Covers machine learning for people who intend to build with it rather than read about it: what the architectures actually do, what the benchmarks actually measure, and which claims survive contact with a dataset you did not choose.
Attention, explained through a single nine-token sentence: what the weights mean, why the upper triangle of the matrix is empty, and what that mask has to do with the model's failure modes.
Four serious positions on AGI, what each treats as the binding constraint, and — for each — the observation that would refute it. A guide to holding the question rather than picking a side.
AI, machine learning, deep learning and generative models are nested, not synonymous. Here is what distinguishes each layer, and which one people now mean when they say AI.
