{"title":"人工智能在测量系统中的作用","authors":"R. Taymanov, K. Sapozhnikova","doi":"10.1109/MMA52675.2021.9610828","DOIUrl":null,"url":null,"abstract":"The development of artificial intelligence (AI) and AI systems has opened up new perspectives for measurements due to the ability to process a number of measurement information streams using networks, recognize images in them, predict their changes and correlations with others, self-learn during operation, as well as make decisions. The level of capabilities to implement these functions in AI-provided measuring systems produced by different manufacturers can be different. Decisions made by the AI, if a manufacturer and user do not sufficiently consider the specifics of a particular system, can result in dangerous consequences. The experience of working in a Committee on Standardization that considers dozens of standard drafts related to AI systems for various applications has given grounds for presenting an analysis of trends in the development of such measuring systems and substantiating recommendations aimed at improving their efficiency.","PeriodicalId":287017,"journal":{"name":"2021 XXXI International Scientific Symposium Metrology and Metrology Assurance (MMA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Role of Artificial Intelligence in Measuring Systems\",\"authors\":\"R. Taymanov, K. Sapozhnikova\",\"doi\":\"10.1109/MMA52675.2021.9610828\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The development of artificial intelligence (AI) and AI systems has opened up new perspectives for measurements due to the ability to process a number of measurement information streams using networks, recognize images in them, predict their changes and correlations with others, self-learn during operation, as well as make decisions. The level of capabilities to implement these functions in AI-provided measuring systems produced by different manufacturers can be different. Decisions made by the AI, if a manufacturer and user do not sufficiently consider the specifics of a particular system, can result in dangerous consequences. The experience of working in a Committee on Standardization that considers dozens of standard drafts related to AI systems for various applications has given grounds for presenting an analysis of trends in the development of such measuring systems and substantiating recommendations aimed at improving their efficiency.\",\"PeriodicalId\":287017,\"journal\":{\"name\":\"2021 XXXI International Scientific Symposium Metrology and Metrology Assurance (MMA)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 XXXI International Scientific Symposium Metrology and Metrology Assurance (MMA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MMA52675.2021.9610828\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 XXXI International Scientific Symposium Metrology and Metrology Assurance (MMA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MMA52675.2021.9610828","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Role of Artificial Intelligence in Measuring Systems
The development of artificial intelligence (AI) and AI systems has opened up new perspectives for measurements due to the ability to process a number of measurement information streams using networks, recognize images in them, predict their changes and correlations with others, self-learn during operation, as well as make decisions. The level of capabilities to implement these functions in AI-provided measuring systems produced by different manufacturers can be different. Decisions made by the AI, if a manufacturer and user do not sufficiently consider the specifics of a particular system, can result in dangerous consequences. The experience of working in a Committee on Standardization that considers dozens of standard drafts related to AI systems for various applications has given grounds for presenting an analysis of trends in the development of such measuring systems and substantiating recommendations aimed at improving their efficiency.