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Argonne: Where AI research meets education and training
Last September, in the Chicago suburb of Lemont, Ill., Argonne National Laboratory hosted its first AI STEM Education Summit. More than 180 educators from high schools, community colleges, and universities; STEM administrators; and experts in various disciplines convened at “One Ecosystem, Many Pathways–Building an AI-Ready STEM Workforce” to discuss how artificial intelligence is reshaping STEM-related industries, including the implications for the nuclear engineering classroom and workforce.
Sidney Oldberg, Jr., Ronald A. Christensen
Nuclear Technology | Volume 37 | Number 1 | January 1978 | Pages 40-47
Technical Paper | Fuel | doi.org/10.13182/NT78-A32089
Articles are hosted by Taylor and Francis Online.
Received December 27, 1976 Accepted for Publication September 7, 1977 A review of the characteristics of available fuel rod reliability models reveals an extremely wide range of opinion regarding the scale of complexity appropriate to the problem. It is argued that this diversity of opinion is symptomatic of a model building style in which no attention is formally paid to the uncertainty in the model predictions. An information-theory-based methodology is suggested as a means for systematically building a model in which the information content of the prediction is no more and no less than the information content of the supporting data.