Connectionists: Stephen Hanson in conversation with Geoff Hinton

[Juyang Weng]

Dear Asim,
You wrote: "We teach the system composition of objects from parts and also
the connectivity between the parts. It?s similar to how we teach humans
about parts of objects."

You are doing manual image annotations like many have done.
Unfortunately, we have been on the wrong track of manually annotating
images for too long.

Sorry, I started it all, i.e., the annotation practice. From the end of
1990, we at UIUC started Cresceptron on learning from natural images of
cluttered scenes (published at IJCNN 1992, ICCV 1993 and IJCV 1997).
Nobody did that before us, as far as I know.   We at UIUC  were trying to
solve the general vision problem using a learning neural network called
Cresceptron.  Namely, detection, recognition and segmentation of 3D objects
from all cluttered natural scenes!

A flood of similar works followed Crescetron, slowly and after several
years, but many did not cite our Cresceptron.  (I do not want to mention
those big shot names.)   I do not understand why.  I chatted with
Narendra Ahuja about this unethical plagiarism.  He explained well. The
Cresceptron appeared in arguably the "best" neural network conference, the
"best" computer vision conference and the "best" computer vision journal.

However, enough is enough.  We must go beyond manual annotation of images,
although this line has created a lot of business.   Many AI companies
contracted with large companies to do just manual image annotations.

We must cut it out!   No more image annotations!

In a paper I just submitted to IJCNN 2022 today, the first million-dollar
problem solved is:

(1) the image annotation problem (e.g., retina is without bounding box to
learn, unlike ImageNet)

Let me list them all:

(2) the sensorimotor recurrence problem (e.g., all big data sets are
invalid),

(3) the motor-supervision problem (e.g., impractical to supervise motors
throughout lifetime),

(4) the sensor calibration problem (e.g., a life calibrates the eyes
automatically),

(5) the inverse kinematics problem (e.g., a life calibrates all redundant
limbs automatically),

(6) the government-free problem (i.e., no intelligent homunculus inside a
brain),

(7) the closed-skull problem (e.g., supervising hidden neurons is
biologically implausible),

(8) the nonlinear controller problem (e.g., a brain is a nonlinear
controller but task-nonspecific),

(9) the curse of dimensionality problem (e.g., a set of global features is
insufficient for a life),

(10) the under-sample problem (i.e., few available examples in a life),

(11) the distributed vs. local representations problem (i.e., how both
representations emerge),

(12) the frame problem (also called symbol grounding problem, thus must be
free from any symbols),

(13) the local minima problem (so, avoid error-backprop learning and
Post-Selections),

(14) the abstraction problem (i.e., require various invariances and
transfers),

(15) the compositionality problem (e.g., metonymy beyond those composable
from sentences),

(16) the smooth representations problem (e.g., brain representations are
globally smooth),

(17) the motivation problem (e.g., including reinforcements and various
emotions),

(18)  the global optimality problem (e.g., avoid catastrophic memory loss and
Post-Selections),

(19) the auto-programming for general purposes (APFGP) problem,

(20) the brain-thinking problem.

The paper discusses also why the proposed holistic solution of conscious
learning solves each.


Best regards,

-John




-- 
Juyang (John) Weng
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