Despite a global decline in tobacco use,smoking remains a leading cause of preventable death,with rising vaping rates among adolescents and young adults further complicating nicotine cessation efforts.Digital interven...Despite a global decline in tobacco use,smoking remains a leading cause of preventable death,with rising vaping rates among adolescents and young adults further complicating nicotine cessation efforts.Digital interventions,particularly chatbots,have gained attention for their potential to support tobacco and vaping cessation by simulating human-like conversations and providing instant feedback.However,evidence of their effectiveness is limited.The emergence of generative artificial intelligence(AI)chatbots,such as ChatGPT,offers a promising avenue for more personalised and effective cessation support.This article reviews existing literature on traditional chatbot interventions for cessation services,explores the potential of AI chatbots,namely ChatGPT,in continuing to support tobacco and vaping cessation efforts,and identifies areas for future research.It highlights the need to further monitor the reliability and accuracy of AI-generated content and to develop frameworks ensuring healthcare professionals receive adequate training in using these new tools effectively to support patients in quitting smoking and/or vaping.展开更多
Intelligent chatbots powered by large language models(LLMs)have recently been sweeping the world,with potential for a wide variety of industrial applications.Global frontier technology companies are feverishly partici...Intelligent chatbots powered by large language models(LLMs)have recently been sweeping the world,with potential for a wide variety of industrial applications.Global frontier technology companies are feverishly participating in LLM-powered chatbot design and development,providing several alternatives beyond the famous ChatGPT.However,training,fine-tuning,and updating such intelligent chatbots consume substantial amounts of electricity,resulting in significant carbon emissions.The research and development of all intelligent LLMs and software,hardware manufacturing(e.g.,graphics processing units and supercomputers),related data/operations management,and material recycling supporting chatbot services are associated with carbon emissions to varying extents.Attention should therefore be paid to the entire life-cycle energy and carbon footprints of LLM-powered intelligent chatbots in both the present and future in order to mitigate their climate change impact.In this work,we clarify and highlight the energy consumption and carbon emission implications of eight main phases throughout the life cycle of the development of such intelligent chatbots.Based on a life-cycle and interaction analysis of these phases,we propose a system-level solution with three strategic pathways to optimize the management of this industry and mitigate the related footprints.While anticipating the enormous potential of this advanced technology and its products,we make an appeal for a rethinking of the mitigation pathways and strategies of the life-cycle energy usage and carbon emissions of the LLM-powered intelligent chatbot industry and a reshaping of their energy and environmental implications at this early stage of development.展开更多
With the development of cities and the prevalence of networks,interpersonal relationships have become increasingly distant.When people crave communication,they hope to find someone to confide in.With the rapid advance...With the development of cities and the prevalence of networks,interpersonal relationships have become increasingly distant.When people crave communication,they hope to find someone to confide in.With the rapid advancement of deep learning and big data technologies,an enabling environment has been established for the development of intelligent chatbot systems.By effectively combining cutting-edge technologies with humancentered design principles,chatbots hold the potential to revolutionize our lives and alleviate feelings of loneliness.A multi-topic chat companion robot based on a state machine has been proposed,which can engage in fluent dialogue with humans and meet different functional requirements.It can chat with users about movies,music,and other related topics,and recommend movies and music that may interest them to alleviate their loneliness and provide companionship.The interaction platform of the companion robot is realized through the QQ communication platform,with two chat modes:Conversation mode and recommendation mode.First,the KdConv open-source corpus was selected,and Python was used to crawl information on movies and music from Douban and QQ Music to establish and pre-process the dataset.Then,the dialogue function was implemented using generative language models and retrieval systems,while the recommendation function was achieved using user profiling and collaborative filtering.Finally,a state machine algorithm was used to achieve real-time switching between the two chat modes of the companion robot.In conclusion,test participants gave high ratings for the accuracy of the companion robot’s responses and the satisfaction with its content recommendations.Compared to traditional large-scale integrated models,this robot employs a state-machine framework to achieve diverse functions through seamless state transitions,thereby enhancing computational speed and precision.Additionally,the robot can recommend movies and music,providing companionship and alleviating loneliness for users,which is of great significance in modern society where interpersonal relationships are increasingly alienated.展开更多
文摘Despite a global decline in tobacco use,smoking remains a leading cause of preventable death,with rising vaping rates among adolescents and young adults further complicating nicotine cessation efforts.Digital interventions,particularly chatbots,have gained attention for their potential to support tobacco and vaping cessation by simulating human-like conversations and providing instant feedback.However,evidence of their effectiveness is limited.The emergence of generative artificial intelligence(AI)chatbots,such as ChatGPT,offers a promising avenue for more personalised and effective cessation support.This article reviews existing literature on traditional chatbot interventions for cessation services,explores the potential of AI chatbots,namely ChatGPT,in continuing to support tobacco and vaping cessation efforts,and identifies areas for future research.It highlights the need to further monitor the reliability and accuracy of AI-generated content and to develop frameworks ensuring healthcare professionals receive adequate training in using these new tools effectively to support patients in quitting smoking and/or vaping.
基金supported by the National Natural Science Foundation of China(72061127004 and 72104164)the System Science and Enterprise Development Research Center(Xq22B04)+1 种基金financial support from the Engineering and Physical Sciences Research Council(EPSRC)Programme(EP/V030515/1)financial support from the Science and Technology Support Project of Guizhou Province([2019]2839).
文摘Intelligent chatbots powered by large language models(LLMs)have recently been sweeping the world,with potential for a wide variety of industrial applications.Global frontier technology companies are feverishly participating in LLM-powered chatbot design and development,providing several alternatives beyond the famous ChatGPT.However,training,fine-tuning,and updating such intelligent chatbots consume substantial amounts of electricity,resulting in significant carbon emissions.The research and development of all intelligent LLMs and software,hardware manufacturing(e.g.,graphics processing units and supercomputers),related data/operations management,and material recycling supporting chatbot services are associated with carbon emissions to varying extents.Attention should therefore be paid to the entire life-cycle energy and carbon footprints of LLM-powered intelligent chatbots in both the present and future in order to mitigate their climate change impact.In this work,we clarify and highlight the energy consumption and carbon emission implications of eight main phases throughout the life cycle of the development of such intelligent chatbots.Based on a life-cycle and interaction analysis of these phases,we propose a system-level solution with three strategic pathways to optimize the management of this industry and mitigate the related footprints.While anticipating the enormous potential of this advanced technology and its products,we make an appeal for a rethinking of the mitigation pathways and strategies of the life-cycle energy usage and carbon emissions of the LLM-powered intelligent chatbot industry and a reshaping of their energy and environmental implications at this early stage of development.
基金supported by the Guangxi Key Laboratory of Automatic Detecting Technology and Instruments(YQ21207)the Qinglan Project of Jiangsu Province.
文摘With the development of cities and the prevalence of networks,interpersonal relationships have become increasingly distant.When people crave communication,they hope to find someone to confide in.With the rapid advancement of deep learning and big data technologies,an enabling environment has been established for the development of intelligent chatbot systems.By effectively combining cutting-edge technologies with humancentered design principles,chatbots hold the potential to revolutionize our lives and alleviate feelings of loneliness.A multi-topic chat companion robot based on a state machine has been proposed,which can engage in fluent dialogue with humans and meet different functional requirements.It can chat with users about movies,music,and other related topics,and recommend movies and music that may interest them to alleviate their loneliness and provide companionship.The interaction platform of the companion robot is realized through the QQ communication platform,with two chat modes:Conversation mode and recommendation mode.First,the KdConv open-source corpus was selected,and Python was used to crawl information on movies and music from Douban and QQ Music to establish and pre-process the dataset.Then,the dialogue function was implemented using generative language models and retrieval systems,while the recommendation function was achieved using user profiling and collaborative filtering.Finally,a state machine algorithm was used to achieve real-time switching between the two chat modes of the companion robot.In conclusion,test participants gave high ratings for the accuracy of the companion robot’s responses and the satisfaction with its content recommendations.Compared to traditional large-scale integrated models,this robot employs a state-machine framework to achieve diverse functions through seamless state transitions,thereby enhancing computational speed and precision.Additionally,the robot can recommend movies and music,providing companionship and alleviating loneliness for users,which is of great significance in modern society where interpersonal relationships are increasingly alienated.
