On Wed, Apr 17, 2013 at 2:48 AM, Sergio Donal <[email protected]> wrote:
Please, could you explain it a little bit further?
I do not understand it.
Okay, that's fair.
The premise is that the input is structured, why is structure important?
Because that's where the useful information is.
What is 'f'?
Do you mean that 'f' is the intelligence-function, so intelligence is about
finding the structure in the input?
Yes.
Why does 's' have to be a 'proper subset'? This point confuses me.
Well, the AI is not omnicient. Furthermore the input might be ambiguous.
So therefore 's' is the structure that is recoverable from the input.
And also, what happens if the intelligence has some kind of structure itself
(like memory from other derivations) so it is able to make sense of I and make
it useful for solving a problem, even when I was just random?
If I is random then 's' is null because there is no useful information.
The function can trivially be made iterative by:
f(I,s) --> s'
Which is obviously an improvement...
Now, how do you get AI?
First you need input. There are two ways that this can be done, both
should be applied.
1. Generate a structure at random and then generate an image or sound
from it. (this way you have a known "gold standard" of the structure).
2. Use sound/pictures/videos from your CD/DVD collection and from around
the web.
Second, you need a way to feed this into f... I'm not exactly sure how
this would be done.
Third, you need to develop f...
Fourth you need to take s' and compare it with the gold standard s too
see if they're equivalent.
Or, if you were using stock photos/videos/sounds etc, then you will have
to use the recovered structure to attempt to re-generate the original.
The success of your algorithm can then be measured by how closely the
re-created data matches the input.
I believe that I am missing the subject and object of the statement, like is
the intelligence a property of someone who finds the structure in I, or
something like that.
Given some structured input I, with structure S
AI is defined as
f(I) --> s
where s is a proper subset of S that can be derived from I with minimal
ambiguity.
This definition is partial but covers all forms of perception.
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