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Silicon Brains That Think As Fast As a Fly Can Smell
Nerval's Lobster writes "Researchers in Germany have discovered what they say is a way to get computers to do more than execute all the steps of a problem-solving calculation as fast as possible – by getting them to imitate the human brain's habit of finding shortcuts to the right answer. A team of scientists from Freie Universität Berlin, the Bernstein Center Berlin, and Heidelberg University have refined the idea of parallel computing into one they describe as neuromorphic computing. In their design, a whole series of processors designed as silicon neurons rather than ordinary CPUs are linked together in a network similar to the highly interconnected mesh that links nerve cells in the human brain. Problems fed into the neuro mesh are broken up and processed in parallel, but not always using the same process. The method by which neuromorphic processors handle problems varies with the way they're linked together, as is the case with neurons in the brain. The chips are designed to copy the layout and functions of brain cells, but the way they're interconnected is based on another highly efficient biological model. 'The design of the network architecture has been inspired by the odor-processing nervous system of insects,' said one of the researchers. 'This system is optimized by nature for a highly parallel processing of the complex chemical world.' In tests using real-world datasets, the prototype was able to match the performance of specialized Bayeseian pattern-matching systems. Even better, the stable decisions reached by 'output neuron populations' take approximately 100 milliseconds, which is the same speed required by the insect nervous systems on which the network design is based, according to the paper."
In order to properly evaluate this story I would need to know the rate at which flies smell. although presumably silicon ICs can move faster than that.
... isn't a parsec a unit of distance, not time?
Priest: "Universe from nothing, no laws of physics, sped up time"+ huge discrepancies. Creationism? No. Big Bang Theory
That's the last thing we need: robot overlords who keep taking shortcuts. Next thing you know, they'll kill all humans and then go bankrupt from ill-advised mortgages!
Solutions that evolution produces (whether real or simulated) typically suck, as they are typically just good enough for the training criteria and may even completely fail longer term. This really is nonsense, unless you have very low quality requirements. And, unlike a solution based on understanding how to solve something, this bio-inspired stuff cannot easily be improved incrementally from seeing how it performs in practice.
Most ACs are not even worth the keystrokes to insult them. Be generically insulted by this and ignored otherwise.
You're pointing to articles on high end mammalian brain structures when TFA is referring to the most basic structures in an insect brain. Also, this blanket assumption that no one could possibly understand the complexity of a small group of neurons is way out of date.
Olfactory circuits are pretty well understood. This isn't the first simulation of neurons mimicking an olfactory bulb at the single neuron level. We've been watching videos and seeing presentations of these models for years now. What is neat, here, is that they're modeling a somewhat realistic hardware instantiation of a model (as in, this is something which maybe could be built).
I come at this from the other end. I make the chemical sensing hardware that mimics the response of a biological chemical sensor (an artificial insect 'nose'). There are long running collaborations between my field and neuromorphic computing folks to develop a combined sensor-processor that can electronically understand smell in the same way a living thing does. I have to sit through their talks on modeling neurons, and they have to sit through my talks on nanosensor arrays.
I come at this from the other end.
The butt?
systemd is Roko's Basilisk.
I get the idea our brains are wired very similar to the old-school analog computers ( yes, analog ... integrators, summers, multipliers, dividers, log/antilog, whatever ).
I am talking patch-boards. pots, switches, lots of meters and analog chart recorders here, fellas, without a keyboard in sight. One programmed these with straight math, where you literally wired your equation into the machine.
Back in my day, I could solve problems on those things thousands of times faster than I could on a digital minicomputer ( DEC PDP series ). However I never got the exact same answer on duplicate runs of the analog machine. It was the original "fuzzy logic"... the slightest bit of noise or variance in the analog logic and the answer would come out different.
Nothing is very concrete in the analog world.
Note that in an analog computer, all processes run in parallel. The basic functions - integration and differentiation - all took place realtime no matter how many of 'em I used... however I rarely used more than a couple of dozen. I seem to have literally millions of them running in me - each one running at only several Hz, but in parallel - they come up with answers about as fast as I remember my old analog machines solving simple nonlinear differential equations - and also like the old analog computer - the logic at which I arrive at conclusions is often quite fuzzy and is apt to give a wildly different result upon the slightest adjustment of the input parameters.
"Prove all things; hold fast that which is good." [KJV: I Thessalonians 5:21]
What's with the syntactically ambiguous title?
Is it
Silicon Brains That Think As Fast As (a Fly Can Smell)
or
Silicon Brains That (Think As Fast As a Fly) Can Smell
And are those flies time flies or fruit flies??
See On The Origin of Circuits:
"As predicted, the principle of natural selection could successfully produce specialized circuits using a fraction of the resources a human would have required. And no one had the foggiest notion how it worked."
"Dr. Thompson peered inside his perfect offspring to gain insight into its methods, but what he found inside was baffling. The plucky chip was utilizing only thirty-seven of its one hundred logic gates, and most of them were arranged in a curious collection of feedback loops. Five individual logic cells were functionally disconnected from the rest-- with no pathways that would allow them to influence the output-- yet when the researcher disabled any one of them the chip lost its ability to discriminate the tones. Furthermore, the final program did not work reliably when it was loaded onto other FPGAs of the same type."
"It seems that evolution had not merely selected the best code for the task, it had also advocated those programs which took advantage of the electromagnetic quirks of that specific microchip environment. The five separate logic cells were clearly crucial to the chip's operation, but they were interacting with the main circuitry through some unorthodox method-- most likely via the subtle magnetic fields that are created when electrons flow through circuitry, an effect known as magnetic flux. There was also evidence that the circuit was not relying solely on the transistors' absolute ON and OFF positions like a typical chip; it was capitalizing upon analogue shades of gray along with the digital black and white.'"
Dr. Thompson's publications seem to be difficult to find in free viewing form on the Internet, but the daminteresting article gives the gist of it: evolution will eventually make use of whatever characteristics are available to solve a problem.