A record-tying eighth planet has been found by NASA and Google in a faraway solar system, and like Earth, the new planet is the third rock from its sun but can orbit it in just 14 days.

Key points: Google helps NASA find Kepler-90i planet in faraway solar system

Google helps NASA find Kepler-90i planet in faraway solar system The solar system has as many planets as our own

The solar system has as many planets as our own It's the first time an artificial neural network has been used to find a new world

Even more amazing, machines and not humans made the discovery.

This eighth planet orbits the star known as Kepler-90, some 2,545 light years away.

Like Earth, this new planet, Kepler-90i, is the third rock from its sun.

But it is much closer to its sun, and is therefore a scorching 427 degrees Celsius at the surface.

In fact, all eight planets are scrunched up around this star, orbiting closer than Earth does to our sun.

This is the only eight-planet solar system found like ours — so far — tying for the most planets observed around a single star.

Planets in the Kepler-90 System are closer to its sun than planets in the Solar System. ( NASA )

Our solar system had nine planets until Pluto was demoted to a dwarf planet in 2006 by the International Astronomical Union, a decision that still stands.

Google used data collected by NASA's planet hunter, the Kepler Space Telescope, to develop its machine-learning computer program.

It focuses on weak planetary signals — so feeble and numerous it would take humans ages to examine.

While machine learning has been used before in the search for planets beyond our solar system, it is believed to be the first time an artificial neural network like this has been used to find a new world.

"This is a really exciting discovery, and we consider it to be a successful proof of concept to be using neural networks to identify planets, even in challenging situations where the signals are very weak," said Christopher Shallue, senior software engineer at Google in Mountain View, California.

Neither NASA nor Google expect to put astronomers out of business.

Mr Shallue sees this as a tool to help astronomers have more impact and increase their productivity.

"It certainly will not replace them at all," he said.

How Google crunched data that helped NASA

The research by Google and the University of Texas at Austin, that used data from NASA, raised the prospects of new insights into the universe by feeding data into computer programs that can churn through information faster and more in-depth than humanly possible, a technique known as machine learning.

In this case, software learned differences between planets and other objects by analysing thousands of data points, achieving 96 per cent accuracy, NASA said at a news conference.

The data came from the Kepler telescope, which NASA launched into space in 2009 as part of a planet-finding mission that is expected to end next year as the spacecraft runs out of fuel.

Kepler was designed to survey part of the galaxy to see how frequent Earth-size and potentially habitable planets are. ( Supplied: NASA )

The software's artificial "neural network" combed through data from about 670 stars, which led to the discovery of new planets.

"As the application of neutral networks to Kepler data matures, who knows what might be discovered," said Jessie Dotson, a NASA project scientist for the Kepler space telescope.

"I'm on the edge of my seat."

Mr Shallue and Andrew Vanderburg, an astronomer at the University of Texas at Austin, said they plan to continue their work by analysing Kepler data on more than 150,000 other stars.

Advancements in hardware and new techniques for machine learning have made it possible in recent years for automated software to tackle data analysis in science, finance and other industries.

Machine learning had not been applied to data acquired by the Kepler telescope until Mr Shallue came up with the idea.

"In my spare time, I started Googling for 'finding exoplanets with large data sets' and found out about the Kepler mission and the huge data set available," he said.

"Machine learning really shines in situations where there is so much data that humans can't search it for themselves."

AP/Reuters