Transformers: Age of Attention

In 2017, a group of researchers (from Google and the University of Toronto) introduced a new way to handle natural language processing (NLP) tasks. Their revolutionary paper “Attention is All You Need”  presented the Transformer model, an architecture that has since become the basis of many advanced AI systems today. The model's performance, scalability, and versatility have led to its widespread adoption, forming the backbone of state-of-the-art models like BERT (Bidirectional Encoder Representations) and GPT (Generative Pre-trained Transformers). Before the Transformer model, most AI models that processed language relied heavily on a type of neural network called a Recurrent Neural Network (RNN) or its improved version, the Long Short-Term Memory Network (LSTM). In particular, problems like language modeling and machine translation (also called sequence transduction). These models processed words in a sequence, one by one, from left to right (or vice versa). While this approach made sense because words in a sentence often depend on the previous words, it had some significant drawbacks: