Artificial intelligence can accurately remove tattoos from photos of people's faces – potentially helping face recognition systems, which can be flummoxed by such tattoos, work accurately. In previous research, Mathias Ibsen at Darmstadt University of Applied Sciences in Germany and his colleagues had identified that face painting and tattoos can impair the performance of face recognition systems.

To hurtle around a corner along the fastest "racing line" without losing control, race car drivers must brake, steer and accelerate in precisely timed sequences. The process depends on the limits of friction, and they are governed by known physical laws--which means self-driving cars can learn to complete a lap at the fastest possible speed (as some have already done). But this becomes a much knottier problem when the automated driver has to share space with other cars. Now scientists have unraveled the challenge virtually by training an artificial intelligence program to outpace human competitors at the ultrarealistic racing game Gran Turismo Sport. The findings could point self-driving car researchers toward new ways to make this technology function in the real world.

Scientists have built a shape-shifting robotic drone that transforms from a ground vehicle to a quadcopter, an advance that may lead to novel machines that can perform diverse functions under different conditions and self-heal after being damaged. The researchers from Virginia Tech in the US first developed a multifunctional material that could change shape, hold the shape, and return to the original configuration, and to do this over many cycles. "One of the challenges was to create a material that was soft enough to dramatically change shape, yet rigid enough to create adaptable machines that can perform different functions," Michael Bartlett, assistant professor in mechanical engineering, said in a statement. The scientists then turned to the Japanese art of kirigami, which involves making shapes out of paper by cutting, to create a structure that could be morphed. Then they developed an endoskeleton made of a low melting point alloy (LMPA) embedded inside a rubber skin.

This article is part of our series that explores the business of artificial intelligence. Digital technologies, and at their forefront artificial intelligence, are triggering fundamental shifts in society, politics, education, economy, and other fundamental aspects of life. These changes provide opportunities for unprecedented growth across different sectors of the economy. But at the same time, they entail challenges that organizations must overcome before they can tap into their full potential. In a recent talk at an online conference organized by Stanford Human-Centered Artificial Intelligence (HAI), Stanford professor Erik Brynjolfsson discussed some of these opportunities and challenges.

Tesla Chief Executive Elon Musk often touts the arrival of completely autonomous vehicles as imminent, but exactly how close that future is for the electric automaker remains murky. Meanwhile, the company is launching new features in a US regulatory environment that has often taken a laissez-faire approach to emerging technologies, while using terms like Full Self Driving (FSD) that critics view as misleading. Videos posted online by Tesla owners show an erratic performance in "FSD Beta," the latest update on Tesla's driver-assistance system. Cars can be seen turning awkwardly, knocking down safety cones and lurching unexpectedly. Earlier this month, Tesla initiated a recall of some 54,000 vehicles equipped with FSD Beta to disable a feature that had allowed the cars to go through a stop sign without fully halting in certain situations. The episode highlights a downside to Musk's envelope-pushing approach, which has also been credited with making electric vehicles a mainstream option in the United States and other markets.

The University of Southern California has unveiled a pair of programs--one in partnership with Amazon--designed to meet the tech industry's accelerating demand for engineers in the realm of artificial intelligence. On Tuesday, USC announced the launch of the new Center for Autonomy and AI at its Viterbi School of Engineering. Co-directed by USC professors Rahul Jain and Jyo Deshmukh, the center aims to study and develop AI applications ranging from self-driving cars and autonomous robots. The university also rolled out a new partnership with Amazon to launch a summer residency program for undergraduate engineering students. Amazon is sponsoring the program, which will see 30 students in science, technology, engineering and mathematics (STEM) disciplines from across the U.S. work with USC faculty during an eight-week on-campus program.

Whether it's autonomous vehicles or assistive technology in healthcare that can do things like help the elderly do core tasks like feeding themselves, some of the most challenging problems in the field of robotics involve how robots interact with humans, with all of our many complexities. Drawing from fields as varied as cognitive neuroscience, psychology, and behavioral economics, Stanford computer scientist Dorsa Sadigh is exploring how to train robots to better understand humans – and how to give humans the skills to more seamlessly work with robots. Stanford HAI's mission is to advance AI research, education, policy and practice to improve the human condition.

Today's AI technology, much like humans, learns from examples. AI systems are developed on datasets containing text, images, audio, and other information that serve as a ground truth. By figuring out the relationships between these examples, AI systems gradually "learn" to make predictions, like which word is likely to come next in a sentence or whether objects in a picture are inanimate. The technique holds up remarkably well in the language domain, for example, where systems like OpenAI's GPT-3 can write content from essays to advertisements in human-like ways. But similar in character to humans, AI that isn't supplied fresh, new data eventually grows stale in its predictions -- a phenomenon known as model drift.

Researchers at Ecole Polytechnique Fédérale de Lausanne have developed a machine-learning program that can be connected to a human brain and used to command a robot. The program can alter the robot’s movements based on electrical signals from the brain.  These new advancements could assist tetraplegic patients who are unable to speak or perform movements. […]

In the winter of 1997 Carver Mead lectured on an unusual topic for a computer scientist: the nervous systems of animals, such as the humble fly. Mead, a researcher at the California Institute of Technology, described his earlier idea for an electronic problem-solving system inspired by nerve cells, a technique he had dubbed "neuromorphic" computing. A quarter-century later, researchers have designed a carbon-based neuromorphic computing device--essentially an organic robot brain--that can learn to navigate a maze. A neuromorphic chip memorizes information similarly to the way an animal does. When a brain learns something new, a group of its neurons rearrange their connections so they can communicate more quickly and easily.