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Analog and "non-exact" computation is not something that sounds very useful. The only reason we've been able to minimise so much is the tremendous noise rejection of binary systems.

Besides, modern computers are spending most of their time on actions of marginal or negative utility, mostly data-bureaucratic rather than computational.



Non-exact computation is useful for some stuff: machine vision, deep-learning, and probably other types of machines learning.

Those have a wide variety of uses and they demand lots of compute.

Also in places where performance is critical and money is spent on optimization, isn't lots of this bureaucracy removed ?


> Analog and "non-exact" computation is not something that sounds very useful.

Floating point operations are non exact in general. Physics simulations work at various degrees of approximation. Non exact computations would be useful as long as we can quantify the "non-exactness".




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