Recently,the rapid development of artificial intelligence technology—and its application in the military field—has attracted extensive attention in the public,policy,and academic circles.However,different research p...Recently,the rapid development of artificial intelligence technology—and its application in the military field—has attracted extensive attention in the public,policy,and academic circles.However,different research paradigms and debates on the nature and aspects of AI challenges to international security exist in theory and policy.In the process of the international community’s exploration of AI global security governance,effectively integrating the views of all parties and constructing consensus international norms is difficult.This paper summarizes and analyzes the current debates on the challenges and governance of AI technology to international security,the multiple evolutions of international security governance norms in the era of AI,and the cooperation and competition between China and the United States in the field of AI international security.Sorting out and exploring these arguments can provide a new research direction and perspective for exploring the global governance path of artificial intelligence.展开更多
Artificial intelligence is a new technological science that researches and develops theories,methods,technologies and application systems for simulating,extending and expanding human intelligence.It simulates certain ...Artificial intelligence is a new technological science that researches and develops theories,methods,technologies and application systems for simulating,extending and expanding human intelligence.It simulates certain human thought processes and intelligent behaviors(such as learning,reasoning,thinking,planning,etc.),and produces a new type of intelligent machine that can respond in a similar way to human intelligence.In the past 30 years,it has achieved rapid development in various industries and related disciplines such as manufacturing,medical care,finance,and transportation.展开更多
Purpose:This study aims to explore the trend and status of international collaboration in the field of artificial intelligence(AI)and to understand the hot topics,core groups,and major collaboration patterns in global...Purpose:This study aims to explore the trend and status of international collaboration in the field of artificial intelligence(AI)and to understand the hot topics,core groups,and major collaboration patterns in global AI research.Design/methodology/approach:We selected 38,224 papers in the field of AI from 1985 to 2019 in the core collection database of Web of Science(WoS)and studied international collaboration from the perspectives of authors,institutions,and countries through bibliometric analysis and social network analysis.Findings:The bibliometric results show that in the field of AI,the number of published papers is increasing every year,and 84.8%of them are cooperative papers.Collaboration with more than three authors,collaboration between two countries and collaboration within institutions are the three main levels of collaboration patterns.Through social network analysis,this study found that the US,the UK,France,and Spain led global collaboration research in the field of AI at the country level,while Vietnam,Saudi Arabia,and United Arab Emirates had a high degree of international participation.Collaboration at the institution level reflects obvious regional and economic characteristics.There are the Developing Countries Institution Collaboration Group led by Iran,China,and Vietnam,as well as the Developed Countries Institution Collaboration Group led by the US,Canada,the UK.Also,the Chinese Academy of Sciences(China)plays an important,pivotal role in connecting the these institutional collaboration groups.Research limitations:First,participant contributions in international collaboration may have varied,but in our research they are viewed equally when building collaboration networks.Second,although the edge weight in the collaboration network is considered,it is only used to help reduce the network and does not reflect the strength of collaboration.Practical implications:The findings fill the current shortage of research on international collaboration in AI.They will help inform scientists and policy makers about the future of AI research.Originality/value:This work is the longest to date regarding international collaboration in the field of AI.This research explores the evolution,future trends,and major collaboration patterns of international collaboration in the field of AI over the past 35 years.It also reveals the leading countries,core groups,and characteristics of collaboration in the field of AI.展开更多
Hepatitis A virus(HAV)infection is still an important health issue worldwide.Although several effective HAV vaccines are available,it is difficult to perform universal vaccination in certain countries.Therefore,it may...Hepatitis A virus(HAV)infection is still an important health issue worldwide.Although several effective HAV vaccines are available,it is difficult to perform universal vaccination in certain countries.Therefore,it may be better to develop antivirals against HAV for the prevention of severe hepatitis A.We found that several drugs potentially inhibit HAV internal ribosomal entry site-dependent translation and HAV replication.Artificial intelligence and machine learning could also support screening of anti-HAV drugs,using drug repositioning and drug rescue approaches.展开更多
Artificial general intelligence (AGI) is the ability of an artificial intelligence (AI) agent to solve somewhat-arbitrary tasks in somewhat-arbitrary environments. Despite being a long-standing goal in the field of AI...Artificial general intelligence (AGI) is the ability of an artificial intelligence (AI) agent to solve somewhat-arbitrary tasks in somewhat-arbitrary environments. Despite being a long-standing goal in the field of AI, achieving AGI remains elusive. In this study, we empirically assessed the generalizability of AI agents by applying a deep reinforcement learning (DRL) approach to the medical domain. Our investigation involved examining how modifying the agent’s structure, task, and environment impacts its generality. Sample: An NIH chest X-ray dataset with 112,120 images and 15 medical conditions. We evaluated the agent’s performance on binary and multiclass classification tasks through a baseline model, a convolutional neural network model, a deep Q network model, and a proximal policy optimization model. Results: Our results suggest that DRL agents with the algorithmic flexibility to autonomously vary their macro/microstructures can generalize better across given tasks and environments.展开更多
文摘Recently,the rapid development of artificial intelligence technology—and its application in the military field—has attracted extensive attention in the public,policy,and academic circles.However,different research paradigms and debates on the nature and aspects of AI challenges to international security exist in theory and policy.In the process of the international community’s exploration of AI global security governance,effectively integrating the views of all parties and constructing consensus international norms is difficult.This paper summarizes and analyzes the current debates on the challenges and governance of AI technology to international security,the multiple evolutions of international security governance norms in the era of AI,and the cooperation and competition between China and the United States in the field of AI international security.Sorting out and exploring these arguments can provide a new research direction and perspective for exploring the global governance path of artificial intelligence.
文摘Artificial intelligence is a new technological science that researches and develops theories,methods,technologies and application systems for simulating,extending and expanding human intelligence.It simulates certain human thought processes and intelligent behaviors(such as learning,reasoning,thinking,planning,etc.),and produces a new type of intelligent machine that can respond in a similar way to human intelligence.In the past 30 years,it has achieved rapid development in various industries and related disciplines such as manufacturing,medical care,finance,and transportation.
基金We acknowledge the National Natural Science Foundation of China(Grant No.71673143)the National Social Science Foundation of China(Grant No.19BTQ062)for thier financial support.
文摘Purpose:This study aims to explore the trend and status of international collaboration in the field of artificial intelligence(AI)and to understand the hot topics,core groups,and major collaboration patterns in global AI research.Design/methodology/approach:We selected 38,224 papers in the field of AI from 1985 to 2019 in the core collection database of Web of Science(WoS)and studied international collaboration from the perspectives of authors,institutions,and countries through bibliometric analysis and social network analysis.Findings:The bibliometric results show that in the field of AI,the number of published papers is increasing every year,and 84.8%of them are cooperative papers.Collaboration with more than three authors,collaboration between two countries and collaboration within institutions are the three main levels of collaboration patterns.Through social network analysis,this study found that the US,the UK,France,and Spain led global collaboration research in the field of AI at the country level,while Vietnam,Saudi Arabia,and United Arab Emirates had a high degree of international participation.Collaboration at the institution level reflects obvious regional and economic characteristics.There are the Developing Countries Institution Collaboration Group led by Iran,China,and Vietnam,as well as the Developed Countries Institution Collaboration Group led by the US,Canada,the UK.Also,the Chinese Academy of Sciences(China)plays an important,pivotal role in connecting the these institutional collaboration groups.Research limitations:First,participant contributions in international collaboration may have varied,but in our research they are viewed equally when building collaboration networks.Second,although the edge weight in the collaboration network is considered,it is only used to help reduce the network and does not reflect the strength of collaboration.Practical implications:The findings fill the current shortage of research on international collaboration in AI.They will help inform scientists and policy makers about the future of AI research.Originality/value:This work is the longest to date regarding international collaboration in the field of AI.This research explores the evolution,future trends,and major collaboration patterns of international collaboration in the field of AI over the past 35 years.It also reveals the leading countries,core groups,and characteristics of collaboration in the field of AI.
基金Supported by The Japan Agency for Medical Research and Development,No.JP20fk0210075.
文摘Hepatitis A virus(HAV)infection is still an important health issue worldwide.Although several effective HAV vaccines are available,it is difficult to perform universal vaccination in certain countries.Therefore,it may be better to develop antivirals against HAV for the prevention of severe hepatitis A.We found that several drugs potentially inhibit HAV internal ribosomal entry site-dependent translation and HAV replication.Artificial intelligence and machine learning could also support screening of anti-HAV drugs,using drug repositioning and drug rescue approaches.
文摘Artificial general intelligence (AGI) is the ability of an artificial intelligence (AI) agent to solve somewhat-arbitrary tasks in somewhat-arbitrary environments. Despite being a long-standing goal in the field of AI, achieving AGI remains elusive. In this study, we empirically assessed the generalizability of AI agents by applying a deep reinforcement learning (DRL) approach to the medical domain. Our investigation involved examining how modifying the agent’s structure, task, and environment impacts its generality. Sample: An NIH chest X-ray dataset with 112,120 images and 15 medical conditions. We evaluated the agent’s performance on binary and multiclass classification tasks through a baseline model, a convolutional neural network model, a deep Q network model, and a proximal policy optimization model. Results: Our results suggest that DRL agents with the algorithmic flexibility to autonomously vary their macro/microstructures can generalize better across given tasks and environments.
