In plain words: A survey series interviews machine-learning researchers about where the field stands, covering interpretable AI, whether language benchmarks still measure real progress, how well we understand deep learning, and academia's future. Rather than one verdict, it offers a snapshot of expert views to compare against later editions.

Abstract · Perspectives on the State and Future of Deep Learning - 2023

The goal of this series is to chronicle opinions and issues in the field of machine learning as they stand today and as they change over time. The plan is to host this survey periodically until the AI singularity paperclip-frenzy-driven doomsday, keeping an updated list of topical questions and interviewing new community members for each edition. In this issue, we probed people's opinions on interpretable AI, the value of benchmarking in modern NLP, the state of progress towards understanding deep learning, and the future of academia.

Micah Goldblum, Anima Anandkumar, Richard Baraniuk, Tom Goldstein, Kyunghyun Cho, Zachary C Lipton, Melanie Mitchell, Preetum Nakkiran, Max Welling, Andrew Gordon Wilson
arXiv:2312.09323 · cs.AI, cs.LG · submitted Dec 7, 2023 · updated Dec 19, 2023
abstract · pdf · html