British researchers have identified 50 new planets using artificial intelligence, marking a technological breakthrough in astronomy science. 

The development was announced in a news statement by the research team from the University of Warwick.  

The research team includes astronomers as well as computer scientists. 

According to the statement, these 50 exoplanets (planets outside our solar system), which orbit around other stars, range in size from as large as Neptune to smaller than Earth. 

Some of their orbits are as long as 200 days, and some as short as a single day. 

"In terms of planet validation, no-one has used a machine learning technique before," said David Armstrong of the University of Warwick, one of the study's lead authors, in the news release. 

"Machine learning has been used for ranking planetary candidates but never in a probabilistic framework, which is what you need to truly validate a planet."

According to the University the team built a machine learning algorithm to dig through old NASA data containing thousands of potential planet candidates.

Normally when scientists search for exoplanets they look for dips in light that indicate a planet passing between the telescope and their star. 

But these dips could also be caused by other factors, like background interference or even errors in the camera.

As opposed to the other technologies however, the new AI ( the breakthrough AI) which the team used can tell the difference.

Therefore, the team trained the algorithm by having it go through data collected by NASA's Kepler Space Telescope (now-retired), which spent nine years in deep space on a world-hunting mission. 

Once the algorithm learned to accurately separate real planets from false positives, it was used to analyze old data sets that had not yet been confirmed hence discovering the 50 exoplanets, reads part of the statement. 

Following this milestone, researchers hope to use the AI for current and future telescope missions. 

Once properly trained, the AI is faster than current techniques, and can be automated to perform on its own, the University in a statement. 

The algorithm could "validate thousands of unseen candidates in seconds," the study indicated. 

Besides, because it's based on machine learning, it can still be improved upon, and can continue to become more effective with each new discovery.

In their study, the research team argues that astronomers should use multiple validation techniques — including this new algorithm — to confirm future exoplanet discoveries. 

According to Armstrong, to date, about 30% of all known planets were validated using only one method, which is "not ideal,". 

We still have to spend time training the algorithm, but once that is done it becomes much easier to apply it to future candidates," he added. 

Additionally, Armstrong said that the algorithm could be used to analyze data from NASA's Transiting Exoplanet Survey Satellite (TESS). 

TESS is an all-sky survey mission technology. 

By mapping about 75% of the sky, TESS identified 66 new confirmed exoplanets, and nearly 2,100 potential candidates by the end of its primary mission which ended on July 4, according to CNN.