Abstract
Generative AI, such as generative pre-trained transformer (GPT), has seen rapid advancements in recent years, offering a wide range of applications, but it also presents several challenges and opportunities. GPT can automate content generation for various industries, including journalism, marketing, and entertainment, reducing the need for manual content creation. Generative AI can personalize content and recommendations in e-commerce, streaming services, and more, enhancing user experiences. Generative AI, such as GPT, offers immense potential across various sectors but requires careful management to address bias, ethics, and quality control challenges. As technology evolves, finding the right balance between control and creativity will be crucial for maximizing its benefits while minimizing risks. Based on the above, the authors systematically review the bibliometric literature on how Generative AI and generative pre-trained transformer applications challenge opportunities using the Scopus database by analysing 49 academic and/or scientific documents.
TopMethodological Approach
The investigator carried out a methodical bibliometric literature review to assess the influence of Generative AI and the applications of Generative Pre-trained Transformers. This approach encompasses the organized identification, examination, and integration of pertinent scholarly literature. It facilitated the acquisition of a thorough comprehension of the current knowledge about the research subject, unveiling pivotal trends and patterns in the literature. Applied to Generative AI and Generative Pre-trained Transformer Applications, this methodology has the potential to unveil the emergence and development of novel technologies, their ongoing effects, and the strategies businesses employ to navigate through disruptions.
Key Terms in this Chapter
Natural Language Generation (NLG): Is the use of artificial intelligence (AI) programming to produce written or spoken narratives from a data set.
Generative Pre-Trained Transformer (GPT): Interactive experience that enhances the real world with computer-generated perceptual information.
Natural Language Understanding (NLU): Is the ability of a computer to understand human language.
Artificial Intelligence: Simulation of human intelligence processes by machines, especially computer systems.
DALL-E: Is an artificial intelligence program that creates images from textual descriptions.
Big Data: Area of knowledge that studies how to treat, analyze and obtain information from very large data sets.
Machine Learning: Is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions.
Natural Language Processing: A branch of artificial intelligence that enables computers to comprehend, generate, and manipulate human language.
Compound Annual Growth Rate (CAGR): Is the rate of return (RoR) that would be required for an investment to grow from its beginning balance to its ending balance, assuming the profits were reinvested at the end of each period of the investment’s life span.
GitHub Copilot: Is an artificial intelligence tool developed by GitHub in conjunction with OpenAI, in order to assist users of integrated development environments such as Visual Studio Code, Visual Studio, Neovim and JetBrains by suggesting code for automatic completion.