Artificial intelligence is used in a wide range of industries today. One particular implementation of AI that has made headlines recently is in relation to space exploration. NASA reports that they have partnered up with Google, IBM, and Intel in order to develop advanced artificial intelligence technologies that would help drive the future of space sciences. In this post, we take a closer look at how artificial intelligence will be used to drive the mission to explore space. NASA has already started with these implementations, and we are sure to see several new technologies arise from this partnership in the near future.

Our planet-hunting telescopes have gotten so good at their jobs that they've located thousands of possible planets outside our solar system. That means scientists have to sift through a whole lot of data to figure out what's a real planet and what's a pretender. A research team led by David Armstrong at the University of Warwick in the UK has worked out how to harness artificial intelligence to handle some of the heavy-lifting of planet confirmation, giving astronomers a new tool to help validate distant worlds. Get the latest science stories from CNET every week. Telescopes like NASA's Transiting Exoplanet Survey Satellite (TESS) look for a telltale dip in brightness that indicates something is passing by a star.

Fifty potential planets have had their existence confirmed by a new machine learning algorithm developed by University of Warwick scientists. For the first time, astronomers have used a process based on machine learning, a form of artificial intelligence, to analyse a sample of potential planets and determine which ones are real and which are'fakes', or false positives, calculating the probability of each candidate to be a true planet. Their results are reported in a new study published in the Monthly Notices of the Royal Astronomical Society, where they also perform the first large scale comparison of such planet validation techniques. Their conclusions make the case for using multiple validation techniques, including their machine learning algorithm, when statistically confirming future exoplanet discoveries. Many exoplanet surveys search through huge amounts of data from telescopes for the signs of planets passing between the telescope and their star, known as transiting. This results in a telltale dip in light from the star that the telescope detects, but it could also be caused by a binary star system, interference from an object in the background, or even slight errors in the camera.

In a groundbreaking first, British researchers have identified 50 new planets using artificial intelligence. A research team led by David Armstrong at the University of Warwick in the United Kingdom created a machine learning algorithm and trained it using data on confirmed planets and false-positives from NASA's retired Kepler mission. The scientists then used the algorithm on a dataset of still unconfirmed planetary candidates from Kepler, resulting in fifty new confirmed planets and the first to be validated by machine learning. Also read: Growing dent on Earth's magnetic field can damage satellites and navigation Their results were reported in a new study published in the Monthly Notices of the Royal Astronomical Society, where they also perform the first large scale comparison of such planet validation techniques. Previous machine learning techniques have ranked candidates, but never determined the probability that a candidate was a true planet by themselves, a required step for planet validation. By confirming that these fifty planets are real, astronomers can now prioritise these for further observations with dedicated telescopes.

Researchers from the University of Warwick have identified 50 new planets using machine learning algorithms that were used to determine the real, fake or false positives by calculating the probability of each candidate to be a true planet. This is the first time ever that astronomers have used used a process based on machine learning to analyse a sample of potential planets. Previous ML techniques have ranked candidates, but never determined the probability that a candidate was a true planet by themselves. The ML algorithm was built on large samples of thousands of candidates found by telescope missions such as NASA's Kepler and TESS, by researchers from Warwick's Departments of Physics and Computer Science, as well as The Alan Turing Institute. It was trained to recognise real planets using two large samples of confirmed planets and false positives from the now-retired Kepler mission.

For the first time, astronomers have used a process based on machine learning, a form of artificial intelligence, to analyse a sample of potential planets and determine which ones are real and which are'fakes', or false positives, calculating the probability of each candidate to be a true planet. Their results are reported in a new study published in the Monthly Notices of the Royal Astronomical Society, where they also perform the first large scale comparison of such planet validation techniques. Their conclusions make the case for using multiple validation techniques, including their machine learning algorithm, when statistically confirming future exoplanet discoveries. Many exoplanet surveys search through huge amounts of data from telescopes for the signs of planets passing between the telescope and their star, known as transiting. This results in a telltale dip in light from the star that the telescope detects, but it could also be caused by a binary star system, interference from an object in the background, or even slight errors in the camera. These false positives can be sifted out in a planetary validation process.

Scientists in the United Kingdom have developed artificial intelligence that has identified 50 new planets. The University of Warwick says a new machine learning algorithm developed by astronomers and computer scientists at the school and The Alan Turing Institute identified the exoplanets by examining data from NASA's now-retired Kepler telescope mission. The results are published in the journal Monthly Notices of the Royal Astronomical Society. Astronomers generally sift through large amounts of telescope data looking for a dip in light to indicate a planet passing between the telescope and its star. However, among the thousands of planetary candidates, there are many false positives caused by a binary star system, interference from an object in the background or camera errors.

Using artificial intelligence, they've identified possibly 50 new planets from old NASA data. University of Warwick astronomers and computer scientists built a machine learning algorithm, and with it they examined old data containing thousands of potential planet candidates. The new AI can hone in on details that help verify whether any are planets. The university said these 50 exo-planets, which orbit around other stars, range in size from as large as Neptune to smaller than Earth. Now that astronomers know the planets are real, they can prioritize them for further observation.

Hong Kong (CNN Business)British researchers have identified 50 new planets using artificial intelligence, marking a technological breakthrough in astronomy. Astronomers and computer scientists from the University of Warwick built a machine learning algorithm to dig through old NASA data containing thousands of potential planet candidates. It's not always clear, however, which of these candidates are genuine. When scientists search for exoplanets (planets outside our solar system), they look for dips in light that indicate a planet passing between the telescope and their star. But these dips could also be caused by other factors, like background interference or even errors in the camera.

An algorithm has confirmed the existence of 50 new planets in a world-first for machine learning in astronomy. Scientists from Warwick University made the discovery by analyzing data collected by space telescopes, such as NASA's Kepler and TESS. This passage produces a distinctive dip in light emerging from the star. However, this effect can also be caused by a binary star system, interference from other objects, or problems with the camera. The new system was designed to separate these false positives from observations of real planets.