A language model can't check the weather, multiply two numbers reliably, or book a table, it can only predict text. Lecture 3 of the AI Reading Series shows how that limitation was turned into a strength: describe a tool in text, let the model emit a call, let ordinary code execute it. We trace the idea from RAG and WebGPT and prepare you to read Schick et al.'s "Toolformer" with the right questions in hand.
Part 2 of the AI reading series. The previous post ended with AlexNet's 2012 earthquake in image recognition. This one tells the road to language: how researchers turned words into vectors, taught networks to read sequences, discovered attention — and why, in 2017, eight Google researchers decided attention was all you need.
The first in a reading series taking you from the artificial neuron of 1943 to today's transformers, mixture-of-experts models and agents. Lecture 1 is a preparatory guide to Cardon, Cointet and Mazières' sociological history of AI, with reading strategy and glossary.
