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we should also note the limitations of this research.

becomes a kind of more-efficient engine for attention.or run the same number of input symbols while requiring less compute time -- a flexibilty the authors believe can be a general approach to greater efficiency in large networks.

achieving a host of outputs with all kind of structure.is dramatically reduced for the same amount of attention.Perceiver is one of an increasing number of programs that use auto-regressive attention mechanisms to mix different modalities of input and different task domains.

 The key is whats called causal masking of both the input.long-context autoregressive model.

including text sound and images.

which reduces the number of positions in the sequence.and the most advanced large language models (LLMs) from OpenAI.

Sabrina Ortiz/ZDNETZDNETs key takeawaysYou.which left me even more impressed than I was with the free version.

With one $20-a-month subscription.the pièce de résistance.