The AI Pocket Book with Emmanuel Maggiori

About this episode

Allen Wyma interviews Emmanuel Maggiori to explore the core concepts behind Large Language Models and demystify AI.

AI is everywhere, from coding assistants to chatbots, but what's really happening under the hood? It often feels like a "black box," but it doesn't have to be. In this episode, Allen sits down with Manning author and AI expert Emmanuel Maggiori to demystify the core concepts behind Large Language Models (LLMs). Emmanuel, author of "The AI Pocket Book," breaks down the entire pipeline - from the moment you type a prompt to the second you get a response. He explains complex topics like tokens, embeddings, context windows, and the controversial training methods that make these powerful tools possible. IN THIS EPISODE 00:00 - Welcome & Why "The AI Pocket Book" is a Must-Read 15:20 - The Basic LLM Pipeline Explained 8:05 - What Are Tokens? 21:30 - Understanding the Context Window 25:50 - How Embeddings Represent Meaning 35:45 - Controlling Creativity with Temperature 39:30 - How LLMs Learn From Internet Data 45:25 - Fine-Tuning with Human Feedback (RLHF) 51:15 - Why AI Hallucinates 56:45 - When Not to Use 📘 GET THE BOOK! Dive deeper into the concepts discussed in this episode with Emmanuel's book, "The AI Pocket Book". Get 45% off with code FHWFmaggiori at checkout. 🔗…

People in this episode

Host: Allen Wyma

Guest: Emmanuel Maggiori

Topics covered

• Artificial Intelligence
• Large Language Models
• Machine Learning
• Tokens
• Embeddings
• Context Windows
• AI Training Methods

Keywords

• AI
• Large Language Models
• tokens
• embeddings
• context windows
• training methods
• AI hallucinations

Sponsors

Manning

Mentioned in this episode

Books & works: The AI Pocket Book

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