Exploring the Determinants of Artificial Intelligence (AI) Literacy: Digital Divide,Computational Thinking,Cognitive Absorption

To effectively utilize artificial intelligence (AI)-based technologies such as ChatGPT and realize their novel ethical issues, individuals must have a variety of knowledge and skills about AI. Such knowledge and skills have led to the emergence of AI literacy. Despite the importance of AI literacy in everyday life, little is known about its determinants. To better understand the determinants of AI literacy, we attempted to build a research model relying on previous research and different theoretical frameworks. The model incorporated digital divide, cognitive absorption, and computational thinking. As a major finding from the current study, computational thinking was found to be a significant determinant of AI literacy, which facilitate using, recognizing, and evaluating AI-based technologies. Moreover, we found out that individuals with physical access to information and communication technologies (ICTs) are more expected to use and recognize AI. Also, motivation and skills in using ICTs enable individuals to better evaluate the outcomes of AI-based technologies. The findings also showed that convenient access to ICTs contributes to a deep involvement with AI-based technologies in the use. Further, individuals with higher motivation and skills to use AI technologies are likely to have a pleasant experience after using these technologies.     

从计算到人类知识：ChatGPT与智能演化

深入探讨了智能的演化过程和ChatGPT的实现效果,以及人工智能通用性提高和长期发展方向。从自然界中智能的诞生过程开始,介绍了人工智能的发展历程和现状,分析了ChatGPT作为一种具有强大泛化能力的深度神经网络模型在人工智能领域中的重要意义。还探讨了人工智能通用性提高对世界的潜在影响,介绍了一些新兴应用领域。通过阅读本文,读者可以更好地理解人工智能技术的发展趋势和未来方向。     

DAO to HANOI via DeSci: AI Paradigm Shifts from AlphaGo to ChatGPT

From AlphaGo to ChatGPT,the field of AI has launched a series of remarkable achievements in recent years.Analyzing,comparing,and summarizing these achievements at the paradigm level is important for future AI innovation,but has not received sufficient attention.In this paper,we give an overview and perspective on machine learning paradigms.First,we propose a paradigm taxonomy with three levels and seven dimensions from a knowledge perspective.Accordingly,we give an overview on three basic and tw...     

ChatGPT大模型技术发展与应用

通过回顾深度学习、语言模型、语义表示和预训练技术的发展历程,全面解析了ChatGPT的技术渊源和演进路线。在语言模型方面,从早期的N-gram统计方法逐步演进到神经网络语言模型,通过对机器翻译技术的研究和影响也催生了Transformer的出现,继而又推动了神经网络语言模型的发展。在语义表示和预训练技术发展方面,从早期的TF-IDF、pLSA和LDA等统计方法发展到Word2Vec等基于神经网络的词向量表示,继而发展到ELMo、BERT和GPT-2等预训练语言模型,预训练框架日益成熟,为模型提供了丰富的语义知识。GPT-3的出现揭示了大语言模型的潜力,但依然存在幻觉问题,如生成不可控、知识谬误及逻辑推理能力差等。为了缓解这些问题,ChatGPT通过指令学习、监督微调、基于人类反馈的强化学习等方式在GPT-3.5上进一步与人类进行对齐学习,效果不断提升。ChatGPT等大模型的出现,标志着该领域技术进入新的发展阶段,为人机交互以及通用人工智能的发展开辟了新的可能。     

Houston,we have a problem!: The use of ChatGPT in responding to customer complaints

This study aims to explore the use of ChatGPT-4 in generating management responses to customer reviews or complaints posted on Tripadvisor. Customer reviews and management responses are viewed as information sources for holidaymakers when they are making their decisions. A negative customer review about a hotel accommodation experience from TripAdvisor together with the response made by hotel management to this review, and the ChatGPT-4 generated management response to the same customer review were evaluated by 40 industry experts based on six dimensions of a service recovery model and three dimensions of justice that are frequently used by researchers. The findings suggest several practical implications mainly that the ChatGPT-4 generated management response satisfies the requirements of an efficient and effective management response. The quality of ChatGPT-4 generated management responses tends to be extremely high and they may be generated within seconds and with little effort. In addition to the several above practical implications, as ChatGPT-4 can measure the severity of service failures based on customer complaints the study has important implications for service failures and recovery literature.     

A Brief Overview of ChatGPT: The History,Status Quo and Potential Future Development

ChatGPT, an artificial intelligence generated content (AIGC) model developed by OpenAI, has attracted worldwide attention for its capability of dealing with challenging language understanding and generation tasks in the form of conversations. This paper briefly provides an overview on the history, status quo and potential future development of ChatGPT, helping to provide an entry point to think about ChatGPT. Specifically, from the limited open-accessed resources, we conclude the core techniques of ChatGPT, mainly including large-scale language models, in-context learning, reinforcement learning from human feedback and the key technical steps for developing ChatGPT. We further analyze the pros and cons of ChatGPT and we rethink the duality of ChatGPT in various fields. Although it has been widely acknowledged that ChatGPT brings plenty of opportunities for various fields, mankind should still treat and use ChatGPT properly to avoid the potential threat, e.g., academic integrity and safety challenge. Finally, we discuss several open problems as the potential development of ChatGPT.      

Is ChatGPT scary good? How user motivations affect creepiness and trust in generative artificial intelligence

Few studies have examined user motivations to use generative artificial intelligence (AI). This research aims to address this gap by examining how user motivations for ChatGPT usage affect perceived creepiness, trust, and the intention to continue using AI chatbot technology. The findings of an online survey (N = 421) reveal a negative relationship between personalization and creepiness, while task efficiency and social interaction are positively associated with creepiness. Increased levels of creepiness, in turn, result in decreased continuance intention. Furthermore, task efficiency and personalization have a positive impact on trust, leading to increased continuance intention. The results contribute to the field of human–computer interaction by investigating the motivations for utilizing generative AI chatbots and advancing our comprehension of AI creepiness, trust, and continuance intention. The practical ramifications of this research can inform the design of user interfaces and the development of features for generative AI chatbots.     

类ChatGPT大模型发展、应用和前景

生成式人工智能技术自ChatGPT发布以来，不断突破瓶颈，吸引了资本规模投入、多领域革命和政府重点关注。本文首先分析了大模型的发展动态、应用现状和前景，然后从以下3个方面对大模型相关技术进行了简要介绍：1）概述了大模型相关构造技术，包括构造流程、研究现状和优化技术；2）总结了3类当前主流图像—文本的大模型多模态技术；3）介绍了根据评估方式不同而划分的3类大模型评估基准。参数优化与数据集构建是大模型产品普及与技术迭代的核心问题；多模态能力是大模型重要发展方向之一；设立评估基准是比较与约束大模型的关键方法。此外，本文还讨论了现有相关技术面临的挑战与未来可能的发展方向。现阶段的大模型产品已有强大的理解能力和创造能力，在教育、医疗和金融等领域已展现出广阔的应用前景。但同时，它们也存在训练部署困难、专业知识不足和安全隐患等问题。因此，完善参数优化、优质数据集构建、多模态等技术，并建立统一、全面、便捷的评估基准，将成为大模型突破现有局限的关键。