<div dir="ltr"><div><span style="font-size:9.75pt">Dear All,</span></div><div><div><span style="font-size:9.75pt"> This is the first time to have issue 3 in a year in the newsletter's history!</span></div><div><span style="font-size:9.75pt"> The new issue Vol. 18, No. 3 is now available!</span></div><div><span style="font-size:9.75pt"> </span><span style="font-family:"Times New Roman";font-size:16px;color:blue"><a href="https://www.cse.msu.edu/amdtc/amdnl/CDSNL-V18-N3.pdf" id="m_8356044403630256268m_7647865871941303595OWAad5d25ac-ed01-abad-13ed-7831b3a20693" target="_blank" style="color:blue">CDS TC Newsletter Vol. 18, No. 3, 2024</a></span></div></div><div>IEEE CDS NEWSLETTERS<br>Volume 18, Number 3 ISSN 1550-1914 July 2024<br>Development of Natural and Artificial Intelligence<br>Contents<br>1 [AI Crisis] Dialogue 1: False “Great Leap Forward” in AI 2<br>2 [AI Crisis] Dialogue 2: Your DN Must Also Have Done Post-Selection 4<br>3 [AI Crisis] Dialogue 3: Why DN Does Not Do Post-Selection 5<br>4 [AI Crisis] Dialogue 4: Post-Selection in Biology and Show Comparison 7<br>5 [AI Crisis] Dialogue 5: Point-to-Point Replies to Dialogue 4 8<br>6 [AI Crisis] Dialogue 6: Cool Things a Bit and Present Views Openly 11<br>7 [AI Crisis] Dialogue 7: Let This AI Crisis Dialogue Continue 12<br>8 [Judicial Crisis] Dialogue Initiation: Do They Represent U.S. Courts? 13<br>9 The Dawn of Artificial Intelligence: Alan Turing and Early AI Research 18<br>10 IEEE TCDS Table of Contents 19</div><font color="#888888"><span class="gmail_signature_prefix">--</span><br><div dir="ltr" class="gmail_signature"><div dir="ltr">Juyang (John) Weng</div></div></font></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Tue, Jul 16, 2024 at 2:52 PM Juyang Weng <<a href="mailto:juyang.weng@gmail.com">juyang.weng@gmail.com</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"><div dir="ltr">Dear Asim,<div> Sorry, this response is too late for Vol. 18, No. 3, 2024. You wrote that you would not respond anymore. </div><div> I saw it just now after publishing No. 3. See <a href="https://www.cse.msu.edu/amdtc/amdnl/CDSNL-V18-N3.pdf" id="m_-1089405898624325193m_8356044403630256268m_7647865871941303595OWAad5d25ac-ed01-abad-13ed-7831b3a20693" style="color:blue;font-family:"Times New Roman";font-size:16px" target="_blank">CDS TC Newsletter Vol. 18, No. 3, 2024</a></div><div> I suggest that you compose the material as a formal [AI Crisis] Dialogue and submit it to me (the dialogue initiator) with a CC to EIC Dongshu Wang. Otherwise, I will include it in issue Vol. 18, No. 4, 2024 which will appear in Nov. 2024.</div><div> Best regards,</div><div>-John</div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Mon, Jul 15, 2024 at 12:10 AM Asim Roy <<a href="mailto:ASIM.ROY@asu.edu" target="_blank">ASIM.ROY@asu.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"><div>
<div lang="EN-US">
<div>
<p class="MsoNormal"><span style="font-size:11pt">Dear John,<u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt"><u></u> <u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt">This will be my last response to the issues you have raised. Since you are posting my responses in your newsletter, please post them without any changes. I am going to be selective in my response since I don’t
think the rest of your arguments matter that much.<u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt"><u></u> <u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt">1)<u></u><u></u></span></p>
<p class="MsoNormal"><b><u><span style="font-size:11pt">Asim Roy wrote</span></u></b><span style="font-size:11pt">, "In fact, there is plenty of evidence in biology that it can create new circuits and reuse old circuits and cells/neurons. Thus, throwing
out bad solutions happens in biology too." <u></u><u></u></span></p>
<p class="MsoNormal"><b><u><span style="font-size:11pt">John Weng’s response</span></u></b><span style="font-size:11pt">:
<u><span style="background:yellow">This is irrelevant</span></u>, as your mother is not inside your skull, but a human programmer is doing that inside the "skull."<u></u><u></u></span></p>
<p class="MsoNormal"><b><u><span style="font-size:11pt">Asim Roy response</span></u></b><span style="font-size:11pt">: By saying “this is irrelevant,” you are admitting that “throwing out bad solutions happens in biology too." You have not contested that
claim. If throwing out bad solutions happens in biology, there is nothing wrong in replicating that process in post-hoc selection of good solutions. It is similar to a biological process. Post-hoc selection is the main issue in all of your arguments and I
think you should apologize to all for making a false accusation, that the post-hoc selection process doesn’t have a biological basis.<u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt"><u></u> <u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt">2)<u></u><u></u></span></p>
<p class="MsoNormal"><b><u><span style="font-size:11pt">Asim Roy wrote</span></u></b><span style="font-size:11pt">, "I still recall Horace Barlow’s ... note to me on the grandmother cell theory: ... though I fear that what I have written will not be universally
accepted, at least at first!”. <u></u><u></u></span></p>
<p class="MsoNormal"><b><u><span style="font-size:11pt">John Weng’s response</span></u></b><span style="font-size:11pt">: If you understand DN3, the first model for conscious learning that starts from a single cell, you will see how the grandmother cell
theory is naive. <u></u><u></u></span></p>
<p class="MsoNormal"><b><u><span style="font-size:11pt">Asim Roy response</span></u></b><span style="font-size:11pt">: The existence of grandmother-type cells will not be proven by any mathematical model, least of all by your DN3 model. The existence will
be proven by further neurophysiological studies. By the way, I challenge you to create the kind of abstract cells like the Jennifer Aniston cell with your development network DN3.
</span><span style="font-size:11pt;font-family:Calibri,sans-serif">Take a look at the concept cell findings (Jennifer Aniston cells). Here’s from
<a href="https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2020.00059/full#B6" target="_blank">
<span style="background:rgb(247,247,247)">Reddy and Thorpe (2014)</span></a><span style="color:rgb(40,40,40);background:rgb(247,247,247)">: “<span style="background:yellow">concept cells have “<strong><i><u><span style="font-family:Calibri,sans-serif">meaning</span></u></i></strong> of
a given stimulus in a manner that is <strong><i><span style="font-family:Calibri,sans-serif">invariant</span></i></strong> to different representations of that stimulus.”</span> Can you replicate that phenomenon in DN3?<u></u><u></u></span></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif;color:rgb(40,40,40);background:rgb(247,247,247)"><u></u> <u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif">3)<u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif">The following arguments are so basic about optimization that it would be silly to try to respond to them with such scholars in the field.
<u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif"><u></u> <u></u></span></p>
<p class="MsoNormal"><b><u>Asim Roy:</u></b> "<span style="font-size:11pt">he does use an optimization method to weed out bad solutions."
<u></u><u></u></span></p>
<p class="MsoNormal"><b><u><span style="font-size:11pt">John Weng: </span></u></b><span style="font-size:11pt">This is false. DN does not weed out bad solutions, since it has only one solution.<u></u><u></u></span></p>
<p class="MsoNormal"><b><u><span style="font-size:11pt">Asim’s Response</span></u></b><span style="font-size:11pt">: Just imagine, he claims he finds a globally optimal solution in a complex network without weeding out bad solutions. That is almost magical.<u></u><u></u></span></p>
<p class="MsoNormal"><u></u> <u></u></p>
<p class="MsoNormal"><b><u><span style="font-size:11pt">Asim Roy:</span></u></b><span style="font-size:11pt"> "In optimization, we only report the best solution."
<u></u><u></u></span></p>
<p class="MsoNormal"><b><u><span style="font-size:11pt">John Weng:</span></u></b><span style="font-size:11pt"> This is misconduct, if you hide bad-looking data, like hiding all other students in your class.<u></u><u></u></span></p>
<p class="MsoNormal"><b><u><span style="font-size:11pt">Asim’s Response</span></u></b><span style="font-size:11pt">: My god, how do I respond to this!!! That’s what we do in the optimization field. No one ever told me or anyone else that reporting the best
solution is misconduct.<u></u><u></u></span></p>
<p class="MsoNormal"><u></u> <u></u></p>
<p class="MsoNormal"><b><u><span style="font-size:11pt">Asim Roy:</span></u></b><span style="font-size:11pt"> "There is no requirement to report any non-optimal solutions."
<u></u><u></u></span></p>
<p class="MsoNormal"><b><u><span style="font-size:11pt">John Weng:</span></u></b><span style="font-size:11pt"> This is not true for scientific papers and business reports.<u></u><u></u></span></p>
<p class="MsoNormal"><b><u><span style="font-size:11pt">Asim’s Response</span></u></b><span style="font-size:11pt">: Again, how do I respond to that. We do that all the time.<u></u><u></u></span></p>
<p class="MsoNormal"><u></u> <u></u></p>
<p class="MsoNormal"><b><u><span style="font-size:11pt">Asim Roy:</span></u></b><span style="font-size:11pt"> "If someone is doing part of the optimization manually, post-hoc, there is nothing wrong with that either." <u></u><u></u></span></p>
<p class="MsoNormal"><b><u><span style="font-size:11pt">John Weng</span></u></b><span style="font-size:11pt">: This is false because the so-called post-hoc solution did not have a test!<u></u><u></u></span></p>
<p class="MsoNormal"><b><u><span style="font-size:11pt">Asim’s Response</span></u></b><span style="font-size:11pt">: For Imagenet and other competitions, there is always an independent test set. When we create our own data, we do cross-validation and other
kinds of random training and testing. What is he talking about?</span><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif"><u></u> <u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif">John, this is my last response. You can post it in your newsletter, but without any changes. I will not respond anymore. I think I have responded to your fundamental argument,
that post-selection is non-biological. It is indeed biological and you have admitted that.<u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:Calibri,sans-serif"><u></u> <u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt">Thanks,</span><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt">Asim Roy</span><u></u><u></u></p>
<p class="MsoNormal">Professor, Information Systems<u></u><u></u></p>
<p class="MsoNormal">Arizona State University<u></u><u></u></p>
<p class="MsoNormal"><a href="https://search.asu.edu/profile/9973" target="_blank">Asim Roy | ASU Search</a><u></u><u></u></p>
<p class="MsoNormal"><a href="https://urldefense.com/v3/__https:/lifeboat.com/ex/bios.asim.roy__;!!IKRxdwAv5BmarQ!aCvWF-PEaRtFT0lr5G-TVd1WSX7BloN_D524nbIUhctg9BC609q63-E91LYTCtXzoEQMZbkc5gnl53le6QZXPE1y$" target="_blank">Lifeboat Foundation Bios: Professor
Asim Roy</a><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt"><u></u> <u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt"><u></u> <u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt"><u></u> <u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt"><u></u> <u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt"><u></u> <u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt"><u></u> <u></u></span></p>
<div style="border-right:none;border-bottom:none;border-left:none;border-top:1pt solid rgb(225,225,225);padding:3pt 0in 0in">
<p class="MsoNormal"><b><span style="font-size:11pt;font-family:Calibri,sans-serif">From:</span></b><span style="font-size:11pt;font-family:Calibri,sans-serif"> Juyang Weng <<a href="mailto:juyang.weng@gmail.com" target="_blank">juyang.weng@gmail.com</a>>
<br>
<b>Sent:</b> Friday, July 5, 2024 6:23 PM<br>
<b>To:</b> Asim Roy <<a href="mailto:ASIM.ROY@asu.edu" target="_blank">ASIM.ROY@asu.edu</a>><br>
<b>Cc:</b> Russell T. Harrison <<a href="mailto:r.t.harrison@ieee.org" target="_blank">r.t.harrison@ieee.org</a>>; Akira Horose <<a href="mailto:ahirose@ee.t.u-tokyo.ac.jp" target="_blank">ahirose@ee.t.u-tokyo.ac.jp</a>>; Hisao Ishibuchi <<a href="mailto:hisao@sustech.edu.cn" target="_blank">hisao@sustech.edu.cn</a>>; Simon See <<a href="mailto:ssee@nvidia.com" target="_blank">ssee@nvidia.com</a>>; Kenji Doya <<a href="mailto:doya@oist.jp" target="_blank">doya@oist.jp</a>>; Robert Kozma <<a href="mailto:rkozma55@gmail.com" target="_blank">rkozma55@gmail.com</a>>; Simon See <<a href="mailto:Simon.CW.See@gmail.com" target="_blank">Simon.CW.See@gmail.com</a>>;
Yaochu Jin <<a href="mailto:Yaochu.Jin@surrey.ac.uk" target="_blank">Yaochu.Jin@surrey.ac.uk</a>>; Xin Yao <<a href="mailto:xiny@sustech.edu.cn" target="_blank">xiny@sustech.edu.cn</a>>; <a href="mailto:amdnl@lists.cse.msu.edu" target="_blank">amdnl@lists.cse.msu.edu</a>; Danilo Mandic <<a href="mailto:d.mandic@imperial.ac.uk" target="_blank">d.mandic@imperial.ac.uk</a>>; Irwin King <<a href="mailto:irwinking@gmail.com" target="_blank">irwinking@gmail.com</a>>; Jose Principe <<a href="mailto:principe@cnel.ufl.edu" target="_blank">principe@cnel.ufl.edu</a>>; Marley Vellasco <<a href="mailto:marley@ele.puc-rio.br" target="_blank">marley@ele.puc-rio.br</a>><br>
<b>Subject:</b> Re: False "Great Leap Forward" in AI<u></u><u></u></span></p>
</div>
<p class="MsoNormal"><u></u> <u></u></p>
<div>
<p class="MsoNormal">Dear Asim,<u></u><u></u></p>
<div>
<p class="MsoNormal"> Thank you for your response, so that people on this email list can get important benefits. The subject is very new. I can raise these misconducts because we have a holistic solution to the 20 million-dollar problems. <u></u><u></u></p>
<div>
<p class="MsoNormal"> You wrote, "<span style="font-size:11pt">he does use an optimization method to weed out bad solutions." This is false. DN does not weed out bad solutions, since it has only one solution.</span><u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11pt"> You wrote, "In optimization, we only report the best solution." This is misconduct, if you hide bad-looking data, like hiding all other students in your class.</span><u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11pt"> You wrote, "There is no requirement to report any non-optimal solutions." This is not true for scientific papers and business reports.</span><u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11pt"> You wrote, "If someone is doing part of the optimization manually, post-hoc, there is nothing wrong with that either." This is false because the so-called post-hoc solution did not have a test!</span><u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11pt"> You wrote, "In fact, there is plenty of evidence in biology that it can create new circuits and reuse old circuits and cells/neurons. Thus, throwing out bad solutions happens in biology too." This is
irrelevant, as your mother is not inside your skull, but a human programmer is doing that inside the "skull."</span><u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11pt"> You wrote, "at a higher level, there’s natural selection and survival of the fittest. So, building many solutions (networks) and picking the best fits well with biology." As I wrote before, this is false,
since biology has built Aldof Hitler and many German soldiers who acted during the Second World War. We report them, not hiding them.</span><u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11pt"> You wrote, "J</span>ohn calls this process `cheating' and a `misdeed".” Yes, I still do.<u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"> You wrote, "<span style="font-size:11pt">he claims his algorithm gets the globally optimal solution, doesn’t get stuck in any local minima." This is true, since we do not have a single objective function as you assumed. Such a single
objective function is a restricted environment or government. Instead, the maximum likelihood computation in DN is conducted in a distributed way by all neurons, each of them having its own maximum likelihood mechanism (optimal Hebbain mechanism). Read
a book, Juyang Weng, Natural and Artificial Intelligence, available at Amonzon.</span><u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11pt"> You wrote, "If that is true, he should get far better results than the folks who are “cheating” through post-selection." Off course, we did as early as 2016. See "</span>Luckiest from Post vs Single
DN" in the attached file 2024-06-30-IJCNN-Tutorial-1page.pdf<span style="font-size:11pt">. Furthermore, the luckiest from the cheating is only a fitting error on the validation set (not test), the single DN is a test error because DN does not fit the validation
set. The latter should not be compared with the former, but we compared with them anyway. </span><u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11pt"> You wrote, </span>"My hunch is, his algorithm falls short and can’t compete with the other ones." <span style="font-size:11pt"> Your hunch is wrong. See above as you can see how wrong you are. DN
is a lot better than even the false performance.</span><u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11pt"> You wrote, "</span>And that’s the reason for this outrage against others.<span style="font-size:11pt">" I am honest. All others should be honest too. Do not cheat like many Chinese in the Great Leap
Forward. </span><u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11pt"> You wrote, "</span>I would again urge IEEE to take action against John Weng for harassing plenary speakers at this conference and accusing them of “misdeeds.”<span style="font-size:11pt"> I am simply
trying to exercise my freedom of speech driven by my care for our community.</span><u></u><u></u></p>
</div>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11pt"> Do you all see a "Great Leap Forward in AI" like the "Great Leap Forward" in 1958 in China?</span><u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11pt"> Best regards,</span><u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11pt">-John</span><u></u><u></u></p>
</div>
</div>
<p class="MsoNormal"><u></u> <u></u></p>
<div>
<div>
<p class="MsoNormal">On Fri, Jul 5, 2024 at 9:01<span style="font-family:Arial,sans-serif"> </span>AM Asim Roy <<a href="mailto:ASIM.ROY@asu.edu" target="_blank">ASIM.ROY@asu.edu</a>> wrote:<u></u><u></u></p>
</div>
<blockquote style="border-top:none;border-right:none;border-bottom:none;border-left:1pt solid rgb(204,204,204);padding:0in 0in 0in 6pt;margin:5pt 0in 5pt 4.8pt">
<div>
<div>
<div>
<p class="MsoNormal"><span style="font-size:11pt">Dear All,</span><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt"> </span><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt">Without getting into the details of his DN algorithm, he does use an optimization method to weed out bad solutions. In optimization, we only report
the best solution. There is no requirement to report any non-optimal solutions. If someone is doing part of the optimization manually, post-hoc, there is nothing wrong with that either. In fact, there is plenty of evidence in biology that it can create new
circuits and reuse old circuits and cells/neurons. Thus, throwing out bad solutions happens in biology too. And, of course, at a higher level, there’s natural selection and survival of the fittest. So, building many solutions (networks) and picking the best
fits well with biology. However, John calls this process “cheating” and a “misdeed.” He also claims having a strong background in biology. So he should be aware of these processes.</span><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt"> </span><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt">In addition, he claims his algorithm gets the globally optimal solution, doesn’t get stuck in any local minima. If that is true, he should get far
better results than the folks who are “cheating” through post-selection. He should be able to demonstrate his superior solutions through the public competitions such as with Imagenet data. My hunch is, his algorithm falls short and can’t compete with the other
ones. And that’s the reason for this outrage against others.</span><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt"> </span><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt">I would again urge IEEE to take action against John Weng for harassing plenary speakers at this conference and accusing them of “misdeeds.”
</span><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt"> </span><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt">Best,</span><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt">Asim</span><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt"> </span><u></u><u></u></p>
<div style="border-right:none;border-bottom:none;border-left:none;border-top:1pt solid rgb(225,225,225);padding:3pt 0in 0in">
<p class="MsoNormal"><b><span style="font-size:11pt;font-family:Calibri,sans-serif">From:</span></b><span style="font-size:11pt;font-family:Calibri,sans-serif"> Juyang Weng <</span><a href="mailto:juyang.weng@gmail.com" target="_blank"><span style="font-size:11pt;font-family:Calibri,sans-serif">juyang.weng@gmail.com</span></a><span style="font-size:11pt;font-family:Calibri,sans-serif">>
<br>
<b>Sent:</b> Thursday, July 4, 2024 8:14 AM<br>
<b>To:</b> Asim Roy <</span><a href="mailto:ASIM.ROY@asu.edu" target="_blank"><span style="font-size:11pt;font-family:Calibri,sans-serif">ASIM.ROY@asu.edu</span></a><span style="font-size:11pt;font-family:Calibri,sans-serif">><br>
<b>Cc:</b> Russell T. Harrison <</span><a href="mailto:r.t.harrison@ieee.org" target="_blank"><span style="font-size:11pt;font-family:Calibri,sans-serif">r.t.harrison@ieee.org</span></a><span style="font-size:11pt;font-family:Calibri,sans-serif">>;
Akira Horose <</span><a href="mailto:ahirose@ee.t.u-tokyo.ac.jp" target="_blank"><span style="font-size:11pt;font-family:Calibri,sans-serif">ahirose@ee.t.u-tokyo.ac.jp</span></a><span style="font-size:11pt;font-family:Calibri,sans-serif">>; Hisao Ishibuchi
<</span><a href="mailto:hisao@sustech.edu.cn" target="_blank"><span style="font-size:11pt;font-family:Calibri,sans-serif">hisao@sustech.edu.cn</span></a><span style="font-size:11pt;font-family:Calibri,sans-serif">>; Simon See <</span><a href="mailto:ssee@nvidia.com" target="_blank"><span style="font-size:11pt;font-family:Calibri,sans-serif">ssee@nvidia.com</span></a><span style="font-size:11pt;font-family:Calibri,sans-serif">>;
Kenji Doya <</span><a href="mailto:doya@oist.jp" target="_blank"><span style="font-size:11pt;font-family:Calibri,sans-serif">doya@oist.jp</span></a><span style="font-size:11pt;font-family:Calibri,sans-serif">>; Robert Kozma <</span><a href="mailto:rkozma55@gmail.com" target="_blank"><span style="font-size:11pt;font-family:Calibri,sans-serif">rkozma55@gmail.com</span></a><span style="font-size:11pt;font-family:Calibri,sans-serif">>;
Simon See <</span><a href="mailto:Simon.CW.See@gmail.com" target="_blank"><span style="font-size:11pt;font-family:Calibri,sans-serif">Simon.CW.See@gmail.com</span></a><span style="font-size:11pt;font-family:Calibri,sans-serif">>; Yaochu Jin <</span><a href="mailto:Yaochu.Jin@surrey.ac.uk" target="_blank"><span style="font-size:11pt;font-family:Calibri,sans-serif">Yaochu.Jin@surrey.ac.uk</span></a><span style="font-size:11pt;font-family:Calibri,sans-serif">>;
Xin Yao <</span><a href="mailto:xiny@sustech.edu.cn" target="_blank"><span style="font-size:11pt;font-family:Calibri,sans-serif">xiny@sustech.edu.cn</span></a><span style="font-size:11pt;font-family:Calibri,sans-serif">>;
</span><a href="mailto:amdnl@lists.cse.msu.edu" target="_blank"><span style="font-size:11pt;font-family:Calibri,sans-serif">amdnl@lists.cse.msu.edu</span></a><span style="font-size:11pt;font-family:Calibri,sans-serif">; Danilo Mandic <</span><a href="mailto:d.mandic@imperial.ac.uk" target="_blank"><span style="font-size:11pt;font-family:Calibri,sans-serif">d.mandic@imperial.ac.uk</span></a><span style="font-size:11pt;font-family:Calibri,sans-serif">>;
Irwin King <</span><a href="mailto:irwinking@gmail.com" target="_blank"><span style="font-size:11pt;font-family:Calibri,sans-serif">irwinking@gmail.com</span></a><span style="font-size:11pt;font-family:Calibri,sans-serif">><br>
<b>Subject:</b> Re: False "Great Leap Forward" in AI</span><u></u><u></u></p>
</div>
<p class="MsoNormal"> <u></u><u></u></p>
<div>
<p class="MsoNormal">Dear Asim and All,<u></u><u></u></p>
<div>
<p class="MsoNormal"> I am happy that Asim responded so that he gave us all an opportunity to interactively participate in an academic discussion. We can defeat the false "Great Leap Forward".<u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"> During the banquet of July 3, 2024, I was trying to explain to Asim why our Developmental Network (DN) only trains a single network, not multiple networks as all other methods
do (e.g., neural networks with error-backprop, genetic algorithms, and fuzzy sets). (Let me know if there are other methods where one network is optimal and therefore is free from the local minima problem.)<u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"> This single-network property is important because normally every developmental network (genome) must succeed in single-network development, from inception to birth, to death. <u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"> Post-selection: A human programer trains multiple (n>1) predictors based on a fit set F, and then picks up the luckiest predictor based on a validation set (which is in the
possession of the program). He suffers from the following two misconducts:<br>
Misconduct 1: Cheating in the absence of a test (because the test set T is absent).<u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"> Misconduct 2: Hiding bad-looking data (other less lucky predictors).<u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"> A. I told Asim that DN tests its performance from birth to death, across the entire life!<u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"> B. I told Asim that DN does not hide any data because it trains a single brain and reports all its lifetime errors! <u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"> Asim did not read our DN papers that I sent to him, or did not read them carefully, especially the proof of the maximum likelihood of DN-1. See Weng IJIS 2015,
<a href="https://urldefense.com/v3/__https:/www.scirp.org/journal/paperinformation?paperid=53728__;!!IKRxdwAv5BmarQ!aCvWF-PEaRtFT0lr5G-TVd1WSX7BloN_D524nbIUhctg9BC609q63-E91LYTCtXzoEQMZbkc5gnl53le6ch2TgX4$" target="_blank">
https://www.scirp.org/journal/paperinformation?paperid=53728</a>.<u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"> At the banquet, I told Asim that the representation of DN is "distributed" like the brain and it collectively computes the maximum likelihood representation by very neuron using
a limited resource and a limited amount of life experience. I told him that every brain is optimal, including his brain, my brain, and Aldolf Hitler's brain. However, every brain has a different experience. However, Asim apparently did not understand me
and did not continue to ask what I meant by "distributed" maximum likelihood representation. Namely, every neuron incrementally computes the maximum likelihood representation of its own competition zone.<u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"> Asim gave an expression about the maximum likelihood implying that every nonlinear objective function has many local minima! That seems to be a lack of understanding of my
proof in IJIS 2015.<u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"> (1) I told Asim that every (positive) neuron computes its competitors automatically (assisted by its dedicated negative neuron), so that every (positive) neuron has a different
set of (positive) neuronal competitors. Because every neuron has a different competition zone, the maximum likelihood representation is distributed. <u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"> (2) Through the distributed computing by all (limited number of) neurons that work together inside the DN, the DN computes the distributed maximum likelihood representations.
Namely, every (positive) neuron computes its maximum likelihood representation incrementally for its unique competition zone. This is proven in IJIS 2015, based on the dual-optimality of Lobe Component Analysis. Through the proof, you can see how LCA converts
a highly nonlinear problem for each neuron into a linear problem for each neuron, by defining observation as a response-weighted input (i.e., dually-optimal Hebbian learning). Yes, with this beautifully converted linear problem (inspired by the brain), neuronal
computation becomes computing an incremental mean through time in every neuron. Therefore, a highly nonlinear problem of computing lobe components becomes a linear one. We know that there is no local minima problem in computing the mean of a time sequence. <u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"> (3) As I presented in several of my IJCNN tutorials, neurons in DN start from random weights, but different random weights lead to the same network, because the initial weights
only change the neuronal resources, but not the resulting network.<u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"> In summary, the equation that Asim listed is for each neuron, but each neuron has a different instance of the expression. There is no search, not that Asim implied (without
saying)! This corresponds to a holistic solution the 20-million dollar problems (i.e., the local minuma problem solved by the maximum-likelihood optimality). See <a href="https://urldefense.com/v3/__https:/ieeexplore.ieee.org/document/9892445__;!!IKRxdwAv5BmarQ!aCvWF-PEaRtFT0lr5G-TVd1WSX7BloN_D524nbIUhctg9BC609q63-E91LYTCtXzoEQMZbkc5gnl53le6cX7cdu2$" target="_blank">https://ieeexplore.ieee.org/document/9892445</a><u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"> However, all other learning algorithms have not solved this local minima problem. Therefore, they have to resort to trials and errors through training many predictors.<u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"> Do you have any more questions?<br>
Best regards,<u></u><u></u></p>
</div>
<div>
<p class="MsoNormal">-John<u></u><u></u></p>
</div>
</div>
<p class="MsoNormal"> <u></u><u></u></p>
<div>
<div>
<p class="MsoNormal">On Thu, Jul 4, 2024 at 4:20<span style="font-family:Arial,sans-serif"> </span>PM Asim Roy <<a href="mailto:ASIM.ROY@asu.edu" target="_blank">ASIM.ROY@asu.edu</a>> wrote:<u></u><u></u></p>
</div>
<blockquote style="border-top:none;border-right:none;border-bottom:none;border-left:1pt solid rgb(204,204,204);padding:0in 0in 0in 6pt;margin:5pt 0in 5pt 4.8pt">
<div>
<div>
<div>
<p class="MsoNormal"><span style="font-size:11pt">Dear All,</span><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt"> </span><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt">There’s quite a bit of dishonesty here. John Weng can be accused of the same “misconduct” that he is accusing others of. He didn’t quite disclose
what we discussed at the banquet last night. He is hiding all that. </span><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt"> </span><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt">His basic argument is that we pick the best solution and report results on that basis. In a sense, when you formulate a machine learning problem as
an optimization problem, that’s essentially what you are trying to do – get the best solution and weed out the bad ones. And HE DOES THE SAME IN HIS DEVELOPMENT NETWORK. When I asked him how his DN algorithm learns, he said it uses the maximum likelihood method,
which is an old statistical method (</span><a href="https://urldefense.com/v3/__https:/en.wikipedia.org/wiki/Maximum_likelihood_estimation__;!!IKRxdwAv5BmarQ!aCvWF-PEaRtFT0lr5G-TVd1WSX7BloN_D524nbIUhctg9BC609q63-E91LYTCtXzoEQMZbkc5gnl53le6RquWOgs$" target="_blank">Maximum
likelihood estimation - Wikipedia</a>). I quote from Wikipedia:<u></u><u></u></p>
<p class="MsoNormal"> <u></u><u></u></p>
<p style="margin:0in;background:white"><span style="font-family:Arial,sans-serif;color:rgb(32,33,34);background:yellow">The goal of maximum likelihood estimation is to find the values of the model parameters that
<b><u>maximize the likelihood function over the parameter space</u></b>,</span><span style="color:black"><a href="https://urldefense.com/v3/__https:/en.wikipedia.org/wiki/Maximum_likelihood_estimation*cite_note-:0-6__;Iw!!IKRxdwAv5BmarQ!aCvWF-PEaRtFT0lr5G-TVd1WSX7BloN_D524nbIUhctg9BC609q63-E91LYTCtXzoEQMZbkc5gnl53le6VGxpEh8$" target="_blank"><sup><span style="font-size:9.5pt;font-family:Arial,sans-serif;background:yellow;text-decoration:none">[6]</span></sup></a></span><span style="font-family:Arial,sans-serif;color:rgb(32,33,34);background:yellow"> that
is</span><u></u><u></u></p>
<p class="MsoNormal" style="margin-left:0.5in;background:white">
<span style="font-size:14pt;font-family:"Cambria Math",serif;color:rgb(32,33,34);background:yellow">𝜃</span><span style="font-size:14pt;font-family:Arial,sans-serif;color:rgb(32,33,34);background:yellow">^=argmax</span><span style="font-size:14pt;font-family:"Cambria Math",serif;color:rgb(32,33,34);background:yellow">𝜃∈</span><span style="font-size:14pt;font-family:Arial,sans-serif;color:rgb(32,33,34);background:yellow">Θ</span><span style="font-size:14pt;font-family:"Cambria Math",serif;color:rgb(32,33,34);background:yellow">𝐿𝑛</span><span style="font-size:14pt;font-family:Arial,sans-serif;color:rgb(32,33,34);background:yellow">(</span><span style="font-size:14pt;font-family:"Cambria Math",serif;color:rgb(32,33,34);background:yellow">𝜃</span><span style="font-size:14pt;font-family:Arial,sans-serif;color:rgb(32,33,34);background:yellow">;</span><span style="font-size:14pt;font-family:"Cambria Math",serif;color:rgb(32,33,34);background:yellow">𝑦</span><span style="font-size:14pt;font-family:Arial,sans-serif;color:rgb(32,33,34);background:yellow">) .</span><span style="color:black"><img border="0" width="32" height="32" style="width: 0.3333in; height: 0.3333in;" id="m_-1089405898624325193m_-7304702940137014181m_-7700355691914837950m_1189456624056520499m_-8001754654975784104Picture_x0020_1" src="cid:ii_190bcdd1d4d4cff311" alt="{\displaystyle {\hat {\theta }}={\underset {\theta \in \Theta }{\operatorname {arg\;max} }}\,{\mathcal {L}}_{n}(\theta \,;\mathbf {y} )~.}"></span><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt"> </span><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt">So, by default, HE ALSO HIDES ALL THE BAD SOLUTIONS AND DOESN’T REPORT THEM. He never talks about all of this. He never mentions that I had talked
about this in particular.</span><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt"> </span><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt">I would suggest that based on his dishonest accusations against others and, in particular, against one of the plenary speakers here at the conference,
that IEEE take some action against him. This nonsense has been going on for a longtime and it’s time for some action.
</span><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt"> </span><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt">By the way, I am not a member of IEEE. I am expressing my opinion only because he has falsely accused me also and I had enough of it. I have added
Danilo Mandic and Irwin King to the list.</span><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt"> </span><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt">Thanks,</span><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt">Asim Roy</span><u></u><u></u></p>
<p class="MsoNormal">Professor, Information Systems<u></u><u></u></p>
<p class="MsoNormal">Arizona State University<u></u><u></u></p>
<p class="MsoNormal"><a href="https://search.asu.edu/profile/9973" target="_blank">Asim Roy | ASU Search</a><u></u><u></u></p>
<p class="MsoNormal"><a href="https://urldefense.com/v3/__https:/lifeboat.com/ex/bios.asim.roy__;!!IKRxdwAv5BmarQ!aCvWF-PEaRtFT0lr5G-TVd1WSX7BloN_D524nbIUhctg9BC609q63-E91LYTCtXzoEQMZbkc5gnl53le6QZXPE1y$" target="_blank">Lifeboat
Foundation Bios: Professor Asim Roy</a><u></u><u></u></p>
<p class="MsoNormal"><span style="font-size:11pt"> </span><u></u><u></u></p>
<div style="border-right:none;border-bottom:none;border-left:none;border-top:1pt solid rgb(225,225,225);padding:3pt 0in 0in">
<p class="MsoNormal"><b><span style="font-size:11pt;font-family:Calibri,sans-serif">From:</span></b><span style="font-size:11pt;font-family:Calibri,sans-serif"> Juyang Weng <</span><a href="mailto:juyang.weng@gmail.com" target="_blank"><span style="font-size:11pt;font-family:Calibri,sans-serif">juyang.weng@gmail.com</span></a><span style="font-size:11pt;font-family:Calibri,sans-serif">>
<br>
<b>Sent:</b> Wednesday, July 3, 2024 5:54 PM<br>
<b>To:</b> Russell T. Harrison <</span><a href="mailto:r.t.harrison@ieee.org" target="_blank"><span style="font-size:11pt;font-family:Calibri,sans-serif">r.t.harrison@ieee.org</span></a><span style="font-size:11pt;font-family:Calibri,sans-serif">><br>
<b>Cc:</b> Akira Horose <</span><a href="mailto:ahirose@ee.t.u-tokyo.ac.jp" target="_blank"><span style="font-size:11pt;font-family:Calibri,sans-serif">ahirose@ee.t.u-tokyo.ac.jp</span></a><span style="font-size:11pt;font-family:Calibri,sans-serif">>;
Hisao Ishibuchi <</span><a href="mailto:hisao@sustech.edu.cn" target="_blank"><span style="font-size:11pt;font-family:Calibri,sans-serif">hisao@sustech.edu.cn</span></a><span style="font-size:11pt;font-family:Calibri,sans-serif">>; Simon See <</span><a href="mailto:ssee@nvidia.com" target="_blank"><span style="font-size:11pt;font-family:Calibri,sans-serif">ssee@nvidia.com</span></a><span style="font-size:11pt;font-family:Calibri,sans-serif">>;
Kenji Doya <</span><a href="mailto:doya@oist.jp" target="_blank"><span style="font-size:11pt;font-family:Calibri,sans-serif">doya@oist.jp</span></a><span style="font-size:11pt;font-family:Calibri,sans-serif">>; Robert Kozma <</span><a href="mailto:rkozma55@gmail.com" target="_blank"><span style="font-size:11pt;font-family:Calibri,sans-serif">rkozma55@gmail.com</span></a><span style="font-size:11pt;font-family:Calibri,sans-serif">>;
Simon See <</span><a href="mailto:Simon.CW.See@gmail.com" target="_blank"><span style="font-size:11pt;font-family:Calibri,sans-serif">Simon.CW.See@gmail.com</span></a><span style="font-size:11pt;font-family:Calibri,sans-serif">>; Yaochu Jin <</span><a href="mailto:Yaochu.Jin@surrey.ac.uk" target="_blank"><span style="font-size:11pt;font-family:Calibri,sans-serif">Yaochu.Jin@surrey.ac.uk</span></a><span style="font-size:11pt;font-family:Calibri,sans-serif">>;
Xin Yao <</span><a href="mailto:xiny@sustech.edu.cn" target="_blank"><span style="font-size:11pt;font-family:Calibri,sans-serif">xiny@sustech.edu.cn</span></a><span style="font-size:11pt;font-family:Calibri,sans-serif">>; Asim Roy <</span><a href="mailto:ASIM.ROY@asu.edu" target="_blank"><span style="font-size:11pt;font-family:Calibri,sans-serif">ASIM.ROY@asu.edu</span></a><span style="font-size:11pt;font-family:Calibri,sans-serif">>;
</span><a href="mailto:amdnl@lists.cse.msu.edu" target="_blank"><span style="font-size:11pt;font-family:Calibri,sans-serif">amdnl@lists.cse.msu.edu</span></a><span style="font-size:11pt;font-family:Calibri,sans-serif"><br>
<b>Subject:</b> False "Great Leap Forward" in AI</span><u></u><u></u></p>
</div>
<p class="MsoNormal"> <u></u><u></u></p>
<div>
<div>
<p class="MsoNormal">Dear Asim,<u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"> It is my great pleasure to finally have somebody who argued with me about this important subject. I have attached the summary of this important issue in pdf.<u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"> I alleged widespread false data in AI from the following two misconducts:<br>
Misconduct 1: Cheating in the absence of a test.<u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"> Misconduct 2: Hiding bad-looking data.<u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"> The following is a series of events during WCCI 2024 in Yokohama Japan.<u></u><u></u></p>
</div>
<p class="MsoNormal">These examples showed that some active researchers in the WCCI community were probably not aware of the severity and urgency of the issue.<br>
July 1, in public eyes, Robert Cozma banned the chance for Simon See at NVidea to respond to my question pointing to a False "Great Leap Forward" in AI. <br>
July 1, Kenji Doya suggested something like "let misconduct go ahead without a correction" because the publications are not cited. But he still did not know that I alleged that AlphaFold as well as many almost all published Google's deep learning products
suffer from the same Post-Selection misconduct. <br>
July 1, Asim Roy said to me "We need to talk" but he did not stay around to talk. I had a long debate during the Banquet last night. He seems to imply that post-selections of few networks and hiding the performance information of the entire population
is "survival of the fittest". He did not seem to agree that all 3 billion human populations need to be taken into account in human evolution, at least a large number of samples like in human sensus.<br>
July 3, Yaochu Jin did not let me ask questions after a keynote talk. Later he seemed to admit that many people in AI only report the data they like.<u></u><u></u></p>
<div>
<p class="MsoNormal"> July 3, Kalanmoy Deb said that he just wanted to find a solution using genetic algorithms but did not know that his so-called solution did not have a test at all.<u></u><u></u></p>
<div>
<p class="MsoNormal"> July 1, I saw all books on the display on the Springer Table appear to suffer from Post-Selection misconduct.<u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"> Do we have a false data flooded "Great Leap Forward" in AI? Why?<u></u><u></u></p>
</div>
<div>
<p class="MsoNormal"> I welcome all those interested to discuss this important issue.<br>
Best regards,<br>
-John Weng<br>
-- <u></u><u></u></p>
<div>
<div>
<p class="MsoNormal">Juyang (John) Weng<u></u><u></u></p>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</blockquote>
</div>
<p class="MsoNormal"><br clear="all">
<u></u><u></u></p>
<div>
<p class="MsoNormal"> <u></u><u></u></p>
</div>
<p class="MsoNormal">--
<u></u><u></u></p>
<div>
<div>
<p class="MsoNormal">Juyang (John) Weng<u></u><u></u></p>
</div>
</div>
</div>
</div>
</div>
</blockquote>
</div>
<p class="MsoNormal"><br clear="all">
<u></u><u></u></p>
<div>
<p class="MsoNormal"><u></u> <u></u></p>
</div>
<p class="MsoNormal"><span>-- </span><u></u><u></u></p>
<div>
<div>
<p class="MsoNormal">Juyang (John) Weng<u></u><u></u></p>
</div>
</div>
</div>
</div>
</div></blockquote></div><br clear="all"><div><br></div><span class="gmail_signature_prefix">-- </span><br><div dir="ltr" class="gmail_signature"><div dir="ltr">Juyang (John) Weng<br></div></div>
</blockquote></div><br clear="all"><div><br></div><span class="gmail_signature_prefix">-- </span><br><div dir="ltr" class="gmail_signature"><div dir="ltr">Juyang (John) Weng<br></div></div>