(Molla 2023). While new technologies certainly can cause disruptions, they may ultimately lead to more and better-quality work, much like the impact of the personal computer or the internet. University instruction is already striving to rethink how to inte- grate such tools into their curriculum and promote best practices (Yang 2023). It is critical to under- stand how to create appropriate prompts for getting the best information, while also understanding the risk and quality issues of the output. One of the biggest issues going forward concerns plagiarism, copyright concerns for human content providers, and how this technology could be better regulated. Artists and writers are begin- ning to take action to defend their intellectual property from so-called “fair use” (DelSignore 2023). Daniel Gervais, a professor at Vanderbilt Law School who specializes in intellectual property law states that it hinges on the following: “What’s the purpose or nature of the use and what’s the impact on the market” (DelSignore 2023). Basically, it comes down to how you are using the output. Is it for research or commercial purposes? If commercial, one needs to be extremely careful. These questions and concerns are going to greatly impact the future of this tech- nology, and how widely and rapidly it will be used. The regulation of AI is expected to evolve rapidly and must address the safe application of this technology. To date, regulation is being led by the EU and China, while the US response has been fairly limited in scope. The White House’s Blueprint for an AI Bill of Rights highlights the need for better decision-making including explanations: “Automated systems should provide explanations that are technically valid, meaningful, and useful to you and to any operators or others who need to understand the system, and calibrated to the level of risk based on the context” (Klein 2023). But experts generally agree we have made almost no progress on explaining what is happening inside these LLMs (Klein 2023). There is a clear need to be able to comprehensively validate AI performance, but this appears to be greatly complicated by how complex these algorithms have become. Work on Explainable AI—a set of tools that help one understand and interpret the outputs generated by ML algorithms— is progressing, but it will take time to get there. One consideration for our community: What if we created our own NDT Chatbot, let’s say residing behind the ASNT login, trained using ASNT- copyrighted materials, for example back issues of Materials Evaluation, and maybe even handbooks? Based on what GPT-4 is doing, it is clear such a tool could pass a Level III exam. If done right, this could be a valuable resource for the community. Of course, we’d have to first ensure that the answers are consistently correct, just as we have reviewers ensure our handbooks and publications are as error-free as possible. I feel the technology would also need to produce the source(s) for its answer to the user, so we have a record to check and verify that the answer is correct. If poor responses are discovered, we must also have the means of correcting it. While we can imagine all of the positive uses for such AI agents, they can just as easily be deployed for nefarious causes today. For example, these tools will likely improve the social engineering that is being used to fleece people of personal informa- tion and money through predatory emails, robo- calls, and social media. It is critical to consider the trade-offs of organizing our body of knowledge into one easily accessible place. Ripi Singh has some very important insight on this going forward: “The ‘Vulnerable World Hypothesis’ is a topic that deserves our undivided attention at every ASNT conference as a single body of professionals com- mitted to Creating a Safer World!® We can start with Generative AI as the first item on the list to be addressed, now” (Singh and Garg 2023). While I don’t have all the answers and definitely can’t predict the future, I do want to encourage more discussion and feedback on this important topic within ASNT. This topic has been brought up in the ASNT AI/ML Committee recently and we plan to explore possible guidance for the use of gen- erative AI in NDT going forward. (As well, please feel free to share your thoughts with me at aldrin @computationaltools.com or get involved with the ASNT AI/ML Committee.) AUTHOR John C. Aldrin: Computational Tools, Gurnee, Illinois 60031, USA aldrin@computationaltools.com CITATION Materials Evaluation 81 (7): 28–34 https://doi.org/10.32548/2023.me-04361 ©2023 American Society for Nondestructive Testing REFERENCES Alkaissi, H., and S. I. McFarlane. 2023. “Artificial hallucina- tions in ChatGPT: Implications in scientific writing.” Cureus 15 (2). https://doi.org/10.7759/cureus.35179. Bisle, W. 2023. “ChatGPT3 writing your inspection proce- dure?” NDT.net forum. 29 January 2023. https://www.ndt. net/forum/thread.php?msgID=84722. DelSignore, P. 2023. “AI and the copyright problem: Making sense of generative AI copyright issues.” Medium. 4 March 2023. https://medium.com/geekculture/ai-and-the-copy- right-problem-97da479a9ccd. 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