Hi Ivan,

I see. Well, it's probably not so much that we've lost interest and more like we're too busy with other stuff, and so less important stuff get forgotten. Don't hesitate to re-ping when necessary.

Nil

On 9/21/21 12:28, Ivan V. wrote:
Hi Nil,

I don't know, everybody suddenly lost interest (IIRC Linas was the only one interested), and I'm not in position to lead the visualization project on my own. Lastly, this thread mysteriously vanished from my google groups and my mail account a while ago. Now that it is back again, the visualization topic seems a bit strayed from this thread about Atomspace, so if anybody is interested in mentoring me toward a useful (still a open question) bit of visualization fragment of OpenCog, we can continue this conversation in the original thread (this thread is branched from the original "MOZI web UI" thread).

Thank you,
Ivan V.

uto, 21. ruj 2021. u 09:48 'Nil Geisweiller' via opencog <[email protected] <mailto:[email protected]>> napisao je:

    On 7/12/21 21:30, Ben Goertzel wrote:

    A reasonable step would be for Nil to send you some real PLN and
    URE inference histories and see what your visualizer does with them...


    Is that still needed?


    Nil



    On Mon, Jul 12, 2021, 10:59 AM Ivan V. <[email protected]
    <mailto:[email protected]>> wrote:

        I made a small infinity test
        <http://ocog.atspace.cc/infinite/> too. Each parent virtually
        has an infinite number of children. Rolling ovals around,
        zooming ovals in, zooming ovals out, ... Surely it's not
        exactly perfect, but I could live with it.

        pon, 12. srp 2021. u 17:48 Linas Vepstas
        <[email protected] <mailto:[email protected]>>
        napisao je:

            Hi Ivan,

            On Mon, Jul 12, 2021 at 6:00 AM Ivan V.
            <[email protected] <mailto:[email protected]>> wrote:


                Thank you for asking, and my thoughts are pretty
                obvious. As I understand, URE and PLN are all about
                proofs, so my thoughts may go in that direction.
                Suppose we have a natural deduction proof composition:
                *
                  --- ---   ---     ---   ---   ---     ---   ---   ---
                   I     J     K       L     M     N       P     Q     R
                 -----------------   ----------------- -----------------
                         A                   B     C
                -----------------------------------------------------------
                                             X*

                You can already see the tree-like composition, but as
                it may span over a very wide and tall area, it may be
                required to represent it within an on-demand scaling
                system. This example <http://ocog.atspace.cc/> roughly
                shows what I have imagined for proof representation.
                In the example you can play with ovals, dragging them
                around and in or out the central area, zooming proof
                parts of the current interest. Notice how it is
                possible to represent and navigate nearly infinite
                length proofs, assuming enough memory space.


            Re: navigating trees: if you don't already know this, then
            I suggest that you really, really should study hyperbolic
            rotations aka mobius transformations on the poincare disk.
            They implement your example.  I recall seeing a demo of
            this at SIGGRAPH two or three decades ago. As you pan
            around on the hyperbolic disk, different parts of the
            graph get magnified at the center. And, like an MC Escher
            print, the rest of the graph remains compressed at the edges.

            For scale-free networks, this doesn't work. And from what
            I can tell, learning really does result in something close
            to scale-free networks.  What this means in practice is
            that there's one vertex with a million edges coming off of
            it.  There are two, with half-a-million each. Four, with a
            quarter-million each, and so on. So almost all vertexes
            have just a handful of edges connected to them, but as you
            move around, from vertex to vertex, you bump into these
            monsters. And you can't really draw them: try drawing a
            vertex with a thousand edges on your 2Kx2K monitor: most
            of those edges will be less than one pixel from
            each-other. It'll be just a big blob.

            It's important to "eat your own dog-food", as they say, or
            "smoke your own dope": use your own code to solve actual,
            real-world problems. This very quickly highlights where
            all that beautiful theory doesn't quite work out in practice.

            --linas

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