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

TL;DR

This paper demonstrates how crowdsourced reverse engineering can enhance reproducibility in neural network research, using DeepMind's Alpha Zero as a case study to showcase the potential of collaborative efforts in verifying scientific claims.

Contribution

It introduces a method of crowdsourced reverse engineering for neural networks and applies it to replicate Alpha Zero's results, highlighting its effectiveness.

Findings

Successful reverse engineering of Alpha Zero's performance

Crowdsourcing accelerates reproducibility efforts

Enhanced transparency in neural network research

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.