Researchers at the US Department of Energy's (DOE's) National Renewable Energy Laboratory (NREL) have developed a novel machine learning approach to quickly enhance the resolution of wind velocity data by 50 times and solar irradiance data by 25 times--an enhancement that has never been achieved before with climate data. The researchers took an alternative approach by using adversarial training, in which the model produces physically realistic details by observing entire fields at a time, providing high-resolution climate data at a much faster rate. This approach will enable scientists to complete renewable energy studies in future climate scenarios faster and with more accuracy. "To be able to enhance the spatial and temporal resolution of climate forecasts hugely impacts not only energy planning, but agriculture, transportation, and so much more," said Ryan King, a senior computational scientist at NREL who specializes in physics-informed deep learning. Recommended AI News: Interlink Electronics Welcomes Aboard Edward Suski As Chief Product Officer King and NREL colleagues Karen Stengel, Andrew Glaws, and Dylan Hettinger authored a new article detailing their approach, titled "Adversarial super-resolution of climatological wind and solar data," which appears in the journal Proceedings of the National Academy of Sciences of the United States …

Artificial Intelligence (AI) -- and its attendant term, 'Machine Learning' (ML) -- is described as the capability of a computer system to perform tasks that normally require human intelligence, such as visual perception, speech recognition and decision-making. Almost all AI/ML examples in commercial as well as military use today rely on data stores that drive deep learning and natural language processing.[1] The defining feature of an AI/ML system is its ability to learn and solve problems. There has been a gradual change in our understanding of what exactly constitutes AI. While advancements in computer hardware and more efficient software have led to the development of AI systems, hitherto computer-resource-intensive tasks, such as optical character recognition (OCR) are now considered a routine technology and, hence, no longer included in any contemporary discussion of AI/ML.

A machine-learned AI system used to assess recidivism risks in Broward County, Fla., often gave higher risk scores to African Americans than to whites, even when the latter had criminal records. The popular sentence-completion facility in Google Mail was caught assuming that an "investor" must be a male.A celebrated natural language generator called GPT, with an uncanny ability to write polished-looking essays for any prompt, produced seemingly racist and sexist completions when given prompts about minorities. Amazon found, to its consternation, that an automated AI-based hiring system it built didn't seem to like female candidates.Commercial gender-recognition systems put out by industrial heavy-weights, including Amazon, IBM and Microsoft, have been shown to suffer from high misrecognition rates for people of color. Another commercial face-recognition technology that Amazon tried to sell to government agencies has been shown to have significantly higher error rates for minorities. And a popular selfie lens by Snapchat appears to "whiten" people's faces, apparently to make them more attractive.ADVERTISEMENTThese are not just academic curiosities.

Traceable, a startup developing an end-to-end cloud app security solution, today emerged from stealth with $20 million in venture equity financing. Newly flush with capital, CEO Jyoti Bansal intends to focus on acquiring customers globally while growing Traceable's team and accelerating R&D. Cloud-native apps are often built with hundreds or even thousands of API microservices (i.e., loosely coupled services), making them difficult to protect at scale. Gartner predicts that by 2022, API abuses will be the most frequent attack vector, which isn't surprising considering API calls represented 83% of web traffic as of 2018. Traceable ostensibly protects these APIs with machine learning algorithms that analyze app activity from the user and the session all the way down to the code.

Developing policy informed by science and technology is now more complex than ever. Policymakers must address supply chains, climate change, inequality, technological breakthroughs, misinformation and more. Using artificial intelligence (AI) to mine the literature could put policymaking on a sounder footing. Advanced big-data and natural-language-processing models enable decision makers to look beyond conventional indicators and expert discussions. Millions of scientific articles, patents and market reports can be readily analysed to identify megatrends or fading topics, and to provide predictive opportunities (see go.nature.com/31snkp5). Machine learning can create maps of national competencies and centres of excellence of science and technology.

HAYWARD, California – Robots that can cook -- from flipping burgers to baking bread -- are in growing demand as virus-wary kitchens try to put some distance between workers and customers. Starting this fall, the White Castle burger chain will test a robot arm that can cook french fries and other foods. The robot, dubbed Flippy, is made by Pasadena, California-based Miso Robotics. White Castle and Miso have been discussing a partnership for about a year. Those talks accelerated when COVID-19 struck, said White Castle Vice President Jamie Richardson.

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Skydio, a startup that makes autonomous drones that fly themselves with little human intervention, is entering the commercial drone market with its new X2 model. The X2 is Skydio's first non-consumer device and it's marketed toward government agencies, the military, and other organizations that require aerial surveillance or surveying, with its own built-in infrared thermal camera. The X2 announcement coincides with Skydio's new round of $100 million in funding, led by German multinational company Siemens' Next47 firm. Skydio first entered the market a little more than two years ago with the Skydio R1. The R1 was an autonomous drone that sported impressive artificial intelligence-powered obstacle avoidance and other sensors and software features that let it seamlessly fly itself through complex outdoor environments like wooded trails while following subjects.

It is not surprising that Automation will change the industry landscape while impacting the employment percentage of the common man. Robots have been replacing humans over the past decades in several countries. While some experts believe that it will lead to a future without work, others are unconvinced of this forecast. A study co-authored by Daron Acemoglu, an MIT economist and Pascual Restrepo, an assistant professor of economics at Boston University, states the statistics on this trend and how the impact of robots differs by industry and region and may play a notable role in exacerbating income inequality in the USA. According to the study, from the period of 1990 to 2007, the addition of one robot per 1000 workers reduced the national employment-to-population ratio by an average of 0.2 percent.

The Pentagon's Joint Artificial Intelligence Center is focused on overlaying artificial intelligence tools on the military's mega information-sharing platform effort, called Joint All Domain Command and Control. Nand Mulchandani, JAIC's acting director, told reporters during a July 8 news briefing the center is "spending a lot of time and resources focused on building the AI components on top of JADC2," which is a patchwork quilt of platforms to improve coordination and information sharing. This involves figuring out how to build AI components, such as data, AI modeling, training and deployment, across all domains including cyber, he said. Mulchandani said JAIC is also investing in cognitive assistance technologies, helping human operators make better decisions, using "predictive analytics or picking out particular things of interest, and those types of information overload cleanup." Working through objections to the Defense Department's use of AI in weapons systems is still a chief concern, however.