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]> 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]>
napisao je:
Hi Ivan,
On Mon, Jul 12, 2021 at 6:00 AM Ivan V. <[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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