Connectionists: Rising Stars in AI Symposium at KAUST

[Juyang Weng]

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:
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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>

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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
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