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
People occasionally interact with each other through conversation.In particular,we communicate through dialogue and exchange emotions and information from it.Emotions are essential characteristics of natural language....People occasionally interact with each other through conversation.In particular,we communicate through dialogue and exchange emotions and information from it.Emotions are essential characteristics of natural language.Conversational artificial intelligence is an integral part of all the technologies that allow computers to communicate like humans.For a computer to interact like a human being,it must understand the emotions inherent in the conversation and generate the appropriate responses.However,existing dialogue systems focus only on improving the quality of understanding natural language or generating natural language,excluding emotions.We propose a chatbot based on emotion,which is an essential element in conversation.EP-Bot(an Empathetic PolarisX-based chatbot)is an empathetic chatbot that can better understand a person’s utterance by utilizing PolarisX,an autogrowing knowledge graph.PolarisX extracts new relationship information and expands the knowledge graph automatically.It is helpful for computers to understand a person’s common sense.The proposed EP-Bot extracts knowledge graph embedding using PolarisX and detects emotion and dialog act from the utterance.Then it generates the next utterance using the embeddings.EP-Bot could understand and create a conversation,including the person’s common sense,emotion,and intention.We verify the novelty and accuracy of EP-Bot through the experiments.展开更多
Background: Chatbots are easy to use and simulate a human conversation through text or voice via smartphones or computers. In the field of health, chatbots can improve patient information, monitoring, or treatment adh...Background: Chatbots are easy to use and simulate a human conversation through text or voice via smartphones or computers. In the field of health, chatbots can improve patient information, monitoring, or treatment adherence. Method: The objective of this article is to describe how a chatbot dedicated to disease monitoring and support of patients can interact with them and how data are exploited to be safe. Results: Wefight designed a chatbot named Vik to empower patients with cancers or chronic diseases and their relatives via personalized text messages. Natural Language Processing models were used. We built several Vik for each disease. Each Vik has its contents, its own NLP model and interacts its way with the patient. Conclusion: Conversational agents may help patients with minor health concerns without seeing a real physician. If the quality of these softwares is not thoroughly assessed, they could be dangerous. If chatbots are effective and safe, they could be prescribed like a drug to improve patient information, monitoring, or treatment adherence.展开更多
Artificial intelligent based dialog systems are getting attention from both business and academic communities.The key parts for such intelligent chatbot systems are domain classification,intent detection,and named ent...Artificial intelligent based dialog systems are getting attention from both business and academic communities.The key parts for such intelligent chatbot systems are domain classification,intent detection,and named entity recognition.Various supervised,unsupervised,and hybrid approaches are used to detect each field.Such intelligent systems,also called natural language understanding systems analyze user requests in sequential order:domain classification,intent,and entity recognition based on the semantic rules of the classified domain.This sequential approach propagates the downstream error;i.e.,if the domain classification model fails to classify the domain,intent and entity recognition fail.Furthermore,training such intelligent system necessitates a large number of user-annotated datasets for each domain.This study proposes a single joint predictive deep neural network framework based on long short-term memory using only a small user-annotated dataset to address these issues.It investigates value added by incorporating unlabeled data from user chatting logs into multi-domain spoken language understanding systems.Systematic experimental analysis of the proposed joint frameworks,along with the semi-supervised multi-domain model,using open-source annotated and unannotated utterances shows robust improvement in the predictive performance of the proposed multi-domain intelligent chatbot over a base joint model and joint model based on adversarial learning.展开更多
Clinical applications of Artificial Intelligence(AI)for mental health care have experienced a meteoric rise in the past few years.AIenabled chatbot software and applications have been administering significant medical...Clinical applications of Artificial Intelligence(AI)for mental health care have experienced a meteoric rise in the past few years.AIenabled chatbot software and applications have been administering significant medical treatments that were previously only available from experienced and competent healthcare professionals.Such initiatives,which range from“virtual psychiatrists”to“social robots”in mental health,strive to improve nursing performance and cost management,as well as meeting the mental health needs of vulnerable and underserved populations.Nevertheless,there is still a substantial gap between recent progress in AI mental health and the widespread use of these solutions by healthcare practitioners in clinical settings.Furthermore,treatments are frequently developed without clear ethical concerns.While AI-enabled solutions show promise in the realm of mental health,further research is needed to address the ethical and social aspects of these technologies,as well as to establish efficient research and medical practices in this innovative sector.Moreover,the current relevant literature still lacks a formal and objective review that specifically focuses on research questions from both developers and psychiatrists in AI-enabled chatbotpsychologists development.Taking into account all the problems outlined in this study,we conducted a systematic review of AI-enabled chatbots in mental healthcare that could cover some issues concerning psychotherapy and artificial intelligence.In this systematic review,we put five research questions related to technologies in chatbot development,psychological disorders that can be treated by using chatbots,types of therapies that are enabled in chatbots,machine learning models and techniques in chatbot psychologists,as well as ethical challenges.展开更多
People often communicate with auto-answering tools such as conversational agents due to their 24/7 availability and unbiased responses.However,chatbots are normally designed for specific purposes and areas of experien...People often communicate with auto-answering tools such as conversational agents due to their 24/7 availability and unbiased responses.However,chatbots are normally designed for specific purposes and areas of experience and cannot answer questions outside their scope.Chatbots employ Natural Language Understanding(NLU)to infer their responses.There is a need for a chatbot that can learn from inquiries and expand its area of experience with time.This chatbot must be able to build profiles representing intended topics in a similar way to the human brain for fast retrieval.This study proposes a methodology to enhance a chatbot’s brain functionality by clustering available knowledge bases on sets of related themes and building representative profiles.We used a COVID-19 information dataset to evaluate the proposed methodology.The pandemic has been accompanied by an“infodemic”of fake news.The chatbot was evaluated by a medical doctor and a public trial of 308 real users.Evaluationswere obtained and statistically analyzed tomeasure effectiveness,efficiency,and satisfaction as described by the ISO9214 standard.The proposed COVID-19 chatbot system relieves doctors from answering questions.Chatbots provide an example of the use of technology to handle an infodemic.展开更多
India imposed the largest lockdown in the world in response tofight the spread of the Novel Coronavirus disease(COVID-19)from 19 March till 31 May 2020.The onset of the pandemic left the general public feeling psycho-s...India imposed the largest lockdown in the world in response tofight the spread of the Novel Coronavirus disease(COVID-19)from 19 March till 31 May 2020.The onset of the pandemic left the general public feeling psycho-socially distressed,helpless,and anxious.The researcher developed a Messenger supported Chatbot,based on the broaden and build model,to cater to the healthy general public to promote positivity and mental well-being.31 participants between 22 and 45 years old consensually took a pre-test,Chatbot intervention,and post-test.The Chatbot provided guided activities out of which positive affirmations,meditation,and exercises were mostly used.The qualitative data from the study shows that the majority of the participants strongly feel positivity is within themselves and that the tool provided a self-help approach to be me well,mentally during the lockdown.The intervention helped significantly reducing symptoms of psychosocial distress in six of the individual’s post-chatbot interventions.Participants’impressions of the tool suggest more preponderant opportunities for future research in technology-driven mental health support.展开更多
The coronavirus(nCOV-19),which was discovered,has now spread around the world.However,managing the flow of a large number of cases has proven to be a significant issue for hospitals or healthcare professionals.It is b...The coronavirus(nCOV-19),which was discovered,has now spread around the world.However,managing the flow of a large number of cases has proven to be a significant issue for hospitals or healthcare professionals.It is becoming increasingly challenging to speak with a medical expert after the epidemic’s initial wave has passed,particularly in rural areas.Thus,it becomes clear that a Chatbot that is well-designed and implemented can assist patients who are located far away by advocating preventive actions,and viral updates in various cities,and minimising the psychological harm brought on by dread.In this study,a sophisticated Chabot’s design for diagnosing individuals who have been exposed to COVID-19 is presented,along with recommendations for immediate safety measures.Additionally,when symptoms grow serious,this virtual assistant makes contact with specialised medical professionals.展开更多
The rise of artificial intelligence(AI)in procurement has transformed how organizations engage with suppliers,optimize spending,and drive contract negotiations.Traditional procurement negotiations rely on human intuit...The rise of artificial intelligence(AI)in procurement has transformed how organizations engage with suppliers,optimize spending,and drive contract negotiations.Traditional procurement negotiations rely on human intuition,historical knowledge,and manual research.However,with the advancement of AI-driven Smart Negotiation Assistants,procurement teams can leverage real-time market intelligence,price benchmarks,and predictive analytics to autonomously negotiate contracts.This paper introduces an AI-powered Pro curement Chatbot,capable of conducting supplier negotiations with minimal human intervention.The system utilizes machine learning(ML),natural lan guage processing(NLP),and historical transaction data to negotiate terms,secure cost savings,and ensure compliance with procurement policies.Realworld case studies,including automated software licensing negotiations and dynamic supplier pricing adjustments,demonstrate how AI-driven negotiations can save millions in procurement costs,reduce cycle times by up to 40%,and mitigate supplier risks[1].The paper also explores technical architecture,algorithmic models,and deployment strategies for integrating AI negotiation assistants into enterprise procurement workflows.Furthermore,it highlights regulatory and ethical considerations in AI-driven procurement,emphasizing transparency and fairness.By leveraging AI-driven negotiation chatbots,businesses can achieve autonomous,efficient,and data-driven procurement processes,ensuring better supplier relationships and long-term cost savings.展开更多
The application of artificial intelligence(AI)in customer service becomes ubiquitous.In response to the advocacy in the“2021 Coordinated Plan on Artificial Intelligence”,it is crucial to understand how to leverage A...The application of artificial intelligence(AI)in customer service becomes ubiquitous.In response to the advocacy in the“2021 Coordinated Plan on Artificial Intelligence”,it is crucial to understand how to leverage AI customer service chatbots for societal welfare.Across two scenario studies and one lab experiment,this research investigates the impact of AI chatbots’communication styles on consumers’subsequent prosocial intentions irrelevant to the AI-human interaction contents.The combined evidence suggests that consumers exhibit higher prosocial intentions after interacting with social-oriented(vs.task-oriented)AI chatbots.The findings reveal the chain-mediating roles of social presence and empathy.Moreover,the current research investigates the boundary effect of consumers’goal focus(process focus vs.outcome focus),and shows that AI chatbots’communication styles have stronger impact on prosocial intentions for customers with outcome focus.These results revealed the important externality of the AI application in marketplace and provide a novel perspective for companies to implement the corporate social responsibility(CSR)strategy.展开更多
Since OpenAI opened access to ChatGPT,large language models(LLMs)become an increasingly popular topic attracting researchers’attention from abundant domains.However,public researchers meet some problems when developi...Since OpenAI opened access to ChatGPT,large language models(LLMs)become an increasingly popular topic attracting researchers’attention from abundant domains.However,public researchers meet some problems when developing LLMs given that most of the LLMs are produced by industries and the training details are typically unrevealed.Since datasets are an important setup of LLMs,this paper does a holistic survey on the training datasets used in both the pre-train and fine-tune processes.The paper first summarizes 16 pre-train datasets and 16 fine-tune datasets used in the state-of-the-art LLMs.Secondly,based on the properties of the pre-train and fine-tune processes,it comments on pre-train datasets from quality,quantity,and relation with models,and comments on fine-tune datasets from quality,quantity,and concerns.This study then critically figures out the problems and research trends that exist in current LLM datasets.The study helps public researchers train and investigate LLMs by visual cases and provides useful comments to the research community regarding data development.To the best of our knowledge,this paper is the first to summarize and discuss datasets used in both autoregressive and chat LLMs.The survey offers insights and suggestions to researchers and LLM developers as they build their models,and contributes to the LLM study by pointing out the existing problems of LLM studies from the perspective of data.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.
基金supported by Basic Science Research Program through the NRF(National Research Foundation of Korea)the MSIT(Ministry of Science and ICT),Korea,under the National Program for Excellence in SW supervised by the IITP(Institute for Information&communications Technology Promotion)and the Gachon University research fund of 2019(Nos.NRF2019R1A2C1008412,2015-0-00932,GCU-2019-0773).
文摘People occasionally interact with each other through conversation.In particular,we communicate through dialogue and exchange emotions and information from it.Emotions are essential characteristics of natural language.Conversational artificial intelligence is an integral part of all the technologies that allow computers to communicate like humans.For a computer to interact like a human being,it must understand the emotions inherent in the conversation and generate the appropriate responses.However,existing dialogue systems focus only on improving the quality of understanding natural language or generating natural language,excluding emotions.We propose a chatbot based on emotion,which is an essential element in conversation.EP-Bot(an Empathetic PolarisX-based chatbot)is an empathetic chatbot that can better understand a person’s utterance by utilizing PolarisX,an autogrowing knowledge graph.PolarisX extracts new relationship information and expands the knowledge graph automatically.It is helpful for computers to understand a person’s common sense.The proposed EP-Bot extracts knowledge graph embedding using PolarisX and detects emotion and dialog act from the utterance.Then it generates the next utterance using the embeddings.EP-Bot could understand and create a conversation,including the person’s common sense,emotion,and intention.We verify the novelty and accuracy of EP-Bot through the experiments.
文摘Background: Chatbots are easy to use and simulate a human conversation through text or voice via smartphones or computers. In the field of health, chatbots can improve patient information, monitoring, or treatment adherence. Method: The objective of this article is to describe how a chatbot dedicated to disease monitoring and support of patients can interact with them and how data are exploited to be safe. Results: Wefight designed a chatbot named Vik to empower patients with cancers or chronic diseases and their relatives via personalized text messages. Natural Language Processing models were used. We built several Vik for each disease. Each Vik has its contents, its own NLP model and interacts its way with the patient. Conclusion: Conversational agents may help patients with minor health concerns without seeing a real physician. If the quality of these softwares is not thoroughly assessed, they could be dangerous. If chatbots are effective and safe, they could be prescribed like a drug to improve patient information, monitoring, or treatment adherence.
基金This research was supported by the BK21 FOUR(Fostering Outstanding Universities for Research)funded by the Ministry of Education(MOE,Korea)and National Research Foundation of Korea(NFR).
文摘Artificial intelligent based dialog systems are getting attention from both business and academic communities.The key parts for such intelligent chatbot systems are domain classification,intent detection,and named entity recognition.Various supervised,unsupervised,and hybrid approaches are used to detect each field.Such intelligent systems,also called natural language understanding systems analyze user requests in sequential order:domain classification,intent,and entity recognition based on the semantic rules of the classified domain.This sequential approach propagates the downstream error;i.e.,if the domain classification model fails to classify the domain,intent and entity recognition fail.Furthermore,training such intelligent system necessitates a large number of user-annotated datasets for each domain.This study proposes a single joint predictive deep neural network framework based on long short-term memory using only a small user-annotated dataset to address these issues.It investigates value added by incorporating unlabeled data from user chatting logs into multi-domain spoken language understanding systems.Systematic experimental analysis of the proposed joint frameworks,along with the semi-supervised multi-domain model,using open-source annotated and unannotated utterances shows robust improvement in the predictive performance of the proposed multi-domain intelligent chatbot over a base joint model and joint model based on adversarial learning.
基金This work was supported by the grant“Development of an intellectual system prototype for online-psychological support that can diagnose and improve youth’s psychoemotional state”funded by the Ministry of Education of the Republic of Kazakhstan.Grant No.IRN AP09259140.
文摘Clinical applications of Artificial Intelligence(AI)for mental health care have experienced a meteoric rise in the past few years.AIenabled chatbot software and applications have been administering significant medical treatments that were previously only available from experienced and competent healthcare professionals.Such initiatives,which range from“virtual psychiatrists”to“social robots”in mental health,strive to improve nursing performance and cost management,as well as meeting the mental health needs of vulnerable and underserved populations.Nevertheless,there is still a substantial gap between recent progress in AI mental health and the widespread use of these solutions by healthcare practitioners in clinical settings.Furthermore,treatments are frequently developed without clear ethical concerns.While AI-enabled solutions show promise in the realm of mental health,further research is needed to address the ethical and social aspects of these technologies,as well as to establish efficient research and medical practices in this innovative sector.Moreover,the current relevant literature still lacks a formal and objective review that specifically focuses on research questions from both developers and psychiatrists in AI-enabled chatbotpsychologists development.Taking into account all the problems outlined in this study,we conducted a systematic review of AI-enabled chatbots in mental healthcare that could cover some issues concerning psychotherapy and artificial intelligence.In this systematic review,we put five research questions related to technologies in chatbot development,psychological disorders that can be treated by using chatbots,types of therapies that are enabled in chatbots,machine learning models and techniques in chatbot psychologists,as well as ethical challenges.
基金The authors extend their appreciation to the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia,for funding this research work(Project Number UB-2-1442).
文摘People often communicate with auto-answering tools such as conversational agents due to their 24/7 availability and unbiased responses.However,chatbots are normally designed for specific purposes and areas of experience and cannot answer questions outside their scope.Chatbots employ Natural Language Understanding(NLU)to infer their responses.There is a need for a chatbot that can learn from inquiries and expand its area of experience with time.This chatbot must be able to build profiles representing intended topics in a similar way to the human brain for fast retrieval.This study proposes a methodology to enhance a chatbot’s brain functionality by clustering available knowledge bases on sets of related themes and building representative profiles.We used a COVID-19 information dataset to evaluate the proposed methodology.The pandemic has been accompanied by an“infodemic”of fake news.The chatbot was evaluated by a medical doctor and a public trial of 308 real users.Evaluationswere obtained and statistically analyzed tomeasure effectiveness,efficiency,and satisfaction as described by the ISO9214 standard.The proposed COVID-19 chatbot system relieves doctors from answering questions.Chatbots provide an example of the use of technology to handle an infodemic.
文摘India imposed the largest lockdown in the world in response tofight the spread of the Novel Coronavirus disease(COVID-19)from 19 March till 31 May 2020.The onset of the pandemic left the general public feeling psycho-socially distressed,helpless,and anxious.The researcher developed a Messenger supported Chatbot,based on the broaden and build model,to cater to the healthy general public to promote positivity and mental well-being.31 participants between 22 and 45 years old consensually took a pre-test,Chatbot intervention,and post-test.The Chatbot provided guided activities out of which positive affirmations,meditation,and exercises were mostly used.The qualitative data from the study shows that the majority of the participants strongly feel positivity is within themselves and that the tool provided a self-help approach to be me well,mentally during the lockdown.The intervention helped significantly reducing symptoms of psychosocial distress in six of the individual’s post-chatbot interventions.Participants’impressions of the tool suggest more preponderant opportunities for future research in technology-driven mental health support.
文摘The coronavirus(nCOV-19),which was discovered,has now spread around the world.However,managing the flow of a large number of cases has proven to be a significant issue for hospitals or healthcare professionals.It is becoming increasingly challenging to speak with a medical expert after the epidemic’s initial wave has passed,particularly in rural areas.Thus,it becomes clear that a Chatbot that is well-designed and implemented can assist patients who are located far away by advocating preventive actions,and viral updates in various cities,and minimising the psychological harm brought on by dread.In this study,a sophisticated Chabot’s design for diagnosing individuals who have been exposed to COVID-19 is presented,along with recommendations for immediate safety measures.Additionally,when symptoms grow serious,this virtual assistant makes contact with specialised medical professionals.
文摘The rise of artificial intelligence(AI)in procurement has transformed how organizations engage with suppliers,optimize spending,and drive contract negotiations.Traditional procurement negotiations rely on human intuition,historical knowledge,and manual research.However,with the advancement of AI-driven Smart Negotiation Assistants,procurement teams can leverage real-time market intelligence,price benchmarks,and predictive analytics to autonomously negotiate contracts.This paper introduces an AI-powered Pro curement Chatbot,capable of conducting supplier negotiations with minimal human intervention.The system utilizes machine learning(ML),natural lan guage processing(NLP),and historical transaction data to negotiate terms,secure cost savings,and ensure compliance with procurement policies.Realworld case studies,including automated software licensing negotiations and dynamic supplier pricing adjustments,demonstrate how AI-driven negotiations can save millions in procurement costs,reduce cycle times by up to 40%,and mitigate supplier risks[1].The paper also explores technical architecture,algorithmic models,and deployment strategies for integrating AI negotiation assistants into enterprise procurement workflows.Furthermore,it highlights regulatory and ethical considerations in AI-driven procurement,emphasizing transparency and fairness.By leveraging AI-driven negotiation chatbots,businesses can achieve autonomous,efficient,and data-driven procurement processes,ensuring better supplier relationships and long-term cost savings.
基金supported in part by the National Natural Science Foundation of China(NSFC),under Grants Nos.72301034 and 72272016Fundamental Research Funds for the Central Universities under Grant No.2025ZZ048.
文摘The application of artificial intelligence(AI)in customer service becomes ubiquitous.In response to the advocacy in the“2021 Coordinated Plan on Artificial Intelligence”,it is crucial to understand how to leverage AI customer service chatbots for societal welfare.Across two scenario studies and one lab experiment,this research investigates the impact of AI chatbots’communication styles on consumers’subsequent prosocial intentions irrelevant to the AI-human interaction contents.The combined evidence suggests that consumers exhibit higher prosocial intentions after interacting with social-oriented(vs.task-oriented)AI chatbots.The findings reveal the chain-mediating roles of social presence and empathy.Moreover,the current research investigates the boundary effect of consumers’goal focus(process focus vs.outcome focus),and shows that AI chatbots’communication styles have stronger impact on prosocial intentions for customers with outcome focus.These results revealed the important externality of the AI application in marketplace and provide a novel perspective for companies to implement the corporate social responsibility(CSR)strategy.
文摘Since OpenAI opened access to ChatGPT,large language models(LLMs)become an increasingly popular topic attracting researchers’attention from abundant domains.However,public researchers meet some problems when developing LLMs given that most of the LLMs are produced by industries and the training details are typically unrevealed.Since datasets are an important setup of LLMs,this paper does a holistic survey on the training datasets used in both the pre-train and fine-tune processes.The paper first summarizes 16 pre-train datasets and 16 fine-tune datasets used in the state-of-the-art LLMs.Secondly,based on the properties of the pre-train and fine-tune processes,it comments on pre-train datasets from quality,quantity,and relation with models,and comments on fine-tune datasets from quality,quantity,and concerns.This study then critically figures out the problems and research trends that exist in current LLM datasets.The study helps public researchers train and investigate LLMs by visual cases and provides useful comments to the research community regarding data development.To the best of our knowledge,this paper is the first to summarize and discuss datasets used in both autoregressive and chat LLMs.The survey offers insights and suggestions to researchers and LLM developers as they build their models,and contributes to the LLM study by pointing out the existing problems of LLM studies from the perspective of data.
