Supervised/Unsupervised/RL

In this episode, we break down three of the most important learning paradigms in modern artificial intelligence: supervised learning, unsupervised learning, and reinforcement learning. Each of these approaches teaches machines in a fundamentally different way, and together they form the backbone of nearly every AI system we interact with today.We start by exploring what it really means for an AI system to learn. Rather than receiving hand-crafted rules, machines discover patterns, structures, or strategies from data and experience. That shift changed the trajectory of AI and made learning-based systems central to the field.From there, we walk through each paradigm in clear, simple terms:Supervised learning, where models learn from labelled examplesUnsupervised learning, where models discover hidden structure in unlabelled dataReinforcement learning, where agents learn by interacting with an environment and receiving rewardsTo make these ideas intuitive, we use relatable stories, everyday analogies, and real-world applications—from recommendation systems and language models to clustering algorithms and game-playing agents.This episode covers:What “learning from data” means at a conceptual levelHow supervised learning pairs inputs with correct answersWhy labelled data is so powerful—and sometimes limitingHow unsupervised learning finds structure without any labelsClustering, grouping, and pattern discovery in intuitive termsHow reinforcement learning works through actions, rewards, and trial-and-errorWhy RL is especially useful for control, robotics, and decision-makingThe strengths and challenges of each learning paradigmHow these three approaches fit together in modern AI systemsThis episode is part of the Adapticx AI Podcast. You can listen using the link provided, or by searching “Adapticx” on Apple Podcasts, Spotify, Amazon Music, or most podcast platforms.Sources and Further ReadingRather than listing individual books or papers here, you can find all referenced materials, recommended readings, foundational papers, and extended resources directly on our website:👉 https://adapticx.co.ukWe continuously update our reading lists, research summaries, and episode-related references, so check back frequently for new material.