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.
@article{arxiv.2312.09323,
title = {Perspectives on the State and Future of Deep Learning - 2023},
author = {Micah Goldblum and Anima Anandkumar and Richard Baraniuk and Tom Goldstein and Kyunghyun Cho and Zachary C Lipton and Melanie Mitchell and Preetum Nakkiran and Max Welling and Andrew Gordon Wilson},
journal= {arXiv preprint arXiv:2312.09323},
year = {2023}
}