Aaron,

I have just sent out a post to PM wh. applies equally to you.

This is waffle.

You have to identify -

what are the COMMON ELEMENTS  - and COMMON RELATIONSHIPS OF THOSE ELEMENTS – 
that will enable you or your semantic net to identify these different figures 
as belonging to the same class of “chair”  and not “collages of wood” or “piles 
of assorted forms”  or “computer desk” or “collections of tools”?

ARE there any common elements?

You haven’t identified any

You have to provide a direct clue as to how you are going to solve this problem 
– the problem of AGI – and not just waffle.



From: [email protected] 
Sent: Tuesday, October 23, 2012 6:34 PM
To: AGI 
Subject: Re: [agi] Re: Superficiality Produces Misunderstanding - Not Good 
Enough

The thing which typifies the category "chair" is not the shape, but the 
combination of capability to support a behind and the potential inclination of 
a person to take advantage of that capability (or intention of the creator to 
provide such an artifact). These are things that are easy to represent in 
semantic nets, and difficult to represent as rules about shape.

If I have a representation of an object as a semantic net describing its parts 
and their physical relationships to each other, I can write a straight forward 
algorithm to analyze the transitive "supports" and "is connected to" relations 
in that description to determine whether the spot I intend to sit is supported. 
I can also determine whether or not my behind, when placed there, will itself 
be supported, or whether I'll slide off or topple over.

The network generating algorithm can be designed to provide the information 
needed to perform this simulation (simulation being the reason you say images 
are necessary in the first place). Once the simulation has been performed the 
first time, the node representing the chair as a whole object can be labeled 
with a summary of the results, acting as a cache for relevant information so 
that the expensive operation of full physical simulation can be avoided next 
time the information is needed. It is this caching ability that gives 
hierarchical semantic nets their leg up over other ways of representing the 
problem.




-- Sent from my Palm Pre


--------------------------------------------------------------------------------
On Oct 23, 2012 11:30 AM, Mike Tintner <[email protected]> wrote: 


PM & Aaron,

You do realise that whatever semantic net system you use must apply to not just 
one chair, but chair after chair – image after image?

Bearing that in mind, explain the elements of your semantic net which you will 
use to analyse these fairly simple figures as **chairs**::

http://image.shutterstock.com/display_pic_with_logo/95781/95781,1218564477,2/stock-vector-modern-chair-vector-16059484.jpg

Let’s label these chairs 1-25  (going L to R from the top down, row after row)

Start with just 1. and 2. top left and explain how your net will recognize 2 as 
another example of 1.

How IOW do you define a “chair” in terms of simple abstract forms?

Then we can apply your system, successively, to 3. 4. etc.

This is the problem that has defeated all AGI-ers and all psychologists and 
philosophers so far. 

But Aaron (and PM?) has a semantic net solution to it -   if you can solve 
jungle scenes, this should be a piece of cake.

I am saying, Aaron, you do not understand this problem – the problem of  visual 
object recognition/conceptualisation//applicability of semantic nets.

You are saying you do – and it’s me who is confused. Show me.





From: Piaget Modeler 
Sent: Tuesday, October 23, 2012 4:41 PM
To: AGI 
Subject: RE: [agi] Re: Superficiality Produces Misunderstanding - Not Good 
Enough

Mike,  

When you type "Chair" what should happen is the AGI's model should activate the 
chair concept
first at a perceptual level to form the pixels into the words, then at a 
linguistic level to form letters
into a word, then at a conceptual level, then at a simulation level where 
images of chair instances 
are evoked.  

This is just simple activation.  Semantic networks tied into perception and 
simulation would achieve 
the necessary effect you seek.  Transformations on these 
perception-simulation-semantic networks 
is what much of Piaget's work was about.

~PM.


--------------------------------------------------------------------------------
From: [email protected]
To: [email protected]
Subject: Re: [agi] Re: Superficiality Produces Misunderstanding - Not Good 
Enough
Date: Tue, 23 Oct 2012 15:09:30 +0100


CHAIR

...

It should be able to handle any transformation of the concept, as in

DRAW ME (or POINT TO/RECOGNIZE)  A CHAIR IN TWO PIECES –..

..SQUASHED
..IN PIECES
-HALF VISIBLE
..WITH AN ARM MISSING
...WITH NO SEAT
..IN POLKA DOTS
...WITH RED STRIPES

Concepts are designed for a world of everchanging, everevolving multiform 
objects (and actions).  Semantic networks have zero creativity or adaptability 
– are applicable only to a uniform set of objects, (basically a database) -  
and also, crucially, have zero ability to physically recognize or interact with 
the relevant objects. I’ve been into it at length recently. You’re the one not 
paying attention.

The suggestion that networks or similar can handle concepts is completely 
absurd.

This is yet another form of the central problem of AGI, which you clearly do 
not understand – and I’m not trying to be abusive  – I’ve been realising this 
again recently – people here are culturally punchdrunk with concepts like 
*concept* and *creativity*, and just don’t understand them in terms of AGI.

From: Jim Bromer 
Sent: Tuesday, October 23, 2012 2:04 PM
To: AGI 
Subject: Re: [agi] Re: Superficiality Produces Misunderstanding - Not Good 
Enough

Mike Tintner <[email protected]> wrote:
AI doesn’t handle concepts.
 

Give me one example to prove that AI doesn't handle concepts.
Jim Bromer



On Tue, Oct 23, 2012 at 4:24 AM, Mike Tintner <[email protected]> wrote:

  Jim: Mike refuses to try to understand what I am saying because he would have 
to give up his sense of a superior point of view in order to understand it

  Concepts have nothing to do with semantic networks. 
  AI doesn’t handle concepts.
  That is the challenge for AGI.
  The form of concepts is graphics.
  The referents of concepts are infinite realms..

  What are you saying that is relevant to this, or that can challenge this – 
from any evidence?

















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