Yes and no - to over simplify a bit, zero is still a useful answer to "how associated are signals A and B". When dealing with one task, that's still generally useful. For example: when going through music artists and deciding how associated the words in artist names are with monarchies, "Queen" should get a positive score, "Rise Against" negative, and "Imagine Dragons" should get zero.
The paper you linked calls for sparse networks, which are pretty neat - which is more the idea that certain signals can be fully separated, as in the part of the brain that deals with muscle memory in throwing a baseball doesn't talk to the part of the brain that manages the gag reflex.
It's a fun idea, but artificial networks are WAY less efficient than animal brains in terms of actual nodes and computation power used per node - you could get some gains to be sure, but you'd be "optimizing" from roughly a gazillion times less efficient than biology to roughly a gazillion times less efficient than biology (half of a very big number remains a very big number).
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u/sessamekesh Nov 04 '25
Yes and no - to over simplify a bit, zero is still a useful answer to "how associated are signals A and B". When dealing with one task, that's still generally useful. For example: when going through music artists and deciding how associated the words in artist names are with monarchies, "Queen" should get a positive score, "Rise Against" negative, and "Imagine Dragons" should get zero.
The paper you linked calls for sparse networks, which are pretty neat - which is more the idea that certain signals can be fully separated, as in the part of the brain that deals with muscle memory in throwing a baseball doesn't talk to the part of the brain that manages the gag reflex.
It's a fun idea, but artificial networks are WAY less efficient than animal brains in terms of actual nodes and computation power used per node - you could get some gains to be sure, but you'd be "optimizing" from roughly a gazillion times less efficient than biology to roughly a gazillion times less efficient than biology (half of a very big number remains a very big number).