Xu Zhu, Siliang Chen, Xinbin Liang, Xinqiao Jin, Zhimin Du
{"title":"新一代通用能源人工智能,为智慧能源导航","authors":"Xu Zhu, Siliang Chen, Xinbin Liang, Xinqiao Jin, Zhimin Du","doi":"10.1016/j.xcrp.2024.102192","DOIUrl":null,"url":null,"abstract":"<p>The rapid advancement of highly flexible and reliable artificial intelligence (AI) holds the promise of unlocking transformative capabilities in response to imminent energy and environmental challenges. Toward future energy, we propose this perspective and introduce a groundbreaking paradigm for a versatile energy AI, termed artificial general intelligence for energy (AGIE). AGIE is designed to address a spectrum of energy-related issues with flexibility, drawing upon information such as energy parameters, equipment images, and expert voice feedback. The applications of AGIE are diverse, ranging from energy diagnostics and operational optimization to offering advice on energy policies. By incorporating human-in-the-loop interactions and leveraging domain knowledge, AGIE has the capacity to assimilate the habits of energy users. Through continuous reinforcement learning, it aspires to establish a new paradigm of explainable reasoning, paving the way for the development of credible energy robots with attributes similar to human understanding. We anticipate that AGIE-enabled applications will lead to new approaches in energy usage and the consideration of serious technical and societal challenges ranging from data integration to privacy and security concerns, environmental impacts, and constraints in hardware and software. Addressing these issues is crucial for realizing the full potential of generalist energy intelligence, leading to enhanced energy efficiency and contributing to the resolution of global energy problems.</p>","PeriodicalId":9703,"journal":{"name":"Cell Reports Physical Science","volume":null,"pages":null},"PeriodicalIF":7.9000,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Next-generation generalist energy artificial intelligence for navigating smart energy\",\"authors\":\"Xu Zhu, Siliang Chen, Xinbin Liang, Xinqiao Jin, Zhimin Du\",\"doi\":\"10.1016/j.xcrp.2024.102192\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The rapid advancement of highly flexible and reliable artificial intelligence (AI) holds the promise of unlocking transformative capabilities in response to imminent energy and environmental challenges. Toward future energy, we propose this perspective and introduce a groundbreaking paradigm for a versatile energy AI, termed artificial general intelligence for energy (AGIE). AGIE is designed to address a spectrum of energy-related issues with flexibility, drawing upon information such as energy parameters, equipment images, and expert voice feedback. The applications of AGIE are diverse, ranging from energy diagnostics and operational optimization to offering advice on energy policies. By incorporating human-in-the-loop interactions and leveraging domain knowledge, AGIE has the capacity to assimilate the habits of energy users. Through continuous reinforcement learning, it aspires to establish a new paradigm of explainable reasoning, paving the way for the development of credible energy robots with attributes similar to human understanding. We anticipate that AGIE-enabled applications will lead to new approaches in energy usage and the consideration of serious technical and societal challenges ranging from data integration to privacy and security concerns, environmental impacts, and constraints in hardware and software. Addressing these issues is crucial for realizing the full potential of generalist energy intelligence, leading to enhanced energy efficiency and contributing to the resolution of global energy problems.</p>\",\"PeriodicalId\":9703,\"journal\":{\"name\":\"Cell Reports Physical Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.9000,\"publicationDate\":\"2024-09-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cell Reports Physical Science\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1016/j.xcrp.2024.102192\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cell Reports Physical Science","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1016/j.xcrp.2024.102192","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
Next-generation generalist energy artificial intelligence for navigating smart energy
The rapid advancement of highly flexible and reliable artificial intelligence (AI) holds the promise of unlocking transformative capabilities in response to imminent energy and environmental challenges. Toward future energy, we propose this perspective and introduce a groundbreaking paradigm for a versatile energy AI, termed artificial general intelligence for energy (AGIE). AGIE is designed to address a spectrum of energy-related issues with flexibility, drawing upon information such as energy parameters, equipment images, and expert voice feedback. The applications of AGIE are diverse, ranging from energy diagnostics and operational optimization to offering advice on energy policies. By incorporating human-in-the-loop interactions and leveraging domain knowledge, AGIE has the capacity to assimilate the habits of energy users. Through continuous reinforcement learning, it aspires to establish a new paradigm of explainable reasoning, paving the way for the development of credible energy robots with attributes similar to human understanding. We anticipate that AGIE-enabled applications will lead to new approaches in energy usage and the consideration of serious technical and societal challenges ranging from data integration to privacy and security concerns, environmental impacts, and constraints in hardware and software. Addressing these issues is crucial for realizing the full potential of generalist energy intelligence, leading to enhanced energy efficiency and contributing to the resolution of global energy problems.
期刊介绍:
Cell Reports Physical Science, a premium open-access journal from Cell Press, features high-quality, cutting-edge research spanning the physical sciences. It serves as an open forum fostering collaboration among physical scientists while championing open science principles. Published works must signify significant advancements in fundamental insight or technological applications within fields such as chemistry, physics, materials science, energy science, engineering, and related interdisciplinary studies. In addition to longer articles, the journal considers impactful short-form reports and short reviews covering recent literature in emerging fields. Continually adapting to the evolving open science landscape, the journal reviews its policies to align with community consensus and best practices.