Like in our solar system, the smaller planets in the Kepler-90 system are found nearer the star, and the bigger planets further away.
Kepler-90i was discovered by training a computer to scan massive amounts of star data collected by NASA΄s Kepler space telescope, which has scanned more than 150,000 stars since its launch in 2009.
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. The planet completes an orbit around its star in 14.4 Earth days which shows its fast pace.
NASA calculated its average temperature at about 800 degrees Fahrenheit (426 Celsius) - as hot as Mercury, the closest planet to the Sun.
All of the planets in the Kepler-90 system are closely situated to its star. In the Kepler 90 system, the most distant gas giant is 93 million miles from its star, the same distance from Earth to the sun.
For the first time, our Solar System's star is not the only one surrounded by eight different planets.
We can't claim our solar system is unique anymore.
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In recent years, to tackle data analysis in science, finance and other industries by automated software is possible due to advancements in hardware and new techniques in machine learning.
Machine learning has previously been used in searches of the Kepler database, and this continuing research demonstrates that neural networks are a promising tool in finding some of the weakest signals of distant worlds.
The find sets a new record for the most exoplanets around a single star and, for the first time, ties with our own.
Shallue said he became interested in applying Google΄s machine learning technology to astronomy when he learned that "the Kepler mission had collected so much data that it was impossible for scientists to examine it all manually". However, the weakest signals often are missed using these methods. Nasa's Astronomer Andrew Vanderburg and Google's Christopher Shallue worked together and trained the AI to pick out transits from 15,000 signals which were from exoplanets.
Regardless, it means that Kepler 90i, third rock though it may be, is too hot to be habitable.
It turned out the neural network correctly identified true planets and false positives 96 percent of the time. The result is an extremely stable system, similar to the seven planets in the TRAPPIST-1 system. "If you have a finer sieve then you will catch more rocks but you might catch more jewels, as well", he said.
Their findings will be published in The Astronomical Journal.
"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.
Shallue, a senior software engineer at Google AI, made a decision to apply a neural network concept to the vast amounts of Kepler data in his spare time.