Overview
Syllabus
00:00 - The acceleration of AI viewed through programming agents
01:46 - Level-setting on AI agents: nomenclature, evals, and experimentation
03:18 - Experimentation types and processes
05:30 - Optimizing each step of the experiment loop
08:10 - Bringing W&B Launch back to solve knotty experimentation problems
11:50 - Optimizing the research phase in Weave
14:43 - Can we use AI to automatically improve the experimental loop?
17:39 - What changes with the resurgence of reinforcement learning
19:12 - OpenPipe’s Kyle Corbit on building reliable agents with RL
23:14 - Overcoming the limitation of evals with researcher agents
26:08 - How close are we to self-improving AI?
Taught by
Weights & Biases