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One difference is that baking mathematical models into electronic analogs is older than integrated circuits. The reason we deviated from that model is because the re-programmability and cost of general purpose, digital computers was way more economical than bespoke hardware for expensive and temperamental single purpose analog computers. The unit economics basically killed analog computing. What Extropic (and others) have identified is that in the case of machine learning, the pendulum might have to swing back because we do have a large scale need for bespoke hardware. We'll see if they're right.

Quantum computing has been exploring an entirely new model of computation for which it's hard to even articulate the problems it can solve. Whereas using analog computers in place of digital is already well defined.



A lot of quantum computing companies have the same idea of hard-baked analog computing for a useful algorithm. D-Wave was the biggest one to go bust.




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