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Recent advances in Machine Learning (ML) and Artificial Intelligence (AI) follow a familiar structure: A firm releases a large, pretrained model.
Language models are few-shot learners
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Strategic classification. In Proceedings of the 2016 ACM conference on innovations in theoretical computer science . Association for Computing Machinery, Cambridge, Massachusetts, 111–122
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Convolutional neural networks for medical image analysis: Full training or fine tuning?
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Growing a brain: Fine-tuning by increasing model capacity. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 2471–2480
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Why the lean start-up changes everything
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Universal language model fine-tuning for text classification
A comprehensive survey on transfer learning
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On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 2021
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Artificial intelligence as a general-purpose technology: an historical perspective
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Model-sharing games: Analyzing federated learning under voluntary participation. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 35. AAAI, Virtual, 5303–5311
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Learning transferable visual models from natural language supervision. In International conference on machine learning . PMLR, Virtual, 8748–8763
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Jeremy Howard and Sebastian Ruder. 2018 · 2018
Cited alongside, same era.
AI as the next GPT: a Political-Economy Perspective
Manuel Trajtenberg. 2018 · 2018
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Collusion and bargaining in asymmetric Cournot duopoly—An experiment
Christian Fischer and Hans-Theo Normann. 2019 · 2019
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The disparate effects of strategic manipulation. In Proceedings of the Conference on Fairness, Accountability, and Transparency . Association for Computing Machinery, Atlanta, GA, 259–268
Lily Hu, Nicole Immorlica, and Jennifer Wortman Vaughan. 2019 · 2019
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The social cost of strategic classification. In Proceedings of the Conference on Fairness, Accountability, and Transparency . Association for Computing Machinery, Atlanta, Georgia, 230–239
Smitha Milli, John Miller, Anca D Dragan, and Moritz Hardt. 2019 · 2019
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To tune or not to tune? adapting pretrained representations to diverse tasks
Matthew E Peters, Sebastian Ruder, and Noah A Smith. 2019 · 2019
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Roles for computing in social change. In Proceedings of the 2020 conference on fairness, accountability, and transparency . Association for Computing Machinery, New York, NY, United States, 252–260
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Revisiting the Black Box Society by rethinking the political economy of big data
Benedetta Brevini and Frank Pasquale. 2020 · 2020
Cited alongside, same era.
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Florence: A new foundation model for computer vision
Lu Yuan, Dongdong Chen, Yi-Ling Chen, Noel Codella, Xiyang Dai, Jianfeng Gao, Houdong Hu, Xuedong Huang, Boxin Li, Chunyuan Li, et al · 2021
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Towards artificial general intelligence via a multimodal foundation model
Nanyi Fei, Zhiwu Lu, Yizhao Gao, Guoxing Yang, Yuqi Huo, Jingyuan Wen, Haoyu Lu, Ruihua Song, Xin Gao, Tao Xiang, et al · 2022
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Fine-tuning can distort pretrained features and underperform out-of-distribution
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Strategic ranking. In International Conference on Artificial Intelligence and Statistics . PMLR, Virtual, 2489–2518
Lydia T Liu, Nikhil Garg, and Christian Borgs. 2022 · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Bargaining over a jointly produced pie: The effect of the production function on bargaining outcomes
Ai Takeuchi, Róbert F Veszteg, Yoshio Kamijo, and Yukihiko Funaki. 2022 · 2022
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Emergent abilities of large language models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al · 2022
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Ecosystem Graphs: The Social Footprint of Foundation Models
Rishi Bommasani, Dilara Soylu, Thomas I Liao, Kathleen A Creel, and Percy Liang. 2023 · 2023
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Understanding accountability in algorithmic supply chains. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency . Association for Computing Machinery, New York, NY, United States, 1186–1197
Jennifer Cobbe, Michael Veale, and Jatinder Singh. 2023 · 2023
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Gpts are gpts: An early look at the labor market impact potential of large language models
Tyna Eloundou, Sam Manning, Pamela Mishkin, and Daniel Rock. 2023 · 2023
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Could machine learning be a general purpose technology? a comparison of emerging technologies using data from online job postings
Avi Goldfarb, Bledi Taska, and Florenta Teodoridis. 2023 · 2023
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Algorithmic Collective Action in Machine Learning
Moritz Hardt, Eric Mazumdar, Celestine Mendler-Dünner, and Tijana Zrnic. 2023 · 2023
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Strategic Evaluation. In Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization . ACM, Boston, MA, 1–12
Benjamin Laufer, Jon Kleinberg, Karen Levy, and Helen Nissenbaum. 2023 · 2023
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Dislocated accountabilities in the “AI supply chain”: Modularity and developers’ notions of responsibility
David Gray Widder and Dawn Nafus. 2023 · 2023
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