Attention, Transformers, and Self-Supervised Foundation Models

The previous lesson's Remark identified RNNs' core weakness: information from early time steps has to survive being carried, step by step, all the way to wherever it is needed, and the vanishing-gradient mechanism makes that carry unreliable over long sequences. Attention mechanisms solve this by letting a model look directly back at any earlier position, rather than relying on a compressed running state — and the Transformer architecture takes this to its logical extreme, discarding recurrence entirely and building a state-of-the-art sequence model out of attention alone. This closing survey note of Topic 6 introduces both ideas at introductory depth, then points to self-supervised pretraining and generative models without developing them here. For QKV formalism, gated memory, seq2seq, and generative deep learning, continue in the Neural Networks and AI unit.