On Wed, Jun 24, 2015 at 10:36 PM, Piaget Modeler <[email protected]>
wrote:

> Why is this at all important?
> ~PM
>
>
For example, I said, > > 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.

I assume that you do think that kind of thing is important to AGI.
Andrew replied,
> You can do it all within a single (exotic) quasi-spatial indexing
structure. It is how it is done in real systems.

If Andrew is right then how would it not be important? So, if my first
assumption is correct then my second assumption would be that you are
asking Andrew a rhetorical question. However, I have learned that my
assumptions about what people mean in these groups are often inaccurate.

I would like Andrew to explain what he is talking about so that I could
make a reasonable guess about what he is talking about.

Jim Bromer

On Wed, Jun 24, 2015 at 10:36 PM, Piaget Modeler <[email protected]>
wrote:

> Why is this at all important?
>
> ~PM
>
> > Subject: Re: [agi] Re: Could Brain Emulation be NP-Hard?
> > From: [email protected]
> > Date: Wed, 24 Jun 2015 11:41:18 -0700
> > To: [email protected]
>
> >
> >
> > > 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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