What we covered
The talk is structured as a story rather than a syllabus, because the thing that makes LLMs click for most people is understanding what problem they were the answer to.
- The early dream of intelligent machines, the chapters that followed, and the plot twist that stalled it.
- The rise of LLMs, and the paper that turned the corner — "Attention Is All You Need".
- What a language model is actually doing, built up through examples rather than equations.
- The classic natural language problems, and how much of that field collapsed into one interface.
- Prototyping fast: prompting, endpoints, and how little it now takes to try an idea.
- From words to worlds — generation beyond text — with a live demo.
- The rise of agents, and what changes when a model is given a job rather than a question.
- Open source and ethics, and the responsibilities that came with the capability.
- Writing your own chapter: what a career in AI looks like from the start of it.
Format
A talk with a demo, pitched at people meeting this properly for the first time. No prior machine learning background assumed.
Who it's for
Students, career changers, and developers from other disciplines who want an honest mental model of LLMs before deciding how much of their time to bet on them.