Example Concept: Traditional neural networks, like RNNs, process sequential data step-by-step, which can lead to difficulties in capturing long-range dependencies due to vanishing gradients. Transformers, however, use self-attention to weigh the importance of different elements in the input sequence simultaneously, allowing them to capture dependencies regardless of their distance in the sequence. This approach not only improves performance on tasks involving long sequences but also enables parallel processing, making transformers more computationally efficient.