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<p class="MsoNormal">Dear John,<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">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"><o:p> </o:p></p>
<p class="MsoNormal">Best,<o:p></o:p></p>
<p class="MsoNormal">Asim Roy<o:p></o:p></p>
<p class="MsoNormal">Professor, Information Systems<o:p></o:p></p>
<p class="MsoNormal">Arizona State University<o:p></o:p></p>
<p class="MsoNormal"><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"><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"><o:p> </o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"> <img border="0" width="480" height="360" style="width:5.0in;height:3.75in" id="Picture_x0020_1" src="cid:image001.jpg@01D81E14.9BCB49C0" 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="Picture_x0020_3" src="cid:image002.jpg@01D81E14.9BCB49C0" 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="Picture_x0020_6" src="cid:image004.jpg@01D81E16.F4C785A0" alt="An aerial view of a city
Description automatically generated with medium confidence"><o:p></o:p></p>
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<p class="MsoNormal"><b>From:</b> Connectionists <connectionists-bounces@mailman.srv.cs.cmu.edu>
<b>On Behalf Of </b>Juyang Weng<br>
<b>Sent:</b> Wednesday, February 9, 2022 3:19 PM<br>
<b>To:</b> Post Connectionists <connectionists@mailman.srv.cs.cmu.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">Dear Gary,<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">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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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">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"><o:p> </o:p></p>
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<p class="MsoNormal">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"><br>
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">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>
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<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">-- <o:p></o:p></p>
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<p class="MsoNormal">Juyang (John) Weng<o:p></o:p></p>
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