Search Results for « Generative Pre-trained Transformer (GPT) technology »

Abstract: Since its public release in November 2022, the usage of ChatGPT (Open AI, USA) has been unprecedented. This large language model (LLM) can produce human-like text from deep-learning techniques. LLMs are rapidly approaching human-level performance. ChatGPT can potentially help democratize the ability to code, by allowing clinicians to be able to develop basic artificial intelligence (AI) techniques. By leveraging AI models, these clinicians can expand the scope of their research abilities, and this can potentially lead to an AI in medicine revolution, where clinicians are able to generate clinically-focused AI techniques with the goal of improving patient outcomes across all domains. In this paper, we examine the performance of ChatGPT at developing an AI program for medicine and its associated limitations and challenges. Similar to the majority of AI models, the ethical concerns surrounding its application in medicine remains, which includes biases, patient autonomy, and confidentiality, transparency, and accuracy of data. ChatGPT must also be used in accordance with local healthcare regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States. All things considered, ChatGPT and future generative AI technologies will democratize the ability to code and develop AI, likely leading to breakthroughs in the medical AI sector.

The Generative Pre-trained Transformer (GPT) represents a notable breakthrough in the domain of natural language processing, which is propelling us toward the development of machines that can understand and communicate using language in a manner that closely resembles that of humans. GPT is based on the transformer architecture, a deep neural network designed for natural language processing tasks. Due to their impressive performance on natural language processing tasks and ability to effectively converse, GPT have gained significant popularity among researchers and industrial communities, making them one of the most widely used and effective models in natural language processing and related fields, which motivated to conduct this review. This review provides a detailed overview of the GPT, including its architecture, working process, training procedures, enabling technologies, and its impact on various applications. In this review, we also explored the potential challenges and limitations of a GPT. Furthermore, we discuss potential solutions and future directions. Overall, this paper aims to provide a comprehensive understanding of GPT, its enabling technologies, their impact on various applications, emerging challenges, and potential solutions.

With the development of artificial intelligence (AI), natural language processing (NIP) has clearly become the key to enhancing AI capabilities, especially through the deployment of GPT. This paper explores the major advances and related commercial applications of GPT, as it is at the cutting edge of AI technology with far-reaching capabilities in terms of text generation, language understanding, and human-machine interaction. Developed by OpenAI, GPT uses innovative transformer architecture to make breakthroughs in a variety of language tasks, marking a major evolution in the potential applications of AI in various industries. This research explores the integration of GPT in sectors, highlighting how its complex text processing capabilities can be utilized to improve efficiency and innovative service delivery. The study highlights the transformative impact of GPT on business efficiency, strategic decision-making, and customer engagement, thanks to its scalable and adaptable AI framework. Finally, the document reflects the need for a strong ethical framework and regulatory supervision to guide the deployment of GPT technologies. As the Global Trade Agreement evolves, it is committed to shaping the future of global industry by promoting innovation while calling for a balanced approach to ethical considerations and social impacts.

In anticipation of space exploration where astronauts are traveling away from Earth, and for longer durations with an increasing communication lag, artificial intelligence (AI) frameworks such as large language learning models (LLMs) that can be trained on Earth can provide real-time answers. This emerging technology may be helpful for acute medical emergencies, particularly in austere and distant space environments. In this manuscript, we provide an overview of generative pre-trained transformer (GPT) technology, a rapidly emerging AI technology, and implications, considerations, and limitations of such technology for space health.

Introduction: the Generative Pre-trained Transformer (GPT) is a deep learning language model architecture developed by OpenAI. Aim: to describe the knowledge networks (both at the theoretical and country levels) of the Generative Pre-trained Transformer (GPT) as an emerging technology. Results: 222 Documents were identified, of which 69 were articles, 50 were conference papers, 36 were editorials, 29 were notes, 19 were letters, 14 were reviews, 3 were conference reviews, and 2 were short surveys. In terms of the number of documents per year, 2 were found in 2019, 10 in 2020, 22 in 2021, 44 in 2022, and 144 in 2023. The year-on-year growth rate was over 100 % in all years. The subject area with the highest number of documents was Computer Science with 90 documents. The most productive countries in relation to GPT were the United States with 60 documents, followed by China with 19, the United Kingdom with 18, India with 15, and Australia with 12. Co-occurrence illustrated the centrality of Artificial Intelligence, Natural Language Processing, Deep Learning, and the term Human around ChatGPT and GPT. Conclusions: this bibliometric study aimed to describe the knowledge networks of the Generative Pre-trained Transformer (GPT) as an emerging technology. Although only 222 documents were found, this study revealed a high level of international scientific collaboration in the field. The results suggest that GPT is a highly relevant technology with a wide range of potential applications in natural language processing, artificial intelligence, and deep learning. Moreover, the study was able to qualitatively characterize the main thematic areas surrounding GPT, including its applications in chatbots, text generation, machine translation, sentiment analysis, and more.

We evaluated the capability of generative pre-trained transformers (GPT), to pass assessments in introductory and intermediate Python programming courses at the postsecondary level. Discussions of potential uses (e.g., exercise generation, code explanation) and misuses (e.g., cheating) of this emerging technology in programming education have intensified, but to date there has not been a rigorous analysis of the models' capabilities in the realistic context of a full-fledged programming course with diverse set of assessment instruments. We evaluated GPT on three Python courses that employ assessments ranging from simple multiple-choice questions (no code involved) to complex programming projects with code bases distributed into multiple files (599 exercises overall). Further, we studied if and how successfully GPT models leverage feedback provided by an auto-grader. We found that the current models are not capable of passing the full spectrum of assessments typically involved in a Python programming course (<70% on even entry-level modules). Yet, it is clear that a straightforward application of these easily accessible models could enable a learner to obtain a non-trivial portion of the overall available score (>55%) in introductory and intermediate courses alike. While the models exhibit remarkable capabilities, including correcting solutions based on auto-grader's feedback, some limitations exist (e.g., poor handling of exercises requiring complex chains of reasoning steps). These findings can be leveraged by instructors wishing to adapt their assessments so that GPT becomes a valuable assistant for a learner as opposed to an end-to-end solution.

This study explores the potential benefits of employing generative pre-trained transformers (GPTs) in recreational tourism, the difficulties that arise when implementing such technology, the impact it has on tourist behaviour, and the ways it can be utilised in recreational tourism management. The original aspect of the study is that it is the first to give detailed information about the use of GPT in recreational tourism. ChatGPT was used as an interviewer in the study. ChatGPT is a software application that utilizes the high-powered machine learning software called Generative Pre-trained Transformer (GPT-3), developed by the OpenAI organization. Six questions were posed on the ChatGPT query screen (https://chat.openai.com/chat). The interview queries were prepared with reference to the study by Fusté-Forné and Orea-Giner (2023). The question statements in this study on the use of GPT in gastronomy tourism were changed to recreational tourism. ChatGPT's replies were tabulated and presented descriptively. Inferences and suggestions were made in line with the answers given by ChatGPT. The study showed that using GPT technology in recreational-based tourism can offer better customer interaction, decision-making, and a personalized travel experience. ChatGPT underlined that new technologies continue to evolve, and recreational-based tourism will become more personalized, informative, and immersive, ultimately enhancing the overall travel experience, and contributing to the growth and sustainability of the tourism industry. ChatGPT also noted GPT models have the potential to shape and enhance the entire travel experience for tourists, from the initial planning stages to the actual trip itself. Although GPT makes significant contributions to recreational tourism management and recreational tourists, there are deficiencies in ethical, privacy, and authenticity concerns.

As if 2020 was not a peculiar enough year, its fifth month saw the relatively quiet publication of a preprint describing the most powerful natural language processing (NLP) system to date—GPT-3 (Generative Pre-trained Transformer-3)—created by the Silicon Valley research firm OpenAI. Though the software implementation of GPT-3 is still in its initial beta release phase, and its full capabilities are still unknown as of the time of this writing, it has been shown that this artificial intelligence can comprehend prompts in natural language, on virtually any topic, and generate relevant original text content that is indistinguishable from human writing. Moreover, access to these capabilities, in a limited yet worrisome enough extent, is available to the general public. This paper presents examples of original content generated by the author using GPT-3. These examples illustrate some of the capabilities of GPT-3 in comprehending prompts in natural language and generating convincing content in response. I use these examples to raise specific fundamental questions pertaining to the intellectual property of this content and the potential use of GPT-3 to facilitate plagiarism. The goal is to instigate a sense of urgency, as well as a sense of present tardiness on the part of the academic community in addressing these questions.