Nearly 10,000 square kilometers of the Brazilian Amazon, an area the size of Lebanon, is at high risk of being cleared, according to a new tool using artificial intelligence technology to help forecast deforestation before it actually happens. Named PrevisIA (from the Portuguese previsão for "forecast" and IA for "artificial intelligence"), the tool analyzes images provided by European Space Agency satellites, and through an algorithm created by the Brazilian conservation nonprofit Imazon, finds areas prone to deforestation. Imazon studies published in scientific journals show that 95% of accumulated deforestation in the Amazon is located within a 5.5-kilometer (3.4-mile) radius of roads; 90% of annual fires occur 4 km (2.5 mi) from illegal roads built in the middle of the forest for logging, mining and land grabbing. In 2006, the non-profit started to monitor satellite images manually to find these roads before the area around them was cleared of trees, but the laborious and time-consuming work prevented it from scaling up -- a problem that the new technology aims to solve. The tool mapped so far the Brazilian Amazon, but could potentially be expanded to any forested area on Earth, the developers say.

Cassie the robot, invented at Oregon State University and produced by OSU spinout company Agility Robotics, has made history by traversing 5 kilometres outdoors in just over 53 minutes. The robot was developed under the direction of robotics professor Jonathan Hurst with a 16-month, $1 million grant from the Advanced Research Projects Agency of the U.S. Department of Defense. Since Cassie's introduction in 2017, OSU students funded by the National Science Foundation have been exploring machine learning options for the robot. "The Dynamic Robotics Laboratory students in the OSU College of Engineering combined expertise from biomechanics and existing robot control approaches with new machine learning tools," said Hurst, who co-founded Agility in 2017. "This type of holistic approach will enable animal-like levels of performance.

Enemies planning to attack often deliberately move in short, unexpected spurts to elude detection, emerge from undetectable areas and often use decoys or dummies to confuse overhead drones and surveillance planes. Insurgents, enemy vehicles, dismounted fighters and even some aircraft try to vary their patterns, change routines and regularly take specific steps to reduce their chances of being seen by drones. This is why there is so much work now taking Processing Exploitation Dissemination (PED) to a new level using artificial intelligence (AI) to sift through hours of video data and identify those critical moments of importance to commanders. The PED process, which regularly faces the challenging task of organizing massive volumes of incoming data from electro-optical/infrared cameras and infrared sensors, increasingly draws upon advanced computer algorithms to bounce new information against an existing database and perform analytics to support fast decisionmaking. With a similar goal in mind, Defense Advanced Research Projects Agency (DARPA) and several industry partners such as Raytheon Intelligence & Space, Northrop Grumman and BAE Systems are now fast-tracking a technological system engineered to find and transmit only images or pixels that have "changed" in order to pinpoint moments of relevance.

China on Monday dispatched its advanced J-20 stealth fighters for the first time as it began a joint five-day military drill with Russia in the Northwest region of the country. Besides the fighter plane, the People's Liberation Army (PLA) also deployed many of its newest weapons, including the Y-20 large transport planes, during the exercise, the first since the COVID-19 outbreak, reported The South China Morning Post. Over 10,000 troops will take part in the Zapad/Interaction-2021 exercise held at the Ningxia Hui autonomous region where innovative combat tactics like emergency troop and heavy weapon drops, long-range strikes by J-16 fighter bombers, and the use of drones, will be displayed. Liu Xiaowu, commander-in-chief of the Chinese troops, told state broadcaster CCTV that 81% of weapons being used in the drill were "brand new." "That includes the J-20 [stealth fighter jet], KJ-500 [airborne early-warning and control aircraft], and J-16, while surveillance and combat drones and new armored vehicles will also [be involved]," he said.

One evening in July of 2020, a woman named Kathryn went to the hospital in excruciating pain. A 32-year-old psychology grad student in Michigan, Kathryn lived with endometriosis, an agonizing condition that causes uterine-like cells to abnormally develop in the wrong places. Menstruation prompts these growths to shed--and, often, painfully cramp and scar, sometimes leading internal organs to adhere to one another--before the whole cycle starts again. For years, Kathryn had been managing her condition in part by taking oral opioids like Percocet when she needed them for pain. But endometriosis is progressive: Having once been rushed into emergency surgery to remove a life-threatening growth on her ovary, Kathryn now feared something just as dangerous was happening, given how badly she hurt.

Though NASA's Perseverance rover is on the Mars surface looking for signs of ancient life, the US space agency believes that robots looking in caves may help the US space agency find life outside this planet. As such, it is working with a number of contractors, including Boston Dynamics, on a project known as BRAILLE (Biologic and Resource Analog Investigations in Low Light Environments), exploring Mars-like caves on Earth in hopes that one day they will be used for future missions. Fully autonomous robots, like Boston Dynamics' Spot, could help explore these caves, which are believed to be hundreds of feet long and make communicating with Earth difficult, if not impossible. NASA is training robots like Boston Dynamics' Spot (pictured) to help traverse caves on Earth for future missions to Mars It is part of NASA's BRAILLE (Biologic and Resource Analog Investigations in Low Light Environments) project Fully autonomous robots could help explore Martian caves, believed to be hundreds of feet long. On Earth, NASA has incorporated its autonomy and artificial intelligence system, NeBula, into Spot, to help it explore the moon, Mars and other places in the solar system. 'Future potential human exploration missions can benefit from robots in many different ways,' Ali Agha, the project's research lead, told CBS News. 'Particularly, robots can be sent in precursor missions to provide more information about the destination before humans land on those destinations.

A Minecraft agent, operated by computer science master's student Ayushi Agrawal, explores a rainy environment. Researchers plan to test next-generation artificial intelligence skills within the game. Alan Wagner, assistant professor of aerospace engineering at Penn State, and Sarah Rajtmajer, assistant professor of information sciences and technology, recently received a $900,000 grant from the United States Air Force Office of Scientific Research to investigate next-generation artificial intelligence (AI) skills and perhaps make Steve a little smarter. The suggestion that people create abstract mental representations to plan for future events and solve multi-step tasks is identified in the literature as the Construal Level Theory, according to Wagner. People think about future events, consciously or unconsciously, using memories and experiences to generate a plan for a future situation.

Read about any new cybersecurity product today and the chances are that it will be keen to stress its use of AI in some form. But are we expecting too much from AI and are companies adopting it just because it's on trend? We spoke to Nadav Arbel, co-founder and CEO of managed SOC platform CYREBRO, to find out more about AI's role and why the human factor is still important. BN: Is the security industry placing too much faith in AI? NA: Many things from the virtual world are beginning to make significant impacts in the physical world. Artificial intelligence just made it possible for Virgin Galactic to kick off the commercialization of spaceflight, but a human is still at the helm.

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The large and still increasing popularity of deep learning clashes with a major limit of neural network architectures, that consists in their lack of capability in providing human-understandable motivations of their decisions. In situations in which the machine is expected to support the decision of human experts, providing a comprehensible explanation is a feature of crucial importance. The language used to communicate the explanations must be formal enough to be implementable in a machine and friendly enough to be understandable by a wide audience. In this paper, we propose a general approach to Explainable Artificial Intelligence in the case of neural architectures, showing how a mindful design of the networks leads to a family of interpretable deep learning models called Logic Explained Networks (LENs). LENs only require their inputs to be human-understandable predicates, and they provide explanations in terms of simple First-Order Logic (FOL) formulas involving such predicates. LENs are general enough to cover a large number of scenarios. Amongst them, we consider the case in which LENs are directly used as special classifiers with the capability of being explainable, or when they act as additional networks with the role of creating the conditions for making a black-box classifier explainable by FOL formulas. Despite supervised learning problems are mostly emphasized, we also show that LENs can learn and provide explanations in unsupervised learning settings. Experimental results on several datasets and tasks show that LENs may yield better classifications than established white-box models, such as decision trees and Bayesian rule lists, while providing more compact and meaningful explanations.