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Adding an additional comment here, as I'm thinking to myself about how I'd clarify the issue further. Suppose we write a program in `C` which requires 32 bit a
by mjburgess 4y ago
Adding an additional comment here, as I'm thinking to myself about how I'd clarify the issue further.
Suppose we write a program in `C` which requires 32 bit array, sets an input state, operates on the array, and produces an output state. This program is the logical model, here requiring say 32bits and 10 operations/bit average. So we have 320 logical changes to the array (which btw, wont be purely parallel or serial).
Suppose the most efficient CPU we have requires E_thermo energy for this whole process (setting the 32bit input, operating, reading the output).
Now, using somewhat disputed ideas about physical limits, E_thermo say implies we've used 320,000 'bits' thermodynamically, ie., somehow the CPU has produced an equivalent of 320,000 "energetic changes".
So the CPU has an efficiency, in some suspicious sense, of 100,000 thermo-bits / logical-bit. For each single on-average change to the array we see in the `C program`, we theoretically measure an equivalent heat of 100,000 changes.
Now, how efficient is the brain at computation? Well since we have no logical model of the computation its performing, there's absolutely no way of answering that question.
So why do people say it's efficient? Well because any apparently equivalent algorithm we come up with (in, eg., `C`) to do even basic things that the brain does, requires vast amounts more energy. Eg., processing images on a GPU requires, say 300 W, and our whole brain uses (a claimed) 20 W.
I think the resolution to this problem is that the brain(-environment-body) system implements computation in a radically different manner than anything like a CPU, GPU, etc. I suspect that computation happens at every scale: molecular, sub-cellular, cellular, neuronal, inter-neuronal, nervous-system, body, body-environment, etc. And across the whole body, and using energy in the environment to maintain state.
However, I find the use of the term "computer" and "computational" mostly just productive of pseudoscience. It is a basically meaningless term that only produces confusion. The only useful area where it helps is when handling logical models of algorithms -- models largely absent from the whole of empirical and theoretical science.
- rad88 4y agoWhat did you mean by "computation" then? Honestly curious, because it seems you could also say a digital computer does it at all scales: semiconductor junctions, capacitors, clocks, their multipliers, transistors, up through logic gates, blocks, buses, ..., and all the way out to the fans, power supply, etc?
- mjburgess 4y agoI mean that if "intelligence (etc.)" in the relevant sense can be described by an algorithm, its implementation will not just depend on properties neurones have as neurones. It'll depend on properties at various scales. This isnt quite true for digital computers, in the sense that the only property which implements the algorithm is the electrical switching state... that this state depends on, eg., silicon isnt quite the same as silicon-properties the ones "doing the work". This is part of the trouble talking about "computation" at all, which is a nearly empty term in my view. Consider an example. A "baking a cake" algorithm can be implemented by a person or a factory, if specified correctly. In one case, the organic properties of a person appear "essential to the implementation", but they arent. But there are some algorithms which do require organic properties (eg., if we consider the behaviour of a cell wall an algorithm) to implement. The question is in what sense are the implementation properties "doing the computational work". Any electrical system which provides switching states is "doing the work in the same way", ie., there are certain "macro-properties" which do the work, regardless of their micro-property dependencies. I think in the case of intelligence (the brain-body, etc.) the properties "doing the computational work" arent the macro-properties of neuronal interaction. They're properties at various scales (subcelluar protein behaviours; celluar interation; neuronal communication; bodily organization; environmental driving; etc.). In particular, i think it's the self-adapting self-organizing properties of certain organic systems that allow them to implement "the intelligence algorithm" -- this algorithm, if it can ever be specified, I do not think will be implemented only by macroscopic neurone firing -- i'd say that'll be a very minor part of its implementation. I think most of it will come from self-organization properties from the subcell to the body.
- rad88 4y agoI find that plausible, but I am a layman. At the very least there's no evidence that the scales can be separated, because despite all attempts a brain in an organism is the only thing recognized as showing "intelligence". What you describe seems very similar to what's happening in an ecosystem. Our understanding of ecology is maturing though while these fields seem not to be. We have made very impressive calculators of course, and I don't mean to minimize them per se -- mainly people are just reckless with their claims. It an interesting connection, I think, that natural selection is the only other process that is arguably doing something intelligent. This is a controversial idea and might well be wrong, but I don't believe that's proven merely by the fact that some key mechanisms have no intrinsic "goal". I agree that the language is imprecise, and (especially for what I just said) has misleading connotations that are unfortunate... I'm really curious to learn more about the processes you've alluded to and their relations, if you can recommend any reading or care to elaborate.