Core Concepts & Building Blocks of AI

In this episode, we step away from the historical storyline and focus on the essential ingredients that make modern artificial intelligence possible. Instead of diving into equations or heavy technical jargon, we unpack the core ideas and building blocks that sit underneath every AI system — from simple recommendation engines to large-scale neural networks.We explore how data, models, algorithms, computation, and human expertise come together to form complete AI pipelines. Along the way, we use intuitive analogies and simple explanations to make each concept feel accessible and meaningful, even if you've never taken a computer science course.This episode covers:What an AI system is at a conceptual levelWhy data is the foundation of all modern AIHow raw information becomes structured and usableWhat models and algorithms do, and how they “learn”The role of training, validation, and generalizationThe difference between machine learning and deep learningHow neural networks work at a high levelWhy compute and hardware matter so muchHow humans contribute expertise, labels, and feedbackHow all these components fit together to create end-to-end AI systemsSources 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.

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