Beetle-size machine can climb, crawl, and carry heavy objects—all without batteries.
Category: robotics/AI – Page 1,760
The Tetrad Automated Causal Discovery Platform, a software and text project developed by Peter Spirtes, Clark Glymour, Richard Scheines and Joe Ramsey of Carnegie Mellon University’s Department of Philosophy, earned the “Leader” Award at the 2020 World Artificial Intelligence Conference this past July.
The Leader Award is one of four awards presented at the conference that aim to recognize “the best in terms of impact and innovation in AI”. There were over 800 nominees for the awards, including projects by Amazon, Bosch, Huawei, Nvidia, Open AI Lab, and Siemens, among others.
Researchers at Oxford University, in collaboration with DeepMind, University of Basel and Lancaster University, have created a machine learning algorithm that interfaces with a quantum device and ‘tunes’ it faster than human experts, without any human input. They are dubbing it “Minecraft explorer for quantum devices.”
Classical computers are composed of billions of transistors, which together can perform complex calculations. Small imperfections in these transistors arise during manufacturing, but do not usually affect the operation of the computer. However, in a quantum computer similar imperfections can strongly affect its behavior.
In prototype semiconductor quantum computers, the standard way to correct these imperfections is by adjusting input voltages to cancel them out. This process is known as tuning. However, identifying the right combination of voltage adjustments needs a lot of time even for a single quantum device. This makes it virtually impossible for the billions of devices required to build a useful general-purpose quantum computer.
This approach to increasing capacity will be particularly important as robots shrink to the microscale and below—scales at which current stand-alone batteries are too big and inefficient.
“Robot designs are restricted by the need for batteries that often occupy 20% or more of the available space inside a robot, or account for a similar proportion of the robot’s weight,” said Nicholas Kotov, the Joseph B. and Florence V. Cejka Professor of Engineering, who led the research.
Applications for mobile robots are exploding, from delivery drones and bike-lane take-out bots to robotic nurses and warehouse robots. On the micro side, researchers are exploring swarm robots that can self-assemble into larger devices. Multifunctional structural batteries can potentially free up space and reduce weight, but until now they could only supplement the main battery.
Can A.I. understand poetry?
Posted in business, robotics/AI
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Combing through historical seismic data, researchers using a machine learning model have unearthed distinct statistical features marking the formative stage of slow-slip ruptures in the earth’s crust months before tremor or GPS data detected a slip in the tectonic plates. Given the similarity between slow-slip events and classic earthquakes, these distinct signatures may help geophysicists understand the timing of the devastating faster quakes as well.
“The machine learning model found that, close to the end of the slow slip cycle, a snapshot of the data is imprinted with fundamental information regarding the upcoming failure of the system,” said Claudia Hulbert, a computational geophysicist at ENS and the Los Alamos National Laboratory and lead author of the study, published today in Nature Communications. “Our results suggest that slow-slip rupture may well be predictable, and because slow slip events have a lot in common with earthquakes, slow-slip events may provide an easier way to study the fundamental physics of earth rupture.”
Slow-slip events are earthquakes that gently rattle the ground for days, months, or even years, do not radiate large-amplitude seismic waves, and often go unnoticed by the average person. The classic quakes most people are familiar with rupture the ground in minutes. In a given area they also happen less frequently, making the bigger quakes harder to study with the data-hungry machine learning techniques.
Newswise — Most of modern medicine has physical tests or objective techniques to define much of what ails us. Yet, there is currently no blood or genetic test, or impartial procedure that can definitively diagnose a mental illness, and certainly none to distinguish between different psychiatric disorders with similar symptoms. Experts at the University of Tokyo are combining machine learning with brain imaging tools to redefine the standard for diagnosing mental illnesses.
“Psychiatrists, including me, often talk about symptoms and behaviors with patients and their teachers, friends and parents. We only meet patients in the hospital or clinic, not out in their daily lives. We have to make medical conclusions using subjective, secondhand information,” explained Dr. Shinsuke Koike, M.D., Ph.D., an associate professor at the University of Tokyo and a senior author of the study recently published in Translational Psychiatry.
“Frankly, we need objective measures,” said Koike.
For decades, Hollywood has made millions off of our fears that artificial intelligences such as HAL in 2001: A Space Odyssey and Skynet in The Terminator could one day control us or even wipe out humanity.
Two Chinese air force J-20 stealth fighters have appeared at an air base in China’s far west as the mountain stand-off between India and Chine enters its fourth month.
The twin-engine J-20s are visible in commercial satellite imagery of Hotan air base, in the Uighur autonomous region of Xinjiang. Chinese social-media users first spotted the planes.
The J-20 deployment, however temporary, signals Beijing’s resolve as China wrestles with India for influence over a disputed region of the Himalayas. But a pair of warplanes, no matter how sophisticated, don’t represent much actual combat power.