[Amdnl] Fwd: Connectionists: The symbolist quagmire

[Juyang Weng]

---------- Forwarded message ---------
From: Juyang Weng <juyang.weng at gmail.com>
Date: Tue, Jun 21, 2022 at 5:13 PM
Subject: Re: Connectionists: The symbolist quagmire
To: Gary Marcus <gary.marcus at nyu.edu>
Cc: Post Connectionists <connectionists at mailman.srv.cs.cmu.edu>


Dear Gary,

You wrote: "My own view is that arguments around symbols per se are not
very productive, and that the more interesting questions center around what
you *do* with symbols once you have them.  If you take symbols to be
patterns of information that stand for other things, like ASCII encodings,
or individual bits for features (e.g. On or Off for a thermostat state),
then practically every computational model anywhere on the spectrum makes
use of symbols. For example the inputs and outputs (perhaps after a
winner-take-all operation or somesuch) of typical neural networks are
symbols in this sense, standing for things like individual words,
characters, directions on a joystick etc."

I respectfully do not agree, since that is why "practically every
computational model anywhere" cannot learn consciousness.  They are
basically pattern recognition machines for a specific task.

I  skip "data selection" in deep learning here.   Deep learning not
only hits a wall.  All its published data appear to be invalid.

Gary, this issue is probably too fundamental if you do not try to
understand the conscious learning algorithm (see below), first ever in the
world, as far as I humbly aware of.

Let me try in intuitive terms:

(1) You have a series of ASCII symbols, e.g., ASCII-1, ASCII-2, ASCII-3,
ASCII-4 ...  You have 1 million such ASCII symbols.  Any number, as long as
it is a large number.

(2) You specify the meanings of such ASCII symbols in your design documents:
 ASCII-1: forward-move-of-joystick-A,
ASCII-2: backward-move-of-joystick-A,
ASCII-3:left-move-of-joystick-A,
ASCII-4: right-move-of-joystick-A
...
You have at  least 1 millions of lines.

(3) Your machine does not read your design document in (2), they cannot
think about your design document in (2).  They only learn the mapping from
sensory inputs to one of these ASCII symbols.

(4) Therefore, your machine is not able to understand the consciousness
that is required to judge that it is doing a joystick work (e.g., driving
using a joystick) well, because your knowledge hierarchy (using these 1
million symbols) are static.  The machine cannot recompose new meanings
from these symbols, because it does not understand any symbols at all!  Why
do I understand my moving forward?   I do not have (2).  Moving forward is
my own intent, my own volition!  I feel the effects of my volition and
decide whether I want to repeat.

(5) Without consciousness, machine learning is static.   Consciousness must
go beyond any static hierarchy.
(a) My children do.  They told me some views (and intents) that surprise
me.  I did not teach such views.
(b) That is also why a human brain can do research.  My subject research
surprised my father-in-law and he does not believe I can do what I told him
I can.

In summary, all ASCII symbols are a dead end.  They like AI drugs, are
addictive, and waste our resources in AI.

As the first ever conscious learning algorithm, the DN-3 neural network
must autonomously create any fluid hierarchy that any consciousness
requires during human-like thinking.
Please read the first conscious learning algorithm that will be able to do
scientific research in the future:

Peer reviewed version:

@INPROCEEDINGS{WengCLAIEE22

,AUTHOR= "J. Weng"

,TITLE= "An Algorithmic Theory of Conscious Learning"

,BOOKTITLE= "2022 3rd Int'l Conf. on Artificial Intelligence in Electronics
Engineering"

,ADDRESS= "Bangkok, Thailand"

,PAGES= "1-10"

,MONTH= "Jan. 11-13"

,YEAR= "2022"

,NOTE="\url{
http://www.cse.msu.edu/~weng/research/ConsciousLearning-AIEE22rvsd-cite.pdf
}"

}


Not yet peer reviewed:

@misc{WengDN3-RS22

,AUTHOR= "J. Weng"

,TITLE= "A Developmental Network Model of Conscious Learning in Biological
Brains"

,Howpublished= "Research Square"

,PAGES= "1-32"

,MONTH= "June 7"

,YEAR= "2022"

,NOTE="doi: \url{https://doi.org/10.21203/rs.3.rs-1700782/v2},
desk-rejected by {\em Nature}, {\em Science}, {\em PNAS}, {\em Neural
Networks} and {\em ArXiv}"

}


Please kindly read them, get excited and ask questions.


Best regards,

-John
-- 
Juyang (John) Weng


-- 
Juyang (John) Weng
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