[CDSNL] False "Great Leap Forward" in AI
Juyang Weng
juyang.weng at gmail.com
Sun Nov 17 17:15:32 EST 2024
Dear Asim,
The main subject is in the title of this email. I did not change the
subject.
You wrote, "you are no longer arguing that post-selection is not
biological". This is not true, but I helped you with mathematics when you
raised mathematics. Mathematics is another angle to model biological
processes. You did not address Theorem 1 and its proof.
You wrote, "There is no “cheating” or “hiding” in these competitions."
This is not true. Why? The awarded AI systems are predictors that must
generalize from new samples even after these competitions. Otherwise, my
NNWT and PGNN should have beat any groups. Please read the preprint
"Invalitidy of the Experimental Protocol in Two Nobel Prizes", which raised
three frauds in Two Nobel Prizes: (1) Cheating, (2) Hiding, and (3)
Exaggerating the prediction accuracy. You did not address Theorem 1 and
its proof.
You wrote, "Can we stop this now?" No, because this fraud issue is too
important for scientists to stop. The frauds even got Nobel prizes! The
scientific establishment should change (as explained in my article "Nobet
Frauds Rooted in Bureaucracy" in the latest CDS Newsletter). See you at
Thursday's ZOOM meeting.
Best regards,
-John Weng
On Sun, Nov 17, 2024 at 4:27 PM Asim Roy <ASIM.ROY at asu.edu> wrote:
> Dear John,
>
>
>
> I call it dishonesty because you are no longer arguing that post-selection
> is not biological. But that was your main argument all along since the
> conference in Japan in June. And now you are running away from it.
>
>
>
> If you read carefully, I did address your statistical argument. There is
> no “cheating” or “hiding” in these competitions. That’s how they are set
> up. And I have given you the Olympics example. In any given Olympics, one
> person may win in a certain run, say a 100 meter dash, but that might not
> hold if you repeat that run the next day or the next Olympics. It’s the
> nature of these competitions that you are objecting to. They can report
> those average values that you are asking for, but that’s of very little
> significance. The organizers do report the top 3 or top 5. You can ask them
> to give you performance values for all of the submissions. But NOT
> REPORTING the average values does not mean there is “cheating” or “hiding”
> or “misdeeds” by any of the parties in this competitions. They don’t report
> them because no one might be interested in them. These are false
> accusations.
>
>
>
> Can we stop this now? I am sick and tired of this.
>
>
>
> Best,
>
> Asim
>
>
>
> *From:* Juyang Weng <juyang.weng at gmail.com>
> *Sent:* Sunday, November 17, 2024 1:55 PM
> *To:* Asim Roy <ASIM.ROY at asu.edu>
> *Cc:* Dongshu Wang (王东署) <wangdongshu at zzu.edu.cn>; Russell T. Harrison <
> r.t.harrison at ieee.org>; Akira Horose <ahirose at ee.t.u-tokyo.ac.jp>; Hisao
> Ishibuchi <hisao at sustech.edu.cn>; Simon See <ssee at nvidia.com>; Kenji Doya
> <doya at oist.jp>; Robert Kozma <rkozma55 at gmail.com>; Simon See <
> Simon.CW.See at gmail.com>; Yaochu Jin <Yaochu.Jin at surrey.ac.uk>; Xin Yao <
> xiny at sustech.edu.cn>; amdnl at lists.cse.msu.edu; Danilo Mandic <
> d.mandic at imperial.ac.uk>; Irwin King <irwinking at gmail.com>; Jose Principe
> <principe at cnel.ufl.edu>; Marley Vellasco <marley at ele.puc-rio.br>; Ali
> Minai <minaiaa at gmail.com>; Kim Plunkett <kim.plunkett at psy.ox.ac.uk>
> *Subject:* Re: False "Great Leap Forward" in AI
>
>
>
> Dear Asim,
>
> I refuse to reply to your personal attack: "dishonesty". Otherwise, I
> would violate The Robert Rules of Order as you did several times before.
>
> I wrote, "Since you are a mathematician, please try to understand the
> Minimum Mean Square Error (MMSE) principle (Theorem 1 and Proof in [11]) in
> the attached preprint titled `Invalidity of the Experimental Protocol in
> Two Nobel Prizes'." From the term "mathematics" and the title `Invalidity
> of the Experimental Protocol in Two Nobel Prizes', you should address
> Theorem 1 in mathematical correctness (instead of out of context).
> However, you failed to do so.
>
> Best regards,
>
> -John
>
>
>
> On Sun, Nov 17, 2024 at 2:03 PM Asim Roy <ASIM.ROY at asu.edu> wrote:
>
> Dear John,
>
>
>
> I wish you would stop this. You started by saying post-selection is not
> part of biology and I pointed out that it’s very much part of biology. I
> didn’t see any biological arguments in the attached note. You knew that
> very well and sending this paper is part of your dishonesty because it’s
> switching away from the biological argument.
>
>
>
> Your arguments in that note are also flawed. As I mentioned before, there
> is not much value in reporting all solutions. Think of the Olympics. Each
> country selects their best athletes. You are asking why they don’t report
> on the performance of all the athletes that competed. The data is there and
> each country might have internal use for it, but, in the end, they send
> their best ones. At the Olympics, we award the top three with medals. You
> can ask the Olympic committee to provide the average time for the 100
> meters dash, but that does not have much value. The data is there and could
> be interest to someone. But, to the general public, the medal winners
> matter. There’s no “misdeed” or “cheating” or “hiding” in this process.
> Olympics, in general, is a very fair process. It’s all based on the winners
> that year and under whatever conditions existed that year (temperature,
> weather, etc.).
>
>
>
> I think you are misguided in your arguments both on the statistical side
> and the biological side. Reporting all results may not be of much interest
> in general. I am sure, these competition organizers can disclose that
> information if needed. But there is no “cheating” or “hiding” anything. The
> rules of these competition are well-defined.
>
>
>
> If that article was your final argument, then let’s stop emailing at this
> point.
>
>
>
> Best,
>
> Asim
>
>
>
> *From:* Juyang Weng <juyang.weng at gmail.com>
> *Sent:* Sunday, November 17, 2024 9:10 AM
> *To:* Asim Roy <ASIM.ROY at asu.edu>
> *Cc:* Dongshu Wang (王东署) <wangdongshu at zzu.edu.cn>; Russell T. Harrison <
> r.t.harrison at ieee.org>; Akira Horose <ahirose at ee.t.u-tokyo.ac.jp>; Hisao
> Ishibuchi <hisao at sustech.edu.cn>; Simon See <ssee at nvidia.com>; Kenji Doya
> <doya at oist.jp>; Robert Kozma <rkozma55 at gmail.com>; Simon See <
> Simon.CW.See at gmail.com>; Yaochu Jin <Yaochu.Jin at surrey.ac.uk>; Xin Yao <
> xiny at sustech.edu.cn>; amdnl at lists.cse.msu.edu; Danilo Mandic <
> d.mandic at imperial.ac.uk>; Irwin King <irwinking at gmail.com>; Jose Principe
> <principe at cnel.ufl.edu>; Marley Vellasco <marley at ele.puc-rio.br>; Ali
> Minai <minaiaa at gmail.com>; Kim Plunkett <kim.plunkett at psy.ox.ac.uk>
> *Subject:* Re: False "Great Leap Forward" in AI
>
>
>
> Dear Asim,
>
> We must not discuss issues in the context.
>
> You wrote, "When we report results of various algorithms, we are all
> aware that we report the best results." This is out of context. To report
> the behavior of an algorithm, we must report the distribution of its
> behavior, NOT the luckiest result.
>
> Since you are a mathematician, please try to understand the Minimum
> Mean Square Error (MMSE) principle (Theorem 1 and Proof in [11]) in the
> attached preprint titled "Invalidity of the Experimental Protocol in Two
> Nobel Prizes".
>
> Best regards,
>
> -John
>
>
>
> On Sat, Nov 16, 2024 at 3:57 PM Asim Roy <ASIM.ROY at asu.edu> wrote:
>
> Dear John,
>
>
>
> I wish I can stop this nonsense argument. When we report results of
> various algorithms, we are all aware that we report the best results.
> That’s accepted in science. Biological systems also do trial and error
> processes and learn from it and then pick and use the best process. That’s
> how we operate, as biological systems. We don’t always report our failures
> and I think you are calling “not reporting” cheating and hiding. Of course,
> we as individuals, in that sense, do cheat and hide.
>
>
>
> You are trying to say that in our papers, we should be reporting the bad
> results. That’s not a necessity and never been a necessity. I come from an
> optimization background and have always reported the best results found by
> an algorithm. Even if we had reported the bad results, there is no value in
> it.
>
>
>
> Hope you can carry on your arguments within your community without
> accusing others of “cheating” and “misdeeds.” If we see real misdeeds in
> reporting results, we have a fairly good system to protect against that.
> There are many recent cases.
>
>
>
> Again, please carry on your nonsense discussion within your community that
> subscribes to these views. No need to come to conference and loudly accuse
> people of “misdeeds.” I think I speak for many.
>
>
>
> Best,
>
> Asim
>
>
>
> *From:* Juyang Weng <juyang.weng at gmail.com>
> *Sent:* Saturday, November 16, 2024 1:24 PM
> *To:* Asim Roy <ASIM.ROY at asu.edu>
> *Cc:* Dongshu Wang (王东署) <wangdongshu at zzu.edu.cn>; Russell T. Harrison <
> r.t.harrison at ieee.org>; Akira Horose <ahirose at ee.t.u-tokyo.ac.jp>; Hisao
> Ishibuchi <hisao at sustech.edu.cn>; Simon See <ssee at nvidia.com>; Kenji Doya
> <doya at oist.jp>; Robert Kozma <rkozma55 at gmail.com>; Simon See <
> Simon.CW.See at gmail.com>; Yaochu Jin <Yaochu.Jin at surrey.ac.uk>; Xin Yao <
> xiny at sustech.edu.cn>; amdnl at lists.cse.msu.edu; Danilo Mandic <
> d.mandic at imperial.ac.uk>; Irwin King <irwinking at gmail.com>; Jose Principe
> <principe at cnel.ufl.edu>; Marley Vellasco <marley at ele.puc-rio.br>; Ali
> Minai <minaiaa at gmail.com>; Jay McClelland <jlmcc at stanford.edu>; Kim
> Plunkett <kim.plunkett at psy.ox.ac.uk>
> *Subject:* Re: False "Great Leap Forward" in AI
>
>
>
> Dear Asim,
>
> You shallowly mentioned the term "biology", but you still have no
> biological substances.
>
> I said (1) cheating, (2) hiding and (3) exaggeration are not what
> biology does.
>
> (a) You wrote, "Think about natural selection and survival of the
> fittest." Not applicable. For example, get back to my example about
> Adolf Hitler. Adolf Hitler still lives. He just died from an unnatural
> course. We must report Adolf Hitler, must not hide him, and must not
> exaggerate that he is not in human statistics.
>
> (b) You wrote, "Think about trying out different strategies for doing
> something and then selecting the best." Not applicable, at least you did
> not give any biological evidence that biology did (1) cheating, (2) hiding,
> and (3) exaggeration (e.g., at the genes level). At the agent level. a
> human is conscious to do (1) cheating, (2) hiding, and (3) exaggeration,
> but the facts of all agent level of biology (including his failure cases)
> must be reported.
>
> (c) You wrote, "Think about learning from past failures." Not
> applicable. Whatever he learns, he cannot (1) cheat, (2) hide, and (2)
> exaggerate the mean of his prediction accuracy. Again, learning from
> Hitler's failure does not mean that you can hide his case and not report
> about him.
>
> I give you a hint: As I wrote with Jay McClelland and K. Plunkett
> (CCed), ``Convergent Approaches to the Understanding of Autonomous Mental
> Development,'' editorial for the Special Issue on Autonomous Mental
> Development in the
>
> IEEE Transactions on Evolutionary Computation, vol. 18, no. 2, 2007: No
> evolutional works that we have seen then (and it seems to be true after 17
> years), no evolutional methods do development. Learning is in development
> (that DNs do), not in meiosis (look it up).
>
>
>
> Dear Xin Yao,
>
> Have you done development (e.g., each life must succeed in lifetime
> learning from birth to death)?
>
> Best regards,
>
> -John
>
>
>
>
>
>
>
> On Fri, Nov 15, 2024 at 9:43 PM Asim Roy <ASIM.ROY at asu.edu> wrote:
>
> Dear John,
>
>
>
> I should not be replying to your email. I did mention last time that I
> have interest in continuing this nonsense dialogue. And, I did provide you
> with some examples to justify the biological basis of post-selection. Read
> them carefully. I can add to them. Think about natural selection and
> survival of the fittest. Think about trying out different strategies for
> doing something and then selecting the best. Think about learning from past
> failures. They are all about post-selection, rejecting the bad solutions
> and picking the best. And this is at the individual level. That’s biology.
>
>
>
> As I stated before, I have no interest in continuing this nonsense
> discussion. And I have no idea who is listening to you and subscribes to
> your views. You are free to continue your discussion within your community.
>
>
>
> Best,
>
> Asim
>
>
>
> *From:* Juyang Weng <juyang.weng at gmail.com>
> *Sent:* Thursday, November 14, 2024 7:59 PM
> *To:* Asim Roy <ASIM.ROY at asu.edu>
> *Cc:* Dongshu Wang (王东署) <wangdongshu at zzu.edu.cn>; Russell T. Harrison <
> r.t.harrison at ieee.org>; Akira Horose <ahirose at ee.t.u-tokyo.ac.jp>; Hisao
> Ishibuchi <hisao at sustech.edu.cn>; Simon See <ssee at nvidia.com>; Kenji Doya
> <doya at oist.jp>; Robert Kozma <rkozma55 at gmail.com>; Simon See <
> Simon.CW.See at gmail.com>; Yaochu Jin <Yaochu.Jin at surrey.ac.uk>; Xin Yao <
> xiny at sustech.edu.cn>; amdnl at lists.cse.msu.edu; Danilo Mandic <
> d.mandic at imperial.ac.uk>; Irwin King <irwinking at gmail.com>; Jose Principe
> <principe at cnel.ufl.edu>; Marley Vellasco <marley at ele.puc-rio.br>; Ali
> Minai <minaiaa at gmail.com>
> *Subject:* Re: False "Great Leap Forward" in AI
>
>
>
> Dear Asim,
>
> I am trying to include your last response in the attached newsletter,
> but I cannot because it does not have substance.
>
> You wrote, "John knows fully well that he is falsely accusing others of
> “cheating” and “misdeeds” when post-selection has a biological basis."
>
> But you do not have any substance to substantiate your single-sentence
> claim.
>
> Post-selection does not have a biological basis, because biology (1)
> does not cheat, (2) does not hide, and (3) does not exaggerate prediction
> accuracy.
>
> You will receive the upcoming newsletter Vol. 18, No. 4, 2024 with more
> details about (1), (2) and (3) if you subscribe to the Newsletter. Let me
> know if you cannot find the subscription site.
>
> Best regards,
>
> -John
>
>
>
>
>
> On Wed, Jul 17, 2024 at 7:21 PM Asim Roy <ASIM.ROY at asu.edu> wrote:
>
> Dear John,
>
>
>
> This is your typical dishonesty while you accuse others of “cheating” and
> “misdeeds.” I sent the attached reply to you on July 14 and it could have
> been easily included in your newsletter. Everyone knows that the newsletter
> is online and can be easily amended to include this reply, which is
> attached. So, I request Dongshu Wang to include this in Issue No. 3 because
> it has continuity with the other arguments in that issue. I have no
> interest in starting another nonsense dialogue that you mention.
>
>
>
> To All:
>
>
>
> John knows fully well that he is falsely accusing others of “cheating” and
> “misdeeds” when post-selection has a biological basis.
>
>
>
> Best,
>
> Asim
>
>
>
>
>
> *From:* Juyang Weng <juyang.weng at gmail.com>
> *Sent:* Wednesday, July 17, 2024 2:24 PM
> *To:* Asim Roy <ASIM.ROY at asu.edu>
> *Cc:* Dongshu Wang (王东署) <wangdongshu at zzu.edu.cn>; Russell T. Harrison <
> r.t.harrison at ieee.org>; Akira Horose <ahirose at ee.t.u-tokyo.ac.jp>; Hisao
> Ishibuchi <hisao at sustech.edu.cn>; Simon See <ssee at nvidia.com>; Kenji Doya
> <doya at oist.jp>; Robert Kozma <rkozma55 at gmail.com>; Simon See <
> Simon.CW.See at gmail.com>; Yaochu Jin <Yaochu.Jin at surrey.ac.uk>; Xin Yao <
> xiny at sustech.edu.cn>; amdnl at lists.cse.msu.edu; Danilo Mandic <
> d.mandic at imperial.ac.uk>; Irwin King <irwinking at gmail.com>; Jose Principe
> <principe at cnel.ufl.edu>; Marley Vellasco <marley at ele.puc-rio.br>; Ali
> Minai <minaiaa at gmail.com>
> *Subject:* Re: False "Great Leap Forward" in AI
>
>
>
> Dear Asim,
>
> 1. The Newsletter should not be altered after its publication on July
> 16, 2024.
>
> 2. I have not had time to read your previous email as a formal review
> for Issue Vol. 18, No. 3 either before its publication. You changed
> my mind too late.
>
> 3. Please consider submitting an [AI Crisis] Dialogue for Issue Vol.
> 18, No. 4 instead.
>
> Best regards,
>
> -John
>
>
>
> On Wed, Jul 17, 2024 at 4:47 AM Asim Roy <ASIM.ROY at asu.edu> wrote:
>
> Dear John,
>
>
>
> Please have Dongshu Wang publish my last reply in your just published
> newsletter. It refutes your claim that post-selection is not a biological
> process. And all the other nonsense claims about the need to publish
> non-optimal solutions.
>
>
>
> The newsletter is online and my last note (attached) can be easily added
> to it and a notification sent to all your subscribers about my last reply.
> Otherwise, I will consider it a dishonesty on your part.
>
>
>
> Best,
>
> Asim
>
>
>
> *From:* Juyang Weng <juyang.weng at gmail.com>
> *Sent:* Tuesday, July 16, 2024 11:53 AM
> *To:* Asim Roy <ASIM.ROY at asu.edu>
> *Cc:* Russell T. Harrison <r.t.harrison at ieee.org>; Akira Horose <
> ahirose at ee.t.u-tokyo.ac.jp>; Hisao Ishibuchi <hisao at sustech.edu.cn>;
> Simon See <ssee at nvidia.com>; Kenji Doya <doya at oist.jp>; Robert Kozma <
> rkozma55 at gmail.com>; Simon See <Simon.CW.See at gmail.com>; Yaochu Jin <
> Yaochu.Jin at surrey.ac.uk>; Xin Yao <xiny at sustech.edu.cn>;
> amdnl at lists.cse.msu.edu; Danilo Mandic <d.mandic at imperial.ac.uk>; Irwin
> King <irwinking at gmail.com>; Jose Principe <principe at cnel.ufl.edu>; Marley
> Vellasco <marley at ele.puc-rio.br>; Ali Minai <minaiaa at gmail.com>
> *Subject:* Re: False "Great Leap Forward" in AI
>
>
>
> Dear Asim,
>
> Sorry, this response is too late for Vol. 18, No. 3, 2024. You wrote
> that you would not respond anymore.
>
> I saw it just now after publishing No. 3. See CDS TC Newsletter Vol.
> 18, No. 3, 2024
> <https://urldefense.com/v3/__https:/www.cse.msu.edu/amdtc/amdnl/CDSNL-V18-N3.pdf__;!!IKRxdwAv5BmarQ!df8y5hJWRutTV1ot7ePop959eE92GM_dhD75tdtWYF2lZEofBYgmsLc1_7pL8NhOv4PIlQJbPMVSWs_LQNxiumsb$>
>
> 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.
>
> Best regards,
>
> -John
>
>
>
> On Mon, Jul 15, 2024 at 12:10 AM Asim Roy <ASIM.ROY at asu.edu> wrote:
>
> Dear John,
>
>
>
> 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.
>
>
>
> 1)
>
> *Asim Roy 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."
>
> *John Weng’s response*: *This is irrelevant*, as your mother is not
> inside your skull, but a human programmer is doing that inside the "skull."
>
> *Asim Roy response*: 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.
>
>
>
> 2)
>
> *Asim Roy wrote*, "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!”.
>
> *John Weng’s response*: 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.
>
> *Asim Roy response*: 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. Take a look at the
> concept cell findings (Jennifer Aniston cells). Here’s from Reddy and
> Thorpe (2014)
> <https://urldefense.com/v3/__https:/www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2020.00059/full*B6__;Iw!!IKRxdwAv5BmarQ!df8y5hJWRutTV1ot7ePop959eE92GM_dhD75tdtWYF2lZEofBYgmsLc1_7pL8NhOv4PIlQJbPMVSWs_LQHJKuM0B$>:
> “concept cells have “*meaning* of a given stimulus in a manner that is
> *invariant* to different representations of that stimulus.” Can you
> replicate that phenomenon in DN3?
>
>
>
> 3)
>
> 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.
>
>
>
> *Asim Roy:* "he does use an optimization method to weed out bad
> solutions."
>
> *John Weng: *This is false. DN does not weed out bad solutions, since
> it has only one solution.
>
> *Asim’s Response*: Just imagine, he claims he finds a globally optimal
> solution in a complex network without weeding out bad solutions. That is
> almost magical.
>
>
>
> *Asim Roy:* "In optimization, we only report the best solution."
>
> *John Weng:* This is misconduct, if you hide bad-looking data, like
> hiding all other students in your class.
>
> *Asim’s Response*: 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.
>
>
>
> *Asim Roy:* "There is no requirement to report any non-optimal
> solutions."
>
> *John Weng:* This is not true for scientific papers and business reports.
>
> *Asim’s Response*: Again, how do I respond to that. We do that all the
> time.
>
>
>
> *Asim Roy:* "If someone is doing part of the optimization manually,
> post-hoc, there is nothing wrong with that either."
>
> *John Weng*: This is false because the so-called post-hoc solution did
> not have a test!
>
> *Asim’s Response*: 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?
>
>
>
> 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.
>
>
>
> Thanks,
>
> Asim Roy
>
> Professor, Information Systems
>
> Arizona State University
>
> Asim Roy | ASU Search <https://search.asu.edu/profile/9973>
>
> Lifeboat Foundation Bios: Professor Asim Roy
> <https://urldefense.com/v3/__https:/lifeboat.com/ex/bios.asim.roy__;!!IKRxdwAv5BmarQ!aCvWF-PEaRtFT0lr5G-TVd1WSX7BloN_D524nbIUhctg9BC609q63-E91LYTCtXzoEQMZbkc5gnl53le6QZXPE1y$>
>
>
>
>
>
>
>
>
>
>
>
>
>
> *From:* Juyang Weng <juyang.weng at gmail.com>
> *Sent:* Friday, July 5, 2024 6:23 PM
> *To:* Asim Roy <ASIM.ROY at asu.edu>
> *Cc:* Russell T. Harrison <r.t.harrison at ieee.org>; Akira Horose <
> ahirose at ee.t.u-tokyo.ac.jp>; Hisao Ishibuchi <hisao at sustech.edu.cn>;
> Simon See <ssee at nvidia.com>; Kenji Doya <doya at oist.jp>; Robert Kozma <
> rkozma55 at gmail.com>; Simon See <Simon.CW.See at gmail.com>; Yaochu Jin <
> Yaochu.Jin at surrey.ac.uk>; Xin Yao <xiny at sustech.edu.cn>;
> amdnl at lists.cse.msu.edu; Danilo Mandic <d.mandic at imperial.ac.uk>; Irwin
> King <irwinking at gmail.com>; Jose Principe <principe at cnel.ufl.edu>; Marley
> Vellasco <marley at ele.puc-rio.br>
> *Subject:* Re: False "Great Leap Forward" in AI
>
>
>
> Dear Asim,
>
> 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.
>
> You wrote, "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.
>
> 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.
>
> You wrote, "There is no requirement to report any non-optimal
> solutions." This is not true for scientific papers and business reports.
>
> 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!
>
> 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."
>
> 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.
>
> You wrote, "John calls this process `cheating' and a `misdeed".”
> Yes, I still do.
>
> You wrote, "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.
>
> 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 "Luckiest from Post vs Single DN" in the attached
> file 2024-06-30-IJCNN-Tutorial-1page.pdf. 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.
>
> You wrote, "My hunch is, his algorithm falls short and can’t compete
> with the other ones." Your hunch is wrong. See above as you can see
> how wrong you are. DN is a lot better than even the false performance.
>
> You wrote, "And that’s the reason for this outrage against others."
> I am honest. All others should be honest too. Do not cheat like many
> Chinese in the Great Leap Forward.
>
> You wrote, "I would again urge IEEE to take action against John Weng
> for harassing plenary speakers at this conference and accusing them of
> “misdeeds.” I am simply trying to exercise my freedom of speech driven
> by my care for our community.
>
> Do you all see a "Great Leap Forward in AI" like the "Great Leap
> Forward" in 1958 in China?
>
> Best regards,
>
> -John
>
>
>
> On Fri, Jul 5, 2024 at 9:01 AM Asim Roy <ASIM.ROY at asu.edu> wrote:
>
> Dear All,
>
>
>
> 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.
>
>
>
> 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.
>
>
>
> I would again urge IEEE to take action against John Weng for harassing
> plenary speakers at this conference and accusing them of “misdeeds.”
>
>
>
> Best,
>
> Asim
>
>
>
> *From:* Juyang Weng <juyang.weng at gmail.com>
> *Sent:* Thursday, July 4, 2024 8:14 AM
> *To:* Asim Roy <ASIM.ROY at asu.edu>
> *Cc:* Russell T. Harrison <r.t.harrison at ieee.org>; Akira Horose <
> ahirose at ee.t.u-tokyo.ac.jp>; Hisao Ishibuchi <hisao at sustech.edu.cn>;
> Simon See <ssee at nvidia.com>; Kenji Doya <doya at oist.jp>; Robert Kozma <
> rkozma55 at gmail.com>; Simon See <Simon.CW.See at gmail.com>; Yaochu Jin <
> Yaochu.Jin at surrey.ac.uk>; Xin Yao <xiny at sustech.edu.cn>;
> amdnl at lists.cse.msu.edu; Danilo Mandic <d.mandic at imperial.ac.uk>; Irwin
> King <irwinking at gmail.com>
> *Subject:* Re: False "Great Leap Forward" in AI
>
>
>
> Dear Asim and All,
>
> 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".
>
> 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.)
>
> This single-network property is important because normally every
> developmental network (genome) must succeed in single-network development,
> from inception to birth, to death.
>
> 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:
> Misconduct 1: Cheating in the absence of a test (because the test set
> T is absent).
>
> Misconduct 2: Hiding bad-looking data (other less lucky predictors).
>
> A. I told Asim that DN tests its performance from birth to death,
> across the entire life!
>
> B. I told Asim that DN does not hide any data because it trains a
> single brain and reports all its lifetime errors!
>
> 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,
> https://www.scirp.org/journal/paperinformation?paperid=53728
> <https://urldefense.com/v3/__https:/www.scirp.org/journal/paperinformation?paperid=53728__;!!IKRxdwAv5BmarQ!aCvWF-PEaRtFT0lr5G-TVd1WSX7BloN_D524nbIUhctg9BC609q63-E91LYTCtXzoEQMZbkc5gnl53le6ch2TgX4$>
> .
>
> 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.
>
> 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.
>
> (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.
>
> (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.
>
> (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.
>
> 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
> https://ieeexplore.ieee.org/document/9892445
> <https://urldefense.com/v3/__https:/ieeexplore.ieee.org/document/9892445__;!!IKRxdwAv5BmarQ!aCvWF-PEaRtFT0lr5G-TVd1WSX7BloN_D524nbIUhctg9BC609q63-E91LYTCtXzoEQMZbkc5gnl53le6cX7cdu2$>
>
> However, all other learning algorithms have not solved this local
> minima problem. Therefore, they have to resort to trials and errors
> through training many predictors.
>
> Do you have any more questions?
> Best regards,
>
> -John
>
>
>
> On Thu, Jul 4, 2024 at 4:20 PM Asim Roy <ASIM.ROY at asu.edu> wrote:
>
> Dear All,
>
>
>
> There’s quite a bit of dishonesty here. John Weng can be accused of the
> same “misconduct” that he is accusing others of. He didn’t quite disclose
> what we discussed at the banquet last night. He is hiding all that.
>
>
>
> His basic argument is that we pick the best solution and report results on
> that basis. In a sense, when you formulate a machine learning problem as an
> optimization problem, that’s essentially what you are trying to do – get
> the best solution and weed out the bad ones. And HE DOES THE SAME IN HIS
> DEVELOPMENT NETWORK. When I asked him how his DN algorithm learns, he said
> it uses the maximum likelihood method, which is an old statistical method (Maximum
> likelihood estimation - Wikipedia
> <https://urldefense.com/v3/__https:/en.wikipedia.org/wiki/Maximum_likelihood_estimation__;!!IKRxdwAv5BmarQ!aCvWF-PEaRtFT0lr5G-TVd1WSX7BloN_D524nbIUhctg9BC609q63-E91LYTCtXzoEQMZbkc5gnl53le6RquWOgs$>).
> I quote from Wikipedia:
>
>
>
> The goal of maximum likelihood estimation is to find the values of the
> model parameters that *maximize the likelihood function over the
> parameter space*,[6]
> <https://urldefense.com/v3/__https:/en.wikipedia.org/wiki/Maximum_likelihood_estimation*cite_note-:0-6__;Iw!!IKRxdwAv5BmarQ!aCvWF-PEaRtFT0lr5G-TVd1WSX7BloN_D524nbIUhctg9BC609q63-E91LYTCtXzoEQMZbkc5gnl53le6VGxpEh8$> that
> is
>
> 𝜃^=argmax𝜃∈Θ𝐿𝑛(𝜃;𝑦) .[image: {\displaystyle {\hat {\theta
> }}={\underset {\theta \in \Theta }{\operatorname {arg\;max} }}\,{\mathcal
> {L}}_{n}(\theta \,;\mathbf {y} )~.}]
>
>
>
> So, by default, HE ALSO HIDES ALL THE BAD SOLUTIONS AND DOESN’T REPORT
> THEM. He never talks about all of this. He never mentions that I had talked
> about this in particular.
>
>
>
> I would suggest that based on his dishonest accusations against others
> and, in particular, against one of the plenary speakers here at the
> conference, that IEEE take some action against him. This nonsense has been
> going on for a longtime and it’s time for some action.
>
>
>
> By the way, I am not a member of IEEE. I am expressing my opinion only
> because he has falsely accused me also and I had enough of it. I have added
> Danilo Mandic and Irwin King to the list.
>
>
>
> Thanks,
>
> Asim Roy
>
> Professor, Information Systems
>
> Arizona State University
>
> Asim Roy | ASU Search <https://search.asu.edu/profile/9973>
>
> Lifeboat Foundation Bios: Professor Asim Roy
> <https://urldefense.com/v3/__https:/lifeboat.com/ex/bios.asim.roy__;!!IKRxdwAv5BmarQ!aCvWF-PEaRtFT0lr5G-TVd1WSX7BloN_D524nbIUhctg9BC609q63-E91LYTCtXzoEQMZbkc5gnl53le6QZXPE1y$>
>
>
>
> *From:* Juyang Weng <juyang.weng at gmail.com>
> *Sent:* Wednesday, July 3, 2024 5:54 PM
> *To:* Russell T. Harrison <r.t.harrison at ieee.org>
> *Cc:* Akira Horose <ahirose at ee.t.u-tokyo.ac.jp>; Hisao Ishibuchi <
> hisao at sustech.edu.cn>; Simon See <ssee at nvidia.com>; Kenji Doya <
> doya at oist.jp>; Robert Kozma <rkozma55 at gmail.com>; Simon See <
> Simon.CW.See at gmail.com>; Yaochu Jin <Yaochu.Jin at surrey.ac.uk>; Xin Yao <
> xiny at sustech.edu.cn>; Asim Roy <ASIM.ROY at asu.edu>; amdnl at lists.cse.msu.edu
> *Subject:* False "Great Leap Forward" in AI
>
>
>
> Dear Asim,
>
> 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.
>
> I alleged widespread false data in AI from the following two
> misconducts:
> Misconduct 1: Cheating in the absence of a test.
>
> Misconduct 2: Hiding bad-looking data.
>
> The following is a series of events during WCCI 2024 in Yokohama Japan.
>
> These examples showed that some active researchers in the WCCI community
> were probably not aware of the severity and urgency of the issue.
> 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.
> 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.
> 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.
> 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.
>
> 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.
>
> July 1, I saw all books on the display on the Springer Table appear to
> suffer from Post-Selection misconduct.
>
> Do we have a false data flooded "Great Leap Forward" in AI? Why?
>
> I welcome all those interested to discuss this important issue.
> Best regards,
> -John Weng
> --
>
> Juyang (John) Weng
>
>
>
>
> --
>
> Juyang (John) Weng
>
>
>
>
> --
>
> Juyang (John) Weng
>
>
>
>
> --
>
> Juyang (John) Weng
>
>
>
>
> --
>
> Juyang (John) Weng
>
>
>
>
> --
>
> Juyang (John) Weng
>
>
>
>
> --
>
> Juyang (John) Weng
>
>
>
>
> --
>
> Juyang (John) Weng
>
>
>
>
> --
>
> Juyang (John) Weng
>
--
Juyang (John) Weng
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.cse.msu.edu/pipermail/amdnl/attachments/20241117/4192aae4/attachment-0001.html>
-------------- next part --------------
A non-text attachment was scrubbed...
Name: image001.png
Type: image/png
Size: 183 bytes
Desc: not available
URL: <http://lists.cse.msu.edu/pipermail/amdnl/attachments/20241117/4192aae4/attachment-0001.png>
More information about the Amdnl
mailing list