Data is the fuel of generative artificial intelligence
Creator:Sokołowski Wojciech ; Ficoń Krzysztof ; Florczyk-Żółtowska Marzena
Abstract:TThe aim of the article is to present the generative technology of artificial intelligence and a general analysis of its achievements and technological limitations. In turn, the hypothesis is as follows: data is the main factor ensuring the development of generative AI. The identified research niche includes stimulants of this development, the need to clarify the concept of generative AI and the indication of the most frequently used sources and methods of obtaining data. At the beginning, the concept and genesis of artificial intelligence were explained, which was necessary to move on to further considerations regarding the analyzed generative artificial intelligence. The self-learning mechanisms used by this type of artificial intelligence are discussed, consisting mainly in the analysis of specific data sets, thanks to which computers learn things that have not been known before. The advantages of generative artificial intelligence were also indicated, including increased productivity, greater efficiency and high creativity. Part of the attention was also paid to the limitations of this technology, including inaccurate and fuzzy output information, uncritical analyzes and often biased conclusions, copyright infringement and high requirements for memory and computing power of computers. Two factors have been identified that have recently stimulated the development of generative intelligence, i.e. the growing computing power of computers and easy access to large data sets. The main subject of considerations are large data sets, dynamically and uncontrollably self-replicating in Internet servers. Today, data resources are collected very efficiently, including: in Big Data, Cloud Computing and Internet of Things technologies, and their use is almost cost-free and publicly available. It was enough to improve the technology of artificial neural networks, expand multi-layer machine learning and create deep learning for generative intelligence to gain enormous momentum and become a symbol of the beginning of the 21st century. The whole investigation was summarized with a synthetic ending.
Date issued: Identifier:doi:10.37055/slw/193858 ; oai:editorialsystem.com:article-193858
Electronic ISSN: Print ISSN: Publisher ID: Starting page: Ending page: Volume: Issue: Journal: Keywords:data ; generative intelligence ; artificial intelligence ; neural networks ; machine learning