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We have now entered the era of trillion parameter machine learning models trained on billion-sized datasets scraped from the internet.
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T. McEwan and B. Weerts, “Alt text and basic accessibility,” in Proceedings of HCI 2007 The 21st British HCI Group Annual Conference University of Lancaster, UK 21 , 2007, pp. 1–4
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P. Sharma, N. Ding, S. Goodman, and R. Soricut, “Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2018, pp. 2556–2565
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D. Guinness, E. Cutrell, and M. R. Morris, “Caption crawler: Enabling reusable alternative text descriptions using reverse image search,” in Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems , 2018, pp. 1–11
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Y. You, Z. Zhang, C.-J. Hsieh, J. Demmel, and K. Keutzer, “Imagenet training in minutes,” in Proceedings of the 47th International Conference on Parallel Processing , 2018, pp. 1–10
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C. Gleason, P. Carrington, C. Cassidy, M. R. Morris, K. M. Kitani, and J. P. Bigham, ““it’s almost like they’re trying to hide it”: How user-provided image descriptions have failed to make twitter accessible,” in The World Wide Web Conference , 2019, pp. 549–559
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J. Otterbacher, P. Barlas, S. Kleanthous, and K. Kyriakou, “How do we talk about other people? group (un) fairness in natural language image descriptions,” in Proceedings of the AAAI Conference on Human Computation and Crowdsourcing , vol. 7, no. 1, 2019, pp. 106–114
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2020
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J. Guo and J. Zhou, “Why ai alt text generator fail,” 2020
2020
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C. L. Bennett, C. Gleason, M. K. Scheuerman, J. P. Bigham, A. Guo, and A. To, ““it’s complicated”: Negotiating accessibility and (mis) representation in image descriptions of race, gender, and disability,” in Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems , 2021, pp. 1–19
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D. Gurari, Y. Zhao, M. Zhang, and N. Bhattacharya, “Captioning images taken by people who are blind,” in European Conference on Computer Vision . Springer, 2020, pp. 417–434
2020
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M. R. Morris, “Ai and accessibility,” Communications of the ACM , vol. 63, no. 6, pp. 35–37, 2020
2020
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2020
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2020
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S. Matic, C. Iordanou, G. Smaragdakis, and N. Laoutaris, “Identifying sensitive urls at web-scale,” in Proceedings of the ACM Internet Measurement Conference , 2020, pp. 619–633
2020
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2020
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2020
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2020
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S. Black, G. Leo, P. Wang, C. Leahy, and S. Biderman, “GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow,” Mar. 2021, If you use this software, please cite it using these metadata. [Online]. Available: https://doi.org/10.5281/zenodo.5297715
2021
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A. Andonian, S. Biderman, S. Black, P. Gali, L. Gao, E. Hallahan, J. Levy-Kramer, C. Leahy, L. Nestler, K. Parker, M. Pieler, S. Purohit, T. Songz, P. Wang, and S. Weinbach, “GPT-NeoX: Large scale autoregressive language modeling in pytorch,” 2021. [Online]. Available: http://github.com/eleutherai/gpt-neox
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P. Nayak, “Mum: A new ai milestone for understanding information,” https://blog.google/products/search/introducing-mum/
2021
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Wikipedia, “Wu Dao — Wikipedia, the free encyclopedia,” http://en.wikipedia.org/w/index.php?title=Wu\%20Dao&oldid=1045892362
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A. Zhavoronkov, “Wu dao 2.0 - bigger, stronger, faster ai from china,” https://www.forbes.com/sites/alexzhavoronkov/2021/07/19/wu-dao-20bigger-stronger-faster-ai-from-china/
2021
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C. Feng, “Us-china tech war: Beijing-funded ai researchers surpass google and openai with new language processing model | south china morning post,” https://www.scmp.com/tech/tech-war/article/3135764/us-china-tech-war-beijing-funded-ai-researchers-surpass-google-and
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M. Heikkilä, “Meet wu dao 2.0, the chinese ai model making the west sweat – politico,” https://www.politico.eu/article/meet-wu-dao-2-0-the-chinese-ai-model-making-the-west-sweat/
2021
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A. Tarantola, “China’s gigantic multi-modal ai is no one-trick pony | engadget,” https://www.engadget.com/chinas-gigantic-multi-modal-ai-is-no-one-trick-pony-211414388.html
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G. Goh, N. Cammarata, C. Voss, S. Carter, M. Petrov, L. Schubert, A. Radford, and C. Olah, “Multimodal neurons in artificial neural networks,” Distill , vol. 6, no. 3, p. e30, 2021
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M. Tan and Z. Dai, “Toward fast and accurate neural networks for image recognition,” https://ai.googleblog.com/2021/09/toward-fast-and-accurate-neural.html
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C. Newton, “Facebook will pay $52 million in settlement with moderators who developed ptsd on the job - the verge,” https://www.theverge.com/2020/5/12/21255870/facebook-content-moderator-settlement-scola-ptsd-mental-health
2021
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M. Steiger, T. J. Bharucha, S. Venkatagiri, M. J. Riedl, and M. Lease, “The psychological well-being of content moderators,” in Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, CHI , vol. 21, 2021
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A. Birhane, “Algorithmic injustice: a relational ethics approach,” Patterns , vol. 2, no. 2, p. 100205, 2021
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