Connectionists: Stephen Hanson in conversation with Geoff Hinton

[Juyang Weng]

Dear Asim,

Thank you for saying "we can".
Please provide:
(1) a neural network that does all you said "we can" and
(2) the complexity analysis for all possible combinations among all
possible parts and all possible objects

This chain of conversations is very useful for those who are not yet
familiar with the "complexity of vision" (NP hard) that John Tsotso wrote
papers argued about.

John Tsotso:
Our DN solves this problem like a brain in a constant time (frame time)!
The solution simply pops up.

Best regards,
-John

On Thu, Feb 10, 2022 at 3:01 AM Asim Roy <ASIM.ROY at asu.edu> wrote:

> Dear John,
>
>
>
> We can deal with cluttered scenes. And we can also identify parts of
> wholes in these scenes. Here are some example scenes. In the first two
> scenes, we can identify the huskies along with the ears, eyes, legs, faces
> and so on. In the satellite image below, we can identify parts of the
> planes like the fuselage, tail, wing and so on. That’s the fundamental part
> of DARPA’s XAI model – to be able to identify the parts to confirm the
> whole object. And if you can identify the parts, a school bus will never
> become an ostrich with change of a few pixels. So you get a lot of things
> with Explainable models of this form – a symbolic XAI model, robustness
> against adversarial attacks, and a model that you can trust. Explainable AI
> of this form can become the best defense against adversarial attacks. You
> may not need any adversarial training of any kind.
>
>
>
> Best,
>
> Asim Roy
>
> Professor, Information Systems
>
> Arizona State University
>
> Lifeboat Foundation Bios: Professor Asim Roy
> <https://urldefense.proofpoint.com/v2/url?u=https-3A__lifeboat.com_ex_bios.asim.roy&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=waSKY67JF57IZXg30ysFB_R7OG9zoQwFwxyps6FbTa1Zh5mttxRot_t4N7mn68Pj&s=oDRJmXX22O8NcfqyLjyu4Ajmt8pcHWquTxYjeWahfuw&e=>
>
> Asim Roy | iSearch (asu.edu)
> <https://urldefense.proofpoint.com/v2/url?u=https-3A__isearch.asu.edu_profile_9973&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=waSKY67JF57IZXg30ysFB_R7OG9zoQwFwxyps6FbTa1Zh5mttxRot_t4N7mn68Pj&s=jCesWT7oGgX76_y7PFh4cCIQ-Ife-esGblJyrBiDlro&e=>
>
>
>
>
>
>    [image: A dog and a cat lying on a bed Description automatically
> generated with low confidence]        [image: A wolf walking in the snow
> Description automatically generated with medium confidence]    [image: An
> aerial view of a city Description automatically generated with medium
> confidence]
>
>
>
>
>
> *From:* Connectionists <connectionists-bounces at mailman.srv.cs.cmu.edu> *On
> Behalf Of *Juyang Weng
> *Sent:* Wednesday, February 9, 2022 3:19 PM
> *To:* Post Connectionists <connectionists at mailman.srv.cs.cmu.edu>
> *Subject:* Re: Connectionists: Stephen Hanson in conversation with Geoff
> Hinton
>
>
>
> Dear Gary,
>
>
>
> As my reply to Asim Roy indicated, the parts and whole problem that Geoff
> Hinton considered is ill-posed since it bypasses how a brain network
> segments the "whole" from 1000 parts in the cluttered scene.  Only 10 parts
> belong to the whole.
>
>
>
> The relation problem has also been solved and mathematically proven if one
> understands emergent universal Turing machines using a
> Developmental Network (DN).   The solution to relation is a special case of
> the solution to the compositionality problem which is a special case of the
> emergent universal Turing machine.
>
>
>
> I am not telling you "a son looks like his father because the father makes
> money to feed the son".   The solution is supported by biology and a
> mathematical proof.
>
>
> Best regards,
>
> -John
>
>
>
> Date: Mon, 7 Feb 2022 07:57:34 -0800
> From: Gary Marcus <gary.marcus at nyu.edu>
> To: Juyang Weng <juyang.weng at gmail.com>
> Cc: Post Connectionists <connectionists at mailman.srv.cs.cmu.edu>
> Subject: Re: Connectionists: Stephen Hanson in conversation with Geoff
>         Hinton
> Message-ID: <D0E77E54-78C0-4605-B40C-434E2B8F1E7C at nyu.edu>
> Content-Type: text/plain; charset="utf-8"
>
> Dear John,
>
> I agree with you that cluttered scenes are critical, but Geoff?s GLOM
> paper [https://www.cs.toronto.edu/~hinton/absps/glomfinal.pdf
> <https://urldefense.com/v3/__https:/www.cs.toronto.edu/*hinton/absps/glomfinal.pdf__;fg!!IKRxdwAv5BmarQ!Nz1SeTUV0HTHgPjgQgoT1IgAHrVhxdw8HVVMwgs83QlxthT1NyY5hgDxKe34wLc$>]
> might actually have some relevance. It may well be that we need to do a
> better job with parts and whole before we can fully address clutter, and
> Geoff is certainly taking that question seriously.
>
> Geoff?s ?Stable islands of identical vectors? do sound suspiciously like
> symbols to me (in a good way!), but regardless, they seem to me to be a
> plausible candidate as a foundation for coping with clutter.
>
> And not just cluttered scenes, but also relations between multiple objects
> in a scene, which is another example of the broader issue you raise,
> challenging for pure MLPs but critical for deeper AI.
>
> Gary
>
>
>
> --
>
> Juyang (John) Weng
>


-- 
Juyang (John) Weng
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