Connectionists: A through light after we scientists are in the dark brain-mind tunnel for 70 years

[Juyang Weng]

Dear All:

If we do not get back to much earlier non-computational studies, it was
Alan Turing 1936 who proposed what is now called universal Turing machines
(Alan Tring's meaning "computable numbers" has been enriched later to all
possible computer programs).  Alan Turing 1950 asked: "Can machines
think?".  Let us use 1950 as the starting year for scientists to start this
extremely challenging computational question.   This is probably of one of
the the most important issues for modern time.

John Tsotsos and his coworker has a recent review Jan. 13, 2018, "A Review
of 40 Years of Cognitive Architecture Research: Core Cognitive Abilities
and Practical Applications," available from ArXiv.   From this review, we
can see that the problem we face is an extremely challenging one.

Nov. 10, 2018, Richard Loosemore wrote on this post: "Why is the question
of how concepts relate to neurons so scandalously incoherent, even after
this field has been talking about it for at least 35 years?" Loosemore,
R.P.W. & Harley, T.A. (2010) wrote: "If you look through a journal such as
Science or Nature, you will find that most articles on psychology contain
or refer to imaging. And what now passes as psychology in the popular press
is mostly reports on brain imaging studies. The brain is back in cognitive
psychology. But is this change for the better?"

Nov. 11, 2018, Asim Roy wrote: "I would argue that localist representation
is synonymous with symbolic systems."

All the above questions are good, but such partial questions only lead to
partial solutions that do not address more fundamental issues, only
delaying them.

I proposed to ask a more holistic question below.  This question has been
positively answered through (1) a theory, (2) a fully detailed algorithm,
(3) an optimality proof, (4) experiments with impressive performances
verified by several independent groups during  the AIML Contest 2016.

"How machines auto-program for general purposes (through a single lifetime
learning in the natural world)?"  This question is concise but avoids many
pitfalls that many partial questions left to us and misled us.

The following freely available technical report provides a solution to this
question:  Juyang Weng: "A Model for Auto-Programming for General Purposes"
https://arxiv.org/abs/1810.05764v1

Of course, this report did not solve all problems.   The following are some
key points:

(1) Liberate symbols to overcome the limitation that a set of symbols is
handcrafted only after a specific task is given.  Weng modeled that
emergent numeric vectors correspond to muscles neurons on the motor end.
These numeric vectors liberate (i.e., without the limitations of using)
symbols because they emerge automatically.

(2) As a bridge for understanding, Weng used symbols to explain how a DN
learns emergent Universal Turing machines, which is a model of modern
general-purpose computers.

(3) Generality: Such emergent vectors on the motor end correspond to all
motor-expressible (e.g., spoken or hand-written) spatiotemporal concepts
(not just mental states), such as states, actions, plans, goals, intents,
costs, plans, goodness, badness, and novelties.

(4) Skull-closed: the model (DN)'s skull-internal learning is always
unsupervised, similar to the skull that closes the human brain, off limit
to human teachers.

(5) Top-down weights: Each hidden neuron has two types of weight vectors,
top-down weight vectors (this is fundamentally new compared with all
feedforward and recurrent neural networks) and bottom-up weight vectors
(typically local).

(6) The model is without any rigid boarders between “lower processing” and
“high processing” where “lower” sensory inputs (e.g., vision, audition, and
text) and “higher” context inputs (e.g., cognition, planning, and natural
languages) are tightly integrated.

-John Weng
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