A good dataset serves as the backbone of an Artificial Intelligence system. Data assists in various ways as it helps understand how the system is performing, understand meaning insights and others. At the premier annual Computer Vision and Pattern Recognition conference (CVPR 2020), several datasets have been open-sourced in order to help the community achieve higher accuracies and insights. Below here we have listed the top 10 Computer Vision datasets that are open-sourced at the CVPR 2020 conference. About: FaceScape is a large-scale detailed 3D face dataset that includes 18,760 textured 3D face models, which are captured from 938 subjects and each with 20 specific expressions.
XAOS MOTORS, headquartered in KOREA, challenges the technological progress of autonomous driving. XAOS MOTORS, by launching XCAT LiDAR Sensor now, give OEMs to make fully self-driving cars earlier than the market expected. MEMS LiDAR Sensor XCAT was developed for self-driving cars. With the ability to scan over 300 meters, XCAT can safely cope with high-speed driving. XCAT is designed for mass production, and OEMs can adopt high-performance 3D LiDARs at a low cost.
DJI's new Mavic Air 2 folding-style drone is a huge improvement over the previous model--so much so that for most people, this is the perfect drone. The Mavic Air 2 is the middle child in DJI's consumer drone lineup, sitting between the smaller, lighter, but less capable Mavic Mini, and the more powerful, more capable, but also more expensive, Mavic 2. If you're just getting started with drones, the less expensive Mavic Mini (8/10 WIRED Recommends)--my previous top pick for most people--might be a better buy. That said, the Air 2 offers better collision avoidance systems, higher quality photos and video, and a wide assortment of automated flight features that newcomers and seasoned vets alike can appreciate. The Mavic Air 2 is slightly bigger than its predecessor, at least on paper. The folding design remains compact, and at 1.3 pounds, the drone is plenty portable.
For all the advances enabled by artificial intelligence, from speech recognition to self-driving cars, AI systems consume a lot of power and can generate high volumes of climate-changing carbon emissions. A study last year found that training an off-the-shelf AI language-processing system produced 1,400 pounds of emissions--about the amount produced by flying one person roundtrip between New York and San Francisco. The full suite of experiments needed to build and train that AI language system from scratch can generate even more: up to 78,000 pounds, depending on the source of power. But there are ways to make machine learning cleaner and greener, a movement that has been called "Green AI." Some algorithms are less power-hungry than others, for example, and many training sessions can be moved to remote locations that get most of their power from renewable sources.
The following was issued as a joint release from the MIT AgeLab and Toyota Collaborative Safety Research Center. How can we train self-driving vehicles to have a deeper awareness of the world around them? Can computers learn from past experiences to recognize future patterns that can help them safely navigate new and unpredictable situations? These are some of the questions researchers from the AgeLab at the MIT Center for Transportation and Logistics and the Toyota Collaborative Safety Research Center (CSRC) are trying to answer by sharing an innovative new open dataset called DriveSeg. Through the release of DriveSeg, MIT and Toyota are working to advance research in autonomous driving systems that, much like human perception, perceive the driving environment as a continuous flow of visual information. "In sharing this dataset, we hope to encourage researchers, the industry, and other innovators to develop new insight and direction into temporal AI modeling that enables the next generation of assisted driving and automotive safety technologies," says Bryan Reimer, principal researcher.
When we purchase a new car in today's market, it is often the case that you can choose to add a personal touch for an additional fee: seat warmers, a particular color, wheel rims, and more. However, once the car leaves the showroom and is in a customer's hands, beyond repairs or, perhaps, a rare upgrade, there is little room for manufacturers to generate extra revenue from a sale. With the arrival of mobile solutions, the Internet of Things (IoT), and in-car connectivity, however, the game changed -- and the vehicle industry is now able to capitalize on the same subscription-based models that others, such as streaming content providers, are already shifting to. BMW appears to be keen to cash in on this change in consumer trends. This week, the automaker announced a set of new services in tandem with its upgraded vehicle operating system, OS version 7, including what could become subscription-based bolt-ons for owners.
Edge computing can roughly be defined as the practice of processing and storing data either where it's created or close to where it's generated -- "the edge" -- whether that's a smartphone, an internet-connected machine in a factory or a car. The goal is to reduce latency, or the time it takes for an application to run or a command to execute. While that sometimes involves circumventing the cloud, it can also entail building downsized data centers closer to where users or devices are. Anything that generates a massive amount of data and needs that data to be processed as close to real time as possible can be considered a use case for edge computing: think self-driving cars, augmented reality apps and wearable devices. Edge computing can roughly be defined as the practice of processing and storing data either where it's created or close to where it's generated -- "the edge" -- whether that's a smartphone, an internet-connected machine in a factory or a car.
Secretary of State Mike Pompeo seized on a U.N. report confirming Iranian weapons were used to attack Saudi Arabia in September and were part of an arms shipment seized months ago off Yemen's coast; State Department correspondent Rich Edson reports. A fire and an explosion struck a centrifuge production plant above Iran's underground Natanz nuclear enrichment facility early Thursday, analysts said, one of the most-tightly guarded sites in all of the Islamic Republic after earlier acts of sabotage there. The Atomic Energy Organization of Iran sought to downplay the fire, calling it an "incident" that only affected an under-construction "industrial shed," spokesman Behrouz Kamalvandi said. However, both Kamalvandi and Iranian nuclear chief Ali Akbar Salehi rushed after the fire to Natanz, a facility earlier targeted by the Stuxnet computer virus and built underground to withstand enemy airstrikes. The fire threatened to rekindle wider tensions across the Middle East, similar to the escalation in January after a U.S. drone strike killed a top Iranian general in Baghdad and Tehran launched a retaliatory ballistic missile attack targeting American forces in Iraq. While offering no cause for Thursday's blaze, Iran's state-run IRNA news agency published a commentary addressing the possibility of sabotage by enemy nations such as Israel and the U.S. following other recent explosions in the country.