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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.
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