AI Signals #34: Agent Harnesses, and Geometric Thoughts
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About this event
Agenda
06:00pm - Doors open
06:30pm - Welcome
06:35pm - Alper Nebi Kanli, Optimizely, "How LLMs Represent Concepts Internally: A Tour of the Geometry"
07:05pm - Break
07:20pm - Tun Shwe, Lenses.io "Understanding Agent Harnesses"
08:00pm - Lightning Talks:
- TBA
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Large language models look like they just predict the next word, so it is surprising how much structure they build inside. They actually represent concepts: you can find directions in their activations for things like gender, sentiment, or even truthfulness, and you can use those directions to steer what the model does. This talk starts there, with the evidence that concepts really are represented inside these models. Then it goes one level deeper: what does it actually mean for a concept to be "a direction"? The answer depends on geometry, on how you choose to measure, and that choice does more work than it looks. You will come away seeing the inside of an LLM a little more clearly, with a few surprises about something many of us use every day.
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