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Reproducibility via Crowdsourced Reverse Engineering: A Neural Network Case Study With DeepMind's Alpha Zero

2019/09/05 by Dustin Tanksley, Tanksley, Dustin, Donald C. Wunsch +2 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · #Adversarial Robustness in Machine Learning #Artificial intelligence #Artificial neural network #Cell Image Analysis Techniques #Computer science #Computer security #Computers and Society (cs.CY) #Data science #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Intellectual property #Leverage (statistics) #Reverse engineering #Transparency (behavior) #Zero (linguistics) #cs.CY

paper · pdf · doi:10.48550/arxiv.1909.03032

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2019/09/05 · arxiv created 2019/09/09 · arxiv updated 2019/09/11 · openalex created_date 2019/09/12 · openalex updated_date 2026/07/28

Abstract

The reproducibility of scientific findings are an important hallmark of quality and integrity in research. The scientific method requires hypotheses to be subjected to the most crucial tests, and for the results to be consistent across independent trials. Therefore, a publication is expected to provide sufficient information for an objective evaluation of its methods and claims. This is particularly true for research supported by public funds, where transparency of findings are a form of return on public investment. Unfortunately, many publications fall short of this mark for various reasons, including unavoidable ones such as intellectual property protection and national security of the entity creating those findings. This is a particularly important and documented problem in medical research, and in machine learning. Fortunately for those seeking to overcome these difficulties, the internet makes it easier to share experiments, and allows for crowd-sourced reverse engineering. A case study of this capability in neural networks research is presented in this paper. The significant success of reverse-engineering the important accomplishments of DeepMind's Alpha Zero exemplifies the leverage that can be achieved by a concerted effort to reproduce results.

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