A detailed explanation of the Transformer model, a key architecture in modern deep learning for tasks like neural machine translation, focusing on components like self-attention, encoder and decoder stacks, positional encoding, and training.
Combined with the growing trend of multimodality, or models that combine language, image, and other types of capabilities, we may see a trend of AI models operating more like a committee of different components rather than a monolithic block. This approach actually has many conceptual similarities to a set of interesting ideas described by Marvin Minsky and Seymour Paypert from the early days of AI.