References
- Agostinelli, A., T. I. Denk, Z. Borsos, J. Engel, M. Verzetti, A. Caillon, Q. Huang, A. Jansen, A. Roberts, M. Tagliasacchi, M. Sharifi, N. Zeghidour, and C. Frank. 2023. MusicLM: Generating music from text. https://arxiv.org/abs/2301.11325.
- Alwan, L. C., and H. V. Roberts. 1988. Time-series modeling for statistical process control. Journal of Business & Economic Statistics 6 (1):87–95. doi:10.2307/1391421.
- Begel, A., and N. Nagappan. 2008. Pair programming: What’s in it for me? In Proceedings of the Second ACM-IEEE International Symposium on Empirical Software Engineering and Measurement, 120–8.
- Biswas, S. 2023. ChatGPT and the future of medical writing. Radiology 307 (2). doi:10.1148/radiol.223312.
- Box, G. E. P., and W. H. Woodall. 2012. Innovation, quality engineering, and statistics. Quality Engineering 24 (1):20–9. doi:10.1080/08982112.2012.627003.
- Brown, T., B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al. 2020. Language models are few-shot learners. Advances in Neural Information Processing Systems 33:1877–901.
- Chakraborti, S., S. Human, and M. Graham. 2008. Phase i statistical process control charts: An overview and some results. Quality Engineering 21 (1):52–62. doi:10.1080/08982110802445561.
- Chen, M., J. Tworek, H. Jun, Q. Yuan, H. P. D. O. Pinto, J. Kaplan, H. Edwards, Y. Burda, N. Joseph, G. Brockman, et al. 2021. Evaluating large language models trained on code. https://arxiv.org/abs/2107.03374.
- Choi, K., A. Grover, T. Singh, R. Shu, and S. Ermon. 2020. Fair generative modeling via weak supervision. In Proceedings of the 37th International Conference on Machine Learning, Volume 119 of Proceedings of Machine Learning Research, ed. H. D. III and A. Singh, 1887–98. PMLR, July 13–18.
- Colosimo, B. M., L. A. del Castillo, K. Jones-Farmer and, and Paynabar, E. 2021. Artificial intelligence and statistics for quality technology: An introduction to the special issue. Journal of Quality Technology 53 (5):443–53. doi:10.1080/00224065.2021.1987806.
- Constantz, J. 2023. California’s new gold rush: Big tech moves to gain the edge in AI. Bloomberg. https://www.bloomberg.com/news/articles/2023-02-03/big-tech-earnings-call-mentions-of-ai-spike-after-chatgpt-went-viral.
- Crosier, R. B. 1986. A new two-sided cumulative quality control scheme. Technometrics 28 (3):187–94. doi:10.1080/00401706.1986.10488126.
- Devlin, J., M.-W. Chang, K. Lee, and K. Toutanova. 2018., BERT: Pre-training of deep bidirectional transformers for language understanding. https://arxiv.org/abs/1810.04805.
- Dowling, M., and B. Lucey. 2023. ChatGPT for (finance) research: The Bananarama conjecture. Finance Research Letters 53 103662.
- Editorial. 2023a. The AI writing on the wall. Nature Machine Intelligence 5 (1). doi:10.1038/s42256-023-00613-9.
- Editorial. 2023b. Tools such as ChatGPT threaten transparent science; here are our ground rules for their use. Nature, January. doi:http://dx.doi.org/10.1038/d41586-023-00191-1.
- Gilson, A., T. Safranek, V. Huang, L. Socrates, R. A. Chi, D. Taylor, and Chartash, C. W. 2023. How does ChatGPT perform on the United States Medical Licensing Examination? The implications of large language models for medical education and knowledge assessment. JMIR Medical Education 9 (1):e45312. doi:10.2196/45312.
- Gozalo-Brizuela, R., and E. C. Garrido-Merchan. 2023. ChatGPT is not all you need. a state of the art review of large generative AI models. https://arxiv.org/abs/2301.04655.
- Griffith, E., and C. Metz. 2023. A new area of A.I. booms, even amid the tech gloom, January. Last accessed January 23, 2023. https://www.nytimes.com/2023/01/07/technology/generative-ai-chatgpt-investments.html.
- Hockman, K. K., and W. A. Jensen. 2016. Statisticians as innovation leaders. Quality Engineering 28 (2):165–74. doi:10.1080/08982112.2015.1083107.
- Huang, S., P. Grady, and GPT-3. 2022. Generative AI: A creative new world. Sequoia Capital, September. Accessed January 28, 2023. https://www.sequoiacap.com/article/generative-ai-a-creative-new-world/.
- Huberts, L. C., M. Schoonhoven, and R. J. Does. 2022. Multilevel process monitoring: A case study to predict student success or failure. Journal of Quality Technology 54 (2):127–43. doi:10.1080/00224065.2020.1828008.
- Jardim, F. S., S. Chakraborti, and E. K. Epprecht. 2020. Two perspectives for designing a phase II control chart with estimated parameters: The case of the Shewhart X-bar Chart. Journal of Quality Technology 52 (2):198–217. doi:10.1080/00224065.2019.1571345.
- Ji, Z., N. Lee, R. Frieske, T. Yu, D. Su, Y. Xu, E. Ishii, Y. Bang, A. Madotto, and P. Fung. 2023. Survey of hallucination in natural language generation. ACM Computing Surveys 55 (12):1–38. doi:10.1145/3571730.
- Jones-Farmer, L. A., W. H. Woodall, S. H. Steiner, and C. W. Champ. 2014. An overview of phase i analysis for process improvement and monitoring. Journal of Quality Technology 46 (3):265–80. doi:10.1080/00224065.2014.11917969.
- Karpas, E., O. Abend, Y. Belinkov, B. Lenz, O. Lieber, N. Ratner, Y. Shoham, H. Bata, Y. Levine, K. Leyton-Brown, et al. 2022. Mrkl systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning. https://arxiv.org/abs/2205.00445.
- Kim, K., W. H. Mahmoud and, and Woodall, M. A. 2003. On the monitoring of linear profiles. Journal of Quality Technology 35 (3):317–28. doi:10.1080/00224065.2003.11980225.
- Kirchenbauer, J., J. Geiping, Y. Wen, J. Katz, I. Miers, and T. Goldstein. 2023. A watermark for large language models. https://arxiv.org/abs/2301.10226.
- Knoth, S. 2021. Steady-state average run length(s) – methodology, formulas and numerics. Sequential Analysis 40 (3):405–26. doi:10.1080/07474946.2021.1940501.
- Korinek, A. 2023. Language models and cognitive automation for economic research. In NBER Working Paper. https://www.nber.org/papers/w30957.
- Lim, R., M. Wu, and L. Miller. 2021. Customizing GPT-3 for your application. OpenAI, December. Last accessed February 10, 2023. https://openai.com/blog/customized-gpt-3/.
- Lucas, J. Mand., and M. S. Saccucci. 1990. Exponentially weighted moving average control schemes: Properties and enhancements. Technometrics 32 (1):1–12. doi:10.1080/00401706.1990.10484583.
- Maleki, M. R., P. Amiri and, and Castagliola, A. 2018. An overview on recent profile monitoring papers (2008–2018) based on conceptual classification scheme. Computers & Industrial Engineering 126:705–28. doi:10.1016/j.cie.2018.10.008.
- Markov, A. A. 2006. An example of statistical investigation of the text Eugene Onegin concerning the connection of samples in chains. Science in Context 19 (4):591–600. doi:10.1017/S0269889706001074.
- McKee, F., and D. Noever. 2022. Chatbots in a botnet world. https://arxiv.org/abs/2212.11126.
- Megahed, F. M. 2019. Discussion on “real-time monitoring of events applied to syndromic surveillance. Quality Engineering 31 (1):97–104. doi:10.1080/08982112.2018.1530358.
- Megahed, F. M., and L. A. Jones-Farmer. 2015. Statistical perspectives on “big data”. In Frontiers in statistical quality control, ed. S. Knoth and W. Schmid, Vol. 11, 29–47. Switzerland: Springer International Publishing.
- Megahed, F. M., W. H. Woodall, and J. A. Camelio. 2011. A review and perspective on control charting with image data. Journal of Quality Technology 43 (2):83–98. doi:10.1080/00224065.2011.11917848.
- Mehdi, Y. 2023. Reinventing search with a new AI-powered Microsoft Bing and Edge, your copilot for the web. Microsoft Blog. https://blogs.microsoft.com/blog/2023/02/07/reinventing-search-with-a-new-ai-powered-microsoft-bing-and-edge-your-copilot-for-the-web/.
- Michel, J.-B, and E. L. Aiden. 2011. What we learned from 5 million books. TEDxBoston. https://www.ted.com/talks/jean_baptiste_michel_erez_lieberman_aiden_what_we_learned_from_5_million_books.
- Montgomery, D. 2020. Introduction to statistical quality control. 8th ed. John Wiley & Sons.
- Nature. 2023. For authors: Initial submission guidelines. Last accessed February 12, 2023. https://www.nature.com/nature/for-authors/initial-submission.
- Noorossana, R., A. Saghaei, and A. Amiri. 2013., Statistical analysis of profile monitoring. 1st ed. Hoboken, New Jersey: John Wiley & Sons.
- OpenAI. 2022a. ChatGPT: Optimizing language models for dialogue, November. https://openai.com/blog/chatgpt/. Last accessed on January 28, 2023.
- OpenAI. 2022b. Code completion, November. Last accessed February 09, 2023. https://platform.openai.com/docs/guides/code/introduction.
- OpenAI. 2022c. DALL.E 2, April. Last accessed January 27, 2023. https://openai.com/dall-e-2/.
- Ouyang, L., J. Wu, X. Jiang, D. Almeida, C. L. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, et al. 2022. Training language models to follow instructions with human feedback. https://arxiv.org/abs/2203.02155.
- Radford, A., K. Narasimhan, T. Salimans, and I. Sutskever. 2018. Improving language understanding by generative pre-training. OpenAI. https://cdn.openai.com/ research-covers/language-unsupervised/language_understanding_paper.pdf.
- Ramesh, K., S. Sitaram, and M. Choudhury. 2023. Fairness in language models beyond English: Gaps and challenges. arXiv preprint arXiv:2302.12578.
- Saleh, N. A., M. A. Mahmoud, M. J. Keefe, and W. H. Woodall. 2015. The difficulty in designing Shewhart X and X-bar control charts with estimated parameters. Journal of Quality Technology 47 (2):127–38. doi:10.1080/00224065.2015.11918120.
- Schick, T., J. Dwivedi-Yu, R. Dessì, R. Raileanu, M. Lomeli, L. Zettlemoyer, N. Cancedda, and T. Scialom. 2023. Toolformer: Language models can teach themselves to use tools. https://arxiv.org/abs/2302.04761.
- Schwarz, K., A. Liao and, and Geiger, Y. 2021. On the frequency bias of generative models. Advances in Neural Information Processing Systems 34:18126–36.
- Scrucca, L. 2017. A quick tour of qcc, July. Accessed February 2, 2023. https://cran.r-project.org/web/packages/qcc/ vignettes/qcc_a_quick_tour.html.
- Scrucca, L. 4. 2004. qcc: An R package for quality control charting and statistical process control. R News, Vol. 4/1, July 8. http://www.stat.unipg.it/luca/misc/Rnews_2004-1-pag11-17.pdf.
- Sevilla, J., L. Heim, A. Ho, T. Besiroglu, M. Hobbhahn, and P. Villalobos. 2022., Compute trends across three eras of machine learning. https://arxiv.org/abs/2202.05924.
- Shannon, C. E. 1948. A mathematical theory of communication. Bell System Technical Journal 27 (3):379–423. doi:10.1002/j.1538-7305.1948.tb01338.x.
- Singer, U., A. Polyak, T. Hayes, X. Yin, J. An, S. Zhang, Q. Hu, H. Yang, O. Ashual, O. Gafni, et al. 2022. Make-A-Video: Text-to-video generation without text-video data. https://arxiv.org/abs/2209.14792.
- Singer, U., S. Sheynin, A. Polyak, O. Ashual, I. Makarov, F. Kokkinos, N. Goyal, A. Vedaldi, D. Parikh, J. Johnson, et al. 2023. Text-To-4D dynamic scene generation. https://arxiv.org/abs/2301.11280.
- Srinivasan, R., and K. Uchino. 2021. Biases in generative art: A causal look from the lens of art history. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 41–51.
- Stokel-Walker, Cand., and R. Van Noorden. 2023. What ChatGPT and generative AI mean for science. Nature 614 (7947):214–6. doi:10.1038/d41586-023-00340-6.
- Thorp, H. H. 2023. ChatGPT is fun, but not an author. Science (New York, N.Y.) 379 (6630):313– doi:10.1126/science.adg7879.
- Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. U. Kaiser, and I. Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems, Vol. 30, ed. I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett. Curran Associates, Inc. https://proceedings.neurips.cc/ paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf.
- Weese, M., F. M. Martinez, L. A. Megahed and, and Jones-Farmer, W. 2016. Statistical learning methods applied to process monitoring: An overview and perspective. Journal of Quality Technology 48 (1):4–24. doi:10.1080/00224065.2016.11918148.
- Wei, J., and Y. Tay. 2022. Characterizing emergent phenomena in large language models. Google Research Blog, November. Last accessed January 27, 2023. https://ai.googleblog.com/2022/11/characterizing-emergent-phenomena-in.html.
- Wei, J., Y. Tay, R. Bommasani, C. Raffel, B. Zoph, S. Borgeaud, D. Yogatama, M. Bosma, D. Zhou, D. Metzler, et al. 2022., Emergent abilities of large language models. In Transactions on Machine Learning Research. Accepted paper. https://openreview.net/forum?id=yzkSU5zdwD.
- Weisz, J. D., M. Muller, J. He, and S. Houde. 2023. Toward general design principles for generative AI applications. https://arxiv.org/abs/2301.05578.
- Wells, L. J., F. M. Megahed, C. B. Niziolek, J. A. Camelio, and W. H. Woodall. 2013. Statistical process monitoring approach for high-density point clouds. Journal of Intelligent Manufacturing 24 (6):1267–79. doi:10.1007/s10845-012-0665-2.
- Woodall, W. H. 2007. Current research on profile monitoring. Production 17 (3):420–5. doi:10.1590/S0103-65132007000300002.
- Zhang, M., A. Peng, F. M. Schuh, W. H. Megahed and, and Woodall, Y. 2013. Geometric charts with estimated control limits. Quality and Reliability Engineering International 29 (2):209–23. doi:10.1002/qre.1304.
- Zhao, S., H. Ren, A. Yuan, J. Song, N. Goodman, and S. Ermon. 2018. Bias and generalization in deep generative models: An empirical study. In Advances in Neural Information Processing Systems, Vol. 31.
- Zwetsloot, I. M., W. H. Jones-Farmer and, and Woodall, L. A. 2023. Monitoring univariate processes using control charts: Some practical issues and advice. Submitted for Publication.