On Wed, Jun 24, 2015 at 2:41 PM, J. Andrew Rogers <[email protected]> wrote:
> Let’s keep make it even simpler: please describe an indexing structure for 
> finding cube intersections that is general, parallelizable, and has constant 
> space complexity. A “minor redesign” won’t solve this problem. When it was 
> finally solved in 2007, over a quarter century had passed since anyone had 
> previously made progress on it, and I am willing to bet that you know nothing 
> about what the actual solution looks like.


I was talking about various kinds of conceptual data objects. I don't
see how this relates to topological objects, which have special
relational properties that are not all shared by various conceptual
objects. (If all words, for example, shared something similar to the
relational semantics of prepositions, AGI would be a whole lot
easier.) I hope that you are going to tell me about the method of
finding cube intersections that is general, parallelizable and has
constant space complexity so I will have some idea about why you keep
insisting that this would resolve the np-hard-kind of complications
with AGI.

If you explain this method that you are talking about I might, and if
I understand it, be  able to better express an opinion to its
applicability to AGI.

I really still do not understand what you are getting at. Are you
talking about 3-D cubes? Do you mean that there is some cool formula
that uses some simple representation of the cube or something. I
really cannot see what you are getting at.

I guess I really cannot criticize people for dragging a presentation
out but you really haven't explained your point or even the basis for
your point.
Jim Bromer



On Wed, Jun 24, 2015 at 2:41 PM, J. Andrew Rogers <[email protected]> wrote:
>
>> On Jun 24, 2015, at 9:39 AM, Jim Bromer <[email protected]> wrote:
>>
>> I think Data types with no meaningful order can be difficult but it is
>> not impossible to create things like keyed (I can't remember what the
>> data structure is called) indexes and even more elaborate indexes as
>> needed. But this can become a more serious issue when you have to have
>> a lot of specialized indexes.
>
>
> You can do it all within a single (exotic) quasi-spatial indexing structure. 
> It is how it is done in real systems.
>
>
>> I can intuitively see that data types where intersection
>> and equality are not equivalent could be a problem but I am not sure
>> what you mean.
>
>
> Dynamic search algorithms tend to have very poor selectivity traditionally. 
> O(n) worst-case search is no way to build a scalable computing system.
>
>
>> Since you have derived more than one example of
>> bad-computer science thinking from relational database concepts I am
>> guessing that this has something to do with database processing. So
>> searching on a constraint can become time consuming? But that can be
>> parallelized by minor redesigns.
>
>
> It has nothing to do with databases, though these issues are manifest in 
> large-scale databases. Parallelization doesn’t work the way you think it does.
>
> Let’s keep make it even simpler: please describe an indexing structure for 
> finding cube intersections that is general, parallelizable, and has constant 
> space complexity. A “minor redesign” won’t solve this problem. When it was 
> finally solved in 2007, over a quarter century had passed since anyone had 
> previously made progress on it, and I am willing to bet that you know nothing 
> about what the actual solution looks like.
>
>
>> Many computer scientists have thought about expressing topological
>> relationships between data objects. So it is not in itself a new idea
>> that I have never heard of or thought about before.
>
>
>
> The mathematical concept has existed for half a century. A useful computer 
> science reduction of the mathematics is maybe five years old. I doubt what 
> you think you know about this idea is relevant.
>
>
>
>
>
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