The newly found planet has made our Earth's solar system and Kepler-90's solar system quite similar as both have eight planets revolving around their orbit.
The Kepler space telescope searches for these exoplanets -those planets orbiting stars beyond our solar system - by measuring how the brightness of a star changes when a planet transits, or crosses in front of its disk. But Google Artificial Intelligence - which enables computers to "learn" - looked at archival data obtained by NASA's planet-hunting Kepler telescope and uncovered the eighth planet.
NASA used neural networking to find potential exoplanets from Kepler telescope data.
Kepler-90i is a rocky planet that is about 30 percent larger than the Earth.
Mercury, the first and closest planet in our Solar Systems, takes 88 days to orbit the sun.
"Kepler-90i is not a place I'd like to go visit", Vanderburg said, adding that the planet probably has an average temperature of about 800 degrees Fahrenheit (426 degrees Celsius).
"You have small planets inside and big planets outside, but everything is scrunched in much closer", said Vanderburg, a NASA Sagan Postdoctoral Fellow and astronomer at the University of Texas at Austin.
For the first time, our Solar System's star is not the only one surrounded by eight different planets. Then, with the neural network having "learned" to detect the pattern of a transiting exoplanet, the researchers directed their model to search for weaker signals in 670 star systems that already had multiple known planets.
Many scientists had hoped that Kepler's original mission would last far longer than four years, allowing it to find a significant number of Earth-sized planets in Earth-sized orbits around sun-like stars.
While machine learning has been applied before to the Kepler telescope΄s data, it is believed to be the first time that the technology has unearthed a new world. A combination of computer software running automated tests and human judgement are used to verify the most promising results, but that means that the weakest signals are often missed. After finding thousands of exoplanets in a tiny area of the night sky near the constellation of Lyra over the Milky Way, Kepler's gyroscopes were damaged in 2014.
That's why Shallue and Vanderburg developed a neural network, a type of machine-learning technique that can learn to identify patterns in large data sets.
"There are simply too many weak signals to examine using the existing methods", Shallue said.
But finding them will be a challenge, said study coauthor Christopher Shallue, a senior software engineer with the Google AI research team.
Shallue and Vanderburg plan to keep up the hunt, using the program to scour the 150,000-plus stars observed by Kepler.
Besides, he said that this information would be a "treasure trove" that other experts will be able to use fpr further research.
The TRAPPIST-1 star, an ultra-cool dwarf, has seven Earth-size planets orbiting it. In the statement, the agency says that with the help of advanced computer analysis they have identified two new planets around distant stars. After gazing at one patch of space for four years, the spacecraft now is operating on an extended mission and switches its field of view every 80 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.