{"title":"推进渔业人工智能需要新的跨部门合作","authors":"Kate Wing, Benjamin Woodward","doi":"10.1093/icesjms/fsae118","DOIUrl":null,"url":null,"abstract":"Artificial intelligence, or AI, has the potential to dramatically improve our understanding and management of the ocean. For fisheries, these benefits could include greater monitoring coverage at lower costs, improved estimates of catch and bycatch, identification of illegal fishing, and seafood traceability throughout the supply chain. However, fisheries AI innovation and adoption faces substantial barriers from the highly regulated nature of fisheries and the complex overlap of government policies, diverse user needs, and market pressures. We argue that needed advances in fisheries AI require novel collaborations to share data and methods, encourage new and diverse entrants to the field, and increase baseline technical literacy across the global fisheries community. Unlocking fisheries data to power AI, particularly image data, can only be achieved through partnerships across government managers, AI developers, fishers and vessel owners, and technology service providers, which, in turn, requires a common vocabulary for policy and technical concepts. With a greater shared understanding across the field, fisheries AI providers can deliver desired results, and users can have confidence that systems are performing as advertised, ultimately meeting monitoring demand and sustainability goals.","PeriodicalId":51072,"journal":{"name":"ICES Journal of Marine Science","volume":"100 1","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Advancing artificial intelligence in fisheries requires novel cross-sector collaborations\",\"authors\":\"Kate Wing, Benjamin Woodward\",\"doi\":\"10.1093/icesjms/fsae118\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Artificial intelligence, or AI, has the potential to dramatically improve our understanding and management of the ocean. For fisheries, these benefits could include greater monitoring coverage at lower costs, improved estimates of catch and bycatch, identification of illegal fishing, and seafood traceability throughout the supply chain. However, fisheries AI innovation and adoption faces substantial barriers from the highly regulated nature of fisheries and the complex overlap of government policies, diverse user needs, and market pressures. We argue that needed advances in fisheries AI require novel collaborations to share data and methods, encourage new and diverse entrants to the field, and increase baseline technical literacy across the global fisheries community. Unlocking fisheries data to power AI, particularly image data, can only be achieved through partnerships across government managers, AI developers, fishers and vessel owners, and technology service providers, which, in turn, requires a common vocabulary for policy and technical concepts. With a greater shared understanding across the field, fisheries AI providers can deliver desired results, and users can have confidence that systems are performing as advertised, ultimately meeting monitoring demand and sustainability goals.\",\"PeriodicalId\":51072,\"journal\":{\"name\":\"ICES Journal of Marine Science\",\"volume\":\"100 1\",\"pages\":\"\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-08-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ICES Journal of Marine Science\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.1093/icesjms/fsae118\",\"RegionNum\":2,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"FISHERIES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ICES Journal of Marine Science","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1093/icesjms/fsae118","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"FISHERIES","Score":null,"Total":0}
Advancing artificial intelligence in fisheries requires novel cross-sector collaborations
Artificial intelligence, or AI, has the potential to dramatically improve our understanding and management of the ocean. For fisheries, these benefits could include greater monitoring coverage at lower costs, improved estimates of catch and bycatch, identification of illegal fishing, and seafood traceability throughout the supply chain. However, fisheries AI innovation and adoption faces substantial barriers from the highly regulated nature of fisheries and the complex overlap of government policies, diverse user needs, and market pressures. We argue that needed advances in fisheries AI require novel collaborations to share data and methods, encourage new and diverse entrants to the field, and increase baseline technical literacy across the global fisheries community. Unlocking fisheries data to power AI, particularly image data, can only be achieved through partnerships across government managers, AI developers, fishers and vessel owners, and technology service providers, which, in turn, requires a common vocabulary for policy and technical concepts. With a greater shared understanding across the field, fisheries AI providers can deliver desired results, and users can have confidence that systems are performing as advertised, ultimately meeting monitoring demand and sustainability goals.
期刊介绍:
The ICES Journal of Marine Science publishes original articles, opinion essays (“Food for Thought”), visions for the future (“Quo Vadimus”), and critical reviews that contribute to our scientific understanding of marine systems and the impact of human activities on them. The Journal also serves as a foundation for scientific advice across the broad spectrum of management and conservation issues related to the marine environment. Oceanography (e.g. productivity-determining processes), marine habitats, living resources, and related topics constitute the key elements of papers considered for publication. This includes economic, social, and public administration studies to the extent that they are directly related to management of the seas and are of general interest to marine scientists. Integrated studies that bridge gaps between traditional disciplines are particularly welcome.