Microsoft is currently focusing significant financial and human resources on the development of its AI assistant Copilot and its integration into Windows and Microsoft 365 applications. The company sees this as an opportunity to set itself apart from the competition of Libre Office and Google. Today, users have several alternatives for AI support in Office. This is because ChatGPT from OpenAI, the software that triggered the AI hype, is also suitable for office tasks in conjunction with Word, Excel, and others. Independent developers provide add-ons that allow you to integrate ChatGPT directly into Word so that you always have it at hand. At the same time, there are AI systems, especially from American providers, that help you create presentations online. These presentations can then be downloaded and in many cases converted into PowerPoint format PPTX.

Data annotation generally refers to the labeling or generating of raw data with relevant information, which could be used for improving the efficacy of machine learning models. The process, however, is labor-intensive and costly. The emergence of advanced Large Language Models (LLMs), exemplified by GPT-4, presents an unprecedented opportunity to automate the complicated process of data annotation. While existing surveys have extensively covered LLM architecture, training, and general applications, we uniquely focus on their specific utility for data annotation. This survey contributes to three core aspects: LLM-Based Annotation Generation, LLM-Generated Annotations Assessment, and LLM-Generated Annotations Utilization. Furthermore, this survey includes an in-depth taxonomy of data types that LLMs can annotate, a comprehensive review of learning strategies for models utilizing LLM-generated annotations, and a detailed discussion of the primary challenges and limitations associated with using LLMs for data annotation. Serving as a key guide, this survey aims to assist researchers and practitioners in exploring the potential of the latest LLMs for data annotation, thereby fostering future advancements in this critical field.

The incorporation of Artificial Intelligence (AI) models into various optimization systems is on the rise. Yet, addressing complex urban and environmental management problems normally requires in-depth domain science and informatics expertise. This expertise is essential for deriving data and simulation-driven for informed decision support. In this context, we investigate the potential of leveraging the pre-trained Large Language Models (LLMs). By adopting ChatGPT API as the reasoning core, we outline an integrated workflow that encompasses natural language processing, methontology-based prompt tuning, and transformers. This workflow automates the creation of scenario-based ontology using existing research articles and technical manuals of urban datasets and simulations. The outcomes of our methodology are knowledge graphs in widely adopted ontology languages (e.g., OWL, RDF, SPARQL). These facilitate the development of urban decision support systems by enhancing the data and metadata modeling, the integration of complex datasets, the coupling of multi-domain simulation models, and the formulation of decision-making metrics and workflow. The feasibility of our methodology is evaluated through a comparative analysis that juxtaposes our AI-generated ontology with the well-known Pizza Ontology employed in tutorials for popular ontology software (e.g., prot\'eg\'e). We close with a real-world case study of optimizing the complex urban system of multi-modal freight transportation by generating anthologies of various domain data and simulations to support informed decision-making.

This study explores the integration of the ChatGPT API with GPT-4 model and Microsoft Copilot Studio on the Microsoft Teams platform to develop an intelligent tutoring system. Designed to provide instant support to students, the system dynamically adjusts educational content in response to the learners' progress and feedback. Utilizing advancements in natural language processing and machine learning, it interprets student inquiries, offers tailored feedback, and facilitates the educational journey. Initial implementation highlights the system's potential in boosting students' motivation and engagement, while equipping educators with critical insights into the learning process, thus promoting tailored educational experiences and enhancing instructional effectiveness.

With the rapid advancement of technology, artificial intelligence (AI) and machine learning (ML) have been integrated into the design process, presenting new opportunities and challenges for architects and designers. However, the potential for AI, particularly language models like ChatGPT - a conversational AI model developed by OpenAI (Radford et al. 2021)- to transform the architectural design process has yet to be fully explored. This paper presents a new framework for architectural design that uses ChatGPT and AI-based ideation and visualization tools, Veras ("VERAS" 2023), to make the design process easier and create 3D geometric models, parametric models, and Building Information Models using natural language input. The proposed framework combines ChatGPT and Veras to generate and explore design ideas rapidly. Using natural language input, architects can communicate their design intentions more intuitively, allowing quicker iterations and reducing barriers associated with traditional design tools (Hsu, Yang, and Buehler 2022). Moreover, ChatGPT's ability to understand human design intentions helps to translate the input into Building Information Modeling (BIM) and parametric Generative AI-Powered Parametric Modeling and BIM for Architectural Design 1 models, highlighting the potential of the architectural design process.

Learning new programming skills requires tailored guidance. With the emergence of advanced Natural Language Generation models like the ChatGPT API, there is now a possibility of creating a convenient and personalized tutoring system with AI for computer science education. This paper presents GPTutor, a ChatGPT-powered programming tool, which is a Visual Studio Code extension using the ChatGPT API to provide programming code explanations. By integrating Visual Studio Code API, GPTutor can comprehensively analyze the provided code by referencing the relevant source codes. As a result, GPTutor can use designed prompts to explain the selected code with a pop-up message. GPTutor is now published at the Visual Studio Code Extension Marketplace, and its source code is openly accessible on GitHub. Preliminary evaluation indicates that GPTutor delivers the most concise and accurate explanations compared to vanilla ChatGPT and GitHub Copilot. Moreover, the feedback from students and teachers indicated that GPTutor is user-friendly and can explain given codes satisfactorily. Finally, we discuss possible future research directions for GPTutor. This includes enhancing its performance and personalization via further prompt programming, as well as evaluating the effectiveness of GPTutor with real users.

Since its release in November 2022, ChatGPT has achieved rapid growth, breaking records for the fastest-growing user base. Developers have eagerly anticipated the official API to create applications using this powerful technology. OpenAI recently introduced GPT-4, which incorporates visual inputs, improved accuracy, and larger context windows. This article offers valuable insights and a starter kit for your GPT experiments. It should be easy to upgrade to utilize large context windows and potentially read the content of images too.

Become an expert and get hired. Welcome to the best resource for learning OpenAI API with Python and for integrating the latest OpenAI models into your applications. This OpenAI API with Python Bootcamp covers every model released by OpenAI that has an API, including GPT-3 (Davinci), ChatGPT (gpt-3.5-turbo), By the end of this course, you'll have in-depth knowledge and a vast hands-on experience with the OpenAI API and you'll be an expert able to make your Python applications intelligent. This is a brand new OpenAI API course that will be constantly updated (with GPT-4 included) to teach you the skills required for the future that comes.

Artificial Intelligence (AI) has been revolutionizing the way we interact with technology, and chatbots are one of the most prominent examples of this trend.

ChatGPT, the internet-famous AI text generator, has taken on a new form. Once a website you could visit, it is now a service that you can integrate into software of all kinds, from spreadsheet programs to delivery apps to magazine websites such as this one. Snapchat added ChatGPT to its chat service (it suggested that users might type "Can you write me a haiku about my cheese-obsessed friend Lukas?"), and Instacart plans to add a recipe robot. They will be weirder than you might think. Instead of one big AI chat app that delivers knowledge or cheese poetry, the ChatGPT service (and others like it) will become an AI confetti bomb that sticks to everything.