9:19Transformers Explained: The Discovery That Changed AI Forever
The talk traces the development of Transformer models through three key steps: LSTMs, seq2seq models with attention, and the Transformer architecture. It explains how LSTMs helped neural networks retain information across long sequences, while attention let decoders access encoder states directly. Transformers removed recurrence, allowing parallel processing and better performance on translation benchmarks. The talk also connects Transformer variants such as BERT and GPT to the large language models used in products like ChatGPT and Claude.