Andrej Karpathy, a prominent figure in AI, explains the transformer's design with elements like residual connections and multi-layer perceptrons, highlighting its message-passing scheme. The transformer is not just a translation tool but a powerful, optimizable computer, with the title 'Attention is All You Need' enhancing its impact. Its design allows for efficient optimization using backpropagation and gradient descent, running effectively on hardware like GPUs. The transformer's residual connections facilitate smooth gradient flow, enabling effective algorithm development through sequential learning in layers. The paper 'Attention is All You Need' introduced the Transformer architecture seven years ago, revolutionizing the world of deep learning and becoming widely used across modalities. The term 'transformers' in AI, such as in GPT, raises questions about its origin and significance, as the original paper does not provide an explanation. Multi-Headed Attention is highlighted as a crucial architectural paradigm in machine learning, covering essential mathematical operations within its structure.