We must stop flawed scholarship and start being serious about how to not advance AI

Zachary C. Lipton and Jacob Steinhardt have written an outstanding paper, entitled Troubling Trends in Machine Learning Scholarship, that focuses on patterns that are a trend in the scientific literature from the machine learning community: the failure to distinguish between explanation and speculation, the failure to identify the sources of empirical gains, the mathiness (the use of mathematics that obfuscates or impresses rather than clarifies), and the misuse of language. Those are the same issues that we find, PERMANENTLY!, in the failed efforts to define what (machine) intelligence is and is not, since the very origins of AI. Lipton and Steinhardt elaborate on the following possible causal factors: a complacency in the face of progress, the rapid expansion of the community, the consequent thinness of the reviewer pool, and misaligned incentives of scholarship vs. short-term measures of success. They don't stop there, they even provide suggestions for authors, publishers, and reviewers, and conclude that It is tremendously important that each and every AI researcher, practitioner, author, reviewer, publisher, investor, journalist, user, leader, student, educator, and enthusiast is aware of these implications. We will never advance AI in the right direction by carrying flawed scholarship all the way with us.