DOI: 10.14704/nq.2022.20.8.NQ44468

Machine Learning: Past, Present and Future

Akshita Tyagi, Swetta Kukreja, Meghna Manoj Nair, Amit Kumar Tyagi

Abstract

Recent well-publicized triumphs in Machine Learning have solved issues that were previously believed to take decades to solve, rekindling interest in the fields of Artificial Intelligence (AI) And Machine Learning(ML). For instance, deep learning's recent success and quick commercialization have propelled technological advancements in a variety of industries, such as computer vision, speech recognition, gaming, and machine translation. These accomplishments offer fresh chances to advance Nondestructive Assessment (NDE) methods. The foundational ideas of MLare reviewed, along with the relationship they have to statistics. Then, we go over previous ML for NDE applications and techniques. We also discuss the problems that this discipline is still trying to overcome, namely the lack of trustworthy training data. We then go into recent ML for NDE research that aims to address these issues. Finally, we discuss how current ML developments like deep learning and transfer learning have the potential to fundamentally alter how we develop future NDE solutions.

Keywords

Artificial Intelligence, Internet of Things; Healthcare; Smart Cities; Smart Grid; Supply Chain Management; Machine Learning

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