processing. Conceptually, machine learning connects an input X to an output Y through a function with parameters W. W are also known as the model weights or the numeric values discovered through training. F is where the human intervention happens. It's the model architecture designed by researchers. And the transformer is one convenient way of defining F. If we had infinite resources, we wouldn't need any human intervention. Given a set of neurons, we would define f in the least constraining way possible by connecting all pairs. But this quadratic is intractable. It gets really big really fast. The role of a model architecture is to prune these connections and rearrange the neurons in an informed way. It needs to strike the right balance between cost and expressivity. So, how does the transformer pull that