Module 2: The Transformer Architecture: History - The Bottleneck That Broke Language Models

Shay breaks down why recurrent neural networks (RNNs) struggled with long-range dependencies in language: fixed-size hidden states and the vanishing gradient caused models to forget early context in long texts.

He explains how LSTMs added gates (forget, input, output) to manage memory and improve short-term performance but remained serial, creating a training and scaling bottleneck that prevented using massive parallel compute.

The episode frames this fundamental bottleneck in NLP and sets up the next episode on attention, ending with a brief reflection on persistence and steady effort.