The Kepler-90 planets have a similar configuration to our solar system, with small planets found orbiting close to their star, and the larger planets found farther away.
Google used data collected by NASA's keen planet hunter, the Kepler Space Telescope, to develop the machine-learning computer program.
Researchers trained a computer to learn how to identify the faint signal of transiting exoplanets in Kepler's vast archive of deep-space data.
NASA provided the AI with data collected by its Kepler Space Telescope.
"Machine learning really shines in situations where there is so much data that humans can't search it for themselves", Shallue told CNN. It is the first star known to support as many planets as are orbiting our own sun, and researchers believe that this is the first of many to come.
The new exoplanets are added to the growing list of known worlds found orbiting other stars. Called Kepler-90i, the earth-like planet has a surface temperature that exceed 800 °F which are unlikely for any form of life to exist.
Andrew Vanderburg, a NASA Sagan Postdoctoral Fellow and astronomer at the University of Texas at Austin, one of the researchers who developed the neural network, called Kepler-90 "a mini version of our solar system". Kepler-90h, the outermost planet in the system, orbits its star at a similar distance as Earth does to the Sun.
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"There's a lot of unexplored real estate in the Kepler-90 system", Vanderburg said, "and it would nearly be surprising to me if there weren't any more planets around this star".
"Maybe there are systems out there with so many planets that they make our eight-planet solar system seem ordinary".
Automated tests, and sometimes human eyes, are used to verify the most promising signals in the data, but the weakest signals often are missed, it said.
Our corner of the galaxy now shares the record with another system, Kepler 90, NASA and Google researchers announced Thursday. As in ours, Kepler's small, rocky planets are closest to the sun and its gas giants farthest away.
The planets in our system are far more spread out, however.
After the neural network was trained manually to identify passing exoplanets by analyzing 15,000 signals from the Kepler catalogue, it started giving correct results for true planets and false positives in 96 percent of cases.
And while that search was cut short, this AI-based method could allow researchers to go back into the data and fulfill that goal, said Seager, the deputy science director for NASA's Transiting Exoplanet Survey Satellite, set to launch next year. The research paper reporting this finding has been accepted for publication in The Astronomical Journal. In the statement, the agency says that with the help of advanced computer analysis they have identified two new planets around distant stars. It also has an orbit of 14.4 days.
"This will absolutely work alongside astronomers", Jessie Dotson, Kepler's project scientist at NASA's Ames Research Center in California's Silicon Valley, said in a press briefing. "I'm sure there are more firsts in the data waiting for people to find them".