Brad Porter, who recently defended working conditions at Amazon warehouses, leaves to run Scale’s tech division.
Category: robotics/AI – Page 1680
Adapting the Intelligence Community
As machines become the primary collectors, analysts, consumers, and targets of intelligence, the entire U.S. intelligence community will need to evolve. This evolution must start with enormous investments in AI and autonomization technology as well as changes to concepts of operations that enable agencies to both process huge volumes of data and channel the resulting intelligence directly to autonomous machines. As practically everything becomes connected via networks that produce some form of electromagnetic signature or data, signals intelligence in particular will need to be a locus of AI evolution. So will geospatial intelligence. As satellites and other sensors proliferate, everything on earth will soon be visible at all times from above, a state that the federal research and development center Aerospace has called the “GEOINT Singularity.” To keep up with all this data, geospatial intelligence, like signals intelligence, will need to radically enhance its AI capabilities.
The U.S. intelligence community is currently split up into different functions that collect and analyze discrete types of intelligence, such as signals or geospatial intelligence. The RIA may force the intelligence community to reassess whether these divisions still make sense. Electromagnetic information is electromagnetic information, whether it comes from a satellite or an Internet of Things device. The distinction in origin matters little if no human ever looks at the raw data, and an AI system can recognize patterns in all of the data at once. The division between civilian and military intelligence will be similarly eroded, since civilian infrastructure, such as telecommunications systems, will be just as valuable to military objectives as military communications systems. Given these realities, separating intelligence functions may impede rather than aid intelligence operations.
Amazon received federal approval to operate its fleet of Prime Air delivery drones, the Federal Aviation Administration said Monday, a milestone that allows the company to expand unmanned package delivery.
The approval will give Amazon broad privileges to “safely and efficiently deliver packages to customers,” the agency said. The certification comes under Part 135 of FAA regulations, which gives Amazon the ability to carry property on small drones “beyond the visual line of sight” of the operator.
Amazon said it will use the FAA’s certification to begin testing customer deliveries. The company said it went through rigorous training and submitted detailed evidence that its drone delivery operations are safe, including demonstrating the technology for FAA inspectors.
Amazon Prime Air has cleared a regulatory hurdle, moving the online retail giant one step closer to dropping packages off at your doorstep with drones. The US Federal Aviation Administration on Saturday issued Amazon Prime Air a “a Part 135 air carrier certificate,” allowing it to begin commercial drone deliveries in the US.
“Amazon Prime Air’s concept uses autonomous [unmanned aircraft systems] to safely and efficiently deliver packages to customers,” said a spokesperson for the FAA on Monday. “The FAA supports innovation that is beneficial to the public, especially during a health or weather-related crisis.”
Amazon and other companies are trying to make drones the future of deliveries.
Scientists have unveiled the first ever “living robot,” an organism made up of living cells, which can move around, carry payloads, and even heal itself.
“All of the computational people on the project, myself included, were flabbergasted,” said Joshua Bongard, a computer scientist at the University of Vermont.
Robots made of frog skin and heart cells can crawl, move stuff and heal themselves.
FRAMOS, the global partner for Vision Technologies, has developed an industrial grade version of Intel’s® RealSense™ Suite to provide Gigabit Ethernet connectivity and an IP66 rated housing. The D435e industrial 3D GigE Vision camera leverages the advantages of easy-to-integrate 3D vision in rugged environments; enabling real-time positioning, orientation and tracking of robots, automated guided vehicles, and smart machines.
Christopher Scheubel, Product Manager for Intel at FRAMOS, says: “Providing an ethernet solution in combination with Intel’s® RealSense™ Technology is key to enabling 3D vision applications for industry that require longer cable lengths, dust and water resistance, and locked connections. Applications like robotic pick and place systems, automated guided vehicles (AGVs), retail observation, or automatic patient positioning, benefit from the very robust implementation and high usability.”
When Gary Kasparov was dethroned by IBM’s Deep Blue chess algorithm, the algorithm did not use Machine Learning, or at least in the way that we define Machine Learning today.
Adji Bousso Dieng will be Princeton’s School of Engineering’s first Black female faculty.
Not only has Adji Bousso Dieng, an AI researcher from Senegal, contributed to the field of generative modeling and about to become one of the first black female faculty in Computer Science in the Ivy League, she is also helping Africans in STEM tell their own success stories.
Dieng, who is currently a researcher at Google and an incoming computer science faculty at Princeton, works in an area of Artificial Intelligence called generative modeling.
IAIFI will advance physics knowledge — from the smallest building blocks of nature to the largest structures in the universe — and galvanize AI research innovation.
The U.S. National Science Foundation (NSF) announced last week an investment of more than $100 million to establish five artificial intelligence (AI) institutes, each receiving roughly $20 million over five years. One of these, the NSF AI Institute for Artificial Intelligence and Fundamental Interactions (IAIFI), will be led by MIT ’s Laboratory for Nuclear Science (LNS) and become the intellectual home of more than 25 physics and AI senior researchers at MIT and Harvard, Northeastern, and Tufts universities.
By merging research in physics and AI, the IAIFI seeks to tackle some of the most challenging problems in physics, including precision calculations of the structure of matter, gravitational-wave detection of merging black holes, and the extraction of new physical laws from noisy data.
Autonomous unmanned aerial vehicles (UAVs) have shown great potential for a wide range of applications, including automated package delivery and the monitoring of large geographical areas. To complete missions in real-world environments, however, UAVs need to be able to navigate efficiently and avoid obstacles in their surroundings.
Researchers at Luleå University of Technology in Sweden and California Institute of Technology have recently developed a nonlinear model predictive control (NMPC)-based computational technique that could provide UAVs with better navigation and obstacle avoidance capabilities. The NMPC approach they used, presented in a paper published in IEEE Robotics and Automation Letters, is based on the structure of OpEn (Optimization Engine), a parametric optimization software developed by Dr. Pantelis Sopasakis at Queen’s University Belfast.
“Our team has previously published several works on autonomous obstacle avoidance and navigation for UAVs,” Björn Lindqvist, one of the researchers who carried out the study, told TechXplore. “In our recent study, we set out to extend the notion of obstacle avoidance to include a direct consideration of moving or dynamic obstacles, using NMPC. Our objective was to offer a technical demonstration of how modern and intelligent control structures could allow UAVs to be used in, for example, urban environments where the surroundings are always moving and where collision avoidance is of great importance to ensure the safety of persons and other vehicles.”