Machines with a human touch
Instead of using the ones and zeros of digital electronics to simulate the way
the brain functions, "neuromorphic" engineering relies on nature's biological
short-cuts to make robots that are smaller, smarter and vastly more
PEOPLE have become accustomed to thinking of artificial intelligence and
natural intelligence as being completely different-both in the way they work
and in what they are made of. Artificial intelligence (AI) conjures up images
of silicon chips in boxes, running software that has been written using human
expertise as a guide. Natural intelligence gives the impression of
"wetware"-cells interacting biologically with one another and with the
environment, so that the whole organism can learn through experience. But that
is not the only way to look at intelligence, as a group of electronics
engineers, neuroscientists, roboticists and biologists demonstrated recently
at a three-week workshop held in Telluride, Colorado.
What distinguished the group at Telluride was that they shared a wholly
different vision of AI. Rather than write a computer program from the top down
to simulate brain functions, such as object recognition or navigation, this
new breed of "neuromorphic engineers" builds machines that work (it is
thought) in the same way as the brain. Neuromorphic engineers look at brain
structures such as the retina and the cortex, and then devise chips that
contain neurons and a primitive rendition of brain chemistry. Also, unlike
conventional AI, the intelligence of many neuromorphic systems comes from the
physical properties of the analog devices that are used inside them, and not
from the manipulation of 1s and 0s according to some modelling formula. In
short, they are wholly analog machines, not digital ones.
The payoff for this "biological validity", comes in size, speed and low power
consumption. Millions of years of evolution have allowed nature to come up
with some extremely efficient ways of extracting information from the
environment. Thus, good short-cuts are inherent in the neuromorphic approach.
At the same time, the electronic devices used to implement neuromorphic
systems are crucial. Back in the 1940s, when computers were first starting to
take shape, both analog and digital circuits were used. But the analog devices
were eventually abandoned because most of the applications at the time needed
equipment that was more flexible. Analog devices are notoriously difficult to
design and reprogram. And while they are good at giving trends, they are poor
at determining exact values.
In analog circuits, numbers are represented qualitatively: 0.5 reflecting,
say, a voltage that has been halved by increasing the value of a resistor;
0.25 as a quarter the voltage following a further increase in resistance, etc.
Such values can be added to give the right answer, but not exactly. It is like
taking two identical chocolate bars, snapping both in half, and then swapping
one half from each. It is unlikely that either of the bars will then be
exactly the weight that the manufacturer delivered.
One of the contributions of the father of the field-Carver Mead, professor
emeritus at the California Institute of Technology in Pasadena-was to show
that this kind of precision was not important in neural systems, because the
eventual output was not a number but a behaviour. The crucial thing, he
argued, was that the response of the electronic circuits should be
qualitatively similar to the structures they were supposed to be emulating.
That way, each circuit of a few transistors and capacitors could "compute" its
reaction (by simply responding as dictated by its own physical properties)
instantly. To do the same thing, a digital computer would have to perform many
operations and have enough logic gates (circuits that recognise a 1 or a 0)
for the computation. That would make the device not only slow and
power-hungry, but also huge and expensive. For a fuller account of Carver Mead
and his unique contribution to the whole of information technology, see this
Another advantage of the analog approach is that, partly because of their
speed, such systems are much better at using feedback than their digital
counterparts. This allows neuromorphically designed machines to be far more
responsive to their environment than conventional robots. In short, they are
much more like the biological creatures they are seeking to emulate.
One of the many projects demonstrating this concept at the Telluride meeting
was a robot that could drive in straight lines-thanks to electronics modelled
on the optic lobe in a fly's brain. The vision chip, built by Reid Harrison at
the University of Utah in Salt Lake City, is a "pixellated" light sensor that
reads an image using an array of individual cells, with additional circuitry
built locally into each cell to process the incoming signals. The fact that
these processing circuits are local and analog is crucial to the device's
operation-and is a feature that is borrowed from the biological model.
Dr Harrison and his supervisor at Caltech and co-founder of the Telluride
summer school, Christof Koch, identified the various processes taking place in
the so-called lamina, medulla and lobular-plate cells in a fly's brain as
being worth implementing in silicon. These cells form a system that allows the
fly to detect motion throughout most of its visual field-letting the insect
avoid obstacles and predators while compensating for its own motion.
In the chip, special filters cut out any constant or ambient illumination, as
well as very high frequencies that can be the source of electronic noise in
the system. The purpose is to let the device concentrate on what is actually
changing. In a fly's brain, this filtering role is played by the lamina cells.
In a fly's medulla, adjacent photodetectors are paired together, a time delay
is introduced between the signals, and the two are then multiplied together.
The length of the delay is crucial, because it sets the speed of motion that
the detector is looking for. In the chip, since the delay and the distance
between the two adjacent photo-diodes are known, the speed of an image moving
over the two detectors can be determined from the multiplier output. Large
numbers of these "elementary motion detectors" are then added together in the
final processing stage. This spatial integration, which is similar to that
performed in a fly's large lobular plate cells, ensures that the broad sweep
of the motion is measured, and not just local variations. The same kind of
mechanism for detecting motion is seen in the brains of cats, monkeys and even
To prove that the chip not only worked, but could be useful, Mr Harrison
attached it to a robot that had one of its wheels replaced by a
larger-than-normal one, making it move in circles. When instructed to move in
a straight line, feedback from the vision chip-as it computed the unexpected
sideways motion of the scenery-was fed into the robot's drive mechanism,
causing the larger wheel to compensate by turning more slowly. The result was
a robot that could move in a straight line, thanks to a vision chip that
consumed a mere five millionths of a watt of power.
For comparison, the imaging device on NASA's little Sojourner Rover that
explored a few square metres of the Martian surface in 1997 consumed
three-quarters of a watt-a sizeable fraction of the robot's total power. The
image system that helps make the "Marble" trackball developed by Logitech of
Fremont, California, a handy replacement for a conventional computer mouse,
takes its cue likewise from a fly's vision system. In this case, the
engineering was done mainly by the Swiss Centre for Electronics and
Microtechnology in Neuchatel and Lausanne.
The concept of sensory feedback is a key part of another project shown at the
Telluride workshop. In this case, a biologist, robotics engineer and
analog-chip designer collaborated on a walking robot that used the principle
of a "central pattern generator" (CPG)-a kind of flexible pacemaker that
humans and other animals use for locomotion. (It is a chicken's CPG that
allows it to continue running around after losing its head.) Unlike most
conventional robots, CPG-based machines can learn to walk and avoid obstacles
without an explicit map of their environment, or even their own bodies.
The biological model on which the walking robot is based was developed in part
by Avis Cohen of the University of Maryland at College Park. Dr Cohen had been
studying the way that neural activity in the spinal cord of the lamprey (an
eel-shaped jawless fish) allowed it to move, with the sequential contraction
of muscles propelling it forward in a wave motion. The findings helped her
develop a CPG model that treated the different spinal segments as individual
oscillators that are coupled together to produce an overall pattern of
activity. Tony Lewis, president and chief executive of Iguana Robotics in
Mahomet, Illinois, developed this CPG model further, using it as the basis for
controlling artificial creatures.
In the walking robot, the body is mainly a small pair of legs (the whole thing
is just 14cm tall) driven at the hip; the knees are left to move freely,
swinging forward under their own momentum like pendulums until they hit a stop
when the leg is straight. To make the robot walk, the hips are driven forwards
and backwards by "spikes" (bursts) of electrical energy triggered by the CPG.
This robot has sensors that let it feel and respond to the ground and its own
body. Because outputs from these sensors are fed directly back to the CPG, the
robot can literally learn to walk.
The CPG works by charging and discharging an electrical capacitor. When an
additional set of sensors detect the extreme positions of the hips, they send
electrical spikes to the CPG's capacitor, charging it up faster or letting it
discharge more slowly, depending on where the hips are in the walking cycle.
As the robot lurches forward, like a toddler taking its first steps, the next
set of "extreme spikes" charge or discharge the capacitor at different parts
of the cycle. Eventually, after a bit of stumbling around, the pattern of the
CPG's charging and discharging and the pattern of the electrical spikes from
the sensors at the robot's hip joints begin to converge in a process known as
"entrainment". At that point, the robot is walking like a human, but with a
gait that matches the physical properties of its own legs.
Walking is only the start. Mr Lewis has endowed his robot with an ability to
learn how to step over obstacles (see photo, top). It does this by changing
the length of the three strides before the object, using miniature cameras as
eyes, and the same kind of interaction with the CPG that it uses to
synchronise its hip movement for normal walking.
The interesting thing is that the obstacle does not have to be defined in any
way. It appears simply as an unexpected change in the flow of visual
information from the cameras that the robot uses to see with. This makes the
technique extremely powerful: in theory, it could be applied to lots of other
forms of sensory input. Another factor that makes this project impressive is
that its key component-the CPG chip, designed by Ralph Etienne-Cummings of
Johns Hopkins University in Baltimore, Maryland-consumes less than a millionth
of a watt of power.
The efficiency of CPG-based systems for locomotion has captured commercial
attention. For the first time, parents can now buy their children analog
"creatures", thanks to Mark Tilden, a robotics expert at Los Alamos National
Laboratory in New Mexico. Hasbro, one of America's largest toy makers, is
marketing a product called BIO Bugs based on Dr Tilden's biomechanical
--- --- --- --- ---
Useless hypotheses, etc.:
consciousness, phlogiston, philosophy, vitalism, mind, free will, qualia,
analog computing, cultural relativism, GAC, Cyc, Eliza, cryonics, individual
uniqueness, ego, human values, scientific relinquishment
We move into a better future in proportion as science displaces superstition.
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