| | JUNE 20258By Christopher Suffi, IT Global Senior Manager - Innovation, Architecture SAP RISE & Cloud, AB InBevHOW DO DATA SCIENCE, MACHINE LEARNING, AND ARTIFICIAL INTELLIGENCE DIFFER AND COMPLEMENT EACH OTHER?The distinctions and intersections between Data Science, Machine Learning, and Artificial Intelligence can be complex and controversial. However, understanding their differences and commonalities is crucial to applying them effectively to real-world problems.There are different perspectives not only on these fields but also on their interrelations. Which field encompasses which? What are the overlaps? This article does not intend to settle these distinctions definitively but offers a structured analysis based on a particular academic perspective. While these terms may continue to be used interchangeably, it is essential to recognize their distinctions and, most importantly, understand their real-world applications in corporate and societal contexts.Despite belonging to the same knowledge domain, each field has specific applications and concepts. Most scholars agree that Machine Learning is a subset of Artificial Intelligence. On the other hand, data science is a distinct discipline that significantly overlaps machine learning and AI.An example of the interchangeable use of these terms is the overuse of "AI" to describe any smart system today. Smartphones, HR tools, gaming consoles, banking systems--all claim to use AI. However, many technologies rely on predefined rule-based systems rather than true AI. Expert systems, which have existed for decades, also fall into this category.Artificial Intelligence has become a common term in society. Simplistically, AI enables machines to replicate human intelligence. However, it does not imply the emergence of autonomous robots taking over the world. Instead, AI focuses on teaching systems to learn from past experiences, usually represented as data. Accurate and well-structured data input and self-adjusting mechanisms are essential for effective learning.AI specialists use statistical models, deep learning techniques, and natural language processing to train machines for specific tasks. AI aims to automate repetitive tasks and scale human-dependent processes. Progressive learning enables algorithms to train systems to execute various functions. Some scholars consider AI a subdiscipline of computer science, focusing on building systems with flexible intelligence to solve complex problems, learn from data, and make replicable decisions at scale.Cognitive science has also influenced AI, aiming to enable machines to think like humans. AI is applied in autonomous vehicles, monitoring systems, failure detection sensors, and preventive maintenance applications. AI-equipped devices can collect and process large datasets, adapt to new information, and autonomously take action or generate applicable knowledge. AI applications range from personalized product recommendations to medical diagnostics, facial recognition, computer vision, and content generation.Machine Learning is a subset of AI widely used in Data Science. It enables systems to process data independently, identify patterns, and develop reasoning mechanisms based on discoveries. Unlike traditional statistical models with predefined equations and parameters, Machine Learning algorithms discover these components through training. While some predefined models exist--such as econometric models--parameters are automatically adjusted during training. Even with known input data, output values emerge only after algorithm execution.This process differs from other exact sciences like experimental and theoretical physics. In physics, equations and parameters are explicitly defined, allowing direct inference of outcomes from input data. In contrast, Machine Learning relies on data-driven discovery.Machine Learning techniques fall into three categories: supervised, semi-supervised, and unsupervised learning. In supervised learning, a target variable is classified or estimated, such as purchase events, fraud detection, or financial risk. In unsupervised learning, there is no target variable, and the focus is on discovering structures in the data, such as customer segmentation or market basket analysis. Semi-supervised learning combines labeled (with targets) and unlabeled (without targets) data, using known labels to infer missing ones.Christopher SuffiIN MY OPINION
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