Denis Ostapenko

I kept seeing the same kind of AI system described as a miracle in one headline and a failure in the next. One story gave the model a mind. The next called it useless. Both wanted a reaction before they explained the mechanism. I got tired of borrowing my opinion from headlines. So I went backward. I followed the short chain of papers behind the tools: early neural language models, word vectors, sequence-to-sequence translation, attention, the transformer, scaling laws, and training from human feedback. Then I moved into the papers and documentation on hallucinations, retrieval, long context, evaluation, infrastructure, and the systems that give a model data and tools. There was less magic than the headlines promised. The engineering was more interesting. The split that made the noise quieter My notes kept returning to a simple separation: The model predicts text. The surrounding system supplies documents, memory, tools, and permissions. Evaluation tells you whether the result...