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AI Magic: Shipping 1000s of successful products with no managers and a team of 12 — Jeremy Howard of Answer.ai

Latent Space: The AI Engineer Podcast

NOTE

Encode for Efficiency, Decode for Depth

Using encoder-decoder models enhances the ability to develop effective feature representations for input data, essential for tasks requiring context, like translation. The encoder captures vital information which supports the decoding process, minimizing the effort needed during decoding. In contrast, when the task primarily involves classification rather than generating sequences, decoder-only models lack practical value, as they do not take advantage of the structured context provided by encoding. Thus, the choice between these model architectures should align with the task requirements, weighing the necessity for context against the need for straightforward output generation.

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