首页|New Artificial Intelligence Study Findings Have Been Reported by Investigators a t University of Richmond (On the Compatibility of Established Methods With Emerg ing Artificial Intelligence and Machine Learning Methods for Disaster Risk Analy sis)
New Artificial Intelligence Study Findings Have Been Reported by Investigators a t University of Richmond (On the Compatibility of Established Methods With Emerg ing Artificial Intelligence and Machine Learning Methods for Disaster Risk Analy sis)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Artificial In telligence have been published. According to news reporting out of Richmond, Vir ginia, by NewsRx editors, research stated, “There is growing interest in leverag ing advanced analytics, including artificial intelligence (AI) and machine learn ing (ML), for disaster risk analysis (RA) applications. These emerging methods o ffer unprecedented abilities to assess risk in settings where threats can emerge and transform quickly by relying on ‘learning’ through datasets.” Our news journalists obtained a quote from the research from the University of R ichmond, “There is a need to understand these emerging methods in comparison to the more established set of risk assessment methods commonly used in practice. T hese existing methods are generally accepted by the risk community and are groun ded in use across various risk application areas. The next frontier in RA with e merging methods is to develop insights for evaluating the compatibility of those risk methods with more recent advancements in AI/ML, particularly with consider ation of usefulness, trust, explainability, and other factors. This article leve rages inputs from RA and AI experts to investigate the compatibility of various risk assessment methods, including both established methods and an example of a commonly used AI-based method for disaster RA applications. This article utilize s empirical evidence from expert perspectives to support key insights on those m ethods and the compatibility of those methods.”
RichmondVirginiaUnited StatesNorth and Central AmericaArtificial IntelligenceCyborgsEmerging TechnologiesM achine LearningUniversity of Richmond
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