You might call me out for nitpicking here, but CPUs don’t require system clocks
A key advantage is that clockless CPUs don’t consume energy when they aren’t active. Machines based on synchronous processors, on the other hand, constantly have the “pulse” of the clock traveling through the system (and the frequency at which it “beats” determines the speed of the CPU). The pulse of the clock continuously coursing through all of the circuits also results in “wasted cycles,” meaning power is being used when the CPU isn’t doing anything, and heat is being dissipated for no reason.
I think that if your intent is to simulate the brain using artificial neural networks, then the how the RAM or hard drive works is inconsequential
I’ll admit that it is something worth pointing out to someone who does take the brain/computer analogy too far (which is I guess exactly who you’re targeting here) or doesn’t know much about computers or brains.
And more: if that hardware is a FPGA, it can mutate and self-organize
This one I completely agree with. I always get the feeling when I read philosophy of AI papers that some of the philosophers take the sentiment “the mind is the program being executed on the machine that is the brain” too far. Consequently, and I feel this is actually a central problem with philosophy of AI, they pay too little attention to how the brain actually operates and try to think about how to implement consciousness on a computer without considering how the activities of the brain relate to the mind.
Anyway, I think it would be fair to describe the brain as an asynchronous, analog, and massively parallel computer where the hardware itself is inherently mutable and self-organizing.
Minksy Papert’s conclusions were based on the faulty assumption that neural networks are computationally linear; linearity is a very unusual characteristic for analogue systems. Had MP fully appreciated the analogue nature of the brain, they likely would not have made this faulty assumption.
A consistent thread in your comment is that some differences are merely “implementational” or “architectural” details, and thus are actually unimportant or otherwise superficial. IMO, that attitude is scientifically dangerous (how can you know for sure?) and *very* premature (when we have an artificially intelligent digital computer, I’ll be convinced).
It is also the same attitude that pervaded both classic cognitive psychology and GOFAI (good old-fashioned AI). I don’t think the track record of either is very good: 20th century advances in statistical theory may be responsible for the few successes in both disciplines (just don’t tell Chris at Mixing Memory I said that. 😉
Just adding something to Jonathan’s answer to #6: A computer can run entirely in hardware (actually it’s a very strange affirmation, but you know what I mean).
I think Chris’ arguments would target “today personal computers” and not computers in general, since “computer” is a very wide term. However, I think it was the true target of the article, just with some differences/mistakes in the use of the terms.
Just wanted to chime in on what a great article this is. Not too complex or technical, and gives a great overview of the significant differences.
Rafael, when you say “I think Chris’ arguments would target ‘today personal computers’ and not computers in general, since ‘computer’ is a very wide term”, you have a good point, but that is exactly the computer model on which analogies were (and are) based. Chris’s arguments are about the analogies were drawn, and not not about computers, and not about analogies can be drawn on the basis of future or experimental computer architectures.
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