It is pretty easy to come up with a way for programs to recognize certain
kinds of patterns in certain kinds of situations.  A computer program could
be written to abstract or find abstractions from a number of data storage
types.  Technically it should be feasible to write a program that could
detect a given pattern, if it had enough time, as long as the pattern was
not too obscure.

The problem with a challenge like this is that it is not really the
problem. In AGI, not only does a computer program need to be able to
recognize patterns, but it needs to be able to find the important patterns
that would allow it to leverage the knowledge that it already had to
achieve stronger goals.  It is very easy for a program to find some
abstractions out of a source of data, but it is impossible for a program to
find every possible abstraction (if the source of the data was large enough
- and it would not have to be that large).  The problem is that the
complexity of finding every kind of possible pattern in some data is just
too great.  There are too many possibilities.

That is one example of how the contemporary AGI problem is a complexity
issue.

Jim Bromer

On Sun, Aug 26, 2012 at 1:35 PM, Mike Tintner <[email protected]>wrote:

>  “it's pretty easy to come up with ways to do it in a program.You can't
> see how a pattern is a patterned concept because you don't understand
> classes, subclasses, and instances.”
>
>  Go ahead – give us a hierarchy of classes for “pattern”, & we’ll present
> your program with a pattern and non-pattern or two for recognition. (I
> think you’re totally lost here – we’re talking about what is basically
> visual/sensory object recognition. You/your machine have to be able to
> recognize a “pattern.” You seem to be talking about, basically, database
> operations).
>       <http://www.listbox.com>
>



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