Myth: The Transformer architecture discovered by Google in 2

Myth: The Transformer architecture discovered by Google in 2017 was revolutionary because it eliminated the need for manual feature extraction.

Reality: While the Transformer does learn complex patterns in sequential data without traditional, hand-engineered features, it relies heavily on positional encoding to maintain awareness of input sequence order.

In reality, the Transformer uses learned self-attention mechanisms to weigh the importance of each input element, which relies on positional encoding to keep track of the input sequence. This encoding is learned through the positional encoding vector added to the input embedding to help the Transformer architecture understand the sequential relationships. The idea that the Transformer abandoned manual feature extraction altogether is an oversimplification and does not give credit to the innovative solutions implemented to resolve the positional dependencies inherent in sequential data.

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