Connectionists: Rising Stars in AI Symposium at KAUST: Protocol Flaws

Tue Mar 15 20:00:05 EDT 2022

Dear Danko,

Since this issue is rampant in AI, I take the liberty to give a CC to
connectionist.
(1) Misleading.  Yes, I agree with you.   Before I was using fraud and
research misconduct, but Nature EIC suggested using a softer term which I
agreed to.
(2) Typo: thank you immensely.
(3) "p-value in statistical science":  I agree with you.  My point is to
show the necessity. They are not the same.
(4) "How about other papers in Nature that you did not mention?"  I might
miss some machine learning papers in Nature since 2015.  But almost all
machine learning papers since 2015 in Nature and Science suffer from the
protocol flaw of Post-Selections Using Test Sets.  This protocol flaw is
many more machine learning papers that use neural networks,
reinforcement learning, swarm mode, reservoir mode, evolutionary mode, and
so on.   Please suggest what we should do, as much resource is being wasted.
Best regards,
-John

On Mon, Mar 14, 2022 at 5:54 AM Danko Nikolic <danko.nikolic at gmail.com>
wrote:

> Dear Juyang,
>
> I just read your post and immediately read the linked paper. I find it
> interesting. Thank you for that. I am amazed that the extent of the problem
> is so high.
>
> I also learned something. I am now completing one study and I realize I
> did not decouple the random seeds. Thank you for pointing out the need for
> such decoupling.
>
> Here some comments from my side:
>
>  - you mention that the results are "misleading", but it seems to me that
> a more strong term should be used: the results are "too optimistic". The
> real performances are worse than what has been reported.
>
>   - found a typo: at one place "ransom seed" should be "random seed".
>
>    - a minor comment: You say: " This is similar to, but more transparent
> than, so-called p-value in statistical science. " I would say that  the two
> things are not too similar. Reporting average + SD is more similar to
> standard error of the measurement.
>
>   Finally, I have a question: How about other papers in Nature that you
> did not mention? Are there any other papers that have done it right? Should
> they be mentioned too? Or in other words, do you have any impression of
> which percentage of papers have done it right? Is it maybe that all of the
> papers are misleading? Or maybe the split is 50-50?
>
> I hope you don't mind me sending my thoughts.
>
> Best,
>
> Danko
>
>
>
>
>
>
> Dr. Danko Nikolić
> www.danko-nikolic.com
> https://www.linkedin.com/in/danko-nikolic/
> --- A progress usually starts with an insight ---
>
>
> On Mon, Mar 14, 2022 at 7:53 AM Juyang Weng <juyang.weng at gmail.com> wrote:
>
>> Dear Juergen,
>> Your service is appreciated but what you are doing is risky.
>> I predict that a large number of them, if not all, are "rising stars" of
>> protocol flaws.
>> Please read why:
>> ------------------------------
>> Message: 2
>> Date: Thu, 10 Mar 2022 17:21:37 -0500
>> From: Juyang Weng <juyang.weng at gmail.com>
>> To: Post Connectionists <connectionists at mailman.srv.cs.cmu.edu>
>> Subject: Connectionists: A challenge to Post-Selections in Deep
>>         Learning
>> Message-ID:
>>         <CAJmX=
>> 6Bx139Ux0iA5PwvEEPzjyXiUcU+WQAcUSO9oOQiSCnkxA at mail.gmail.com>
>> Content-Type: text/plain; charset="utf-8"
>>
>> Through a review of AI papers published in Nature since 2015, this report
>> discusses the technical flaws called Post-Selection in the charged papers.
>> This report suggests the appropriate protocol, explains reasons for the
>> protocol, why what the papers have done is inappropriate and therefore
>> yields misleading results. The charges below are applicable to whole
>> systems and system components, and in all learning modes, including
>> supervised, reinforcement, and swarm learning modes, since the concepts
>> about training sets, validation sets, and test sets all apply. A
>> reinforcement-learning algorithm includes not only a handcrafted form of
>> task-specific, desired answers but also values of all answers, desired and
>> undesired. A supervised learning method typically does not provide values
>> for intermediate steps (e.g., hidden features), but in contrast, a
>> reinforcement learning mode must provide values for intermediate steps
>> using a greedy search (e.g., time discount). Casting dice is the key
>> protocol flaw that owes a due transparency about all losers (e.g., how
>> good
>> they are). A commercial product is impractical if it requires every
>> customer to cast dice and almost all trained ?lives? must cause accidents
>> and be punished by deaths except the luckiest ?life?. All the losers and
>> the luckiest are unethically determined by so called ?unseen? (in fact
>> should be called ?first seen?) test sets but the human programmer saw all
>> the scores before he decided who are losers and who is the luckiest. Such
>> a
>> deep learning methodology gives no product credibility.
>>
>>
>> http://www.cse.msu.edu/%7eweng/research/2021-06-28-Report-to-Nature-specific-PSUTS.pdf
>> <http://www.cse.msu.edu/~weng/research/2021-06-28-Report-to-Nature-specific-PSUTS.pdf>
>>
>> ----
>> Date: Fri, 11 Mar 2022 04:57:02 +0000
>> From: Schmidhuber Juergen <juergen at idsia.ch>
>> To: "connectionists at cs.cmu.edu" <connectionists at cs.cmu.edu>
>> Subject: Connectionists: Rising Stars in AI Symposium at KAUST
>> Message-ID: <A95E83F0-0E51-451F-B532-BF0FB9DF0CCD at supsi.ch>
>> Content-Type: text/plain; charset="utf-8"
>>
>> The AI Initiative at KAUST is hosting the inaugural "Rising Stars in AI
>> Symposium" at KAUST from March 13-15. This event is geared towards young
>> researchers (including Ph.D. students, PostDocs and young faculty), who
>> have recently published promising work at leading AI venues. There will be
>> dozens of brief in-person presentations about papers recently accepted at
>> major AI conferences such as NeurIPS, CVPR, EMNLP, ACL, ICML, ICLR, etc.
>> All speakers will start with an intro for non-AI experts.
>>
>> To view the complete program and for more event details, please visit the
>> symposium website:
>>
>> https://cemse.kaust.edu.sa/ai/aii-symp-2022
>>
>> The symposium will be limited to in-person attendance. So, if you are
>> interested in joining the event, please register here:
>>
>>
>> https://docs.google.com/forms/d/e/1FAIpQLSfNnc3N9sGJwkRfePzsZakQSkunhxRadnecGSOd1m7-F7At-A/viewform?hl=en
>>
>> We will try our best to accommodate those who register to attend in
>> person.
>>
>> J?rgen Schmidhuber
>> Director, AI Initiative, KAUST
>> https://people.idsia.ch/~juergen/kaust-2021.html
>> https://people.idsia.ch/~juergen/kaust-2021-hiring.html
>>
>> --
>> Juyang (John) Weng
>>
>

-- 
Juyang (John) Weng
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