Report · Page 185

On the Opportunities and Risks of Foundation Models
185
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford,
Jeffrey Wu, and Dario Amodei. 2020. Scaling Laws for Neural Language Models. ArXiv abs/2001.08361 (2020). https:
//arxiv.org/abs/2001.08361
Siddharth Karamcheti, Dorsa Sadigh, and Percy Liang. 2020. Learning Adaptive Language Interfaces through Decomposition.
In EMNLP Workshop for Interactive and Executable Semantic Parsing (IntEx-SemPar).
Siddharth Karamcheti, Edward C. Williams, Dilip Arumugam, Mina Rhee, Nakul Gopalan, Lawson L. S. Wong, and Stefanie
Tellex. 2017. A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented
Instructions. In First Workshop on Language Grounding for Robotics @ ACL.
Siddharth Karamcheti, Albert J. Zhai, Dylan P. Losey, and Dorsa Sadigh. 2021. Learning Visually Guided Latent Actions for
Assistive Teleoperation. In Learning for Dynamics and Control.
Holden Karnofsky. 2016.
Potential risks from advanced artificial intelligence: the philanthropic opportunity.
Open
Philanthropy Project 6 (2016).
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau
Yih. 2020. Dense Passage Retrieval for Open-Domain Question Answering. In Proceedings of the 2020 Conference on
Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, 6769–6781.
https://doi.org/10.18653/v1/2020.emnlp-main.550
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. 2020. Analyzing and improving
the image quality of stylegan. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
8110–8119.
Nora Kassner, Philipp Dufter, and Hinrich Schütze. 2021. Multilingual LAMA: Investigating Knowledge in Multilingual
Pretrained Language Models. In EACL.
Hirokatsu Kataoka, Kazushige Okayasu, Asato Matsumoto, Eisuke Yamagata, Ryosuke Yamada, Nakamasa Inoue, Akio
Nakamura, and Yutaka Satoh. 2020. Pre-training without natural images. In Proceedings of the Asian Conference on
Computer Vision.
Michael Katell, Meg Young, Dharma Dailey, Bernease Herman, Vivian Guetler, Aaron Tam, Corinne Bintz, Daniella Raz, and
P. M. Krafft. 2020. Toward situated interventions for algorithmic equity. In Proceedings of the 2020 Conference on Fairness,
Accountability, and Transparency. ACM. https://doi.org/10.1145/3351095.3372874
Daniel Martin Katz, Michael J Bommarito, and Josh Blackman. 2017. A general approach for predicting the behavior of the
Supreme Court of the United States. PloS one 12, 4 (2017), e0174698.
Amit Kaushal, Russ Altman, and Curt Langlotz. 2020. Geographic distribution of US cohorts used to train deep learning
algorithms. Jama 324, 12 (2020), 1212–1213.
Matthew Kay, Cynthia Matuszek, and Sean A. Munson. 2015. Unequal Representation and Gender Stereotypes in Image
Search Results for Occupations. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems
(Seoul, Republic of Korea) (CHI ’15). ACM, New York, NY, USA, 3819–3828. https://doi.org/10.1145/2702123.2702520
Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim
Green, Trevor Back, Paul Natsev, et al. 2017. The kinetics human action video dataset. arXiv preprint arXiv:1705.06950
(2017).
Alexander Ke, William Ellsworth, Oishi Banerjee, Andrew Y Ng, and Pranav