首页|Findings on Machine Learning Reported by Investigators at University of Illinois (Explainable, Interpretable, and Trustworthy Ai for an Intelligent Digital Twin: a Case Study On Remaining Useful Life)
Findings on Machine Learning Reported by Investigators at University of Illinois (Explainable, Interpretable, and Trustworthy Ai for an Intelligent Digital Twin: a Case Study On Remaining Useful Life)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learning have been published. According to news originating from Urbana, Illinois, by NewsRx correspondents, research stated, "Artificial intelligence (AI) and Machine learning (ML) are increasingly used for digital twin development in energy and engineering systems, but these models must be fair, unbiased, interpretable, and explainable. It is critical to have confidence in AI's trustworthiness." Financial support for this research came from National Science Foundation (NSF). Our news journalists obtained a quote from the research from the University of Illinois, "ML techniques have been useful in predicting important parameters and improving model performance. However, for these AI techniques to be useful in making decisions, they need to be audited, accounted for, and easy to understand. Therefore, the use of explainable AI (XAI) and interpretable machine learning (IML) is crucial for the accurate prediction of prognostics, such as remaining useful life (RUL), in a digital twin system to make it intelligent while ensuring that the AI model is transparent in its decision-making processes and that the predictions it generates can be understood and trusted by users. By using an explainable, interpretable, and trustworthy AI, intelligent digital twin systems can make more accurate predictions of RUL, leading to better maintenance and repair planning and, ultimately, improved system performance. This paper aims to explain the ideas of XAI and IML and justify the important role of AI/ML for the digital twin components, which requires XAI to understand the prediction better."
UrbanaIllinoisUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningUniversity of Illinois
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