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

[Schmidhuber Juergen]

Regarding the thoughtful comments of Pierre Baldi on cronyism and collusion: Tom Dietterich offered a somewhat more optimistic view, but pointed out that the head of NeurIPS has never changed throughout all these decades. The lack of new blood in senior governance of such conferences may very well be the root of the problem. After all, change is hard. Even in recent years the NeurIPS head has continued to promulgate a revisionist "history" of deep learning [S20] mentioned in Sec. II and XIII of the report https://people.idsia.ch/~juergen/scientific-integrity-turing-award-deep-learning.html - let me cut and paste some text from there:

ACM seems to be influenced by a misleading and rather self-serving "history of deep learning" propagated by LBH & co-authors, e.g., Sejnowski [S20] (see Sec. II, XIII). It goes more or less like this: "In 1969, Minsky & Papert [M69] showed that shallow NNs without hidden layers are very limited and the field was abandoned until a new generation of neural network researchers took a fresh look at the problem in the 1980s." [S20] However, the 1969 book [M69] addressed a "problem" of shallow learning (introduced around 1800 when Gauss & Legendre started to model data through linear regression and the method of least squares [DL1-2]) that had already been solved 4 years prior by Ivakhnenko & Lapa's popular deep learning method of 1965 [DEEP1-2][DL2]. This method was fully capable of learning internal representations in units that were not part of the input or output. Minsky was perhaps unaware of this and failed to correct it later [HIN](Sec. I). Deep learning research was alive and kicking in the 1970s, especially outside of the Anglosphere [DEEP2][BP6][CNN1][DL1-2].


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On 29 Oct 2021, at 21:21, Dietterich, Thomas <tgd at oregonstate.edu> wrote:

Pierre,

Regarding leadership turnover, with the exception of Terry Sejnowski, the makeup of the NIPS Foundation board turns over on a regular basis, as there are term limits for all Board members. The Board consists of previous program chairs. The IMLS Board has had term limits and regular turnover since its founding, and its members are elected. Both organizations are always seeking volunteers for the many tasks involved in running the conference, and future program chairs are drawn from the people who have served in these positions. I encourage the readers of this list who are interested to contact the members of the conference organizing committees and express your interest. 

I have long sought for mechanisms to broaden the set of candidates considered for leadership positions, editorial boards, etc. When people rely on their own professional networks, this naturally limits the set of names that get considered. And this combines with the "founder effect" that still exists because the field was nucleated in North America. Nonetheless, I think the machine learning community is very open compared to other fields. That said, we could do much better!

--Tom

Thomas G. Dietterich, Distinguished Professor Emeritus
School of Electrical Engineering and Computer Science                        
US Mail: 1148 Kelley Engineering Center       

Office: 2067 Kelley Engineering Center       
Oregon State Univ., Corvallis, OR 97331-5501
Voice: 541-737-5559; FAX: 541-737-1300
URL: http://web.engr.oregonstate.edu/~tgd/

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On 28 Oct 2021, at 20:05, Baldi,Pierre <pfbaldi at ics.uci.edu> wrote:

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