Connectionists: Stephen Hanson in conversation with Geoff Hinton

Fri Feb 11 17:39:50 EST 2022

Dear Asim,

We should not just assume your 1, a dog, since there are many articulated
objects and each articulated object looks very different under many
situations.  There are further many other articulated objects other than
the dog.    You will see below.

Since you want explainable AI, you must not start with a single object
symbol like "dog".  A symbol is "abstract" already.  That is way Michael
Jordan complained that neural networks do not abstract well.

Let us do your 2.  How many possible combinations?
The number of shapes of a part:   Suppose a part of a uniform color.  It
has m pixels along its boundary; each pixel has n possible positions, the
number of shapes of a part is O(m^n), already exponential in n.   We
assumed that each pixel has the same color, which is not true.
Suppose that each part is centered at location l, the number of
combinations of p parts of your object (dog) is O(p^l),
another exponential complexity.
Thus the number of combinations of your object (dog) in a clean background,
like Jeffe Hinton proposed to do, is already a product two two exponential
complexities:
O(m^n)O(p^l)=O(m^{n}p^{l}).
Suppose that there are b objects in a cluttered scene, the number of
combination of objects in a cluttered scene is
[O(m^{n}p^{l})]^b=O(m^{nb}p^{lb}).

Of course, the brain network does not remember all shapes and all
configurations of parts.  That is why our DN must do maximum likelihood
optimality, using a limited number of resources to best estimate such a
huge space of cluttered scenes.
I am not talking about abstraction yet, which is another subject about "pop
up" in the brain.

I guess that many people on this list are not familiar with such a
complexity analysis. Gary Marcus, sorry to overload you with this.
That is what I said in several talks that  the brain is an elephant and all
disciplines are blind men.
Even people with a PhD in computer science may not be skillful in such an
exponential complexity in vision, since many computer science programs have
dropped automata theory from their required course lists.

I asked Gary Marcus to suggest how to solve this huge problem.  But
instead, he asked me to try a data set which is a dead end.  Many have
tried and are dead, like ImageNet.

Best regards,
-Joh

On Fri, Feb 11, 2022 at 4:59 PM Asim Roy <ASIM.ROY at asu.edu> wrote:

> Dear John,
>
>
>
>    1. Let’s start with a simple case, say a dog, and enumerate how many
>    possible parts and objects a dog would need to remember or recognize.
>    2. How many possible combinations did you use in your calculation for
>    the DN?
>
>
>
> Best,
>
> Asim
>
>
>
> *From:* Juyang Weng <juyang.weng at gmail.com>
> *Sent:* Friday, February 11, 2022 12:33 PM
> *To:* Asim Roy <ASIM.ROY at asu.edu>; John K Tsotsos <tsotsos at cse.yorku.ca>
> *Cc:* connectionists at mailman.srv.cs.cmu.edu; Gary Marcus <
> gary.marcus at nyu.edu>
> *Subject:* Re: Connectionists: Stephen Hanson in conversation with Geoff
> Hinton
>
>
>
> 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
>

-- 
Juyang (John) Weng
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