A snapshot of the 2019 NeurIPS conference in Vancouver, Canada. Photo: Khari Johnson / VentureBeat |
There’s no question that the momentum reflects an uptick in publicity and funding — and correspondingly, competition — within the AI research community. But some academics suggest the relentless push for progress might be causing more harm than good...
In a recent tweet, Zachary Lipton, an assistant professor at Carnegie Mellon University, jointly appointed in the Tepper School of Business and the machine learning department, proposed a one-year moratorium on papers for the entire community, which he said might encourage “thinking” without “sprinting/hustling/spamming” toward deadlines...
There’s preliminary evidence to suggest the crunch has resulted in research that could mislead the public and stymie future work. In a 2018 meta analysis undertaken by Lipton and Jacob Steinhardt, who is a member of the statistics faculty at the University of California, Berkeley and the Berkeley Artificial Intelligence Lab, the two assert that troubling trends have emerged in machine learning scholarship, including:
- A failure to distinguish between explanation and speculation and to identify the sources of empirical gains
- The use of mathematics that obfuscates or impresses rather than clarifies
- The misuse of language, for example by overloading established technical terms
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Source: VentureBeat