Scientists discover eight alien signals that define the technology using a new machine-learning technique.
The research, which was release on January 30 in the journal Nature Astronomy (opens in new tab), makes no claims to have discover concrete proof of extraterrestrial intelligence.
The results of a short search for the signals identify in this investigation were quiet.
However, the study’s authors assert that studying alien intelligence through the use of artificial intelligence is a promising direction.
According to research co-author and astronomer Cherry Ng of the University of Toronto, “we were astonish with how effectively this technique works in the hunt for intelligent alien life” (opens in new tab).
in a declaration (opens in new tab). “We have high hopes that artificial intelligence will enable us to more precisely measure the
aliens new approach
makes use of what research lead author and University of Toronto student Peter Ma (opens in new tab) refers to as “semi-unsupervise learning.
” Data from people may be labell in a way that facilitates algorithmic predictions to track machine learning. As an alternative, it may be carried out entirely supervise by picking out patterns randomly from a sizable .
A approach that combines both is semi-supervision also. In order to distinguish between radio signals from Earth and radio signals from other sources, researchers first develop an algorithm to do so.
also(Because aliens may travel great distances in space, radio waves are a preferred target in the hunt for extraterrestrial intelligent life (SETI)).
In order to reduce false alarms, researchers explored several algorithms. They looked at 150 terabytes of data.
The new approach makes advantage of Peter Ma’s, the lead also
These signals had two characteristics with signals that may have been produced by sentient aliens, according to scientists with Breakthrough Listen, a significant SETI initiative.
In contrast to local interference, which is often constantly there, they are present when we gaze at the star and disappear when we turn away,
according to Steve Croft (opens in new tab), project scientist for Breakthrough Listen at the Green Bank Telescope. Second, the signals’ frequency changes over time provide an appearance that they are far away from the telescope.