[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-160567-en":3,"doc-seo-160567-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},160567,8814010472675,"Xiajie","https://avatar.qwps.com/avatar/WGlhamll",8,"Research & Report","ChatGPT Vision and Challenges - Research Overview","Artificial intelligence and machine learning are reshaping scientific inquiry, making virtual assistants an increasingly central capability in modern research workflows. This article studies ChatGPT by outlining its technical foundations in the GPT family, summarizing its major popular applications, and assessing its potential impact when integrated with connected technologies such as the Internet of Things. It also identifies key research challenges and future directions, including energy efficiency, cybersecurity, expanded use with robotics and computer vision, improved human–AI communication, and the ethical considerations and emerging trends surrounding deployment.","ChatGPT: Vision and Challenges  \nSukhpal Singh Gill 1 and Rupinder Kaur2  \n1School of Electronic Engineering and Computer Science, Queen Mary University of London, UK  \n2Department of Science, Kings Education, London, UK  \n[s.s.gill@qmul.ac.uk](s.s.gill@qmul.ac.uk), [rupinderchem@gmail.com](rupinderchem@gmail.com)  \nAbstract: Artificial intelligence (AI) and machine learning have changed the nature of scientific inquiry in recent years. Of these, the development of virtual assistants has accelerated greatly in the past few years, with ChatGPT becoming a prominent AI language model. In this study, we examine the foundations, vision, research challenges of ChatGPT. This article investigates into the background and development of the technology behind it, as well as its popular applications. Moreover, we discuss the advantages of bringing everything together through ChatGPT and Internet of Things (IoT) . Further, we speculate on the future of ChatGPT by considering various possibilities for study and development, such as energy-efficiency, cybersecurity, enhancing its applicability to additional technologies (Robotics and Computer Vision), strengthening human-AI communications, and bridging the technological gap. Finally, we discuss the important ethics and current trends of ChatGPT.  \nKeywords: ChatGPT, Artificial Intelligence, Machine Learning, Chatbot, GPT, Generative AI  \n1. Introduction  \nOver the past few years, language models have benefited greatly from the rapid development of Artificial Intelligence (AI) and Natural Language Processing (NLP), making them more accurate, flexible, and useful than ever before [1] . The term “Generative AI” is used to describe a subset of AI models that can generate new information by discovering relevant trends and patterns in already collected information. These models may produce work in a wide range of media, from written to visual to audio [2] . To analyse, comprehend, and produce material that accurately imitates human-generated outcomes, Generative AI models depend on deep learning approaches and neural networks. OpenAI's ChatGPT is one such AI model that has quickly become a popular and versatile resource for a number of different industries. Its humanoid text generation is made possible by its foundation in the Generative Pre-trained Transformer (GPT) architecture [3] . It has the ability to comprehend and produce a broad variety of words since it has been training on an extensive amount of text data. Linguistic transformation, summarised text, and conversation production are just some of the applications that can benefit from its capacity to create natural-sounding content. ChatGPT can be trained to do a variety of activities, including language recognition, question answering, and paragraph completion. It's also useful for building chatbots and other conversational interfaces. In a nutshell, ChatGPT is a robust NLP model that can comprehend and create natural language for a wide range of applications, including text production, language understanding, and interactive programmes [4] .  \nIt is essential to ChatGPT's function in promoting scientific research to comprehend its genesis and evolution. To clarify, the ChatGPT is not a Generative Adversarial Network (GAN) model but rather a linguistic model built on the GPT architecture, which is relevant here. GPT models are tailored to NLP activities including text production and language comprehension, as opposed to GANs, which are more commonly employed for activities including picture generation [5] . The origins of ChatGPT lie in NLP, a subfield of AI that aims to teach computers to comprehend and produce human speech. The motivation behind developing ChatGPT was to establish a powerful and flexible AI language model that could help with a wide range of activities, such as text production, translation, and data analysis.  \n1.1 Motivation and Our Contributions  \nIn order to address some of the limitations of prior sequenceto-sequenc","cbCailGCeNc2s7ie","https://ap.wps.com/l/cbCailGCeNc2s7ie","pdf",506841,1,9,"English","en",105,"# Introduction\n## Motivation and Our Contributions\n## Article Structure\n# Background and Foundations\n## Timeline of GPTs","[{\"question\":\"What is Generative AI and how does it relate to ChatGPT?\",\"answer\":\"Generative AI refers to models that produce new information by learning patterns in existing data. ChatGPT is an AI language model built on the GPT architecture and trained to generate natural conversational responses.\"},{\"question\":\"What technical foundation and model lineage does ChatGPT build on?\",\"answer\":\"ChatGPT is based on the Transformer architecture and follows the GPT family, including earlier versions such as GPT-2 and GPT-3. The document notes GPT-3.5 as the basis for ChatGPT.\"},{\"question\":\"Which future directions and challenges are discussed for ChatGPT?\",\"answer\":\"The article discusses future study and development topics such as energy efficiency, cybersecurity, extending applicability to robotics and computer vision, strengthening human–AI communication, and bridging technological gaps. It also covers important ethics and current trends.\"}]","ChatGPT Vision and Challenges - Research Overview | PDF",1788069003,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"chatgpt-vision-and-challenges-research-overview","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/chatgpt-vision-and-challenges-research-overview/160567/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-30",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is Generative AI and how does it relate to ChatGPT?","Question",{"text":75,"@type":76},"Generative AI refers to models that produce new information by learning patterns in existing data. ChatGPT is an AI language model built on the GPT architecture and trained to generate natural conversational responses.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What technical foundation and model lineage does ChatGPT build on?",{"text":80,"@type":76},"ChatGPT is based on the Transformer architecture and follows the GPT family, including earlier versions such as GPT-2 and GPT-3. The document notes GPT-3.5 as the basis for ChatGPT.",{"name":82,"@type":73,"acceptedAnswer":83},"Which future directions and challenges are discussed for ChatGPT?",{"text":84,"@type":76},"The article discusses future study and development topics such as energy efficiency, cybersecurity, extending applicability to robotics and computer vision, strengthening human–AI communication, and bridging technological gaps. 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