A team of international scientists has developed a laser that can generate 254 trillion random digits per second, more than a hundred times faster than computer-based random number generators (RNG). Though random number generation has been around for thousands of years, it is increasingly important in computing as it forms the basis of cryptography. With more devices online than ever before, the need for faster encryption that can keep out bad actors has become more crucial. That's why the new system could be a game-changer: It can generate 250 terabytes of random bits per second. In fact, it was so fast that the team behind it struggled to record its output using a high-speed camera.

New research demonstrates for the first time that artificial intelligence (AI) can be used to train computers to recognize individual birds, a task humans are unable to do. The research is published in the British Ecological Society journal Methods in Ecology and Evolution. "We show that computers can consistently recognize dozens of individual birds, even though we cannot ourselves tell these individuals apart. In doing so, our study provides the means of overcoming one of the greatest limitations in the study of wild birds--reliably recognizing individuals." Said Dr. André Ferreira at the Center for Functional and Evolutionary Ecology (CEFE), France, and lead author of the study.

Inspired by the biomechanics of cheetahs, researchers have developed a new type of soft robot that is capable of moving more quickly on solid surfaces or in the water than previous generations of soft robots. The new soft robotics are also capable of grabbing objects delicately--or with sufficient strength to lift heavy objects. "Cheetahs are the fastest creatures on land, and they derive their speed and power from the flexing of their spines," says Jie Yin, an assistant professor of mechanical and aerospace engineering at North Carolina State University and corresponding author of a paper on the new soft robots. "We were inspired by the cheetah to create a type of soft robot that has a spring-powered, 'bistable' spine, meaning that the robot has two stable states," Yin says. "We can switch between these stable states rapidly by pumping air into channels that line the soft, silicone robot. Switching between the two states releases a significant amount of energy, allowing the robot to quickly exert force against the ground. This enables the robot to gallop across the surface, meaning that its feet leave the ground. "Previous soft robots were crawlers, remaining in contact with the ground at all times.

Engineers at North Carolina State University have achieved a new record for the fastest moving soft robot. A team from the university's Mechanical and Aerospace Engineering department created a robot capable of moving 2.7 times its own body length each second, more than three times faster than the previous record of 0.8 times body length per second. The tiny robot--it weighs just 1.5 ounces and measures 2.7 inches long--was designed to run like a cheetah, with four bent legs and a long flexible torso made from silicone. A team of engineers at North Carolina State University developed a small'soft' robot modeled after a cheetah, which uses silicone bands to expand and contract in a galloping motion that mimics a cheetah's movement'We were inspired by the cheetah to create a type of soft robot that has a spring-powered, "bistable" spine, meaning that the robot has two stable states,' North Carolina State's Jie Yin told Eurekalert. 'We can switch between these stable states rapidly by pumping air into channels that line the soft, silicone robot.

The use of machine learning is increasing as automation becomes more widespread in our workplaces, financial institutions and even courts of law -- telling us whom to hire, whom to lend money to, and who might re-offend. But it's becoming painfully clear that these complex algorithms can conceal any number of hidden biases -- leading them to inadvertently discriminate against people based on their gender or race -- oftentimes with terrible, life-changing consequences. The problem is that such AI systems are notoriously opaque; more often than not, the mechanisms and reasoning behind their predictions aren't immediately apparent, even to the people who created these systems. So it's little wonder that a growing number of experts are now working to build what is called "interpretable" or "explainable" AI, where the processes that underlie machine predictions are made more transparent and therefore, also more understandable (at least by us humans). In aiming to better understand how and why machines classify images the way they do, one research team from Duke University created a new deep learning neural network whose reasoning process can be deconstructed, analyzed and understood more easily than comparable models.

In work that combines a deep understanding of the biology of soft-bodied animals such as earthworms with advances in materials and electronic technologies, researchers from the United States and China have developed a robotic device containing a stretchable transistor that allows neurological function. Cunjiang Yu, Bill D. Cook Associate Professor of Mechanical Engineering at the University of Houston, said the work represents a significant step toward the development of prosthetics that could directly connect with the peripheral nerves in biological tissues, offering neurological function to artificial limbs, as well as toward advances in soft neurorobots capable of thinking and making judgments. Yu is corresponding author for a paper describing the work, published in Science Advances. He is also a principal investigator with the Texas Center for Superconductivity at the University of Houston. "When human skin is touched, you feel it," Yu said to describe the human capabilities the new device can mimic.

Cunjiang Yu, Bill D. Cook Associate Professor of Mechanical Engineering at the University of Houston, said the work represents a significant step toward the development of prosthetics that could directly connect with the peripheral nerves in biological tissues, offering neurological function to artificial limbs, as well as toward advances in soft neurorobots capable of thinking and making judgments. Yu is corresponding author for a paper describing the work, published in Science Advances. He is also a principal investigator with the Texas Center for Superconductivity at the University of Houston. "When human skin is touched, you feel it," Yu said to describe the human capabilities the new device can mimic. "The feeling originates in your brain, through neural pathways from your skin to the brain."

Most conventional androids are fairly rigid, susceptible to damage and difficult to repair. However, scientists are determined to (literally) give them thicker skins. They've experimented with soft, deformable circuits that are flexible, and could reduce business expenses in the long term -- but are still prone to tearing and puncturing. The solution to these issues may lie in one recent advancement. A group of researchers from Carnegie Mellon University have found a way to counter surface damage and electrical failure commonly observed in soft materials used in engineering robotic electronics.

An MIT project distorted photos of the capitol building and other famous sites using an artificial intelligence algorithm to make horror images. An MIT project distorted photos of the capitol building and other famous sites using an artificial intelligence algorithm to make horror images. Welcome to the "Nightmare Machine," a horror-imagery project created by three researchers at the Massachusetts Institute of Technology. Pinar Yanardag, Manuel Cebrian and Iyad Rahwan used artificial intelligence algorithms "to learn how haunted houses, or toxic cities look. Then, we apply the learnt style to famous landmarks and present [to] you: AI-powered horror all over the world!"