Artificial Intelligence in Natural Science Research: Innovations and Limitations

The field of Artificial Intelligence (AI) has achieved a significant milestone and is currently transitioning toward a phase beyond Deep Learning (DL). AI has made significant advancements in various domains. Individuals are actively identifying the prospective avenues of AI for the forthcoming decade. The increasing attention toward the re-emphasis on knowledge is gaining traction amidst many perspectives. In conjunction with the three fundamental components of contemporary Machine Learning (ML), namely huge data, algorithms, and computing capabilities, knowledge must re-assume its role in aiding DL models in mitigating their inherent constraints, such as subpar generalization and inefficiency. This chapter provides the innovations and limitations of AI in Natural Science research, covering several aspects, including Data Analysis, Image and Pattern Recognition, Natural Language Processing (NLP), Robotics, Evolutionary Algorithms, Predictive modeling, Genomics and Proteomics, Environmental Monitoring, Drug Discovery, and Personalized Medicine. AI has been implemented in many areas, but these areas are becoming popular for AI applications. AI-based applications in these areas have shown significance and improved overall performances but still face several issues and limitations.