The emergence of generative artificial intelligence(AI)has had a huge impact on all areas of life,including the field of education.AI can assist teachers in cultivating talents and promoting personalized learning and ...The emergence of generative artificial intelligence(AI)has had a huge impact on all areas of life,including the field of education.AI can assist teachers in cultivating talents and promoting personalized learning and teaching,but it also prevents individuals from thinking independently and creatively.In the era of generative AI,the rapid development of technology and its significant impact on the field of education are inevitable.There are many educational issues related to it,such as teaching methods,student training goals,teaching philosophy and purposes,and other educational issues,that require re-conceptualization and review.展开更多
This study,drawing on the commonalities between generative artificial intelligence and foreign language writing instruction,outlines the core ideology of digital humanities-based college English writing instruction,in...This study,drawing on the commonalities between generative artificial intelligence and foreign language writing instruction,outlines the core ideology of digital humanities-based college English writing instruction,including auxiliary use of generative artificial intelligence tools,primary focus on humanistic education,and the re-production of knowledge,aiming to foster students’critical thinking,collaborative skills,and creativity.Building on this foundation,the study delves into generative artificial intelligence tools applicable to different stages of process-genre writing and their strategic applications.The use of generative artificial intelligence tools is beneficial for students to present,discuss,and share writing content,encouraging them to enhance their writing,collaboration,critical thinking,and creative abilities through deep interaction with model essays and creative discourses.展开更多
The primary objectives of medical safety education are to provide the public with essential knowledge about medications and to foster a scientific approach to drug usage.The era of using artificial intelligence to rev...The primary objectives of medical safety education are to provide the public with essential knowledge about medications and to foster a scientific approach to drug usage.The era of using artificial intelligence to revolutionize medical safety education has already dawned,and ChatGPT and other generative artificial intelligence models have immense potential in this domain.Notably,they offer a wealth of knowledge,anonymity,continuous availability,and personalized services.However,the practical implementation of generative artificial intelligence models such as ChatGPT in medical safety education still faces several challenges,including concerns about the accuracy of information,legal responsibilities,and ethical obligations.Moving forward,it is crucial to intelligently upgrade ChatGPT by leveraging the strengths of existing medical practices.This task involves further integrating the model with real-life scenarios and proactively addressing ethical and security issues with the ultimate goal of providing the public with comprehensive,convenient,efficient,and personalized medical services.展开更多
Generative Artificial Intelligence(GAI)is attracting the increasing attention of materials community for its excellent capability of generating required contents.With the introduction of Prompt paradigm and reinforcem...Generative Artificial Intelligence(GAI)is attracting the increasing attention of materials community for its excellent capability of generating required contents.With the introduction of Prompt paradigm and reinforcement learning from human feedback(RLHF),GAI shifts from the task-specific to general pattern gradually,enabling to tackle multiple complicated tasks involved in resolving the structure-activity relationships.Here,we review the development status of GAI comprehensively and analyze pros and cons of various generative models in the view of methodology.The applications of task-specific generative models involving materials inverse design and data augmentation are also dissected.Taking ChatGPT as an example,we explore the potential applications of general GAI in generating multiple materials content,solving differential equation as well as querying materials FAQs.Furthermore,we summarize six challenges encountered for the use of GAI in materials science and provide the corresponding solutions.This work paves the way for providing effective and explainable materials data generation and analysis approaches to accelerate the materials research and development.展开更多
The prediction of chemical synthesis pathways plays a pivotal role in materials science research. Challenges, such as the complexity of synthesis pathways and the lack of comprehensive datasets, currently hinder our a...The prediction of chemical synthesis pathways plays a pivotal role in materials science research. Challenges, such as the complexity of synthesis pathways and the lack of comprehensive datasets, currently hinder our ability to predict these chemical processes accurately. However, recent advancements in generative artificial intelligence(GAI), including automated text generation and question–answering systems, coupled with fine-tuning techniques, have facilitated the deployment of large-scale AI models tailored to specific domains. In this study, we harness the power of the LLaMA2-7B model and enhance it through a learning process that incorporates 13878 pieces of structured material knowledge data.This specialized AI model, named Mat Chat, focuses on predicting inorganic material synthesis pathways. Mat Chat exhibits remarkable proficiency in generating and reasoning with knowledge in materials science. Although Mat Chat requires further refinement to meet the diverse material design needs, this research undeniably highlights its impressive reasoning capabilities and innovative potential in materials science. Mat Chat is now accessible online and open for use, with both the model and its application framework available as open source. This study establishes a robust foundation for collaborative innovation in the integration of generative AI in materials science.展开更多
Artificial intelligence generated content(AIGC)is a production method based on artificial intelligence(AI)technology that finds rules through data and automatically generates content.In contrast to computational intel...Artificial intelligence generated content(AIGC)is a production method based on artificial intelligence(AI)technology that finds rules through data and automatically generates content.In contrast to computational intelligence,generative AI,as exemplified by ChatGPT,exhibits characteristics that increasingly resemble human-level comprehension and creation processes.This paper provides a detailed technical framework and history of ChatGPT,followed by an examination of the challenges posed to political security,military security,economic security,cultural security,social security,ethical security,legal security,machine escape problems,and information leakage.Finally,this paper discusses the potential opportunities that AIGC presents in the realms of politics,military,cybersecurity,society,and public safety education.展开更多
文摘The emergence of generative artificial intelligence(AI)has had a huge impact on all areas of life,including the field of education.AI can assist teachers in cultivating talents and promoting personalized learning and teaching,but it also prevents individuals from thinking independently and creatively.In the era of generative AI,the rapid development of technology and its significant impact on the field of education are inevitable.There are many educational issues related to it,such as teaching methods,student training goals,teaching philosophy and purposes,and other educational issues,that require re-conceptualization and review.
文摘This study,drawing on the commonalities between generative artificial intelligence and foreign language writing instruction,outlines the core ideology of digital humanities-based college English writing instruction,including auxiliary use of generative artificial intelligence tools,primary focus on humanistic education,and the re-production of knowledge,aiming to foster students’critical thinking,collaborative skills,and creativity.Building on this foundation,the study delves into generative artificial intelligence tools applicable to different stages of process-genre writing and their strategic applications.The use of generative artificial intelligence tools is beneficial for students to present,discuss,and share writing content,encouraging them to enhance their writing,collaboration,critical thinking,and creative abilities through deep interaction with model essays and creative discourses.
文摘The primary objectives of medical safety education are to provide the public with essential knowledge about medications and to foster a scientific approach to drug usage.The era of using artificial intelligence to revolutionize medical safety education has already dawned,and ChatGPT and other generative artificial intelligence models have immense potential in this domain.Notably,they offer a wealth of knowledge,anonymity,continuous availability,and personalized services.However,the practical implementation of generative artificial intelligence models such as ChatGPT in medical safety education still faces several challenges,including concerns about the accuracy of information,legal responsibilities,and ethical obligations.Moving forward,it is crucial to intelligently upgrade ChatGPT by leveraging the strengths of existing medical practices.This task involves further integrating the model with real-life scenarios and proactively addressing ethical and security issues with the ultimate goal of providing the public with comprehensive,convenient,efficient,and personalized medical services.
基金National Natural Science Foundation of China[grant number 92270124,52073169]National Key Research and Development Program of China[grant number 2021YFB3802101]the Key Research Project of Zhejiang Laboratory[grant number 2021PE0AC02].
文摘Generative Artificial Intelligence(GAI)is attracting the increasing attention of materials community for its excellent capability of generating required contents.With the introduction of Prompt paradigm and reinforcement learning from human feedback(RLHF),GAI shifts from the task-specific to general pattern gradually,enabling to tackle multiple complicated tasks involved in resolving the structure-activity relationships.Here,we review the development status of GAI comprehensively and analyze pros and cons of various generative models in the view of methodology.The applications of task-specific generative models involving materials inverse design and data augmentation are also dissected.Taking ChatGPT as an example,we explore the potential applications of general GAI in generating multiple materials content,solving differential equation as well as querying materials FAQs.Furthermore,we summarize six challenges encountered for the use of GAI in materials science and provide the corresponding solutions.This work paves the way for providing effective and explainable materials data generation and analysis approaches to accelerate the materials research and development.
基金supported by the Informatization Plan of the Chinese Academy of Sciences (Grant No. CASWX2023SF-0101)the Key Research Program of Frontier Sciences, CAS (Grant No. ZDBS-LY-7025)+1 种基金the Youth Innovation Promotion Association CAS (Grant No. 2021167)the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDB33020000)。
文摘The prediction of chemical synthesis pathways plays a pivotal role in materials science research. Challenges, such as the complexity of synthesis pathways and the lack of comprehensive datasets, currently hinder our ability to predict these chemical processes accurately. However, recent advancements in generative artificial intelligence(GAI), including automated text generation and question–answering systems, coupled with fine-tuning techniques, have facilitated the deployment of large-scale AI models tailored to specific domains. In this study, we harness the power of the LLaMA2-7B model and enhance it through a learning process that incorporates 13878 pieces of structured material knowledge data.This specialized AI model, named Mat Chat, focuses on predicting inorganic material synthesis pathways. Mat Chat exhibits remarkable proficiency in generating and reasoning with knowledge in materials science. Although Mat Chat requires further refinement to meet the diverse material design needs, this research undeniably highlights its impressive reasoning capabilities and innovative potential in materials science. Mat Chat is now accessible online and open for use, with both the model and its application framework available as open source. This study establishes a robust foundation for collaborative innovation in the integration of generative AI in materials science.
基金This work was supported by the National Science Foundation of China[NSFC41971366,4231476]Fundamental Research Funds for the Central Universities of China[buctrc202132].
文摘Artificial intelligence generated content(AIGC)is a production method based on artificial intelligence(AI)technology that finds rules through data and automatically generates content.In contrast to computational intelligence,generative AI,as exemplified by ChatGPT,exhibits characteristics that increasingly resemble human-level comprehension and creation processes.This paper provides a detailed technical framework and history of ChatGPT,followed by an examination of the challenges posed to political security,military security,economic security,cultural security,social security,ethical security,legal security,machine escape problems,and information leakage.Finally,this paper discusses the potential opportunities that AIGC presents in the realms of politics,military,cybersecurity,society,and public safety education.
