Mike,
you are wrong, and I have explained why not too long ago. The number of
invariant representations - or hierarchies of block systems, as I call
them
- is infinite numerable. That accounts for all, repeat, all possible
invariant representations of "line" that can exist in the brain, because
the
brain is discrete.
There is one invariant representation for *each* line. It is invariant
because you would recognize *that* particular line even if I turned it
upside down for you.
No, all those invariant represntations are not stored in thebrain ready to
use. They are created, one at a time, when you see that line. Compare R
and
Я. Many are stored. For example R is stored, but Я is usually not unless
you
speak Russian.
Mike, you need to refine your arguments. Your second step is routine, it
is
behind us now. Try to say something new.
Sergio
-----Original Message-----
From: Mike Tintner [mailto:[email protected]]
Sent: Saturday, July 21, 2012 3:14 AM
To: AGI
Subject: Re: [agi] Re: How the Brain Works -- new H+ magazine article, by
me
Arakawa:When a neural system learns some pattern, say that of a line
segment, it recognizes line segments regardless of their orientation or
length (hence 'invariant").
What Sergio was saying was : "wait a moment, do I really understand this -
what Hawkins is saying...?"
To do that, you first have to say as you have done: "ok what say would be
an
invariant representation of a line...?"
At this stage, you can casually float over the problem, and think: " oh
well, there must be an invariant representation of a line... stands to
reason"
But if you take the second step, and actually start thinking about
different
kinds of line, and what could possibly be an invariant representation of
them all - that could be transformed into them all - you will find there
is
no such invariant representation - and Hawkins has neither posited one in
relation to any object, incl. Jennifer Aniston (or a line), nor explained
how it could be universally transformed into any variation of a given
object.
Similarly, people are thinking : "of course the world consists of
patterns..
stands to reason.. I know that this is a "street" and by god it looks like
that "street" to me, and that other one - I recognize all these "streets"
without a problem - therefore there must be a common "pattern",invariant
representation" which enables me to recognize them all.
But people never go to the second step, and start thinking about what form
those patterns (of things like a street) could take. If you do, you'll
find
there are neither such patterns in the world, nor in your mind.
That's not how the brain achieves its magic feat of recognizing the
similarity between diverse forms, objects and scenes.
Everyone here tends to be Platonist - "of course there is an essential
idea/
invariant representation/ pattern for objects - an essential "chair".."
But Plato didn't say what it was, Hawkins hasn't said what it is, in fact
no
one has said it ... and it doesn't exist.
--------------------------------------------------
From: "ARAKAWA Naoya" <[email protected]>
Sent: Saturday, July 21, 2012 3:10 AM
To: "AGI" <[email protected]>
Subject: Re: [agi] Re: How the Brain Works -- new H+ magazine article, by
me
On 2012/07/21, at 4:59, Mike Tintner wrote:
Sergio: I noticed that Jeff Hawkins in On Intelligence writes about
"invariant representations," which are hierarchies, but never
explains how they come into existence. I am just a little confused.
I wonder whether you have an outstanding point there. Everyone
*talks* about "invariant representations". Does anyone anywhere have
any AI-worthy explanation of their nature/origin whatsoever?
(Of course, invariant representations overlap with concepts. There
are psych/phil. explanatory theories of concepts, but that's why I
put in "AI-worthy". I suspect they are all v. vague).
I interpreted "invariant representations" in the writing of Hawkins as
learned patterns.
When a neural system learns some pattern, say that of a line segment,
it recognizes line segments regardless of their orientation or length
(hence 'invariant").
"Invariant representations" in a neural network would be distributed
so that one cannot point out saying, for example, *this* is the
representation of a line segment...
* The Gibsonian invariance might be a different notion while he may
have made the term popular among cognitive scientists (?).
--
Naoya ARAKAWA
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