A team of scientists used a machine learning method called a deep neural network to discern the signal created by the spin orientation of electrons on quantum dots. Researchers led by the Institute of Scientific and Industrial Research (SANKEN) at Osaka University have trained a deep neural network to correctly determine the output state of quantum bits, despite environmental noise. The team's novel approach may allow quantum computers to become much more widely used. Modern computers are based on binary logic, in which each bit is constrained to be either a 1 or a 0. But thanks to the weird rules of quantum mechanics, new experimental systems can achieve increased computing power by allowing quantum bits, also called qubits, to be in "superpositions" of 1 and 0. For example, the spins of electrons confined to tiny islands called quantum dots can be oriented both up and down simultaneously. However, when the final state of a bit is read out, it reverts to the classical behavior of being one orientation or the other.

Privacy campaigners have raised concerns about the use of facial recognition technology on pupils queueing for lunch in school canteens in the UK. Nine schools in North Ayrshire began taking payments for school lunches this week by scanning the faces of their pupils, according to a report in the Financial Times. More schools are expected to follow. The company supplying the technology claimed it was more Covid-secure than other systems, as it was cashless and contactless, and sped up the lunch queue, cutting the time spent on each transaction to five seconds. With break times shortening, schools are under pressure to get large numbers of students through lunch more quickly.

A Waymo self-driving car pulls into a parking lot in Mountain View, Calif., on May 8, 2019. A Waymo self-driving car pulls into a parking lot in Mountain View, Calif., on May 8, 2019. It was a modern mystery. In a tiny neighborhood in San Francisco's Richmond District, self-driving Waymo cars have been converging at all hours of the day and night, mystifying neighbors, KPIX reported earlier this week. Most would drive to the dead-end on 15th Avenue, where they then had no choice but to turn around and leave, according to the outlet -- and neighbors have no idea why.

Exoskeleton devices work, researchers say, for a variety of uses such as speeding up our walking or making running easier. Yet they don't know what exactly makes exoskeletons effective. What is the benefit of customization, for example? And how much does simply getting used to the exoskeleton matter? Researchers in the Stanford Biomechatronics Laboratory at Stanford University examined these questions and found that training plays a remarkably significant role in how well exoskeletons provide assistance.

A new startup that replaces pilots with robots has received $100 million in funding. Reliable Robotics argues that, with many planes now being controlled automatically, that pilots are the most expensive aspects of cargo operations. Trucking is monotonous and uninteresting work, meaning it is also the source of the most mistakes. Its technology is aimed at handling the taxi, takeoff, landing, and parking parts of cargo flights – monitored by licensed pilots remotely in the control centre. It is claimed that autonomous planes could save airlines approximately $60 billion per year.

For the last two years, Facebook AI Research (FAIR) has worked with 13 universities around the world to assemble the largest ever data set of first-person video--specifically to train deep-learning image-recognition models. AIs trained on the data set will be better at controlling robots that interact with people, or interpreting images from smart glasses. "Machines will be able to help us in our daily lives only if they really understand the world through our eyes," says Kristen Grauman at FAIR, who leads the project. Such tech could support people who need assistance around the home, or guide people in tasks they are learning to complete. "The video in this data set is much closer to how humans observe the world," says Michael Ryoo, a computer vision researcher at Google Brain and Stony Brook University in New York, who is not involved in Ego4D.

Military security firm Ghost Robotics has built a mechanical dog capable of carrying a remote-controlled rifle on its back. The Special Purpose Unmanned Rifle (SPUR) is comprised of a 6.5mm Creedmore rifle from weapons company SWORD International combined with the quadruped unmanned ground vehicle from the robotics firm. First seen at the US Army's annual convention in Washington DC, as reported by The Drive, this is apparently one of the first systems like these with an actual weapon attached. It is unclear how much ammunition the gun contains, and how difficult it might be to reload. Ghost Robotics says that the robot dog can be commanded to chamber the first round from an unloaded state, clear the chamber, and'safeing' the gun (when the weapon is not cocked and no ammunition is present). It can fire bullets up to a 1200-metre distance.

U.S. safety investigators want to know why Tesla didn't file recall documents when it updated Autopilot software to better identify parked emergency vehicles, escalating a simmering clash between the automaker and regulators. In a letter to Tesla, the National Highway Traffic Safety Administration told the electric car maker Tuesday that it must recall vehicles if an over-the-internet update deals with a safety defect. "Any manufacturer issuing an over-the-air update that mitigates a defect that poses an unreasonable risk to motor vehicle safety is required to timely file an accompanying recall notice to NHTSA," the agency said in a letter to Eddie Gates, Tesla's director of field quality. The agency also ordered Tesla to provide information about its "Full Self-Driving" software that's being tested on public roads with some owners. The latest clash is another sign of escalating tensions between Tesla and the agency that regulates vehicle safety and partially automated driving systems.

A synthetic, insect-inspired "brain" can guide a robotic dog using much less energy and weight than conventional approaches require. The control system, built by UK start-up company Opteran Technologies, mimics how honeybees and other insects navigate. It contains a computer chip and two cameras, which provide a 360-degree view, and can be connected to various robots and drones. The package weighs only 30 grams and draws less than 3 watts of electricity. That is a fraction of what is required by most robot control systems, says Opteran CEO David Rajan.