NASA could find aliens by teaching the SETI Institute how to interpret their radar readings and artificial signals, a new report suggests.
Researchers working for the US space agency’s Search for Extraterrestrial Intelligence (Seti) Institute say an international effort might help determine whether alien civilisations are very common, or rare.
The research also suggests future efforts to listen for a sign from other civilizations will use artificial signals rather than wireless radios.
“We see SETI as not an engineering question. It’s actually a question of doing a public policy for space. I believe SETI has the potential to change lives and to possibly save lives,” John C Marzulli, a SETI researcher and investigator at NASA’s Ames Research Center, said in a report to NASA published on Tuesday.
Marzulli was leading the research, published in the journal Science, to develop a framework for how the information sent back from space by a civilisation might be understood by scientists.
Each radio frequency emitted by Earth is generated by computers that use electricity to generate signals. To interpret these signals, astronomers use metadata provided by astronomers to piece together the data and decode what it is pointing towards. In simple terms, scientists can work out what kind of radio signal is generating, where it’s coming from and what it is looking for.
According to the researchers, synthetic computer intelligence built on the unique and unwieldy mathematics used to capture data from the atmosphere can “interpret” future radio signals to determine their source and their function.
“We believe that the most exciting opportunities for SETI are not the radio radio efforts, which we have been supporting, but other kinds of experiments which use artificial intelligence,” Marzulli said.
The new artificial intelligence technique is a derivative of cloud computing – the process by which a digital program runs in distributed computing centres around the world.
One of the problems with cloud computing is how efficiently it consumes energy, mainly because processing cycles and computing power can run concurrently. Using artificial intelligence, the researchers built an algorithm to divide workloads into two main classes: programs that are “all consuming”, and those that are “all consuming and some doing something”.
This model can be used to analyse and decipher other kinds of electromagnetic signals – whether they are artificial, external to the satellite or transmitted from Earth, which is the most common type. The researchers suggest that tests could be conducted to see if artificial signals from space were interpreted correctly.
Many SETI programmes have been set up to look for a handful of artificially generated signals on the sun, known as fast radio bursts. But analysing signals at this rate would be too slow for practical use, the researchers say.
Planetary scientists, who have been working for decades to improve understanding of planets around other stars, are increasingly searching for signs of life in the biosphere. Part of this involves looking for signs of organic gases – which can indicate that a planet is warmer than Earth but hotter than hydrogen or helium – over long distances.
Astronomers working for Nasa’s Kepler mission recently announced in May that they had detected wibbly-wobbly gravity waves – small ripples in space and time caused by a collision between two black holes. These waves are extremely faint, but the faint signal gave a rough estimate of the distance between them. They believe they have found traces of living organisms in space.
By building artificial intelligence into SETI, scientists could use tools in cloud computing to test whether digital signals are interpreted correctly.
Scientists have already begun moving these technologies from their largely geeky environments into other parts of the government. Separately, two US networks – the Defense Advanced Research Projects Agency (Darpa) and the National Science Foundation (NSF) – have released a request for proposals to study applying artificial intelligence to biological challenges.