<html><body><div style="font-family: arial,helvetica,sans-serif; font-size: 12pt; color: #000000"><div>Dear all,</div><div><br data-mce-bogus="1"></div><div>essentially all research is based on the current knowledge and the work of </div><div>previous generations.<br></div><div><br data-mce-bogus="1"></div><div>For instance, Albert Einstein knew the Lorentz equations and their meaning <br data-mce-bogus="1"></div><div>from the work of Hendrik Lorentz developed at the end of the 19th century.<br data-mce-bogus="1"></div><div>Henri Poincaré, Oliver Heaviside and many others have worked on them and </div><div>their meaning assuming a world of aether. Then, 1905 Einstein derived them </div><div>without assuming aether in his special relativity theory (surely motivated and </div><div>heavily influenced by these previous studies) and won the Nobel Prize for it in </div><div>1921. Even Einstein's own wife (also a physicist) contributed much in scientific </div><div>discussions with him.<br data-mce-bogus="1"></div><div><br data-mce-bogus="1"></div><div>Axel<br data-mce-bogus="1"></div><div><br data-mce-bogus="1"></div><div><span id="zwchr" data-marker="__DIVIDER__">----- On 9 Oct, 2024, at 01:55, Brad Wyble <bwyble@gmail.com> wrote:<br></span></div><div data-marker="__QUOTED_TEXT__"><blockquote style="border-left:2px solid #1010FF;margin-left:5px;padding-left:5px;color:#000;font-weight:normal;font-style:normal;text-decoration:none;font-family:Helvetica,Arial,sans-serif;font-size:12pt;"><div dir="ltr">We really can trace the current AI boom back to John Carmack who wrote Doom, which ushered in the era of GPU-hungry computing. Credit where it's due please. </div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Tue, Oct 8, 2024 at 4:10 PM Stephen José Hanson <<a href="mailto:jose@rubic.rutgers.edu" target="_blank" rel="nofollow noopener noreferrer">jose@rubic.rutgers.edu</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb( 204 , 204 , 204 );padding-left:1ex"><u></u>
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<p>Hi Steve,</p>
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<p style="line-height:100%;margin-bottom:0in">The problem every writer encounters is what can be concluded as resolved knowledge rather then new/novel knowledge. In the law this is of course “legal precedence”, so does the reference refer to a recent precedent,
or does the one for the 17<sup>th</sup> century hold precedence? In the present case, I agree that calculating gradients of functions using the chain rule was invented (Legendre -- Least squares) far before Rumelhart and Hinton applied it to error gradients
in acyclic/cyclic networks, and of course there were others as you say, in the 20<sup>th</sup> century that also applied error gradient to networks (Parker, Le cun et al). Schmidhuber says all that matters is the “math” not the applied context. However, I
seriously doubt that Legendre could have imagined using gradients of function error through succesive application in a acylic network would have produced a hierarchical kinship relationship (distinguishing between an italian and english family mother, fathers,
sons, aunts, grandparents etc.) in the hidden units of a network, simply by observing individuals with fixed feature relations. I think any reasonable person would maintain that this application is completely novel and could not be predicted in or out of
context from the “math” and certainly not from the 18<sup>th</sup> century. Hidden units were new in this context and their representational nature was novel, in this context. Scope of reference is also based on logical or causal proximity to the reference.
In this case, referencing Darwin or Newton in all biological or physics papers should be based on the outcome of the metaphorical test of whether the recent results tie back to original source in some direct line, for example, was Oswald’s grandfather responsible
for the death of President John F. Kennedy? Failing this test, suggests that the older reference may not have scope. But of course this can be subjective.</p>
<p style="line-height:100%;margin-bottom:0in">Steve<br>
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<div>On 10/8/24 2:38 PM, Grossberg, Stephen wrote:<br>
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<p class="MsoNormal"><span style="font-size:16pt;font-family:'arial' , sans-serif">Actually, Paul Werbos developed back propagation into its modern form, and worked out computational examples, for his 1974 Harvard PhD thesis.<u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:16pt;font-family:'arial' , sans-serif"><u></u> <u></u></span></p>
<p class="MsoNormal"><span style="font-size:16pt;font-family:'arial' , sans-serif">Then David Parker rediscovered it in 1982, etc.<u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:16pt;font-family:'arial' , sans-serif"><u></u> <u></u></span></p>
<p class="MsoNormal"><span style="font-size:16pt;font-family:'arial' , sans-serif">Schmidhuber provides an excellent and wide-ranging history of many contributors to Deep Learning and its antecedents:<u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:16pt;font-family:'arial' , sans-serif"><u></u> <u></u></span></p>
<p class="MsoNormal"><span style="font-size:16pt;font-family:'arial' , sans-serif"><a href="https://www.sciencedirect.com/science/article/pii/S0893608014002135?casa_token=k47YCzFwcFEAAAAA:me_ZGF5brDqjRihq5kHyeQBzyUMYBypJ3neSinZ-cPn1pnyi69DGyM9eKSyLsdiRf759I77c7w" target="_blank" rel="nofollow noopener noreferrer">https://www.sciencedirect.com/science/article/pii/S0893608014002135?casa_token=k47YCzFwcFEAAAAA:me_ZGF5brDqjRihq5kHyeQBzyUMYBypJ3neSinZ-cPn1pnyi69DGyM9eKSyLsdiRf759I77c7w</a><u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:16pt;font-family:'arial' , sans-serif"><u></u> <u></u></span></p>
<p class="MsoNormal"><span style="font-size:16pt;font-family:'arial' , sans-serif">This article has been cited over 23,000 times.<u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:16pt;font-family:'arial' , sans-serif"><u></u> <u></u></span></p>
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<p class="MsoNormal" style="margin-bottom:12pt"><b><span style="font-size:12pt;color:black">From:
</span></b><span style="font-size:12pt;color:black">Connectionists <a href="mailto:connectionists-bounces@mailman.srv.cs.cmu.edu" target="_blank" rel="nofollow noopener noreferrer">
<connectionists-bounces@mailman.srv.cs.cmu.edu></a> on behalf of Stephen José Hanson
<a href="mailto:jose@rubic.rutgers.edu" target="_blank" rel="nofollow noopener noreferrer"><jose@rubic.rutgers.edu></a><br>
<b>Date: </b>Tuesday, October 8, 2024 at 2:25 PM<br>
<b>To: </b>Jonathan D. Cohen <a href="mailto:jdc@princeton.edu" target="_blank" rel="nofollow noopener noreferrer">
<jdc@princeton.edu></a>, Connectionists <a href="mailto:connectionists@cs.cmu.edu" target="_blank" rel="nofollow noopener noreferrer">
<connectionists@cs.cmu.edu></a><br>
<b>Subject: </b>Re: Connectionists: 2024 Nobel Prize in Physics goes to Hopfield and Hinton<u></u><u></u></span></p>
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<p>Yes, Jon good point here, and although there is a through line from Hopfield to Hinton and Sejnowski.. Ie boltzmann machines and onto DL and LLMs<u></u><u></u></p>
<p>Dave of course invented BP, Geoff would always say.. his contribution was to try and talk Dave out of it as it had so many computational problems and could be in no way considered biologically plausible.<u></u><u></u></p>
<p>Steve<u></u><u></u></p>
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<p class="MsoNormal"><span style="font-size:11pt">On 10/8/24 8:47 AM, Jonathan D. Cohen wrote:<u></u><u></u></span></p>
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<pre>I’d like to add, in this context, a note in memoriam of David Rumelhart, who was an integral contributor to the work honored by today’s Nobel Prize.<u></u><u></u></pre>
<pre><u></u> <u></u></pre>
<pre>jdc<u></u><u></u></pre>
<pre><u></u> <u></u></pre>
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<pre>-- <u></u><u></u></pre>
<pre>Stephen José Hanson<u></u><u></u></pre>
<pre>Professor, Psychology Department<u></u><u></u></pre>
<pre>Director, RUBIC (Rutgers University Brain Imaging Center)<u></u><u></u></pre>
<pre>Member, Executive Committee, RUCCS<u></u><u></u></pre>
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<pre>--
Stephen José Hanson
Professor, Psychology Department
Director, RUBIC (Rutgers University Brain Imaging Center)
Member, Executive Committee, RUCCS</pre>
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</blockquote></div><br clear="all"><br><span class="gmail_signature_prefix">-- </span><br><div dir="ltr" class="gmail_signature"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr">Brad Wyble (he/him)</div></div></div></div></div><br></blockquote></div><div><br></div><div data-marker="__SIG_POST__">-- <br></div><div>Axel Hutt<br>Directeur de Recherche<br>Equipe MIMESIS - INRIA Nancy Grand Est <br>Equipe MLMS - iCube Strasbourg<br>Bâtiment NextMed<br>2, rue Marie Hamm<br>67000 Strasbourg, France<br>https://mimesis.inria.fr/members/axel-hutt/<br></div></div></body></html>