Restrictions, Challenges and Opportunities for AI and ML

Artificial intelligence (AI) refers to a collection of techniques that are being developed to address a wide variety of practical problems. Machine learning (ML) is the backbone of artificial intelligence (AI), comprising a suite of algorithms and techniques designedto solve the issues of categorization, clustering, and prediction. There are bright prospects for putting AI and ML to use in the real world. As a result, there is a lot of study being done in this field. However, mainstream adoption of AI in industry andits widespread use in society are still in their infancy. For understanding the obstacles involved with mainstream AI implementations, both the AI (internal problems) and societal (external problems) viewpoints are required. With this in mind, we can determine what has to happen first to get AI technology into the hands of industry and the public. This article identifies and discusses some of the obstacles to using artificial intelligence in resource-based economies and societies. Publications in the fieldform the basis for the systematic application of AI&ML technology. This methodical approach makes it possible to define institutional, human resource, societal, and technological constraints. This paper provides a roadmap for future research in artificialintelligence and machine learning that will help us overcome current obstacles and broaden the range of these technologies' potential uses.