images. All that's necessary >> for it to work on an input class of any topology or dimensionality is >> simply to have the input carved into small, connectable chunks ("blocks") >> of *arbitrary* shape and to provide a "grammar" of how those chunks can be >> connected to each other recursively. The only caveat lies not in the >> ability of semantic nets to represent the structure of arbitrary data, but >> in the combinatoric explosion of ways to put the pieces back together when >> assembling the semantic net, and that can be gotten around using shrewd >> design and/or a faster processor. >> >> >> On Wed, Oct 24, 2012 at 4:03 AM, Mike Tintner >> <[email protected]>wrote: >> >>> Aaron, >>> The problem is the same whether you start with parts or the whole of the >>> object/chair, such as a silhouette. You’re always dealing with sets of >>> multiform [odds and ends] blocks and configurations - and *not* uniform >>> blocks, on wh. semantic nets depend – wh. is why they have never begun to >>> work for AGI. >>> *From:* Aaron Hosford <[email protected]> >>> *Sent:* Wednesday, October 24, 2012 2:16 AM >>> *To:* AGI <[email protected]> >>> *Subject:* Re: [agi] The Fundamental Misunderstanding in AGI [was >>> Superficiality] >>> Maybe parts of a chair are a bad place to start. Maybe you have to >>> look at the whole thing, Mike. Contextual information tells you what a part >>> of an object is, not the part by itself. Is that vertical piece of wood the >>> leg of a chair or a baseball bat leaned against something? You'll never >>> know until you look at the scene it appears in. Is "go" a noun or a verb? >>> You'll never know until you put it into a sentence. Have a "go" at it. It's >>> the relationships between the elements which is key. That's where the >>> pattern lies. Not in any one element looking a certain way. >>> You keep taking things out of their natural contexts, and then demanding >>> we show how our approaches would recognize them. My parser recognizes "go" >>> as both a noun and a verb, depending on use. It builds up a set of trees >>> that show how the elements, the words, must be connected into bigger >>> elements, the phrases, and finally on to the entirety, the sentence. At >>> each step, it weeds out the combinations that make no sense. What we need >>> is not the ability to recognize the tops of chairs taken out of context. (I >>> wouldn't recognize those blobs if I didn't know what they are already, so >>> how is software that's meant to copy my abilities going to do it?) What we >>> need is a "parser" for images. Something that identifies the outlines and >>> fields that make up an image as the base-level elements, and builds up >>> higher level elements out of them, excluding combinations that make no >>> sense along the way, until the whole image has been explained in terms of >>> the *relationships* between those elements. Only then we can look at how >>> the parts fit together to determine an object's shape, and from its shape, >>> its characteristics (being self supporting, being artifactual, having >>> "sittability", etc.), and from its shape and/or characteristics, its >>> identity. >>> >> *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/23050605-bcb45fb4> | > Modify<https://www.listbox.com/member/?&;>Your Subscription > <http://www.listbox.com> > ------------------------------------------- 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
