Connectionists: Stephen Hanson in conversation with Geoff Hinton

[Asim Roy]

Dear John,

If I had the kind of technology that you list here, I wouldn’t waste time on the Connectionist list. That kind of technology could be worth billions of dollars, if not trillions. So, look for investors.

Best of luck,
Asim

From: Juyang Weng <juyang.weng at gmail.com>
Sent: Monday, February 14, 2022 6:02 PM
To: Asim Roy <ASIM.ROY at asu.edu>
Cc: Post Connectionists <connectionists at mailman.srv.cs.cmu.edu>
Subject: Re: Connectionists: Stephen Hanson in conversation with Geoff Hinton

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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