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<p class="MsoNormal">Dear John,<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
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<li class="MsoListParagraph" style="margin-left:0in;mso-list:l0 level1 lfo1">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.<o:p></o:p></li><li class="MsoListParagraph" style="margin-left:0in;mso-list:l0 level1 lfo1">How many possible combinations did you use in your calculation for the DN?<o:p></o:p></li></ol>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Best,<o:p></o:p></p>
<p class="MsoNormal">Asim<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal"><b>From:</b> Juyang Weng <juyang.weng@gmail.com> <br>
<b>Sent:</b> Friday, February 11, 2022 12:33 PM<br>
<b>To:</b> Asim Roy <ASIM.ROY@asu.edu>; John K Tsotsos <tsotsos@cse.yorku.ca><br>
<b>Cc:</b> connectionists@mailman.srv.cs.cmu.edu; Gary Marcus <gary.marcus@nyu.edu><br>
<b>Subject:</b> Re: Connectionists: Stephen Hanson in conversation with Geoff Hinton<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">Dear Asim,<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">Thank you for saying "we can". <br>
Please provide: <o:p></o:p></p>
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<p class="MsoNormal">(1) a neural network that does all you said "we can" and<o:p></o:p></p>
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<p class="MsoNormal" style="margin-bottom:12.0pt">(2) the complexity analysis for all possible combinations among all possible parts and all possible objects<o:p></o:p></p>
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<p class="MsoNormal" style="margin-bottom:12.0pt">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.<o:p></o:p></p>
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<p class="MsoNormal">John Tsotso: <o:p></o:p></p>
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<p class="MsoNormal">Our DN solves this problem like a brain in a constant time (frame time)! The solution simply pops up.<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">Best regards,<o:p></o:p></p>
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<p class="MsoNormal">-John<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">On Thu, Feb 10, 2022 at 3:01 AM Asim Roy <<a href="mailto:ASIM.ROY@asu.edu" target="_blank">ASIM.ROY@asu.edu</a>> wrote:<o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">Dear John,<o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"> <o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">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.<o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"> <o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">Best,<o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">Asim Roy<o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">Professor, Information Systems<o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">Arizona State University<o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><a href="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=" target="_blank">Lifeboat
Foundation Bios: Professor Asim Roy</a><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><a href="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=" target="_blank">Asim
Roy | iSearch (asu.edu)</a><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"> <o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"> <o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">
<img border="0" width="480" height="360" style="width:5.0in;height:3.75in" id="gmail-m_5005686710730097958gmail-m_4384369736864902407Picture_x0020_1" src="cid:image001.jpg@01D81F57.7E4C6020" alt="A dog and a cat lying on a bed
Description automatically generated with low confidence"> <img border="0" width="308" height="231" style="width:3.2083in;height:2.4062in" id="gmail-m_5005686710730097958gmail-m_4384369736864902407Picture_x0020_3" src="cid:image002.jpg@01D81F57.7E4C6020" alt="A wolf walking in the snow
Description automatically generated with medium confidence"> <img border="0" width="1443" height="1452" style="width:15.0312in;height:15.125in" id="gmail-m_5005686710730097958gmail-m_4384369736864902407Picture_x0020_6" src="cid:image003.jpg@01D81F57.7E4C6020" alt="An aerial view of a city
Description automatically generated with medium confidence"><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"> <o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"> <o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><b>From:</b> Connectionists <<a href="mailto:connectionists-bounces@mailman.srv.cs.cmu.edu" target="_blank">connectionists-bounces@mailman.srv.cs.cmu.edu</a>>
<b>On Behalf Of </b>Juyang Weng<br>
<b>Sent:</b> Wednesday, February 9, 2022 3:19 PM<br>
<b>To:</b> Post Connectionists <<a href="mailto:connectionists@mailman.srv.cs.cmu.edu" target="_blank">connectionists@mailman.srv.cs.cmu.edu</a>><br>
<b>Subject:</b> Re: Connectionists: Stephen Hanson in conversation with Geoff Hinton<o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"> <o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">Dear Gary,<o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"> <o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">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.<o:p></o:p></p>
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<div>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"> <o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">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.<o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"> <o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">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.<o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><br>
Best regards,<o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">-John<o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"> <o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">Date: Mon, 7 Feb 2022 07:57:34 -0800<br>
From: Gary Marcus <<a href="mailto:gary.marcus@nyu.edu" target="_blank">gary.marcus@nyu.edu</a>><br>
To: Juyang Weng <<a href="mailto:juyang.weng@gmail.com" target="_blank">juyang.weng@gmail.com</a>><br>
Cc: Post Connectionists <<a href="mailto:connectionists@mailman.srv.cs.cmu.edu" target="_blank">connectionists@mailman.srv.cs.cmu.edu</a>><br>
Subject: Re: Connectionists: Stephen Hanson in conversation with Geoff<br>
Hinton<br>
Message-ID: <<a href="mailto:D0E77E54-78C0-4605-B40C-434E2B8F1E7C@nyu.edu" target="_blank">D0E77E54-78C0-4605-B40C-434E2B8F1E7C@nyu.edu</a>><br>
Content-Type: text/plain; charset="utf-8"<br>
<br>
Dear John,<br>
<br>
I agree with you that cluttered scenes are critical, but Geoff?s GLOM paper [<a href="https://urldefense.com/v3/__https:/www.cs.toronto.edu/*hinton/absps/glomfinal.pdf__;fg!!IKRxdwAv5BmarQ!Nz1SeTUV0HTHgPjgQgoT1IgAHrVhxdw8HVVMwgs83QlxthT1NyY5hgDxKe34wLc$" target="_blank">https://www.cs.toronto.edu/~hinton/absps/glomfinal.pdf</a>]
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.<br>
<br>
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.<br>
<br>
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.<br>
<br>
Gary<br clear="all">
<o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"> <o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">--
<o:p></o:p></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">Juyang (John) Weng<o:p></o:p></p>
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<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">-- <o:p></o:p></p>
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<p class="MsoNormal">Juyang (John) Weng<o:p></o:p></p>
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