Winning the Nobel Prize is one of the highest honors one can achieve. Winners bring their institutions and their countries prestige. I’d like to highlight this year’s prizewinners.
The Nobel Prize in Physics this year was awarded to French scientist Albert Fert and German scientist Peter Grünberg. They were recognized for their independent discovery of giant magnetoresistance. The concept’s a bit esoteric, but the Nobel Prize site, nobelprize.org, has some nice introductory material. In fact, it’s really put together well and you are advised to browse through it for more information about any aspect of the Nobel Prizes.
I especially like their “speed read” summaries. The Physics entry is quite easy to understand and begins as follows:
The Giant within Small Devices
Lying at the heart of the computer which you are using to read this article is a memory retrieval system based on the discoveries for which the 2007 Nobel Prize in Physics was awarded to Albert Fert and Peter Grünberg. They discovered, quite independently, a new way of using magnetism to control the flow of electrical current through sandwiches of metals built at the nanotechnology scale.
And if you have time, you should definitely read a nice 7-page PDF explaining the concept for the layperson, using illustrations and easy-to-understand concepts. I won’t bother going into detail here since the site does such a nice job. There’s no excuse not to know the basics of this discovery!
You can also see videos of the announcement, or read the press release.
A company called Ambient is developing a new wheelchair that is controlled by words the user thinks of. The system, called Audeo, uses a neckband to pick up signals in the nerves that control the larynx, or voice box. Obviously, this requires that the operator still has control of those nerves, though he doesn’t have to have control of the other muscles or the coordination that is required for speech. This has the potential to restore some mobility to those who have very little strength or coordination to make purposeful movements. And as this technology is refined, the potential uses are many: users could control other devices, such as a computer or television. If the “vocabulary” of the system is increased, the system could also function as an artificial speech synthesizer that could sense the words the user was trying to say and construct them directly. See New Scientist for more.
Below is a video demonstrating the system.
The major components of the new prosthesis. The small wearable computer is not included. Credit: Mark Humayun/AAAS. Source: New Scientist.
An article by Gaia Vince in New Scientist reports on a retinal prosthesis designed to help restore vision to blind people. After a prototype was successfully used in six people, further trials are set to begin. While cochlear implants are used to give deaf people some ability to hear, there has been no comparable, practical system for those who cannot see.
The system has several components. The user wears a pair of glasses with a built-in camera. The information is then transmitted to a wireless computer around the size of a mobile telephone that the user must keep with him. This computer processes the data, then transmits it to a receiver implanted in the user’s head. This is connected to a chip on the user’s retina. This all occurs extremely quickly, as discrepancy between perceived movement and visual changes would cause nausea and dizziness.
The device is still preliminary; the resolution is quite limited, naturally. But it is interesting that the brains of the patients seem to adapt to the limited visual input, and their vision improved over time. The article notes one patient’s observation:
At the beginning, it was like seeing assembled dots — “now it’s much more than that,” says Terry Bryant, aged 58, who received the implant in 2002 after 13 years of blindness. “I can go into any room and see the light coming in through the window. When I am walking along the street I can avoid low hanging branches and I can cross a busy street.”
Similar to the cochlear implant, an intact nervous system is required. This prothesis links with the ganglion cells at the back of the eye and the signals travel over the optic nerve to the brain. Damage to any of these components—such as damage to the ganglion cells, injury to the optic nerve, or stroke—will result in blindness that this prosthesis cannot correct. For that, we’ll have to wait for new technology.
There was an interesting article in New Scientist today about research towards developing a “haptic” glove. This glove would simulate tactile information, analagous to the way a television screen simulates visual information or speakers simulate auditory information. However, simulating touch is much more difficult for several reasons.
One of the main ways we determine the texture of something is through vibration. As we run our fingers over it, different textures have different patterns of high and low points, and vibration sensors in our fingertips are stimulated differently. Touch is complex, though, since we may also pick up and manipulate an object. As Tom Simonite writes in New Scientist,
“Virtual fabric” that feels just like the real thing is being developed by a group of European researchers. Detailed models of the way fabrics behave are combined with new touch stimulating hardware to realistically simulate a texture’s physical properties.
Detailed measurements of a fabric’s stress, strain and deformation properties are fed into a computer, recreating it virtually. Two new physical interfaces then allow users to interact with these virtual fabrics – an exoskeleton glove with a powered mechanical control system attached to the back and an array of moving pins under each finger. The “haptic” glove exerts a force on the wearer’s fingers to provide the sensation of manipulating the fabric, while the “touching” pins convey a tactile sense of the material’s texture.
(continue reading at New Scientist)
Of course, the benefits to virtual reality games are obvious. But there are many possible medical and industrial applications as well, such as manipulation of toxic substances or work in dangerous environments, or perhaps remote or robotic surgery.
There does not seem to be any olfactory or gustatory simulation on the horizon, though.
Scientific American has a neat piece of news in its February 2007 issue (“Chipping In” by Anna Griffin; subscription required for full text). For some time, we have had technology that can pick up signals from neurons (brain and nerve cells), for instance, allowing paralyzed patients rudimentary control over a computer or prosthesis.
But a team at the University of Southern California, led by Theodore W. Berger, have taken this a step further. For twenty years he and his team studied the brains of rats; specifically, how neurons communicate in the hippocampus, a region of the brain involved in memory. They developed a model of how the neurons responded to various inputs and built it into a chip. They then took slices of hippocampal tissue, removed part of it, and replaced it with the chip, “[restoring] function by processing incoming neural signals into appropriate output with 90 percent accuracy,” according to the Scientific American article.
I find this to be very exciting. This sort of research could one day lead to devices to help humans with brain damage or memory problems, for instance, though of course that is still far away. Even at this stage, it took some interesting engineering work to figure out how to make a silicon chip interact with brain tissue. The next step will be to design a chip to work with a living brain, instead of tissue slices.
But what really fascinates me is that they were able to model the function of that brain tissue mathematically, to calculate how the section of neurons would respond to various inputs. This brings us closer to understanding just how brain functions such as memory and consciousness arise from the biology and chemistry of the brain.
It does suggest some future ethical and philosophical puzzles, though. Will we eventually be able to reproduce the functioning of the entire rat brain? How about that of a human? Might we one day be able to calculate the functioning of a human mind, to reproduce a mind as software?
My brain looks forward to future advances.
New Scientist reports about an article in this week’s Lancet. Prosthetic limbs are getting quite advanced! The article discusses a prosthetic arm that has been attached to a 26-year-old woman. Motor (movement) nerves have been attached in a way to allow for more intuitive control of the limb. She is able to achieve remarkable control and accomplish activities of daily living such as cooking and dressing, albeit a bit more slowly. Below is a video of this remarkable woman demonstrating use of her new arm.
Take a look at the advantage this prosthesis offers over previous ones.
They also attached the sensory nerves to her chest so that if she is touched there, she feels the sensation as if it is coming from her arm. The next step will be to develop a sensory mechanism for the arm and relay the signal to the nerves.
The hunt for the missing Mars Global Surveyor continues
Mars Global Surveyor has been orbiting Mars since 1997, the first of a fleet of probes now exploring the Red Planet. Well past its intended lifespan, it has provided a wealth of data, but unfortunately went silent several weeks ago, and so far neither Earth nor the other probes have been able to detect or contact it. This is a good opportunity to take a brief look at the many craft busy examining our neighbor in space. There are too many to cover in a single post; subsequent posts will continue the series. In the meantime, you may read the New Scientist article discussing the search for Mars Global Surveyor.
Artist’s concept of MGS orbiting Mars. Artwork Credit: Corby Waste. Courtesy NASA/JPL-Caltech.
Mars Global Surveyor
The Mars Global Surveyor (MGS) was launched by NASA on 7 November 1996; it reached Mars eight months later on 11 September 1997. It was the first U.S. craft to visit Mars in twenty years (the Soviet Union’s Phobos 2 briefly explored Mars in 1998 before prematurely malfunctioning; the United States’ Mars Observer, launched in 1992, failed to function properly). MGS has performed well beyond expectations; it completed its primary mission in 2001 and has had its mission extended several times since then. It has been a highly successful spacecraft, studying Mars extensively and providing more information than all previous missions combined, according to New Scientist. Some of its observations include mapping local magnetic fields (Mars, unlike Earth, does not have a global magnetic field) and discovering repeating weather patterns. And more recently, it had been serving as a communications relay for the other craft exploring the planet, while complementing their observations.
Continue reading “Exploring Mars, Part 1: Mars Global Surveyor“
Credit: Lindsay France/Cornell University. Source: PhysOrg.com.
Josh Bongard and his colleagues at Cornell write in the November 17, 2006, edition of Science (see abstract) about a new robot they have built. As reported on PhysOrg.com (thanks to Food not Bourgeoisie for spotting this), the robot develops a model of self to learn how to move, perhaps somewhat similar to the way human babies learn:
Nothing can possibly go wrong … go wrong … go wrong … The truth behind the old joke is that most robots are programmed with a fairly rigid “model” of what they and the world around them are like. If a robot is damaged or its environment changes unexpectedly, it can’t adapt.
So Cornell researchers have built a robot that works out its own model of itself and can revise the model to adapt to injury. First, it teaches itself to walk. Then, when damaged, it teaches itself to limp.
(continue reading at PhysOrg.com)
The robot is programmed with a list of its parts, but not how they are connected or used. Instead, it uses a process that is a mixture of scientific method and evolution to learn how to move. It activates a single random motor, then, based on the results, it constructs fifteen varying internal models of how it might be put together. Next, it decides on commands to send to its motors, selecting commands that will produce the largest variation between models. It activates its motors and based on the results, the most likely model is selected. Variations on this model are constructed, and the robot again determines which test movement will produce the largest difference in movement between models. (This sort of repeated variation and selection is sometimes called evolutionary computation.) After sixteen cycles, the robot uses its best model of self to determine how to move its motors to move the farthest. It then attempts to move (usually awkwardly, but functional).
In a second part of the experiment, the researchers simulated injury by removing part of a leg. When the robot detects a large discrepancy between its predicted movement and its actual movement, it repeats the sixteen-cycle process, generating a new model of self and new way to walk.
Continue reading “Learning to Walk”