查看更多>>摘要：Reporters obtained the following quote from the background information supplied by the inventors: “Itis generally known that in an industrial production of a p hysical product, errors and failures can occurthat might lead to a loss of qual ity of a respective production line. In particular, in complex chemicalproducti on processes already slight differences in production parameters, like reactor t emperature, canlead to differences in the final product. Some of these differen ces might be acceptable, although they leadto differences to the optimal produc t, whereas some of these differences are not acceptable. For assessingsuch qual ity differences in a physical product and, in particular, to prevent the release of final productswith a not acceptable quality, it is common to retrieve produ ction samples from each production lineand to visually assess the quality. In p articular, human specialists are trained to recognize, identify andoptionally c lassify differences in the test product from the optimal state and to assess the quality of thefinal product based on these differences. However, this process is not only cumbersome and work-intensive,but also prone to human inaccuracies. For example, it is very likely that each quality assessment specialistapplies a slightly different classification and assessment scheme such that the final ou tcome of the qualityassessment depends on the individual specialist having perf ormed the quality assessment.
查看更多>>摘要：The following quote was obtained by the news editors from the background informa tion supplied by theinventors: “Training machine learning models can be a time consuming process that involves a significantamount of manual review and multip le rounds of training. Further, training a machine learning modelmay require a large amount of training data some of which may not significantly contribute to improvingthe accuracy of the machine learning model. Some training data may eve n have a deleterious effect onthe performance of a machine learning model and e xcluding such data may be beneficial. However, theprocess of identifying traini ng data that impacts the pathways, nodes, and weighting of a machine learningmo del is difficult, in addition to being time consuming and resource intensive. Ac cordingly, there is a needto accurately reduce the size of a training dataset i n a manner that does not also reduce the accuracy ofthe machine learning model. ”
查看更多>>摘要：News editors obtained the following quote from the background information suppli ed by the inventors:“BACKGROUND INFORMATION“With the advent of e-commerce and mobile purchasing, it is easier than ever to make a purchase.Whether it be inexpensive purchases such as food delivery, or e xpensive purchases such as electronics,customers can easily make purchases usin g a mobile device or a computer. This greatly benefits consumers,as the purchas ing processing is easy and seamless. This also greatly benefits merchants, as us ers can moreeasily make impulse purchases, which would otherwise not be possibl e. However, in certain circumstances,the ability to easily make a purchase can be detrimental. For example, if a customer is somehow impaired(e.g., consumed t oo much alcohol, has done drugs, has a mental disorder, or has some other mentalimpairment), the customer may be more likely to make purchases that they will r egret when they are nolonger impaired. However, since purchasing has become so easy, there is currently no easy and convenientway to prevent customers from ma king purchases while in an impaired state.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – According to news reporting based on a preprint abstract, our journalists obtained thefollowing quote sourced from os f.io:“The paper discusses the problem of the artificial future in learning the Englis h language. Englishoccupies a leading place in all spheres of human activity.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – According to news reporting based on a preprint abstract, our journalists obtained thefollowing quote sourced from os f.io:“This chapter examines the governance of artificial intelligence (AI) in healthc are in the United States(US) and the European Union (EU) through an analysis of ‘integrated governance’ and ‘digital exceptionalism’.The developments exemplif y a shift towards digital exceptionalism, but that approach maynot ultimately h elp protect patients against the potential harms of AI. The early responses towa rd AIregulation prioritised explorations of how to adapt existing laws to overc ome the unique challenges posedby AI.
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