Researchers at MIT have developed the first integrated-circuit vocal tract, which could eventually make it's way into high-end PDAs. It's biologically inspired and combined with a bionic-ear processor in a feedback loop. This means it can not only be used for producing speech but also recognizing it: the vocal tract can help to model what the ear thinks it is hearing to verify whether it's likely to have it right or not.
Essentially, the system is a biological model of our own method of producing speech that has been implemented in silicon. It is not just good at synthesizing speech but (in conjunction with the ear/feedback) but in interpreting it because it can literally figure out what muscles etc. would have to be used to produce a particular sound and so can 'reverse engineer' what sound was actually intended. F and S may sound similar, but the way the sounds are produced in terms of muscles are quite different. So if you're able to get into the physiology from small differences in what you hear then you can make much better guesses at what's being said.
The chips used (one for the vocal tract and one for the ear) are both based on human have been implemented in custom analog circuitry. Digital computers can be used, but the computational complexity of the problem means that the analog solutions are drastically smaller, faster, and less power-hungry.
There are a number of really interesting commercial applications. The most obvious is robust speech/speaker/language recognition in noisy environments, but they are also building a glove that can drive the chip and a brain machine interface that can be implanted in the brain for speech impaired subjects, and are building muscle interfaces that would allow silent phone calls (you talk silently on the train, the system figures out what sounds you are trying to make and makes them for you down the phone).
I was at a conference about humanoid robotics, and particularly the iCub, yesterday and Mark Lee from the University of Aberystwyth was talking about the difficulties of 'raising' truly developmental robots: robots that learn about their bodies and environments through experience the way we do. This got me thinking.
Being an analog girl at heart, and given that robots have a lot of essentially analog components (even if they are driven with digital controllers), I'd always assumed that truly intelligent humanoids would have to be raised developmentally. Each individual would have to learn about it's unique set of motors and sensors and processors and what they could do and how they could interact with the world before they would be able go out and do things. Now I wonder if it has to be as drastic as that.
My thinking now is that the question turns on just how different each robot will be to its 'siblings': robots turned out in the same batch and that are (at least intended to be) identical. There are bound to be subtle differences because of manufacturing tolerances, but perhaps these don't matter so much for digitally-driven machines. After all, a robot has to be able to cope if some of its components fail or change their performance (for instance) due to changes in temperature. So perhaps we could clone the 'brains' of one robot and successfully transplant it into another, which would just wake up feeling a bit out of sorts and have to re-optimize.
If that's true, it brings up a lot of interesting questions. Is the best idea is to focus energy on the development of a single machine to clone, or to raise a whole bunch of robots to a certain point and then, in a natural-selection-type way, clone the best brains and ditch the rest? Perhaps the latter would be the best way to learn the best teaching methods for robots? And at what point in their development should they be cloned? Presumably you don't want to have to send a 'baby' robot to each new workplace so that it can learn about it's new environment and tasks at the same time as it's learning how to see and control its actuators from scratch. On the other hand you want it to have plenty of scope for adapting to its new environment...
I wonder.
Photo: Yan Wu working with the Imperial College London iCub.
If you've been following my analog posts you'll know that one of my concerns has been that we don't train enough engineers who are really comfortable working in this area.
Analog has two major problems: not only is it just generally more difficult to design (much harder maths!), but once you do you have to go and have a chip fabricated (i.e. spend time and money) to see how it works in practice. Digital designers have an easier job to begin with, better tools, and can reliably simulate using systems like field-programmable gate arrays (FPGAs) if they'd rather not work in software simulation alone. Plus there are a gazillion of them, which also helps them to make progress!
If you're interested in building brains into machines, this matters because analog technology seems to be the most appropriate (in terms of both power and behavior) in which to implement artifical neurons that behave in biologically-plausible or -inspired ways. This is basis of neuromorphic engineering.
Although not a new idea, field programmable analog arrays (FPAAs) may be one way of making possible both the rapid prototyping of chips and rapid training of students. Paul Hasler and his colleagues at Georgia Tech have been working on both improving the FPAA technology itself and the interfaces that designers can use with them. If you're interested in this, please check out my recent article in EE Times on the subject.
Picture: Paul Hasler and PhD student Csaba Petre demonstrate the interface to their FPAA chip. Photo by Gary Meek.
One of the many things that have kept me from my blog in the last month or two has been working on a new progression of a newsletter I edit called The Neuromorphic Engineer. The new format is is more accessible, searchable, and generally usable, plus it allows for different kinds of content including blog posts. I've put a few of my own posts up as well as all the old newsletter archives, and there will be new content every 2-4 weeks.
If you're interested in how people are trying to build technology that emulates the neural systems of various animals (particularly, but not exclusively, using analog technology) then check this out. In general the articles are more technical than my blog, but less technical than journal papers. I'd really like to hear what you think and any suggestions you may have.
While I was at Johns Hopkins University during the summer, I found out about the first demonstration of a new chip that can be used to stimulate locomotion in an animal (tested on a temporarily-paralyzed cat, see right). Unlike previous controllers, this one is tiny and low-power. However, it can still take account of the sensory input coming from the movement of the limbs through a tiny neural network.
I find the work very interesting and, potentially, extremely important. Rather than explain it here, I recommend you check out the story I wrote for EE Times on the subject. Let me know what you think.
Figure: The schematic of the experiment showing locomotion stimulated by a central pattern generator (CPG) chip. For more details of the electronics, click on the picture.
Although I've no doubt that digital computing will be crucial to the development of intelligent robotics, one of my interests is in the other—often neglected—technologies that will also be vital to making it happen. One of these is mechanics. The video shown here (top) is Domo, one of the latest robots being developed at the MIT Computer Science and Artificial Intelligence Laboratory. As well as incorporating many new ideas, this robot builds on one concept that was developed at the AI lab a decade ago: the idea of compliant limbs.
Matthew Williamson, an earlier PhD student of Rodney Brooks, worked on the arms for Cog in the mid to late 1990s. As you can see in the animated image (middle) this robot saws in what looks like a natural way. This is clever because, unlike the robot arms you see in car manufacturing plant (or on the Honda robot, ASIMO, for that matter), the limbs to not work by calculating exactly where they are supposed to be at every moment. Instead, they have some give (compliance), provided by springs in the arm structure. This not only makes the robots safer because they push back, but also means that the robot need not 'micromanage' it's arm to the degree that it would have to otherwise. It can count on the push/pull of the springs to do some of the work for it.
An even more obvious example of how physics can help is with locomotion. In the late 1990s, Andy Ruina, developed a walking robot with his team at Cornell University. This may not seem very impressive: it's certainly not the first. However, it is completely passive: it has no 'brain' of any kind, it simply walks due to its structure and the laws of physics as you can see in the video (bottom). Essentially, the legs below the knee act as pendula, with the knee itself stoping them from swinging forward. A little potential energy (the slope) is all it needs for a fairly convincing walking gait.
Though the programming side of robotics is often thought to be the more glamourous, getting your hands dirty in the machine shop can make even more of a difference to the efficiency of the machine.
Mark Tilden changed my life. In about 1998 I started to become interested in analog computing for intelligence and came across a paper called Living Machines Mark wrote with Brosl Hasslacher a few years earlier. In it they talked about analog electronic creatures that were were very different to any other robots I had seen before. The 'nervous networks' that drove them were made of very few transistors, capacitors, and resistors—dozens rather than hundreds—and yet they, together, performed a rich, natural, and robust set of behaviors. The sun-seeking robots were even being used for interesting applications like satellite guidance and mine-clearing. It was a great story and it helped me understand what was important about intelligence in a way I hadn't before.
What was more important, however, was that researching this piece made me more sensitive to other analog stories and introduced me to the community of neuromorphic engineers: people who try to build in silicon circuits that emulate biological neurons. A case in point was Reid Harrison's work on fly vision at Caltech. Reid, I discovered, was part of a movement started by Carver Mead, who showed in his groudbreaking book that analog circuits could do the same kind of functions that biological neurons did, and at the same time would consume many orders of magnitude less power than digital circuits. I started to get sucked into the field.
About two years later, in 2001, I got the opportunity to attend the Telluride Neuromorphic Engineering Workshop in Colorado: a critical experience in my life. There I learned about floating gates and address-event representation (which I'll get to in later posts), that I could climb from 8000 to 12000 feet in a couple of hours without dying, and that scientists and engineers are not the same. I also got to see the construction of the very first prototype of Mark Tilden's Robosapiens (pictured). I was so inspired that I offered to start a newsletter covering the
field, which is produced on behalf of the Institute of Neuromorphic
Engineering.
Although I, of course, have written about the movement (and will be writing a lot more about it in this blog), I'm amazed at how few people who claim to be interested in artificial intelligence are aware of it. This community has built robots that respond to the calls of the opposite sex, can see using tiny vision chips and optical flow, that have bat-like hearing, that walk using the same kind of internal oscillations that keep a chicken running after it's head's been chopped off.
My theory is that neuromorphic engineering is still such a minority pursuit because it requires people to actually build things (rather than just program them). You have to solder and send chips off to be fabricated and all sorts of expensive and time-consuming things that really make getting your PhD finished difficult: you don't just type and run. Plus you have to do the maths related to analog electronics, which is not for the faint hearted. The 'basic' analog VLSI course I had to do as part of my time at the workshop was well beyond me and my physics degree. In fact, companies are having difficulty recruiting analog engineers because so few bother to train.
Anyway, though small in numbers they're doing some very interesting work. To show some of it off without rambling on for much longer here, below is a little video of the last Telluride event I attended (2005), and the test of some of their robots at the end of the three-week workshop You'll see robots navigating around a maze. Some are very intelligent, some are not (just dumb toys designed to show that the maze can't be navigated by accident). They use vision, sonar, feelers, and even smell as triggers to move around. And then there's Audio Sapiana (I think that's her name), irresistably drawn to the voice of her mate...
Illustrations
Top: This robot is driven by a circuit based on the nervous system of a lobster.
Middle: The first prototype for Robosapiens, built at the Telluride Neuromorphic Engineering Workshop in 2001.
Bottom: Telluride 2005 video by Stuart Arnott of Red Planet.
When I discuss analog computation with most people, their instant reaction is to explain to me that there is no such thing as analog. For instance, one of the comments on an earlier post I wrote on analog vision pointed out that using a film-based camera rather than a digital camera doesn't give you infinite resolution, just a higher, finite resolution. In this case the the limit is grains of silver on the film rather than the number of pixels on a camera array. This is true, but it also misses the point.
One of the main differences between physical computation (the slightly extreme form of analog I discussed in my PhD thesis) and digital computation is that it is good at extracting meaning out of tiny fluctuations. Digital computing, for very good reasons, is designed so that small 'glitches' that could lead a calculation astray are completely ignored. This is great when doing maths, but not when processing signals.
Let me give a concrete example from a piece of research done a few years ago by Dana Anderson and his colleagues at the University of Colorado: a classic example, in my view, of how analog feedback can be exploited to pull signal from noise. Essentially the system (shown) performs a winner-take-all operation to solve the so-called 'cocktail party' problem where lots of noise (literally sounds in this case) overlaps making it impossible to hear what is going on. In his system, one of the components (one of the sounds) will dominate,
extinguishing the rest.
Note: this system is really complicated optically, so don't feel bad if you don't understand my explanation on the first pass.
First,
the sound signals are processed (you can see the details one of their publications on the subject or on their website) and then used to vary the output of an array of laser beams (so the light carries the sound). These beams create a holographic
pattern in the photorefractive medium, a crystal that changes the way it bends light when it is exposed to photons of a particular color. This pattern is then read out by another beam (called the 'loop' beam because of the shape of the circuit) that, as well as picking up information, helps strengthen the hologram where the patterns are similar, and weaken it where the patterns are not.
The loop beam is then made to interfere with a second beam with a similar but not identical frequency. This produces beats (where two high-frequency signals, mixed, produce a frequency that is the difference between the two). This new oscillation is low enough to be picked up by a detector. After being filtered and
amplified, this signal is used to modulate the phase of the loop beam, altering the elements of the hologram that are strengthened and weakened by it.
In English, the loop beam initially reads out a
hologram containing all of the signals. Since one is the strongest
(even if only by a tiny amount) it has
slightly more impact on the beam, and so also on the output of
the detector. The feedback loop is then tweaked to enhance this
effect. This feedback continues until the strongest signal becomes the only signal (winner takes all). You can actually hear this happening in their system if you use the player below the diagram.
We know we have winner-take-all circuits in the eye, so this kind of feedback is not just clever engineering, but biologically sound too. Even the tiniest hint of what's important can be leveraged into fuller knowledge. Of course, you can do feedback with digital too, but that would be silly: essentially you would be asking it to do something it was designed not to be good at. And, as I will discuss in a future post, you will pay a huge power penalty for your trouble.
Figure: This holographic systems pulls out individual sounds from noise using feedback. Click the image to hear this happen.
As well as explaining evolution (see my last post), analog feedback may explain the ability of deaf people to hear through their bodies as the percussionist Evelyn Glennie learned to do. According to her biography,
Evelyn spent a lot of time when she was young (with the help of Ron Forbes her percussion teacher at school) refining her ability to detect vibrations. She would stand with her hands against the classroom wall while Ron played notes on the timpani (timpani produce a lot of vibrations). Eventually Evelyn managed to distinguish the rough pitch of notes by associating where on her body she felt the sound with the sense of perfect pitch she had before losing her hearing. The low sounds she feels mainly in her legs and feet and high sounds might be particular places on her face, neck and chest.
This is pretty amazing. What her brain managed to do was to take physical structures in her body that were not 'designed' or evolved to be sensors, but use them that way anyway.
This is only possible because we are analog systems and physical objects. We cannot help but feel vibrations: our bones and chest cavity have natural frequencies at which they will vibrate. These vibrations cannot help but be passed on by our nervous system. Of course, we normally ignore these sensations. But our brain can, if necessary, focus in on these 'signals' and use them to make sense of the world.
In a digital system there would almost certainly be no such path. No engineer would design a leg based on the idea that it might one day have to be used for something bizarre like hearing. And even if one did put some vibration sensors on the leg, for some other purpose, the digital threshold might well be too high for the relevant information to get through.
One of the disadvantages of analog systems, its sensitivity to noise, is also an advantage. Noise is mostly just stuff you didn't think you wanted to measure, but got stuck with anyway. Usually it's a pain. But when you suddenly discover that there is good information hidden in the noise then you still have access to it: unlike in digital systems where you've already thrown it away by setting a digital threshold that turns a 0.78 (and a 0.563, and a 0.6724) into a uniform 1.
One of the things that has driven my work over the last decade has been an interest in analog systems (which I alluded to in an earlier post) that perform what I call physical computation. What that means is that they are not ‘programmed’ except in the sense that, like all objects, they are forced to obey the laws of physics. When physical objects become sufficiently complicated, then they start to behave in interesting ways while still just doing what comes naturally.
That’s interesting because biological organisms fall into this class of physical or analog machines.
Not everone understands this. Many people mistakenly assume that because information is transmitted through the brain by uniform pulses of electricity, that this means the brain is like a digital computer. Not so. In fact they brain is a much more complex machine, where the time between pulses (which can vary continuously and so is analog) carries the information, not the pulses themselves.
But, from my point of view, what makes analog systems special is that they do not, like most digital systems, have to be designed for a specific purpose in order to be useful. In fact, analog systems can be very bad indeed at particular tasks but get better through feedback, because even the smallest trace of a useful function can be exploited. This is what makes them ideal from an evolutionary point of view.
Think about it: mammals with sophisticated light and sound-sensing organs evolved from single-celled organisms. We know that this happened through evolution. But how does a sense organ evolve when there wasn't one there before? Well, maybe a little hair structure vibrates in the presence of certain acoustic waves (because physics says it has to), and these vibrations accidentally trigger the organism to move in a certain way. If this movement is advantageous for the organism during its life, then the hair structure might become more common—and more sophisticated—in future generations: a feedback loop that could eventually lead to a hearing organ.
Let’s get specific. There has much debate about why the hammerhead shark, which has an oblong-shaped head much wider than its body, looks the way it does. Biologists have considered various possibilities. First, there is an obvious sensory advantage in that binocular vision (depth perception) improves with the separation of the eyes. Another explanation is that this head shape has hydrodynamic advantages, including increased stability while turning. Finally, sharks have electrosensory pores that assist them in catching prey. The wider head means there can be a greater number of these pores for the same density.
If you think about the shark as a physical object, then you don't need to pick one of these theories. Evolution can select for all of them. Any physical changes caused by mutation—no matter how small or how seemingly unrelated to either sensing or locomotion—can be exploited. All that is necessary is that this change somehow supply some kind of advantage, allowing the animal to survive and procreate better than its neighbors. So, a slight widening of the head produced by evolutionary accident may have improved the survival of the hammerhead shark precisely because all three abilities (electro-sensing, depth perception, dynamical stability) were enhanced at once.
Unlike with digital technology, there is no need for the improvement to reach some artificial threshold in order to make a difference. With analog systems, even the most miniscule tweak can be bootstrapped into a feedback loop that ends in major change.
Matthew Williamson, an earlier PhD student of Rodney Brooks, worked on the arms for 
