Self-aware machines are able to assess injuries and make adjustments
Source: “Resilient Machines through Continuous Self-Modeling”
Josh Bongard et al.
Science 314: 1118-1121
Results: By constantly monitoring its own structure, a four-legged robot built by Josh Bongard, a professor of computer science at the University of Vermont, and colleagues at Cornell University can tell if it has damaged or lost a limb and adapt its gait accordingly.
Why it matters: Robots are useful for exploring environments that are too harsh for humans–unless they suffer damage and can’t compensate for it. Previous recovery schemes for damaged robots relied on built-in redundancy such as extra limbs, or preprogrammed contingency plans that anticipated certain failures. Bongard designed a robot that constantly and autonomously monitors itself, adjusting to damage like joint separation or disappearance of a limb. His approach could make robots more useful in harsh environments.
Methods: The robot is equipped with sensors and actuators that collect information about the relative position of its parts. Based on the sensor data, the robot’s onboard computer creates mathematical models of the state of its body. If one of the robot’s limbs is damaged, data from the sensors can be used to generate a new model. A separate algorithm runs simulations of possible gaits, searching for the most efficient one for the damaged robot. The process usually takes about eight hours.
Next steps: Bongard plans to apply his algorithms to a collection of robots. Drawing on the experiences of others in a group could speed up an individual robot’s recovery rate. A damaged robot would send out a query to the other robots in the group, essentially asking if they’d encountered the same injury and how they adjusted.
A system combining a magnetic material and a semiconductor could lead to spintronic devices that pack more data into beams of light
Source: “Reconstruction Control of Magnetic Properties during Epitaxial Growth of Ferromagnetic Mn3-gGa on Wurtzite GaN(0001)”
Erdong Lu et al.
Physical Review Letters 97: 146101
Results: Arthur Smith, a professor of physics at Ohio University, and postdoc Erdong Lu have grown manganese gallium, a metal, on gallium nitride, a semiconductor commonly used to make blue lasers and light-emitting diodes. Smith and Lu believe that the new material could lead to room-temperature lasers that exploit the spin of electrons (spintronics).
Why it matters: Lasers based on spintronics, rather than on conventional electronics, have the potential to increase bandwidth in optical networks. Currently, data is encoded as the frequency and phase characteristics of a beam of light. In a spintronic laser, however, electrons with a certain spin can create photons with a corresponding spin, resulting in polarized light. Using polarization to encode a light beam with data could increase the amount of information it can carry. But until now, researchers have lacked materials suitable for making spintronic lasers.
Methods: Using standard processes, Smith and Lu deposited a thin film of manganese gallium onto gallium nitride. Reflection high-energy electron diffraction revealed a smooth interface between the two materials–a necessity if electrons are to maintain their spin as they travel into the light-emitting semiconductor.
Next steps: Researchers must determine whether the spin characteristics of the electrons are indeed preserved. They must also test the material’s light-emitting properties to determine how well the spin of electrons translates into polarized light.
Meta has built a massive new language AI—and it’s giving it away for free
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The gene-edited pig heart given to a dying patient was infected with a pig virus
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Saudi Arabia plans to spend $1 billion a year discovering treatments to slow aging
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Yann LeCun has a bold new vision for the future of AI
One of the godfathers of deep learning pulls together old ideas to sketch out a fresh path for AI, but raises as many questions as he answers.
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