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NASA will host a media teleconference at 1 p.m. EST Thursday, Dec. 14, to announce the latest discovery made by its planet-hunting Kepler space telescope. The discovery was made by researchers using machine learning from Google. Machine learning is an approach to artificial intelligence, and demonstrates new ways of analyzing Kepler data.
The briefing participants are:
Paul Hertz, Astrophysics Division director at NASA Headquarters in Washington
Christopher Shallue , senior software engineer at Google AI in Mountain View, California
Andrew Vanderburg , astronomer and NASA Sagan Postdoctoral Fellow at The University of Texas, Austin
Jessie Dotson , Kepler project scientist at NASA's Ames Research Center in California’s Silicon Valley
Teleconference audio and visuals will stream live at:
www.nasa.gov...
3 p.m.: After the teleconference, join us for an AMA on Reddit's r/science. Send your questions for planet hunters from NASA's Kepler mission, Google AI and The University of Texas, Austin.
Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining,[8] where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning.[5]:vii[9] Machine learning can also be unsupervised[10] and be used to learn and establish baseline behavioral profiles for various entities[11] and then used to find meaningful anomalies.
Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to "produce reliable, repeatable decisions and results" and uncover "hidden insights" through learning from historical relationships and trends in the data.
Kepler's four-year data set, for example, consists of about 2 quadrillion possible orbits of planets. To verify the most promising signals of planets, automated tests, or sometimes human eyes, are typically used, but often the weakest signals are missed during this process. So, Shallue and Vanderburg thought there could be some more interesting exoplanet discoveries lurking in the data.
The two developed a neural network to search Kepler data for new planets. First, they trained the neural network to identify transiting exoplanets in a set of 15,000 previously vetted signals from the Kepler exoplanet catalog. Then, with the neural network having "learned" to detect the pattern of a transiting exoplanet, the researchers pointed their model at 670 star systems that already had multiple known planets and searched for weaker signals. Their assumption was that multiple-planet systems would be the best places to look for more exoplanets.
originally posted by: TEOTWAWKIAIFF
a reply to: interupt42
Seems to be that way when "major announcements" are made!
NASA will host a media teleconference at 1 p.m. EST Thursday, Dec. 14, to announce the latest discovery made by its planet-hunting Kepler space telescope. The discovery was made by researchers using machine learning from Google. Machine learning is an approach to artificial intelligence, and demonstrates new ways of analyzing Kepler data.
The briefing participants are:
Paul Hertz, Astrophysics Division director at NASA Headquarters in Washington
Christopher Shallue , senior software engineer at Google AI in Mountain View, California
Andrew Vanderburg , astronomer and NASA Sagan Postdoctoral Fellow at The University of Texas, Austin
Jessie Dotson , Kepler project scientist at NASA's Ames Research Center in California’s Silicon Valley
Teleconference audio and visuals will stream live at:
www.nasa.gov...
originally posted by: norhoc
a reply to: interupt42
What a dud of an announcement