AI - magic or maths? A no-jargon guide on how AI actually works.

Last week, Florence helped us get our heads around the right mindset for using AI. But there were a lot of words flying around. Agents. LLMs. Machine learning. What do those things actually mean? And more importantly, does it matter?This week we're joined by Raji Ramakrishnan, a product leader at Lloyds Banking Group who works on agentic AI observability. Which, yes, is a mouthful. But by the end of this episode, you'll actually know what all of those words mean. And that's kind of the point.Raji breaks down the entire AI landscape in a way that finally makes sense. She starts with the basics (AI is not magic, it's maths, data and programming) and walks us through how machines learn using an analogy that anyone who's taught a child flashcards will immediately get. Supervised learning? That's you holding up the flashcard. Unsupervised learning? That's the kid pointing at a cat in the street having figured it out on their own.But this episode isn't just a glossary. It's about why understanding this stuff actually matters. Raji makes a compelling case that AI is coming whether you engage with it or not. Your mobile provider, your bank, your electricity company are all already using it. And the more you understand, the better equipped you are to know when to trust it and when to push back.We also get into hallucinations (why AI confidently makes stuff up), the difference between generative AI and agentic AI, and what banks are actually doing behind the scenes to make sure AI doesn't go rogue. Spoiler: there are real humans watching.In this episode, we cover:AI, machine learning, deep learning, generative AI, agentic AI: what each one actually means and how they connectThe flashcard analogy: how machines learn in a similar way to children (supervised vs unsupervised learning)Why AI is a prediction machine, not a truth machine, and why that distinction mattersHallucinations: what they are, why they happen, and why you should always sense-checkAgentic AI: what changes when AI can take actions on its own, not just generate contentObservability and guardrails: what's actually happening inside banks to keep AI in checkWhy jargon is an unnecessary barrier to entry and how to not let it hold you backThe mobile phone analogy: remember buying minutes for your Nokia 3310? AI adoption is on the same trajectory