On the Opportunities and Risks of Foundation Models

Written for the Research library (claude-opus-5-5) from the paper text. Original — arxiv.org/pdf/2108.07258

Summary

This report from Stanford's Center for Research on Foundation Models (CRFM) names and examines a new way of building AI. Large models are pretrained, usually with self-supervision, on huge and varied data, then adapted to many downstream tasks. The authors call these "foundation models" (for example BERT, GPT-3, CLIP and DALL-E). The name signals that many systems are built on them, even though the models are still unfinished and poorly understood. The work has no new experiments. It is a survey of about 160 pages by more than 100 researchers from many fields. It covers what the models can do (language, vision, robotics, reasoning, interaction), where they are used (healthcare, law, education), the technology (architectures, training, adaptation, evaluation, data, security, robustness, interpretability, theory) and their effects on society (fairness, misuse, environment, law, economics, ethics). The central argument rests on two ideas. Emergence means abilities such as in-context learning come from scale, not from explicit design. Homogenization means many applications are built from a few base models, so both improvements and defects reach every downstream system. The report matters because it gave the field its vocabulary and research agenda. It also argues that these sociotechnical systems can only be understood through collaboration across disciplines.

Key points

The report traces a history in three stages: machine learning (1990s), deep learning (around 2010) and foundation models (from about 2018–2019, with BERT). Each stage lets more of the system be learned instead of hand-built, and makes more of it shared: first the learning algorithm, then the architecture, now the model itself.
Three things made the current scale possible: GPU throughput and memory grew roughly 10× in four years; the Transformer architecture uses that hardware in parallel; and self-supervised objectives (masked language modeling in BERT, next-word prediction in GPT) can learn from unlabeled data.
GPT-3 has 175 billion parameters. With natural-language prompts alone, it handles many tasks reasonably well even though it was never trained on them directly.
After 2019, nearly all state-of-the-art NLP systems were adapted from a few models (BERT, RoBERTa, BART, T5). This creates single points of failure, and the biases of these models pass to everything built on them.
On ethics, the report calls for norms on how models are developed, audited and released. It also calls for data subjects to be able to opt out and to withdraw consent they gave earlier. It notes that AI lacks counterparts to IRB review, FDA-style regulators or professional oaths.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Other

Cited by

851 papers of our library cite it (of the 140673 whose bibliographies we read); 73 of them have a page here, 778 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2108.07258 · arXiv 2108.07258

@misc{bommasani2021opportunities,
  title = {{On the Opportunities and Risks of Foundation Models}},
  author = {Bommasani, Rishi and Hudson, Drew A. and Adeli, Ehsan and Altman, Russ and Arora, Simran and Arx, Sydney von and Bernstein, Michael S. and Bohg, Jeannette and Bosselut, Antoine and Brunskill, Emma and Brynjolfsson, Erik and Buch, Shyamal and Card, Dallas and Castellon, Rodrigo and Chatterji, Niladri and Chen, Annie and Creel, Kathleen and Davis, Jared Quincy and Demszky, Dora and Donahue, Chris and Doumbouya, Moussa and Durmus, Esin and Ermon, Stefano and Etchemendy, John and Ethayarajh, Kawin and Fei-Fei, Li and Finn, Chelsea and Gale, Trevor and Gillespie, Lauren and Goel, Karan and Goodman, Noah and Grossman, Shelby and Guha, Neel and Hashimoto, Tatsunori and Henderson, Peter and Hewitt, John and Ho, Daniel E. and Hong, Jenny and Hsu, Kyle and Huang, Jing and Icard, Thomas and Jain, Saahil and Jurafsky, Dan and Kalluri, Pratyusha and Karamcheti, Siddharth and Keeling, Geoff and Khani, Fereshte and Khattab, Omar and Koh, Pang Wei and Krass, Mark and Krishna, Ranjay and Kuditipudi, Rohith and Kumar, Ananya and Ladhak, Faisal and Lee, Mina and Lee, Tony and Leskovec, Jure and Levent, Isabelle and Li, Xiang Lisa and Li, Xuechen and Ma, Tengyu and Malik, Ali and Manning, Christopher D. and Mirchandani, Suvir and Mitchell, Eric and Munyikwa, Zanele and Nair, Suraj and Narayan, Avanika and Narayanan, Deepak and Newman, Ben and Nie, Allen and Niebles, Juan Carlos and Nilforoshan, Hamed and Nyarko, Julian and Ogut, Giray and Orr, Laurel and Papadimitriou, Isabel and Park, Joon Sung and Piech, Chris and Portelance, Eva and Potts, Christopher and Raghunathan, Aditi and Reich, Rob and Ren, Hongyu and Rong, Frieda and Roohani, Yusuf and Ruiz, Camilo and Ryan, Jack and Ré, Christopher and Sadigh, Dorsa and Sagawa, Shiori and Santhanam, Keshav and Shih, Andy and Srinivasan, Krishnan and Tamkin, Alex and Taori, Rohan and Thomas, Armin W. and Tramèr, Florian and Wang, Rose E. and Wang, William and Wu, Bohan and Wu, Jiajun and Wu, Yuhuai and Xie, Sang Michael and Yasunaga, Michihiro and You, Jiaxuan and Zaharia, Matei and Zhang, Michael and Zhang, Tianyi and Zhang, Xikun and Zhang, Yuhui and Zheng, Lucia and Zhou, Kaitlyn and Liang, Percy},
  year = {2021},
  doi = {10.48550/arxiv.2108.07258},
  eprint = {2108.07258},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2108.07258}
}

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