The Significance of Attention in the history of Neural Networks

Attention in neural networks involves content-based addressing, utilizing information from similar areas to determine new representations, which represents a significant breakthrough in modern neural networks. While LSTM and convolutional neural networks have historical origins, attention brought a genuinely new concept, revolutionizing NLP systems in the mid-2010s. Initially applied in neural machine translation, attention quickly expanded to question answering and summarization systems. The development of different attention mechanisms, such as multiplicative attention, played crucial roles in the evolution of transformers. Despite transformers incorporating other components like fully connected layers and residual connections, attention remains the core, as highlighted in the paper 'Attention is all you need,' marking a pivotal shift towards intensive use of attention in neural networks.