LITERATURE REVIEW ON THE VARIETY KINDS OF AI AND ML TECHNOLOGIES CLASSIFICATIONS AND LIMITATIONS

Abstract

Artificial intelligence (AI) is a collection of advanced technologies that are employed to tackle various practical problems in a dynamic and progressive manner. Machine learning (ML) is a fundamental component of artificial intelligence (AI) that encompasses a range of algorithms and techniques designed to perform various tasks such as classification, segmentation, and forecasting. The utilisation of Artificial Intelligence (AI) and Machine Learning (ML) in practical settings exhibits significant potential, thereby prompting a multitude of scholarly inquiries in this domain. Nevertheless, the implementation of artificial intelligence in industrial contexts and its ubiquity in the community are presently constrained. In order to comprehend the obstacles linked to the extensive integration of AI, a thorough assessment is imperative, encompassing both intrinsic AI-related concerns and extrinsic societal dilemmas. The objective of this study is to ascertain pivotal metrics that can augment the pragmatic execution of artificial intelligence (AI) technologies, their assimilation across various sectors, and involvement of the community. This article scrutinises and evaluates the challenges that ensue from the integration of artificial intelligence (AI) technologies in the economies and societies of nations that possess abundant resources. The process of standardising the deployment of artificial intelligence (AI) and machine learning (ML) technologies is grounded in the extant literature pertaining to these domains. The utilisation of a systematic approach facilitates the assimilation of diverse constraints, encompassing organisational configuration, human capital, societal influences, and technological facets. The current investigation delves into prospective avenues of research within the fields of Artificial Intelligence (AI) and Machine Learning (ML) that possess the capability to surmount particular constraints and broaden the range of AI and ML implementations.