ChatGPT: Vision and Challenges

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

Towards Designing a NLU Model Improvement System for Customer Service Chatbots

Current customer service chatbots often struggle to meet customer expectations. One reason is that despite advances in artificial intelligence (AI), the natural language understanding (NLU) capabilities of chatbots are often far from perfect. In order to improve them, chatbot managers need to make informed decisions and continuously adapt the chatbot’s NLU model to the specific topics and expressions used by customers. Customer-chatbot interaction data is an excellent source of information for these adjustments because customer messages contain specific topics and linguistic expressions representing the domain of the customer service chatbot. However, extracting insights from such data to improve the chatbot’s NLU, its architecture, and ultimately the conversational experience requires appropriate systems and methods, which are currently lacking. Therefore, we conduct a design science research project to develop a novel artifact based on chatbot interaction data that supports NLU improvement

Chatbots for enterprises: Outlook

Chatbots are going to be the main tool for automated conversations with customers. Still, there is no consistent methodology for choosing a suitable chatbot platform for a particular business. This paper proposes a new method for chatbot platform evaluation. To describe the current state of chatbot platforms, two high-level approaches to chatbot platform design are discussed and compared. WYSIWYG platforms aim to simplicity but may lack some advanced features. All-purpose chatbot platforms require extensive technical skills and are more expensive but give their users more freedom in chatbot design. We provide an evaluation of six major chatbot solutions. The proposed method for the chatbot selection is demonstrated on two sample businesses - a large bank and a small taxi service.O

Building chatbots from large scale domain-specific knowledge bases: challenges and opportunities

Popular conversational agents frameworks such as Alexa Skills Kit (ASK) and Google Actions (gActions) offer unprecedented opportunities for facilitating the development and deployment of voice-enabled AI solutions in various verticals. Nevertheless, understanding user utterances with high accuracy remains a challenging task with these frameworks. Particularly, when building chatbots with large volume of domain-specific entities. In this paper, we describe the challenges and lessons learned from building a large scale virtual assistant for understanding and responding to equipment-related complaints. In the process, we describe an alternative scalable framework for: 1) extracting the knowledge about equipment components and their associated problem entities from short texts, and 2) learning to identify such entities in user utterances. We show through evaluation on a real dataset that the proposed framework, compared to off-the-shelf popular ones, scales better with large volume of entities being up to 30% more accurate, and is more effective in understanding user utterances with domain-specific entities

The dawn of the human-machine era: a forecast of new and emerging language technologies

New language technologies are coming, thanks to the huge and competing private investment fuelling rapid progress; we can either understand and foresee their effects, or be taken by surprise and spend our time trying to catch up. This report scketches out some transformative new technologies that are likely to fundamentally change our use of language. Some of these may feel unrealistically futuristic or far-fetched, but a central purpose of this report - and the wider LITHME network - is to illustrate that these are mostly just the logical development and maturation of technologies currently in prototype. But will everyone benefit from all these shiny new gadgets? Throughout this report we emphasise a range of groups who will be disadvantaged and issues of inequality. Important issues of security and privacy will accompany new language technologies. A further caution is to re-emphasise the current limitations of AI. Looking ahead, we see many intriguing opportunities and new capabilities, but a range of other uncertainties and inequalities. New devices will enable new ways to talk, to translate, to remember, and to learn. But advances in technology will reproduce existing inequalities among those who cannot afford these devices, among the world's smaller languages, and especially for sign language. Debates over privacy and security will flare and crackle with every new immersive gadget. We will move together into this curious new world with a mix of excitement and apprehension - reacting, debating, sharing and disagreeing as we always do. Plug in, as the human-machine era dawn

Technical and social challenges in a conversational user interface

(česky) jsou automatizované počítačové programy, které slouží k simulaci výměny zpráv s člověkem, zároveň jsou ale i novým uživatelským rozhraním. V roce 1994 definoval Jacob Nielsen deset heuristických pravidel, které se tradičně používají právě pro design uživa novém oboru konverzačních prostředí a za jakých podmínek. Danou problematiku spondentů, kteří v českém prostředí vytvořili chatboty a následnou analýzou prostřednictvím zakotvené teorie. Teoretická část popisuje popisuje kontext, ve kterém chatboti vznikají i teoretické principy uživatelské zkušenosti, ze kterých následně čerpá(in English): Chatbots are automatised computer programs which are built to simulate an exchange in communication with a human being. Simultaneously, they provide new user interfaces. In 1994, Jacob Nielsen defined ten heuristic rules that are traditionally used within the design of user interfaces. This thesis's aim is to find out whether these ten rules are applicable in new fields of conversational environments. Secondly, what are the conditions under which the exchange becomes possible. The matter was examined through a qualitative research with the sample of eleven respondents who'd created chatbots within the Czech professional environment. The next step was a grounded analysis of existing established theories. The theoretical part explains the context in which chatbots are being created. It also shows theoretical canons of user interfaces which are the base of data and experience for the researches.Ústav informačních studií - studia nových médiíInstitute of Information Studies and Librarianship - New Media StudiesFilozofická fakultaFaculty of Art

Learning from a Generative AI Predecessor -- The Many Motivations for Interacting with Conversational Agents

For generative AI to succeed, how engaging a conversationalist must it be? For almost sixty years, some conversational agents have responded to any question or comment to keep a conversation going. In recent years, several utilized machine learning or sophisticated language processing, such as Tay, Xiaoice, Zo, Hugging Face, Kuki, and Replika. Unlike generative AI, they focused on engagement, not expertise. Millions of people were motivated to engage with them. What were the attractions? Will generative AI do better if it is equally engaging, or should it be less engaging? Prior to the emergence of generative AI, we conducted a large-scale quantitative and qualitative analysis to learn what motivated millions of people to engage with one such 'virtual companion,' Microsoft's Zo. We examined the complete chat logs of 2000 anonymized people. We identified over a dozen motivations that people had for interacting with this software. Designers learned different ways to increase engagement. Generative conversational AI does not yet have a clear revenue model to address its high cost. It might benefit from being more engaging, even as it supports productivity and creativity. Our study and analysis point to opportunities and challenges.Comment: 26 pages, 18 figures, 2 table

Chatbots and virtual assistants vs. human agents in IT customer support

While the integration of technology in customer support continues to thrive, its application in IT settings displays exciting advancements but severe limitations. For years, companies have had to balance operational efficiency with quality human interactions in providing customer support services across the globe. In the interconnected world today, end-user transparency and a seamless user experience are more critical than ever. IT customer support resides within a complicated system of technologies and human-set measures that vary across users and technological capabilities. This master’s thesis goes beyond the dynamics of chatbots and virtual assistants against human agents to research the combined impact of these service providers on customer experiences and operational efficiency. The research focuses on the proportionality of automated and human services in a Ukrainian-Swedish IT outsourcing company to offer an indicator for IT support strategies. This study explores various phenomena through quantitative surveys and qualitative interviews. It examines user satisfaction, the real success quotient of service technologies, and strategically customized inclusion in serious business arrangements. The focus areas cover enhancing communication efficiencies, making customer interaction deeper, and providing more advanced technology. Ultimately, this thesis predicts a future where technology and human service agents merge to produce more user-friendly, empathetic, and intuitive IT customer support. AI and machine learning skills, coupled with other managerial policy designs will help stakeholders meet users’ needs comfortably and optimally exceed user expectation levels, developing a responsive engaged customer support system

A Study on the Implementation of Generative AI Services Using an Enterprise Data-Based LLM Application Architecture

This study presents a method for implementing generative AI services by utilizing the Large Language Model (LLM) application architecture. With recent advancements in generative AI technology, LLMs have gained prominence across various domains. In this context, the research addresses the challenge of information scarcity and proposes specific remedies by harnessing LLM capabilities. The investigation delves into strategies for mitigating the issue of inadequate data, offering tailored solutions. The study delves into the efficacy of employing fine-tuning techniques and direct document integration to alleviate data insufficiency. A significant contribution of this work is the development of a Retrieval-Augmented Generation (RAG) model, which tackles the aforementioned challenges. The RAG model is carefully designed to enhance information storage and retrieval processes, ensuring improved content generation. The research elucidates the key phases of the information storage and retrieval methodology underpinned by the RAG model. A comprehensive analysis of these steps is undertaken, emphasizing their significance in addressing the scarcity of data. The study highlights the efficacy of the proposed method, showcasing its applicability through illustrative instances. By implementing the RAG model for information storage and retrieval, the research not only contributes to a deeper comprehension of generative AI technology but also facilitates its practical usability within enterprises utilizing LLMs. This work holds substantial value in advancing the field of generative AI, offering insights into enhancing data-driven content generation and fostering active utilization of LLM-based services within corporate settings

Toward a linguistically grounded dialog model for chatbot design

The increasing interest in various types of conversational interfaces has been supported by a progressive standardization of the technological frameworks used to build them. However, the landscape of available methodological frameworks for designing conversations is much more fragmented. We propose a highly generalizable methodology for designing conversational flows rooted in a functionalist-pragmatics perspective, with an explicit adherence to a conversationalist approach. In parallel, we elaborate a practical-procedural workflow for undertaking chatbots projects in which we situate the theoretical starting point. At last, we elaborate a general case- study on which we transpose the identified approach in Italian language and using one of the most authoritative NLU platforms