Forwarded by Negativland.
>
>
>
>MACHINES WITH MINDS OF THEIR OWN
>
> Left to evolve on their own, certain
>
> machines can learn to be smarter-
>
> surpassing even humans in some
>
> of the most intellectually demanding
>
> of tasks
>
>
>Mar 22nd 2001
> From The Economist print edition
>
>http://www.economist.com/science/tq/displayStory.cfm?Story_ID=539808
>
>
>
>CAN people build machines capable of evolving into something
>better-able, perhaps, to invent solutions beyond human imagination?
>Using brute-force methods of calculation, computers can nowadays play
>a passable game of chess. In 1997, an IBM supercomputer called Deep
>Blue defeated Garry Kasparov. The world champion described the
>experience as being every bit as gruelling as playing a top-notch
>human challenger. In so doing, Deep Blue satisfied at least one of
>the criteria for artificial intelligence set in the 1950s by Alan
>Turing, the mathematical genius behind the Enigma code-breaking
>effort in wartime Britain.
>
>Yet Deep Blue's victory left the world's artificial-intelligence
>community unimpressed. That was because the machine performed its
>feat merely by crunching numbers faster than any other computer had
>managed before. Its enormous processing power enabled it to predict a
>game's possible course up to 30 moves ahead, while its clever
>programming allowed it to work out which of the millions of possible
>moves would strengthen its position best. On its own, all that Deep
>Blue could do-and do brilliantly-was the mathematics. What it could
>not do was devise its own strategies for playing a game of chess.
>
>
>Self improvement
>
>But what if Deep Blue could have been given the ability to evolve and
>learn to improve itself using its trial-and-error experiences? A new
>technology called "evolvable hardware" (EHW) attempts to do just
>that. Like Deep Blue, EHW seeks solutions through trying billions of
>different possibilities. The difference is that, unlike Deep Blue,
>EHW continually crops and refines its search algorithm-the sequence
>of logical steps it takes to find a solution. It selects the best
>each time and tries that. And it does all this on its own accord, not
>according to some programmed set of instructions.
>
>Conventional wisdom has long held that a machine's abilities are
>limited by the imagination of its creators. But over the past few
>years, the pioneers of EHW have succeeded in building devices that
>can tune themselves autonomously to perform better. In some cases,
>the mechanical progeny appear to outstrip even their creators'
>abilities. In the field of circuit design, for instance, EHW is
>coming up with creative solutions to problems that have defied human
>beings for decades.
>
>The first thing EHW needs is for the hardware in question to be
>reconfigurable. There is no way that a device can evolve if it cannot
>change its shape or way of doing things. Take a Swiss Army knife.
>Given the task of, say, opening a bottle, the user identifies the
>correct tool in the knife, opens it, and thereby transforms the
>device into an implement that can pry off a bottle cap.
>
>In this case, the actual customisation is crude: no matter what the
>size and shape of the bottle cap, the shape of the bottle-opener does
>not alter. For a Swiss Army knife, the "program" (the decision about
>which implement to use) can be adapted, but the "hardware" (the
>bottle-opener) cannot. What EHW engineers are trying to do is invent
>a knife that can customise its shape to any bottle cap-and perform
>this adaptation on its own recognisance.
>
>The trick with evolvable hardware lies in creating a device that
>knows how to make the correct structural adaptation at the correct
>time. To search out the best-suited design, engineers make use of a
>programming tool called a "genetic algorithm"-a software technique
>that deploys trial-and-error learning to mimic the process of natural
>selection that powers evolution in the living world.
>
>The first step that a genetic algorithm takes is to generate a set of
>random blueprints which are used, one by one, to configure the
>device. After each reconfiguration, the device is tested to see how
>well (or otherwise) it carries out the desired task. The
>highest-scoring designs are retained as guidelines ("parents") for a
>new generation of designs. These "offspring" designs are created by
>swapping portions of the parents' blueprints with one another, or by
>making some random changes. This marginally improved population of
>designs then undergoes further testing, and the cycle then repeats
>itself until the device achieves an optimal level of performance.
>
>The target could be determined right at the beginning of the device's
>operation or it could be adjusted continually. Either way, the device
>alters its structure to perform the task at hand in the best way
>possible. In the case of the Swiss Army knife, it would work out what
>shape to morph into on its own and leave its "processor" (the user's
>brain) free to address other matters.
>
>Today, it is possible to contain the entire genetic
>algorithm-blueprint creation, fitness evaluation and
>reconfiguration-within a single microchip, and to run thousands of
>evolutionary trials in a fraction of a second. Although they were
>invented some 30 years ago, genetic algorithms have hitherto been run
>generally in software, where they placed a large and often
>prohibitive burden on the processor's time. EHW avoids this problem
>by running its genetic algorithms in hardware.
>
>That is the crucial difference. In any digital device, wiring
>instructions into the actual hardware, rather than running them as
>part of the software, invariably boosts the speed of operation. In
>EHW, the speed advantage is so significant that the genetic algorithm
>for problems that could not have been solved in software can be
>cracked in real time-ie, with the solutions being produced as fast as
>the problems are fed in. This speed and flexibility makes EHW ideal
>for handling situations that vary rapidly.
>
>
>Designers, resign
>
>The most notable application of EHW so far is in the design of
>analogue circuits. While digital devices have become ubiquitous, they
>still have to communicate with the real world-and the real world
>remains stubbornly analogue. The fact is that people do not talk,
>hear, see, touch and taste in the ones and zeros of digital
>computerspeak. Analogue circuits are needed to measure or produce the
>wave-like signals of light, sound or temperature. Other analogue
>circuits known as A-D and D-A converters are needed to translate
>these continuous wave-like signals to and from the discrete language
>used by digital devices. Analogue circuitry is thus an essential part
>of the sensors, receivers and display units that play such a vital
>role in the modern wireless world.
>
>It is no surprise that, with so much emphasis on digital circuitry
>these days, the design of analogue devices is becoming a serious
>problem. First, coming up with an efficient analogue circuit has as
>much to do with instinct as with physics. John Koza of Stanford
>University in California claims that analogue-circuit design is the
>domain of engineers "off in a room wearing purple hats with gold
>stars." Second, engineers with the necessary skills are in short
>supply. Texas Instruments, for instance, needs to recruit 500
>analogue engineers a year-more than the number that graduate from all
>the universities in America.
>
>A third problem is that even when a good circuit architecture is
>conceived, a large proportion of the devices fabricated turn out to
>be defective. In order to make a complicated job manageable,
>designers of analogue devices tend to assume that the components used
>in their circuits work in a uniform and predictable manner.
>
>In the real world, however, environmental factors such as temperature
>and humidity can cause the electrical properties of a micro-circuit's
>resistors and capacitors to vary by as much as 20%. Such
>discrepancies matter far less in digital circuits, which simply have
>to detect whether an electrical current is more or less on or off.
>But such variations in analogue circuits can render them unusable.
>For instance, a cellular telephone will not work properly if its
>analogue filter allows the transmission frequency to vary by more
>than 1%. Until now, designers of analogue chips have tried to
>circumvent the problem by using larger components whose physical
>properties are more easily measured and controlled. Unfortunately,
>that leads to bulkier circuits that gobble power.
>
>Tetsuya Higuchi and his colleagues at the Electro-Technical
>Laboratory in Japan have solved the stability problem by using EHW to
>accommodate the natural variations that occur between the components.
>His team use genetic algorithms to tweak the irregular analogue
>circuit components until they conform to the design specification. By
>testing the performance of each chip, the algorithm evolves an
>architecture that can adjust automatically for all the variations in
>its resistors and capacitors. The group has found that 95% of
>analogue chips can eventually be coaxed into acceptable performance.
>That is a higher yield than most digital chip plants achieve. Dr
>Higuchi expects the first cellular telephones exploiting evolutionary
>hardware to be on the market by next September. Output of such EHW
>chips will then be running at hundreds of thousands per month.
>
>
>Machines that invent
>
>But it is the work done by Dr Koza at Stanford that gives a real
>glimpse of the future. By running genetic algorithms on analogue
>circuits that have been simulated in a computer, Dr Koza's machines
>have already produced seven circuit designs that he calls
>"human-competitive" because they infringe on patents previously
>issued to human inventors. Currently, each circuit design costs
>around $10,000 to simulate, which means that it is still cheaper to
>do the job manually. But as Dr Koza points out, processing power is
>becoming less expensive all the time, while human designers are
>becoming scarcer to find and costlier to keep. Dr Koza is optimistic
>that, given time, a design for a wholly novel and commercially viable
>circuit will emerge from his "invention machine".
>
>While Dr Koza's simulated circuits are recognisable variations on
>human inventions, Adrian Thompson of Sussex University in Britain has
>evolved a circuit that is literally incomprehensible. Four years ago,
>Dr Thompson performed a seminal "proof of principle" experiment which
>described the evolution in hardware of a simple analogue circuit that
>could discriminate between two different audio tones.
>
>The type of chip that Dr Thompson selected to carry out the evolution
>was a field-programmable gate array (FPGA). Unlike an ordinary chip,
>an FPGA's architecture is not "hardwired". Instead of being fixed, a
>string of bits specifies the chip's design by telling it what
>linkages to forge between its various components (in this case,
>groups of transistors known as logic cells). By changing this bit
>string, the FPGA's circuitry can be altered on the fly. Thus, when a
>genetic algorithm runs on the chip, the effectiveness of each
>configuration can be measured directly on the circuit rather than in
>some costly simulation.
>
>As it turned out, conducting the evolution in hardware produced some
>results that could not have emerged through mere simulation. After
>around 4,000 generations of bit strings, a unique circuit emerged.
>The surprising thing was that, while the new circuit relied directly
>on only a few of the FPGA's logic cells, it appeared somehow to take
>advantage of clusters of other cells nearby. These unconnected
>neighbouring cells could not be removed without damaging the
>circuit's performance. Further investigations revealed that these
>detached cells exerted some subtle electromagnetic influence on the
>wired-up part of the circuit, allowing it to perform its task
>efficiently.
>
>Remarkably, the circuit had adapted itself in a way that allowed it
>to exploit the underlying physics of the FPGA's semiconductor
>material. And it had done this despite the fact that the human
>experimenters were completely unaware of the physical quirks in the
>semiconductor that the genetic algorithm was taking advantage of.
>Four years on, this bizarre circuit has still not been completely
>deciphered. What has become clear, however, is that EHW's ability to
>adapt automatically means that it can exploit the physics of
>materials in ways that researchers do not even consider, let alone
>understand.
>
>Beyond the realm of analogue and digital electronics lie all manner
>of unconventional physical systems-including the microscopic world of
>nanotechnology and quantum dots-where there are no well-developed
>design rules. By testing layouts that would never occur to humans,
>EHW can capitalise on the physical properties of these unconventional
>materials-even when engineers cannot fully account for their
>behaviour.
>
>It may seem ironic that the direction being taken with evolvable
>hardware speaks so eloquently of the ignorance of the human
>architect. But, then, the use of evolution in design is really an
>admission that researchers have not as yet found anything better.
>Over the next few years, evolutionary machines could show humans the
>way.
>
>
>
>________________________________________
>
>DANIEL J LYNCH
>Partner, Creative Director
>Sp3d, Inc.
>Media Design & Production
>
>http://www.sp3d.com
>danATsp3d.com
>(415) 864-3302 - VOX
>(415) 864-3402 - FAX
>
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