I Thought I'd share this article with others that
may not have seen it. The reporter, Bill Softky seems to nail the
"intelligence" problem in a very unusual way by pointing out the
intelligence deficit in today's consumer robotic products. There
clearly is a cost trade off between intelligence and functionality, and
ultimately what the consumer pays for.
But more importantly, I believe he's stressing the point that no "real
intelligence" to date is being seriously worked on for consumer
robotics, despite the promises from technology over the last couple of
decades. And the central problem to that, is the fact that no "killer
app" has been designed or imagined to kick in the imaginations of
software engineers and designers. The home PC never really took off
until the advent of the Internet.
Steven R. Grimm
Robot brains? Can't make 'em, can't sell 'em
Why dopes still beat boffins
Page: 1 2 3 Next >
By Bill Softky → More by this author
Published Thursday 28th June 2007 09:02 GMT
The current generation of "consumer robots" is driven mostly by
robot-love: people enjoy things which move around on their own,
especially if they can build or tinker with the gadgets
themselves. That much became clear at a recent symposium on
Robots, which I described here last month. The consumer robot business
today is manned by avid tinkerers because there is neither a technology
for autonomous gadgets, nor a business model to support them even if
they did exist.
Robot bacterium?
At the symposium, your reporter posed the following question to the
panel:
"The three commercial presenters offer consumer products with
pre-programmed behaviors about equal to those of a bacterium. The
lone researcher demonstrated fancier computer vision, but it took a
dozen graduate students a year to develop, and is still extremely
simple and pre-programmed. When can we expect our robots to have the
sophistication, responsiveness, and robustness of - say - a mouse?"
No one answered the question, of course, but the most enlightening
response came from Colin Angle, CEO of iRobot (which manufactures the
autonomous vacuum-cleaning Roomba):
"The Roomba is actually very sophisticated: it has a multi-threaded
operating system, and was built by over a hundred computer scientist
and a dozen PhDs," he replied.
He's right, of course. The Roomba really is a sophisticated piece of
computer engineering - but sophistication by computer standards does
not translate to biological sophistication. I was tempted to
respond that bacteria are also multi-threaded - they can grow and
eat and reproduce and move all at the same time, too.
Unfortunately, Angle's PhDs have the unenviable task of reproducing in
silicon what Nature has spent a billion years on.
iRobot's Roomba is a great example of how very hard real-life robotics
is. The task for the disk-shaped rolling vacuum seems
simple: roam around a room, vacuum up dirt, and come back to the
dock in time to recharge. But to accomplish that task, the Roomba
needs infra-red locators and "virtual walls" spread around the room to
keep it from getting lost elsewhere in the house.
Perhaps the hardest task is to avoid "getting stuck": not just
physically getting wedged somewhere, but running in circles or
vacuuming the same region over and over. Merely detecting
"stuck-ness" from its sensor data required vast amounts of
trial-and-error programming, as did delineating how to recover.
Meanwhile, the iRobot corporation has been obliged to simplify the
hardware mercilessly, so that the whole package of
motors/wheels/vacuum/software is affordable - say below $200 - an
economising which leaves little room to develop sophisticated planning
and "intelligence."
Moore's Law for gears
Angle's clever lament on the business of building such gadgets -
"Moore's law doesn't apply to gears" - masks a deeper truth. What
he means is that mechanical or hardware costs have not dropped as fast
as chips, memory, and bandwidth, so that the robotic "industry" has not
had the same exponential growth as communications and computation. He
could also mean that selling physical gadgets entails much more than
simply assembling them; it means repairing them and offering warrantees
(an obligation that click-wrap software has wriggled out of), and even
ensuring the safety of customers from potential robots-run-amok.
The truth he didn't mention is that hardware is not the reason we have
no intelligent robots. In fact motors, sensors and even processors are
very cheap now, and a desktop computer core with a video input and a
few motorized wheels could be mass-produced for a few hundred dollars.
But the software to animate it is quite literally priceless, because it
doesn't yet exist. Worse, no one even knows the principles on which to
write it.
Here's why.
Missing the basics
Of course people can write software specialized for specific hardware
to to a specific task (like the Roomba), but such programs won't
generalize to new hardware, sensors, and environments: no one yet has
software which "learns" the way brains do, mostly because science
doesn't even know what brains do. If we don't understand how we (or
even mice) interact gracefully with an uncertain world, how could we
expect to program anything else to?
At every level, even specialists lack conceptual clarity.Let's look at
a few examples taken from current academic debates.
We lack a common mathematical language for generic sensory input -
tactile, video, rangefinder - which could represent any kind of signal
or mixed-up combination of signals. Vectors? Correlations? Templates?
Imagine this example. If one were to plot every picture from a live
video-feed as a single "point" in a high-dimensional space, a day's
worth of images would be like a galaxy of stars. But what shape would
that galaxy have: a blob, a disk, a set of blobs, several parallel
threads, donuts or pretzels? At the point scientists don't even know
the structure in real-world data, much less the best ways to infer
those structures from incomplete inputs, and to represent them
compactly.
And once we do know what kind of galaxies we're looking for, how should
we measure the similarity or difference between two example signals, or
two patterns? Is this "metric" squared-error, bit-wise, or probablistic?
Well, in real galaxies, you measure the distance between stars by the
usual Pythagorean formula. But in comparing binary numbers, one
typically counts the number of different bits (which is like leaving
out Pythagorus' square root). If the stars represented probabilities,
the comparisons would involve division rather than subtraction, and
would probably contain logarithms. Choose the wrong formula, and the
algorithm will learn useless features of the input noise, or will be
unable to detect the right patterns.
There's more: the stars in our video-feed galaxy are strung together in
time like pearls on a string,in sequence. but we don't know what kind
of (generic) patterns to look for among those stars -linear
correlations, data-point clusters, discrete sequences, trends?
Perhaps every time one image ("star") appears, a specific different one
follows, like a black car moving from left to right in a picture. Or
maybe one of two different ones followed, as if the car might be moving
right or left. But if the car is black, or smaller (two very different
images!), would we still be able to use what we learned about large
black moving cars? Or would we need to learn the laws of motion afresh
for every possible set of pixels?
The problems don't end there. We don't know how to learn from mistakes
in pattern-detection, to incorporate errors on-the-fly. Nor do we know
how to assemble small pattern-detection modules into usefully large
systems. Then there's the question of how to construct or evaluate
plans of action or even simple combinations of movements for the robot.
Academics are also riven by the basic question of whether self-learning
systems should ignore surprising input, or actively seek it out? Should
the robot be as stable as possible, or as hyper-sensitive as possible?
If signal-processing boffins can't even agree on basic issues like
these, how is Joe Tinkerer to create an autonomous robot himself? Must
he still specify exactly how many pixels to count in detecting a wall,
or how many degrees to rotate each wheel? Even elementary
motion-detection - "Am I going right or left?" - is way beyond the
software or mathematical prowess of most homebrew roboticists.
Calling engineer Einstein!
So the tinkerers can't do the math, and the boffins can't tinker. To
break that logjam we need an Einstein of engineering. He would be part
hacker, part statistician: a special blend of mathematical
genius, programmer, and tinkerer.
And hopefully a businessman too.
Unique among technologies, robotics faces an insidious competition:
live human beings. Almost every other revolutionary technology - steam
engines, air travel, telephones, computers - accelerated crazily as it
became better and better at doing what no human being could do, so even
the earliest prototypes offered commercial benefits and attracted
customers, reinvestment, and iterative improvement. The earliest
trains, while expensive, nevertheless moved faster than horses: and
that was enough to unleash the investment. But robotic brainpower
is different, because it competes with human brainpower; the
"robotish-ness" is precisely what humans are better at. The dumbest
human still sees, hears, and grasps better than the most expensive
robot.
A similar chicken-and-egg predicament long stymied solar energy:
large-scale investment made little economic sense while oil, coal, and
hydro power were much cheaper. Solar ultimately succeeded
in niche markets where it didn't compete with the mains;
autonomous robotics, likewise, needs a business application with no
hope of human intervention.
Perhaps some applications are on the way. The long-term goal of
Stanford Professor Sebastian Thrun, designer of a prize-winning robot
car, is a self-driving car which will save humans the trouble of
keeping their own eyes glued to the road for hours a day. Such robot
chauffeurs would form a great business, but they are still at least a
decade off. While today, they are possible only because the technology
is specifically tuned to the narrow task of road-driving with lasers,
radar, GPS, and other purpose-built sensors. A robot chauffeur would
not have a robot brain.
One high-profile businessman is working on real robot brains: Jeff
Hawkins, founder of Palm Computing, hopes his new venture Numenta Inc
will spur a business based on automatic, self-learning systems.
His system isn't robotic yet, but he champions a modular software
architecture and generic API templates for coders and customers, so
even if the initial algorithm sputters, it could be iteratively
improved without redesigning all the infrastructure.
Such interfaces are the best news in an otherwise stagnant field.
Toymaker Lego is in cahoots with Microsoft, vacuum-maker iRobot is
creating an open robotics platform, and the general trend is for
standard drivers, modular programming modules, and interlocking parts.
Once the algorithms are equally modularized, perhaps a new generation
of mini-Einsteins will build a prototype or discover a business
application that others can imitate and improve upon. Then we
might finally have real robots in place of promised ones. ®
