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 level
• Why data is the foundation of all modern AI
• How raw information becomes structured and usable
• What models and algorithms do, and how they “learn”
• The role of training, validation, and generalization
• The difference between machine learning and deep learning
• How neural networks work at a high level
• Why compute and hardware matter so much
• How humans contribute expertise, labels, and feedback
• How all these components fit together to create end-to-end AI systems

Sources and Further Reading

Rather 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.uk

We continuously update our reading lists, research summaries, and episode-related references, so check back frequently for new material.