Hmmm... the context is quite different, but I am reminded in some ways
of the object-capabilities model

https://en.wikipedia.org/wiki/Object-capability_model

which has come up in the work we're doing on crypto-currency-based
distributed computing platforms for AI, together with the Economic
Space Agency...

The similarities are total encapsulation, and explicitness of
reference to data...



On Mon, Jul 17, 2017 at 11:26 AM, Curtis Faith <[email protected]> wrote:
> I've been thinking about building graph-optimized hardware and how one might
> use analog function cells as the baseline for a function machine. What do I
> mean by function machine?
>
> An intelligence system device which stores functions and their relationships
> between and towards other functions as well as the results of those
> functions applied to data sets on a periodic basis.
>
> I have a programming model for such a machine that can be modelled via 3D
> interactions of units that can attach to each other in simple ways, so
> programming functions is analogous to connecting some pipes and boxes and
> fittings together.
>
> So you might have a function that takes an input series and another slower
> changing series and outputs a differential equation. A function cell would
> contain both that algorithm / transformation / equation for a given function
> as well as add storage and caching and optimized data retrieval
> instructions, structures, and algorithms.
>
> Function cells are composable, i.e. they can have the connection topology
> equivalent to the different faces of any regular space-filling 3D
> tessalation, starting with the simplest and most flexible, the truncated
> tetrahedron, 4 - hexagon faces and 4 triangle faces. The mapping between
> physical object and mathematical objects and abstractions made possible by
> the real-world connection possibilities afforded by the physical connection
> restrictions creates a better interface for reliably connecting complex
> systems in a fast and efficient manner.
>
> Imagine pipes leading from sensors to sense maker- / detector- / observer-
> systems. There is enough information in the physical connection in the 3D
> model to automate most programming if the system uses a single common global
> semantic lexicon. When a connection is made the  software could
> automatically handle the communication links required between sensors and
> neural-network cells implemented via the function cell topology.
>
> So programming becomes connecting these parts logically and defining their
> internals recursively. Then you assemble them, twiddle a few constants knobs
> and you can address them and query them instantly. You program instantly as
> state changes made to the connected cell graph or to settings for each cell
> flow through all cells during a single update cycle.
>
> If you look at call as a unit of composition that adds storage and hardware
> implementation cell rather than the code that implements a function, then
> some new capabilities emerge with rare benefits that are not obvious because
> the simplifications that arise are trans-dimensional and transitive. The
> equations for this simplification are equally simple to conceive because
> they follow Metcalf's law. This has important implications for distinction
> graphs and series as well as for any classification problems.
>
> But that's a better topic for the whiteboard. And I'd like to explain the
> implications for robotics before everyone heads off to LA. I have drawn a
> picture on the whiteboard in the office with a design which leverages this
> idea in a physical topology suitable for human robot legs.
>
> - Curtis
>
>
>
>



-- 
Ben Goertzel, PhD
http://goertzel.org

"I am God! I am nothing, I'm play, I am freedom, I am life. I am the
boundary, I am the peak." -- Alexander Scriabin

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