This one-hour talk provides a clear and intuitive introduction to Large Language Models (LLMs), focusing on how they work at a conceptual level and why they represent a major shift in artificial intelligence.

The lecture explains what LLMs are: deep neural networks trained on massive amounts of text data to predict and generate human-like language. Instead of relying on explicit rules, these models learn statistical patterns from data, allowing them to produce coherent and context-aware responses.

A key idea covered in the talk is next-token prediction, which is the core training objective behind most modern LLMs. The model learns to predict the next word or token in a sequence, and over time this simple task leads to surprisingly powerful language understanding and generation abilities.

The talk also introduces the transformer architecture, the backbone of modern LLMs. It highlights how attention mechanisms allow models to focus on relevant parts of input text, enabling better context handling compared to earlier approaches like RNNs.

Another important theme is scale: the performance of LLMs improves dramatically with more data, more parameters, and more compute. This scaling behavior explains why recent models have become so capable across a wide range of tasks.

The lecture also touches on limitations such as hallucinations, reasoning errors, and sensitivity to prompts, helping learners understand that LLMs are powerful but not perfect systems.

By the end of the talk, learners gain