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that's not really a good summary of what MoEs are. you can more consider it like sublayers that get routed through (like how the brain only lights up certain pathways) rather than actual separate models.

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The gains from MoE is that you can have a large model that's efficient, it lets you decouple #params and computation cost. I don't see how anthropomorphizing MoE <-> brain affords insight deeper than 'less activity means less energy used'. These are totally different systems, IMO this shallow comparison muddies the water and does a disservice to each field of study. There's been loads of research showing there's redundancy in MoE models, ie cerebras has a paper[1] where they selectively prune half the experts with minimal loss across domains -- I'm not sure you could disable half the brain and notice a stupefying difference.

[1] https://www.cerebras.ai/blog/reap

> I don't see how anthropomorphizing MoE <-> brain affords insight deeper than 'less activity means less energy used'.

I'm not saying it is a perfect analogy, but it is by far the most familiar one for people to describe what sparse activation means. I'm no big fan of over-reliance on biological metaphor in this field, but I think this is skewing a bit on the pedantic side.

re: your second comment about pruning, not to get in the weeds but I think there have been a few unique cases where people did lose some of their brain and the brain essentially routed around it.

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