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A group of scientists from NASA’s Goddard Space Flight Center
Since its launch, AI has attracted international attention with its wide range of capabilities. Even NASA (National Aeronautics and Space Administration) plans to use AI for long-term area exploration and other programs. Recently, in June 2020, the U.S.-area firm announced that it had shaped the AI formula that will lead scientists on their quest to look for symptoms of ancient life on Mars and other planets and moons. The program will be directed through the European Space Agency (ESA) “ExoMars” rover mission Rosalind Franklin. He will head to the red planet in 2022/23, before going further to moons such as Jupiter’s Europe, Enceladus and Titan of Saturn.
The news comes after a team of scientists from NASA’s Goddard Space Flight Center (GSFC) announced the first effects of the new AI systems, which are expected to be installed in area probes. These systems can identify the geochemical signatures of life from rock samples. The appealing feature of these intelligent systems is that their ability to analyze and what to transmit to Earth will trump the serious limits of long-distance data transmission in the search for life on remote planets. It’s those artificial intelligence systems that will be aboard the ExoMars mission. It will be an update of the existing rovers that charge us cash and time by sending all the knowledge to Earth because of their ability to optimize those knowledge sets.
Speaking of this ordinary feat, NASA GSFC Lead Researcher Victoria Da Poian calls it a visionary step in area exploration. “This means that, over time, we will have gone from the concept that humans care to the fullest of everything in the area, to the concept that computers have intelligent systems, and that they are trained to make safe decisions and can prioritize maximum appeal. or urgent information,” Da Poian said in a press release.
Da Poian, who is also an aerospace engineer at NASA Goddard, says scientists waste hours looking to perceive and analyze all knowledge. And assignments like ExoMars can help reduce your load by allowing real-time decisions to be made on the site. This AI mapping will begin in 3 phases. In the first phase, the AI formula includes knowledge and translates it into a readable format. In the moment phase, knowledge is grouped into other groups: attractive knowledge that catches the attention of scientists, not-so-attractive knowledge, and knowledge similar to anything scientists have observed before. When new raw knowledge is received, the software tells scientists which past samples fit the new knowledge. Finally, in the 3rd phase, which is lately in development, would be a neural network capable of suggesting similar knowledge of what scientists have taken note of in the past. This is synonymous with the algorithms used for Netflix recommendations.
Co-researcher Eric Lyness, also from GSFC, has reported that Mars’ knowledge of a rover can charge up to 100,000 times more than our cell phone knowledge, so it’s vital to make those bits as valuable as you can imagine scientifically. This is because we want smart tools for planetary exploration. He added: “During the first collection, the knowledge produced through the Mars Molecule Organic Analyzer (MOMA) toaster size study tool will not shout ‘I discovered life here’, but it will give us opportunities that will want to be analyzed.” The MOMA analyzer is considered the largest tool of the Rosalind Franklin rover. Grind samples, heat them, and reproduce mass spectrometry and fuel chromatography to identify molecules. In addition, the rover is expected to land more likely on Oxia Planum, near the Martian equator. This domain has a comfortable point of contact and also has the possibility of involving preserved biological signatures.
“These effects will tell us a lot about the geochemistry that the tools find. Our goal is for the formula to give orders to scientists, for example, our formula can simply say: “I have 91% confidence that this pattern corresponds to a pattern of real world pattern and I am 87% sure that it is phospholipids, similar to a pattern tested on July 24, 2018 and that’s what knowledge looked like.” We want humans to interpret the effects, but the first clarification will be the AI formula,” he continues. Eric is a software manager at the GSFC Global Environments Laboratory.
In addition, it is not the first or only example of the use of AI in area technologies. The National Astronomical Observatory of Japan (NAOJ) uses AI to effectively classify the morphologies of galaxies with an accuracy of 97.5%. Using the in-depth learning technique, a type of AI, he met about 560,000 spirally patterned galaxies in a vast set of symbol knowledge containing about 80,000 galaxies received with the Subaru telescope. A few months ago, Airbus, the German aeroarea center DLR and IBM introduced the CIMON – Crew Interactive Mobile Companion generation demonstration project – the first synthetic intelligence robot in the area. CIMON, which is created in 3-d printing generation, is a floating couple in the form of a floating sphere that can help astronauts in their daily work. It is able to listen and view and serves through object search, stock management, experiment documentation, videography and photography.
As we are about to explore what our solar formula is and our area beyond, looking for life beyond Earth or in the search for the surface of the moons and many others, LA AI will actually help us make the big leap for humanity, which Neil Armstrong had cited his landing. On the moon. This will be a key point and will advance our search for exploration of the area.
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