Connectionists: Fwd: Scientific Integrity, the 2021 Turing Lecture, etc.

[Baldi,Pierre]

Besides plagiarism, this community would be well-served by taking a 
frank look at the remarkable levels of cronyism, collusion, and 
subtle--but very real--manipulation that have permeated it for several 
decades.  In addition to self- , cross-, and suppressive citation 
issues, there are many other metrics to look at. To get started, one 
could ask the following simple questions and compute the corresponding 
statistics:

1) Over the past four decades, how often has the leadership of any 
relevant machine learning foundation changed?

2) Over the past four decades, what is the degree of over-representation 
by members of any particular organization in things like:
a) organizing and program committees of major machine learning 
conferences? b) AI/ML academic department and now also AI/ML corporate 
departments? c) editorial boards and other centers of power and 
dissemination?

3) What is the degree of over-representation of any particular 
organization in invited talks, workshops, tutorials, or other "special 
events", such as official birthday celebrations or on-stage Q&A sessions 
with rich and famous people, at major machine learning scientific 
conferences?

Cronyism and collusion are nothing new in human affairs, including 
science, and most of the time they are even legal.  But how well do 
these serve science or society?

The tip of the iceberg.

--Pierre Baldi



-------- Forwarded Message --------
Subject: 	Re: Connectionists: Scientific Integrity, the 2021 Turing 
Lecture, etc.
Date: 	Wed, 27 Oct 2021 07:52:09 +0000
From: 	Schmidhuber Juergen <juergen at idsia.ch>
To: 	connectionists at cs.cmu.edu <connectionists at cs.cmu.edu>



Hi, fellow artificial neural network enthusiasts!

The connectionists mailing list is perhaps the oldest mailing list on 
ANNs, and many neural net pioneers are still subscribed to it. I am 
hoping that some of them - as well as their contemporaries - might be 
able to provide additional valuable insights into the history of the field.

Following the great success of massive open online peer review (MOOR) 
for my 2015 survey of deep learning (now the most cited article ever 
published in the journal Neural Networks), I've decided to put forward 
another piece for MOOR. I want to thank the many experts who have 
already provided me with comments on it. Please send additional relevant 
references and suggestions for improvements for the following draft 
directly to me at juergen at idsia.ch:

https://people.idsia.ch/~juergen/scientific-integrity-turing-award-deep-learning.html

The above is a point-for-point critique of factual errors in ACM's 
justification of the ACM A. M. Turing Award for deep learning and a 
critique of the Turing Lecture published by ACM in July 2021. This work 
can also be seen as a short history of deep learning, at least as far as 
ACM's errors and the Turing Lecture are concerned.

I know that some view this as a controversial topic. However, it is the 
very nature of science to resolve controversies through facts. Credit 
assignment is as core to scientific history as it is to machine 
learning. My aim is to ensure that the true history of our field is 
preserved for posterity.

Thank you all in advance for your help!
Jürgen Schmidhuber







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