Tasks

• Keep laying out the book sections
• Formalize the two models (identical architecture, different biases in the training corpora), and as many prompts as needed to see if the “system prompt” has changed or the model has changed. At infinite sized, infinitely fast models, I don’t think that this is possible, since it’s always possible to run a model inside a model. But with respect to computable models, then I think there can be tests. The best way to do this might be to train up a NanoGPT completion model (M1, M2) on two corpora that use random word (alphanumeric?) token sequences that have different biases (according to some rule?). A model can have an arbitrary “system prompt” (S1…SN) prepended to the input. The system (Mx + Sx) is a black box. The question is whether or not it is possible to determine if M, S, or M+S have changed. And if so, the number of prompts required
• 2:00 class

SBIRs

• Finish and submit quarterly report