Slashdot Mirror
Supercomputer Simulates Human Visual System
An anonymous reader writes "What cool things can be done with the 100,000+ cores of the first petaflop supercomputer, the Roadrunner, that were impossible to do before? Because our brain is massively parallel, with a relatively small amount of communication over long distances, and is made of unreliable, imprecise components, it's quite easy to simulate large chunks of it on supercomputers. The Roadrunner has been up only for about a week, and researchers from Los Alamos National Lab are already reporting inaugural simulations of the human visual system, aiming to produce a machine that can see and interpret as well as a human. After examining the results, the researchers 'believe they can study in real time the entire human visual cortex.' How long until we can simulate the entire brain?"
Perhaps the goal should be to make the visual system BETTER than ours?
Visual object recognition systems have been a thorn in the side of robotics since the beginning. The other annoynace of battery power will likely be solved by the nanowire battery - therefore leaving 'sight' as the real final technological step for our lovely robots.
Extrapolating further, a human-quality object recognition system will yield results which we cannot currently imagine (let's avoid some big-brother robot talk for a second, however).
For example; I was looking at some old WWII photographs of troops getting on boat - thousands of faces in these very high-quality photographs. To myself, I thought,'Self. If all historical photographs could be placed in view of a recognition system, perhaps it could be found, interestingly, where certain ancestors of ours did appear.'
Throw in a dash of human-style creativity and reasoning and I'm certain some truly nifty revelations are to be found in our mountains of visual documentation currently lamenting in countless vast archives.
Read my Very Short "Stories"
One word? That makes your spelling error rate 100%.
OMG a super computer! It's so powerful it can probably pop up a consciousness of its own!
Sarcasm aside, computer power and strong AI are two very distinct problems. Computer power is all about scaling up power so you can do more in less time, that doesn't allow you to do anything new, only the same things except faster. Strong AI is all about algorithms, and nobody can tell if such algorithms exist. And anyone who talks about human-like strong AI is a crackpot (Kurzwiel is a crackpot to me for his wacky predictions), as we have yet to see a bug-like strong AI, and if it was just a problem of power we'd already have something working in that field.
You just got troll'd!
That's only 10% lower than my math error rate.
"So long and thanks for all the fish."
In the interest of full disclosure, let me first say that I am one of the co-authors of the model that was executed on the Roadrunner, though I had nothing to do with the actual implementation that was executed (this was done by professional computer scientists, and I am a computational neuroscientist).
Let me clarify what was done, and what will be done in the future.
We simulated about 1 billion neurons communicating with each other and coupled according to theoretically derived arguments, which are broadly supported by experiments, but are a coarse approximation to them. The reason is that we are interested in principles of the neural computation, which will enable us to construct special purpose dedicated hardware for vision in the future. We are not necessarily interested in curing neurological diseases, hence we don't want to reproduce all physiological details in this simulation, but only those that, in our view, are essential to performing the visual computation. This is why we have no glia and other similar things in the model: while important in long-term changes of neuronal properties, they communicate chemically and, therefore, are too slow to help in recognition of an object in ~200 milliseconds.
The simulation was a proof of principle only. We simulated only the V1 area of the brain, and only those neurons in it that detect edges and contours in the images. But the size of V1 we simulated was much larger than in real life, so that we had only a bit smaller total number of neurons than the entire visual system in a human has. Hence we can reliably argue that we will be able to simulate the full visual cortex, almost in real time. This is what will be done in the next year or so.
When we talk about human cognitive power, we only mean the ability to look at images, segment them into objects, and recognize these objects. We are not talking about consciousness, free will, and thinking, etc. -- only visual cognition. This is also why we want to match a human, rather than to beat him: in such visual tasks, humans almost never make any errors (at least, when the images are not ambiguous), while the best computer vision programs make an error in 1 in 10 casesor so (just imagine what your life would be if you didn't see every tenth car on the road). Based mostly on theoretical arguments characterizing neuronal connectivity, and neglecting many important biological details, we may never be able to match a human (or maybe we will -- who knows? this is why it's called research). But we have good reasons to believe that these petascale simulations with biologically inspired, if not fully biological, neurons will decrease error rates by hundreds or thousands. This is also why we are content with simulating the visual system only: some theories suggest that image segmentation and object identification happens in the IT area of the visual cortex (which we plan to simulate). While the rest of the brain certainly influences its visual parts, it seems that the visual system, from the retina to IT, is sufficiently independent of the rest of the brain, so that visual cognitive tasks may be modeled by modeling the visual cortex alone.
Finally, let me add that we got some interesting scientific results from these petascale simulations and the accompanying simulations and analysis on smaller machines. But we need to verify what we found and substantially expand it before we report the results; this will have to wait till the fall, when the RR computer will be available to us again. For now, the fact that we can simulate the system the size of the visual cortex is of interest by itself.
That's all, folks!