Minds of their own

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Sep 5th 2008
From Economist.com

One day, a machine will outsmart its maker


IN ONE of William Gibson’s early mind-bending stories, the protagonist suddenly needs to fly a jump jet. In the cockpit, he finds his employer has thoughtfully stashed a biochip containing all the necessary piloting skills for him to plug into his own nervous system. While your correspondent applauded the idea at the time, he nevertheless dismissed it as pure science-fiction. Today, he’s not so sure.

The progress being made in neuroengineering—devising machines that mimic the way the brain and other bodily organs function—has been literally eye-opening. In the decade since Kevin Warwick, professor of cybernetics at Reading University in Britain, had a silicon chip implanted in his arm so he could learn how to build better prostheses for the disabled, we now have cochlear implants that allow the deaf to hear, and a host of other spare mechanical parts to replace defective organs.

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A mind of whose own?A bionic eye, to help people suffering from macular degeneration, is in the works, and artificial synapses are being tested as possible replacements for damaged optic nerves. An implantable electronic hippocampus—the world’s first brain prosthesis—is being developed for people who lose the ability to store long-term memories following a stroke, epilepsy or Alzheimer’s disease.

Meanwhile, a team at the University of Sheffield in Britain has built a “brainbot” controlled by a mathematical model of the brain’s basal ganglia—the part that helps us decide what to do next. Depending on how much simulated dopamine (the neurotransmitter in the brain that controls movement, behaviour, mood and learning) is dialled into the mathematical model, the brainbot responds differently.

Too much, and the machine has trouble suppressing unwanted actions, or tries to do two incompatible things at once—like patients with Huntington’s disease, Tourette’s syndrome or schizophrenia. Too little digital dopamine, and the machine has difficulty deciding how to move—like patients with Parkinson’s disease.

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Mr Warwick’s team at Reading has now gone a stage further. Instead of using a computer model of part of the brain as a controller, the group’s new “animat” (part animal, part material) relies solely on nerve cells from an actual brain.

Signals from a culture of rodent brain cells in a tiny dish are picked up by an array of electrodes and used to drive a robot’s wheels. The animat’s biological brain learns how and when to steer away from obstacles by interpreting sensory data fed to it by the robot’s sonar array. And it does this without outside help or an electronic computer to crunch the data.

This is not just a clever party trick. Such experiments are essential for understanding how the brain stores specific pieces of data—a crucial first step for helping people with degenerative disorders such as Alzheimer’s and Parkinson’s diseases.

Throughout history, engineers have spent their lives inventing machines that were faster, stronger, more reliable or capable of greater precision than human beings. Whether they were Jacquard looms, combine harvesters or CAD-CAM gear, they were tools for amplifying some human skill or compensating for a weakness. But always they needed human intelligence to function.

That’s now changed. Neuroengineers build tools that think for themselves, making decisions the way humans do.

What if the machines acquired too much of a mind of their own? In the search for solutions, even a modest PC can manipulate data 10m times faster than the human brain. Admittedly, humans can take certain heuristic short-cuts that save time, but it’s now over a decade since Deep Blue, an IBM supercomputer, defeated Garry Kasparov at chess. It did so, of course, by using its brute-force processing to predict, by trial and error, the course of a game up to 30 moves ahead; and then to compute which of the millions of possible moves would strengthen its own position best. What it could not do was devise its own strategies for playing a winning game.

There are machines around today, however, that can do just that. Over the past decade, a new technology known as “evolvable hardware” has emerged. Like traditional brute-force methods, evolvable machines try billions of different possibilities. But the difference is they then continually crop and refine their search algorithm—the sequence of logical steps they take to find a solution.

To do so, they rely on so-called “genetic algorithms”, which use trial-and-error learning to mimic natural selection. With each run of the genetic algorithm, the highest-scoring solutions are retained as “parents” for the next generation. Offspring solutions are created by swapping out portions of the parents’ blueprints, or by introducing some element of randomness to stir things up a bit—as happens in nature.

The evolvable concept, pioneered by Adrian Thompson at the University of Sussex in Britain, has led to some astonishing results. Dr Thompson’s original “proof of principle” experiment—a design for a simple analogue circuit that could tell the difference between two audio tones—worked brilliantly, but to this day no one knows quite why. Left to run for some 4,000 iterations on its own, the genetic algorithm somehow found ways of exploiting physical quirks in the semiconductor material that researchers still don’t fully comprehend.

Similarly, John Koza at Stanford University has been using genetic algorithms to devise analog circuits that are so smart they infringe on patents awarded to human inventors. Mr Koza’s so-called “invention machine” has even earned patents of its own—the first non-human inventor to do so.

How soon before machines become smarter than people? The way self-programming machines are evolving today suggests they will probably begin to match human intelligence in perhaps little over a decade. By 2030, they might look down on us—if we’re lucky—as endangered critters like the blue whale or polar bear and accept we are worth keeping around for our genetic diversity.

But what if visionaries like Mr Gibson are right, and we embrace the bionic future? With our plug-in bio-processors and learning modules, perhaps we’ll be able to outsmart the machines—or, at least, become indistinguishable from them.