Horizon asked a selection of scientists featured in the magazine last year for their opinion on priorities for 2020. READ: Prosperity is about more than money. But what else should count? '2020 is a super-year for international policy action,' said Sandrine Dixson-Declève, co-president of global think tank the Club of Rome and chair of an expert EU group on the economic and societal impact of research (ESIR). An oceans treaty will be agreed, biodiversity targets announced, it's the first opportunity for nations to increase their climate goals, and the start of the decade to scale action for the 2030 Sustainable Development Goals, she says.

A PhD student in Australia has developed an automated system to detect, in real time, mysterious radio pulses emanating from the deep universe. The fleeting signals known as fast radio bursts (FRBs) have baffled scientists since they were first discovered in 2007 by a team poring through archival data. Since then, there have been numerous other instances of their detection – though what exactly causes them remains a mystery. The latest breakthrough could be a huge leap forward for scientists' ability to understand the nature of fast radio bursts, allowing them to be captured in detail the moment they reach Earth. A PhD student in Australia has developed an automated system to detect, in real-time, mysterious radio pulses emanating from the deep universe.

Scientists have developed an automated system that uses artificial intelligence (AI) to detect and capture fast radio bursts (FRBs) in real-time. FRBs are mysterious and powerful flashes of radio waves from space, thought to originate billions of light years from the Earth, said researchers from Swinburne University of Technology in Australia. They last for only a few milliseconds or a thousandth of a second and their cause is one of astronomy's biggest puzzles. Astronomical Society, has already identified five bursts -- including one of the most energetic ever detected, as well as the broadest. Wael Farah from Swinburne University of Technology trained the on-site computer at the Molonglo Radio Observatory in Australia to recognise the signs and signatures of FRBs, and trigger an immediate capture of the finest details seen to date.

Researchers at Breakthrough Listen, a SETI project led by the University of California, Berkeley, have now used machine learning to discover 72 new fast radio bursts from a mysterious source some 3 billion light years from Earth. Fast radio bursts are bright pulses of radio emission mere milliseconds in duration, thought to originate from distant galaxies. The source of these emissions is still unclear, however. Theories range from highly magnetized neutron stars blasted by gas streams from a nearby supermassive black hole, to suggestions that the burst properties are consistent with signatures of technology developed by an advanced civilization. "This work is exciting not just because it helps us understand the dynamic behavior of fast radio bursts in more detail, but also because of the promise it shows for using machine learning to detect signals missed by classical algorithms," said Andrew Siemion, director of the Berkeley SETI Research Center and principal investigator for Breakthrough Listen, the initiative to find signs of intelligent life in the universe.

Artificial intelligence is invading many fields, most recently astronomy and the search for intelligent life in the universe, or SETI. Researchers at Breakthrough Listen, a SETI project led by the University of California, Berkeley, have now used machine learning to discover 72 new fast radio bursts from a mysterious source some 3 billion light years from Earth. Fast radio bursts are bright pulses of radio emission mere milliseconds in duration, thought to originate from distant galaxies. The source of these emissions is still unclear, however. Theories range from highly magnetized neutron stars blasted by gas streams from a nearby supermassive black hole, to suggestions that the burst properties are consistent with signatures of technology developed by an advanced civilization.

One of astronomy's controversial mysteries is now being investigated by artificial intelligence. A team of researchers at Breakthrough Listen, a Search for Extraterrestrial Intelligence (SETI) project spearheaded by the University of California, Berkeley, have developed a machine learning algorithm to sift through cosmic data and identify fast radio bursts, strange and energetic pulses thought to emanate from far-off galaxies. No one knows for sure what causes these radio emissions but theories abound -- from highly magnetized neutron stars battling black holes to signs of alien life. In a recent study, the SETI researchers used a customized A.I. system to discover dozens of previously unidentified fast radio bursts from a source some 3 billion light-years away. The fast radio bursts were picked out from a data set that had already been analyzed by astronomers.

Breakthrough Listen researchers used artificial intelligence to search through radio signals recorded from a fast radio burst, capturing many more than humans could. Artificial intelligence is invading many fields, most recently astronomy and the search for intelligent life in the universe, or SETI. Researchers at Breakthrough Listen, a SETI project led by the University of California, Berkeley, have now used machine learning to discover 72 new fast radio bursts from a mysterious source some 3 billion light years from Earth. Fast radio bursts are bright pulses of radio emission mere milliseconds in duration, thought to originate from distant galaxies. The source of these emissions is still unclear, however.

IMAGE: Breakthrough Listen researchers used artificial intelligence to search through radio signals recorded from a fast radio burst, capturing many more than humans could. They are using a similar algorithm to... view more Artificial intelligence is invading many fields, most recently astronomy and the search for intelligent life in the universe, or SETI. Researchers at Breakthrough Listen, a SETI project led by the University of California, Berkeley, have now used machine learning to discover 72 new fast radio bursts from a mysterious source some 3 billion light years from Earth. Fast radio bursts are bright pulses of radio emission mere milliseconds in duration, thought to originate from distant galaxies. The source of these emissions is still unclear, however.

The perennial optimists at the Search for Extraterrestrial Intelligence, or SETI, have joined the rest of the world in deploying AI to help manage huge data sets -- and their efforts almost instantly bore fruit. Seventy-two new "fast radio bursts" from a mysteriously noisy galaxy 3 billion miles away were discovered in previously analyzed data by using a custom machine learning model. To be clear, this isn't Morse code or encrypted instructions to build a teleporter, à la Contact, or at least not that we know of. But these fast radio bursts, or FRBs, are poorly understood and may very well represent, at the very least, some hitherto unobserved cosmic phenomenon. FRB 121102 is the only stellar object known to give off the signals regularly, and so is the target of continued observation.

Scientists have detected dozens more mysterious signals emanating from deep space, thanks to the help of artificial intelligence. Using machine learning techniques to analyze data from the Green Bank Telescope in West Virginia, the Breakthrough Listen team spotted 72 fast radio bursts (FRBs) coming from an object known as'the repeater.' Evidence of these strange signals has piled in over the last few years – but, scientists still aren't quite sure what's causing them. Using machine learning techniques to analyze data from the Green Bank Telescope in West Virginia (pictured), the Breakthrough Listen team spotted 72 fast radio bursts (FRBs) coming from an object known as'the repeater' The latest batch of fast radio bursts was discovered in Breakthrough Listen's 2017 data after applying the machine learning techniques. The so-called'repeater,' an object known more formally as FRB 121102, has baffled researchers for years.