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NASA's New AI Tool Can Spot Craters On Mars - Analytics India Magazine

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Amid NASA's progress in AI research starting from ML model to predict hurricanes to partnering with Google to make quantum computing accessible, it has now developed a new AI tool to classify a cluster of craters on Mars. The launch of this new AI tool, built on a machine learning algorithm, was aimed at helping scientists to reduce their process time of scanning a single Context Camera image. Thus, researchers from Jet Propulsion Laboratory (JPL), created this tool also called an "automated fresh impact crater classifier", where for the "first time" researchers are leveraging AI to identify unknown craters on the Red Planet, stated by NASA, in their statement. According to their news release, typically scientists and researchers spend hours each day studying images to understand "dust devils, avalanches, and shifting dunes," and approximately 40 minutes to scan a single Context Camera image; however this tool will significantly reduce the processing time and advance the workflow massively. The launch of this tool is a part of a broader NASA's bigger effort -- COSMIC -- capturing onboard summarization to monitor image change that develops technologies for future generations of Mars orbiters.


Artificial intelligence helps classify new craters on Mars – IAM Network

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An innovative artificial intelligence (AI) tool developed by NASA has helped identify a cluster of craters on Mars that formed within the last decade.The new machine-learning algorithm, an automated fresh impact crater classifier, was created by researchers at NASA's Jet Propulsion Laboratory (JPL) in California -- and represents the first time artificial intelligence has been used to identify previously unknown craters on the Red Planet, according to a statement from NASA. Scientists have fed the algorithm more than 112,000 images taken by the Context Camera on NASA's Mars Reconnaissance Orbiter (MRO). The program is designed to scan the photos for changes to Martian surface features that are indicative of new craters. In the case of the algorithm's first batch of finds, scientists think these craters formed from a meteor impact between March 2010 and May 2012. Related: Latest photos from NASA's Mars Reconnaissance Orbiter"AI can't do the kind of skilled analysis a scientist can," Kiri Wagstaff, JPL computer scientist, said in the statement.


Artificial intelligence helps classify new craters on Mars

#artificialintelligence

An innovative artificial intelligence (AI) tool developed by NASA has helped identify a cluster of craters on Mars that formed within the last decade. The new machine-learning algorithm, an automated fresh impact crater classifier, was created by researchers at NASA's Jet Propulsion Laboratory (JPL) in California -- and represents the first time artificial intelligence has been used to identify previously unknown craters on the Red Planet, according to a statement from NASA. Scientists have fed the algorithm more than 112,000 images taken by the Context Camera on NASA's Mars Reconnaissance Orbiter (MRO). The program is designed to scan the photos for changes to Martian surface features that are indicative of new craters. In the case of the algorithm's first batch of finds, scientists think these craters formed from a meteor impact between March 2010 and May 2012.


Generative Models (GANs) - Top Videos, Papers & more

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The past few years has seen great advancement in the world of generative models (GANs), becoming one of the most promising approaches toward collecting all of the easily accessible open-source information available and using it to develop models and algorithms to analyze and understand. The below blog brings together a general explanation of GANs, alongside the newest papers, video presentations, application methods and more. For a model to be "Generative" must fit in a class of statistical models which contrast against discriminative models. Simply, a generative model is one that can generate new data after learning from the dataset. Therefore, "Generative" describes a class of statistical models that contrasts with discriminative models.


Python programming: Microsoft's latest beginners' course looks at developing for NASA projects

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Microsoft has teamed up with NASA to create three project-based learning modules that teach entry-level coders how to use the Python programming language and machine-learning algorithms to explore space, classify space rocks and predict weather and rocket-launch delays. Students need a Windows, Mac or Linux computer to complete the modules, which teach the basics of what a programming language is, how to use Microsoft's Visual Studio Code (VS Code) code editor, install extensions for Python, and how to run a basic Jupyter Notebook within VS Code – some of the key ingredients to get started on a machine-learning project. Microsoft's learning modules don't actually teach anything about how to code in Python but rather offer some ideas, focussing on NASA's space exploration activities, to illustrate how Python could be used in space exploration. It might suit students learning to code who need some ideas for how that knowledge could be applied to solving challenges NASA faces, or those considering programming to see how Python could be used. The Introduction to Python for Space Exploration module contains eight units and offers background on NASA's Artemis lunar exploration program, which aims to land the first woman and the next man on the moon by 2024.


NASA uses Artificial Intelligence to find new Craters on Mars

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Pasadena, CA – Sometime between March 2010 and May 2012, a meteor streaked across the Martian sky and broke into pieces, slamming into the planet's surface. The resulting craters were relatively small – just 13 feet (4 meters) in diameter. The smaller the features, the more difficult they are to spot using Mars orbiters. But in this case – and for the first time – scientists spotted them with a little extra help: artificial intelligence (AI). It's a milestone for planetary scientists and AI researchers at NASA's Jet Propulsion Laboratory in Southern California, who worked together to develop the machine-learning tool that helped make the discovery.


Scientists use AI to find tiny craters on Mars

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The High-Resolution Imaging Science Experiment (HiRISE) camera aboard NASA's Mars Reconnaissance Orbiter took this image of a crater cluster on Mars, the first ever to be discovered by artificial intelligence (AI). NASA said, "These craters were created by several pieces of a single meteor. The largest of the craters is about 13 feet (4 meters) wide. In total, the craters span about 100 feet (30 meters) of the red planet's surface. The craters were found in a region called Noctis Fossae, located at latitude -3.213, longitude 259.415."


AI Is Helping Scientists Discover Fresh Craters on Mars

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Sometime between March 2010 and May 2012, a meteor streaked across the Martian sky and broke into pieces, slamming into the planet's surface. The resulting craters were relatively small - just 13 feet (4 meters) in diameter. The smaller the features, the more difficult they are to spot using Mars orbiters. But in this case - and for the first time - scientists spotted them with a little extra help: artificial intelligence (AI). It's a milestone for planetary scientists and AI researchers at NASA's Jet Propulsion Laboratory in Southern California, who worked together to develop the machine-learning tool that helped make the discovery.


New Research Shows How Deep Learning Can Help Advance Neural Degeneration Studies – IAM Network

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Artificial intelligence (AI) and deep learning models can help advance research on neural degeneration, showing its capabilities in identifying and categorizing its forms on a model organism. Using the organism Caenorhabditis elegans or the roundworm – a 1-millimeter near-transparent nematode – researchers used deep learning to conduct a quantitative image-based analysis of neural degeneration patterns observed in the PVD neuron of the organism. Researchers from North Carolina State University have detailed their work in the journal BMC Biology, September 23. The worms were found alive last week in a biological container that was among the debris from the Space Shuttle Columbia recovered in East Texas. The worms are descendants of those that were part of an experiment that flew on Columbia's last mission before the spacecraft broke up on reentry February 1, killing all seven astronauts.


Tech Behind Nasa's ML Model To Predict Hurricane Intensity

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With part of the world dealing with the adverse effects of hurricanes and intense tropical cyclones, it has become imperative for researchers and scientists to develop a way to predict and analyse these hurricane patterns. Thus in an attempt to forecast future hurricane intensity, scientists at NASA's Jet Propulsion Laboratory in Southern California have proposed a machine learning model that claims to predict rapid-intensification events of the future accurately. The critical factor in understanding the intensity of a hurricane is the wind speed. Traditionally it has been a challenge to predict the severity of storms or hurricanes while it's brewing. However, NASA's new ML model can improve the accuracy of the prediction and provide better results.