Basics of deep learning, gradient descent, optimization.
Attention, transformers, language models
Pre-training, inference-time interventions, how to make a model believe that it is a bridge
Interpretability, activation patching, in-context learning
Alignment: weak-to-strong generalization, debate, guaranteed safe AI