Transformers Explained: The Discovery That Changed AI Forever

The podcast episode traces the historical development of the Transformer architecture, which underpins modern AI systems like ChatGPT. It begins by explaining the Transformer's function in modeling data relationships and generating outputs, then delves into three key precursors: Long Short-Term Memory (LSTM) networks, sequence-to-sequence (Seq2Seq) models with attention, and finally, the Transformer itself. The speaker details how LSTMs addressed the vanishing gradient problem in recurrent neural networks (RNNs) for sequential data but were limited by a fixed-length bottleneck. Seq2Seq models with attention improved upon this by allowing decoders to "attend" to encoder hidden states, significantly boosting machine translation performance. However, RNNs' sequential processing still posed a challenge for parallel computation. The episode concludes with the 2017 "Attention Is All You Need" paper, which introduced Transformers, eliminating recurrence and relying solely on self-attention to enable parallel processing and greater accuracy, leading to the development of models like BERT and GPT.