Transformers: The best idea in AI | Andrej Karpathy and Lex Fridman

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updated 11 Jul 2023

The Transformer architecture in deep learning is considered a beautiful and surprising idea.
It is a general-purpose neural network architecture that can process various sensory modalities such as vision, audio, text, etc.
The Transformer is a powerful, trainable, and efficient computer that can handle a wide range of tasks.
The architecture was introduced in a paper titled "Attention Is All You Need" in 2016 and has had a significant impact on the field.
The authors may not have fully anticipated the extent of its impact, but they were aware of the motivations and design decisions behind it.
The catchy and memeable title of the paper, though not entirely reflective of its significance, contributed to its popularity.
The Transformer is expressive, optimizable, and efficient, making it suitable for various computations and hardware setups.
It supports the learning of short algorithms and gradually extends them during training through residual connections.
The architecture has remained stable over the years, but there is ongoing exploration to improve it further.
Current trends focus on scaling up datasets, improving evaluation, and keeping the architecture unchanged while making advancements in other areas.