Machine Learning - Method or Solution?

Abstract

This article explores the dualistic nature of machine learning (ML) in scientific research, dissecting its role as both a methodological tool and a direct solution to complex problems. It begins by defining ML and its foundational principles, algorithms, and statistical models. The versatility of ML is highlighted through its applications across various disciplines, such as biology, astrophysics, healthcare, environmental science, and business. The article then delves into the challenges and limitations of ML, including data quality issues, ethical concerns, and the risk of over-reliance on automated decisions. It advocates for the integration of ML with classical research methods to ensure robust and ethical scientific practice. Concluding with future perspectives, the article speculates on the evolving role of ML in advancing both methodology and problem-solving in diverse fields, emphasizing the importance of responsible and effective utilization of ML in scientific research.