Brain-Mind Institute 

BMI Press 

Juyang (John) Weng

President 

4460 Alderwood Drive 

Okemos, Michigan 

48864 

USA 

juyang.weng@gmail.com

Tel: (517) 980-6270 

http://www.brain-mind-institute.org/ 

Prof. K.J. Ray Liu Aug. 21, 2022 

IEEE President 2022 

Department of Electrical and Computer Engineering 

2232 Kim Bldg 

University of Maryland 

College Park, MD 20742 

Email: kjrliu@umd.edu 

Phone: (301) 405-6619 

Subject: Misconducts in “Deep Learning” and Tolerance to new science of AMD 

(Autonomous Mental Development) 

Dear Prof Liu, 

Prof. Jim Kelly, the president of IEEE Computational Intelligence Society 

(CIS), informally suggested to me during IJCNN 2022 that for such scales of 

problems, I should contact an IEEE level leader like you. 

There are two problems, (1) misconducts in AI known as so-called “deep 

learning” and (2) lack of tolerance to new science AMD that does not suffer from 

the misconducts in “deep learning”. I did not claim that (2) is due to (1), but the Ad 

Hoc Committee timely appointed by Jim Kelly seems to have distorted my two 

claims by only denying (2) is due to (1). Of course, I should not have the burden to 

prove (2) is due to (1). The Ad Hoc Committee did not address (1) or (2) 

individually. I am not informed whether any systematic measures about either (1) 

or (2) would take place in CIS. 

In terms of (1), let me provide some text in my popular science book titled 

“Conscious Learning: Humans and Machines”, that will be published by WSPC. 

== Start of the quoted text == 

The author believes that the main published performance figures of almost all 

“deep learning” projects, including many media hyped ones, such as AlphaGo, 

AlphaGoZero, AlphaZero, AlphaFold, and the IBM Debater, appear to have been 

grossly inflated. Namely, so called “deep learning” is in a controversial status, as 

we expose below. 

Such misconducts composed of a combination of two types, “data deletion” and 

“test on training data”. 

The First Misconduct: Data Deletions 

Data deletion is serious research misconduct, since the person who conducts an 

experiment deletes data that he dislikes. A proper conduct is to report all data that 

were generated. If the number of performance data is too large to report all data, he 

must at least provide a characteristic of the data distribution. For performance data, 

the reported information for distribution should include the worst case, the average 

case and the best case. 

For example, for a teacher to report his teaching performance of his class for a 

standard test (e.g., TOEFL, Test Of English as a Foreign Language), he should not 

only report the highest score of his class, but also the minimum score and the 

average score of the class. Data deletion happens when he reports only the highest 

score, or few high scores, but deletes all other scores from the report. 

In almost all so-called “deep learning” projects, each project needs to trained 

multiple networks, often many. Each network starts from a different set of random 

network weights and other parameters. The performances of these networks are 

very different, although they use the same training data set. The person who 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

conducted the experiments deleted many performance data from all trained networks. He reports 

only the performance of the luckiest network. This is a gross violation of well-established 

statistical protocol that requires reporting the average performance of all trained networks, instead 

of the luckiest. For the same reason, in a lottery, one should report the average chance for one 

lottery ticket to win money from the lottery. 

We call the stage of selecting a trained network from multiple trained network “PostSelection” stage, selection after training. 

The Second Misconduct: Tests on Training Data Sets 

Furthermore, those who picked up the luckiest network probably based on a test data set, 

instead of only training data set. This is because a trained network that fits the training set well 

does not mean that it will fit the test set well. 

For the same reason, should we test students using examples that are exactly the same as 

taught examples? No. This is because students must demonstrate their capability to extend taught 

examples to similar but different from taught examples. 

Post-Selection using a test set is another violation of well-established statistical protocols. 

This is like testing a student using a training set, so he does not need to understand. All he needs 

is to memorize all the training examples. 

In summary, “deep learning” suffers from two compounding misconducts in the PostSelection stage: the first, data deletion; the second, tests on training sets. 

The first misconduct and the second misconduct are both research misconducts. To reach the 

level of extreme exaggeration of performance data that has misled the public in various countries 

today, it is necessary to superimpose these two misconducts together. The absence of any one 

misconduct of the two is far from enough to reach such a serious level. 

The principles of both misconducts are well-understood by the scientific community. 

However, engineers unintentionally or intentionally ignored it. However, when my paper about 

the misconducts was published and my report was submitted to the journals that published the 

misconduct papers, the correspondence authors of these papers should have known their 

behaviors were wrong. But they still evaded the truth to the public. 

As far as I am aware, the only neural network series that does not have the Post-Selection 

problem is the series from Cresceptron to Developmental Network. Because freedom from PostSelection requires a solution to a million-dollar problem—the local-minima problem in a high 

dimensional space, that is why almost all other neural network projects, including those in large 

publicly listed companies like Google and IBM and all those that the author is aware of, suffer 

from the local-minima problem. 

Furthermore, the author has published a paper titled “20 Million-Dollar Problems for Any 

Brain Models and a Holistic Solution: Conscious Learning” in Proceedings of International Joint 

Conference on Neural Networks (IJCNN) 2022. It reasons that any brain model, like DN-3 

below, must have solved at least 20 million-dollar problems. The local minima problem is one 

of them. 

== End of the quoted text== 

For (2), please read Attachment 1. 

Please acknowledge your receipt of this letter via email and kindly let me know your planned 

actions before the end of your 2022 term. 

Sincerely yours, 

Juyang (John) Weng 

Att: 1: 2022-06-15-CIS-Conferences.pdf; 2: 2022-07-28-AdHoc-Committee.pdf