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So what this is saying is that there are limits to how far you can push photo-lithography. Maybe. Yes, it's probably true, but as for how relevant that is... I

So what this is saying is that there are limits to how far you can push photo-lithography. Maybe. Yes, it's probably true, but as for how relevant that is... I'd like to point out that there's an existence proof for putting the power of ~some very large supercomputer into the space of a human head, running off a hundred watts or so. I am, of course, referring to the brain itself. It's a very different sort of architecture, that's true, but does anyone really think we'll stop before we get close to that?

ajuc 13y ago
Are we sure brain have more raw power than PC? Because when I try to outperform my PC at multiplying floats I got owned every time.. Maybe brain is faster at the things it's faster only because it's hardwired to do these things, and we're incorrectly comparing it to general purpose CPU? And brain has this very slow turing machine emulator on top of millions of special-purpose circuits.
- Filligree 13y ago
  We're quite sure it has more raw power, yes. And a lot of its power comes from special-purpose circuits, yep; you're only wrong in the order of magnitude. (Billions, not millions) It also has a couple other 'advantages'. For instance, running at 100Hz means far lower power consumption. Of course, it also means many problems can only be solved through precaching; notice how much better computers are than people at, say, bouncing a ball, while using ludicrously less cpu-time. For the raw power calculation, though... We don't have a perfect model of neurons yet, but it appears likely that the average neuron can be modelled by a circuit of 500 or so transistors; contrariwise, a single neuron can easily model a single transistor. Neurons are more complex, no question about it; I believe their behaviour was once compared to vacuum tubes, although that's incorrect. The synapse count is more typically used for comparisons; problem is, each neuron has multiple synapses, and they're not quite independent. To make the problem more complex, synapses double as memory devices; details of the chemistry of a single synapse can change fairly quickly, apparently allowing for long-term memory and skill formation without changing the gross structure of the brain (that is, graph connectivity). Though the connectivity also changes. The total storage capacity of the brain has been estimated at anything from a few gigabytes (very, very good compression algorithms; you might even call them AI-complete) to several terabytes, but it's nothing to write home about. Now, there are 6 billion transistors in the most recent CPUs, and 85 billion synapses in the human brain. Naively, this would make the human brain anywhere from 14 to 7000 times more powerful than a modern i7, but of course there are confounding factors. For starters, the aforementioned frequency differences. The i7 can run serial algorithms; the brain essentially cannot. Any given thought you have may be produced over a fairly long period, but it could be no more than a second; in the latter case, this means only 100 cycles. So it's pretty much got to be put together from previously-made components... meanwhile, the i7 can run several billion cycles of serial logic in that period. This doesn't just mean doing single things faster; per Amdahl's law there are some algorithms that cannot be usefully parallelized, so the i7 can run them while you cannot, and it may therefore be able to do the same thing using less overall resources. Okay, so that makes the brain seem less impressive. It's especially the case for motor functions - a great deal of the brain is dedicated to that, largely because there's no time to do longwinded calculations, so it all has to be precached. Which is why you need a lot of practice to reliably throw a basketball. It's an immense waste of space - a very cheap computer can do better, where we've invented the algorithms. Our robots are getting pretty good at moving, in some constrained circumstances. On the flip side there are problems that appear to be inherently parallel, such as face-recognition; all our best approaches so far essentially amount to checking every part of the image against every conceivable possibility. Here the brain has a major advantage, because neurons are damned well suited for the purpose; one might call them well-tuned to running neural networks. Where we're using general-purpose hardware to check several possibilities one after another the brain can have cheap neurons hanging out on the visual cortex, listening for particular signals, and never doing anything else. Conclusion? Heh, can't say I have one, except.. the brain uses special-purpose circuitry everywhere, but half the time it's from a lack of choice. It can't really do anything else. Regardless, technologically it's still a generation ahead of us, packing more power into a smaller space and energy budget than we can hope to.. but not two, or three, and our own speedy general-purpose systems have their own advantages that will eventually let them outperform it. EDIT: I should clarify, by 'generation' I mean a human generation - thirty years, not three. At the current rate of progress, which is likely irrelevant; we've already got good enough computers for AI (finally), we just need to use a lot of them.

ajuc 13y ago

Are we sure brain have more raw power than PC? Because when I try to outperform my PC at multiplying floats I got owned every time.. Maybe brain is faster at the things it's faster only because it's hardwired to do these things, and we're incorrectly comparing it to general purpose CPU? And brain has this very slow turing machine emulator on top of millions of special-purpose circuits.

Filligree 13y ago
We're quite sure it has more raw power, yes. And a lot of its power comes from special-purpose circuits, yep; you're only wrong in the order of magnitude. (Billions, not millions) It also has a couple other 'advantages'. For instance, running at 100Hz means far lower power consumption. Of course, it also means many problems can only be solved through precaching; notice how much better computers are than people at, say, bouncing a ball, while using ludicrously less cpu-time. For the raw power calculation, though... We don't have a perfect model of neurons yet, but it appears likely that the average neuron can be modelled by a circuit of 500 or so transistors; contrariwise, a single neuron can easily model a single transistor. Neurons are more complex, no question about it; I believe their behaviour was once compared to vacuum tubes, although that's incorrect. The synapse count is more typically used for comparisons; problem is, each neuron has multiple synapses, and they're not quite independent. To make the problem more complex, synapses double as memory devices; details of the chemistry of a single synapse can change fairly quickly, apparently allowing for long-term memory and skill formation without changing the gross structure of the brain (that is, graph connectivity). Though the connectivity also changes. The total storage capacity of the brain has been estimated at anything from a few gigabytes (very, very good compression algorithms; you might even call them AI-complete) to several terabytes, but it's nothing to write home about. Now, there are 6 billion transistors in the most recent CPUs, and 85 billion synapses in the human brain. Naively, this would make the human brain anywhere from 14 to 7000 times more powerful than a modern i7, but of course there are confounding factors. For starters, the aforementioned frequency differences. The i7 can run serial algorithms; the brain essentially cannot. Any given thought you have may be produced over a fairly long period, but it could be no more than a second; in the latter case, this means only 100 cycles. So it's pretty much got to be put together from previously-made components... meanwhile, the i7 can run several billion cycles of serial logic in that period. This doesn't just mean doing single things faster; per Amdahl's law there are some algorithms that cannot be usefully parallelized, so the i7 can run them while you cannot, and it may therefore be able to do the same thing using less overall resources. Okay, so that makes the brain seem less impressive. It's especially the case for motor functions - a great deal of the brain is dedicated to that, largely because there's no time to do longwinded calculations, so it all has to be precached. Which is why you need a lot of practice to reliably throw a basketball. It's an immense waste of space - a very cheap computer can do better, where we've invented the algorithms. Our robots are getting pretty good at moving, in some constrained circumstances. On the flip side there are problems that appear to be inherently parallel, such as face-recognition; all our best approaches so far essentially amount to checking every part of the image against every conceivable possibility. Here the brain has a major advantage, because neurons are damned well suited for the purpose; one might call them well-tuned to running neural networks. Where we're using general-purpose hardware to check several possibilities one after another the brain can have cheap neurons hanging out on the visual cortex, listening for particular signals, and never doing anything else. Conclusion? Heh, can't say I have one, except.. the brain uses special-purpose circuitry everywhere, but half the time it's from a lack of choice. It can't really do anything else. Regardless, technologically it's still a generation ahead of us, packing more power into a smaller space and energy budget than we can hope to.. but not two, or three, and our own speedy general-purpose systems have their own advantages that will eventually let them outperform it. EDIT: I should clarify, by 'generation' I mean a human generation - thirty years, not three. At the current rate of progress, which is likely irrelevant; we've already got good enough computers for AI (finally), we just need to use a lot of them.