Scientists in China have developed a laser that can locate a hidden object from a mile away. Researchers hid a mannequin inside an apartment and fired a laser emitter at its location, determining the dummy's location by calculating how long it took photons to hit different parts of the room and travel back to the laser. The technology, known as non-line-of-sight (NLOS) imaging, could be utilized by the military to find enemy targets or rescue teams to find victims. It could also be beneficial in helping self-driving cars detect pedestrians and other vehicles from behind buildings. A team at the University of Science and Technology of China perfected the new technique.
Delseny, Hervé, Gabreau, Christophe, Gauffriau, Adrien, Beaudouin, Bernard, Ponsolle, Ludovic, Alecu, Lucian, Bonnin, Hugues, Beltran, Brice, Duchel, Didier, Ginestet, Jean-Brice, Hervieu, Alexandre, Martinez, Ghilaine, Pasquet, Sylvain, Delmas, Kevin, Pagetti, Claire, Gabriel, Jean-Marc, Chapdelaine, Camille, Picard, Sylvaine, Damour, Mathieu, Cappi, Cyril, Gardès, Laurent, De Grancey, Florence, Jenn, Eric, Lefevre, Baptiste, Flandin, Gregory, Gerchinovitz, Sébastien, Mamalet, Franck, Albore, Alexandre
Machine Learning (ML) seems to be one of the most promising solution to automate partially or completely some of the complex tasks currently realized by humans, such as driving vehicles, recognizing voice, etc. It is also an opportunity to implement and embed new capabilities out of the reach of classical implementation techniques. However, ML techniques introduce new potential risks. Therefore, they have only been applied in systems where their benefits are considered worth the increase of risk. In practice, ML techniques raise multiple challenges that could prevent their use in systems submitted to certification constraints. But what are the actual challenges? Can they be overcome by selecting appropriate ML techniques, or by adopting new engineering or certification practices? These are some of the questions addressed by the ML Certification 3 Workgroup (WG) set-up by the Institut de Recherche Technologique Saint Exup\'ery de Toulouse (IRT), as part of the DEEL Project.
This story was originally published by Undark and is reproduced here as part of the Climate Desk collaboration. Deep in the Mojave Desert, 60 miles from the city of Barstow, is the Slash X Ranch Cafe, a former ranch where dirt bike riders and ATV adventurers can drink beer and eat burgers with fellow daredevils speeding across the desert. Displayed on a wall alongside trucker caps and taxidermy is a plaque that memorializes the 2004 DARPA Grand Challenge, a 142-mile race whose starting point was at Slash X Ranch Cafe. It was the first race in the world without human drivers. Instead, it featured the fever-dream inventions -- robotic motorcycles, monster Humvees -- of a handful of software engineers who were hellbent on creating fully autonomous vehicles and winning the million-dollar prize offered by the Defense Department's Defense Advanced Research Projects Agency.
The Civil Aviation Safety Authority (CASA), alongside Airservices Australia, on Wednesday announced a trial of a new digital, automated process that is aimed at expediting the approval processes of remotely piloted aircraft operations. According to the organisations, the application process currently takes weeks to complete before commercial drone operators are allowed to take flight. With the trial, CASA and Airservices hope to create an application process that reduces the time required from weeks to seconds. "Moving to digital approval processes is a key initiative for CASA, streamlining interactions and making it easier for operators," CASA acting-CEO and Aviation Safety director Graeme Crawford said. The trial digital process will be delivered through CASA's remotely piloted aircraft systems digital platform, with Airservices and the Queensland University of Technology to develop designated maps that will be used to conduct the relevant analysis required for these automated authorisations.
Zhang, Daniel, Mishra, Saurabh, Brynjolfsson, Erik, Etchemendy, John, Ganguli, Deep, Grosz, Barbara, Lyons, Terah, Manyika, James, Niebles, Juan Carlos, Sellitto, Michael, Shoham, Yoav, Clark, Jack, Perrault, Raymond
Welcome to the fourth edition of the AI Index Report. This year we significantly expanded the amount of data available in the report, worked with a broader set of external organizations to calibrate our data, and deepened our connections with the Stanford Institute for Human-Centered Artificial Intelligence (HAI). The AI Index Report tracks, collates, distills, and visualizes data related to artificial intelligence. Its mission is to provide unbiased, rigorously vetted, and globally sourced data for policymakers, researchers, executives, journalists, and the general public to develop intuitions about the complex field of AI. The report aims to be the most credible and authoritative source for data and insights about AI in the world.
On a recent afternoon, Mr. Luckey, dressed as if ready for the beach in a Hawaiian-like shirt, shorts and flip-flops, joined other Anduril employees at the company's testing site near Camp Pendleton, a Marine training facility. As the drone took off and swooped between the hills, Mr. Luckey said it could track an object and capture detailed images from seven football fields away. Using many of the artificial intelligence technologies that underpin self-driving cars, Anduril's drones can identify and track vehicles, people and other objects largely on their own. The drones are not armed, but could be useful for guarding bases or reconnaissance. The same sensor technologies that allow the drones to fly on their own could also be used to identify targets on a battlefield.
Master Python By Implementing Face Recognition & Image Processing In Python Created by Emenwa Global, Zoolord AcademyPreview this Course - GET COUPON CODE Computer vision is an interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do. Computer vision is concerned with the automatic extraction, analysis and understanding of useful information from a single image or a sequence of images. It involves the development of a theoretical and algorithmic basis to achieve automatic visual understanding. As a scientific discipline, computer vision is concerned with the theory behind artificial systems that extract information from images. The image data can take many forms, such as video sequences, views from multiple cameras, or multi-dimensional data from a medical scanner.
The Victorian government has launched an aviation unit within Fire Rescue Victoria (FRV) that will be responsible for using drone technology to assist firefighters and other emergency services who are on-ground. The new unit will be staffed by four specialist firefighters, including qualified Civil Aviation Safety Authority drone pilots and aviation accredited personnel. As part of standing up the new FRV unit, four new drones will be made available to Victorian firefighters, which they can deploy to gather aerial images of fires and other emergencies. More specifically, the new drones, according to the Victorian government, feature high-definition thermal imaging and live-streaming cameras, have the ability to fly for up to 30 minutes, and withstand strong wind conditions to help better monitor fires and other incidents from the air. These new drones will be in addition to the existing drone services that are used by FRV.
When the inquisition required him to drop his study of what the Roman Catholic Church insisted was not a heliocentric solar system, Galileo Galilei turned his energy to the less controversial question of how to stick a telescope onto a helmet. The king of Spain had offered a hefty reward to anyone who could solve the stubborn mystery of how to determine a ship's longitude while at sea: 6,000 ducats up front and another 2,000 per year for life. Galileo thought his headgear, with the telescope fixed over one eye and making its wearer look like a misaligned unicorn, would net him the reward. Determining latitude is easy for any sailor who can pick out the North Star, but finding longitude escaped the citizens of the 17th century, because it required a precise knowledge of time. That's based on a simple principle: Say you set your clock before sailing west from Greenwich.
As we make tremendous advances in machine learning and artificial intelligence technosciences, there is a renewed understanding in the AI community that we must ensure that humans being are at the center of our deliberations so that we don't end in technology-induced dystopias. As strongly argued by Green in his book Smart Enough City, the incorporation of technology in city environs does not automatically translate into prosperity, wellbeing, urban livability, or social justice. There is a great need to deliberate on the future of the cities worth living and designing. There are philosophical and ethical questions involved along with various challenges that relate to the security, safety, and interpretability of AI algorithms that will form the technological bedrock of future cities. Several research institutes on human centered AI have been established at top international universities. Globally there are calls for technology to be made more humane and human-compatible. For example, Stuart Russell has a book called Human Compatible AI. The Center for Humane Technology advocates for regulators and technology companies to avoid business models and product features that contribute to social problems such as extremism, polarization, misinformation, and Internet addiction. In this paper, we analyze and explore key challenges including security, robustness, interpretability, and ethical challenges to a successful deployment of AI or ML in human-centric applications, with a particular emphasis on the convergence of these challenges. We provide a detailed review of existing literature on these key challenges and analyze how one of these challenges may lead to others or help in solving other challenges. The paper also advises on the current limitations, pitfalls, and future directions of research in these domains, and how it can fill the current gaps and lead to better solutions.