UFO researchers believe that artificial intelligence will be able to identify whether flying objects are a bird or a plane … or even something unknown to our planet.
Avi Loeb, the chair of Harvard’s Department of Astronomy claims the human race will be able to determine if alien visitors walk among us with AI equipment that can track the correct data.
The author of Extraterrestrial plans to build 100 specialized telescopes equipped with wide-angle lenses, infrared technology, radio receivers and an audio system to help identify UFOs and monitor other planets under the Galileo Project.
The privately-funded project aims to “bring the search for extraterrestrial technological signatures of Extraterrestrial Technological Civilizations from accidental or anecdotal observations and legends into the mainstream of transparent, validated and systematic scientific research”.
The tech will be used to search for physical objects, rather than radio signals like the long-running Search for Extra-Terrestrial Intelligence (SETI) Institute and Leob claims they can use AI to identify whether objects are coming from Earth or alien neighbors.
He told Sifted: “We will have an artificial intelligence system that will identify whether we are looking at a bird, a drone, an airplane or something else.”
Tzvi Weitzner, the Tel Aviv-based Timbr’s co-founder and chief strategy officer, has revealed that data scientists from his company are working on the algorithm for the project which would be identifying the objects.
He said: “The use of AI to analyze images is widely known, but in Galileo’s case it is not as simple as training a machine-learning algorithm to identify objects, just because we don’t know what we are looking for, or, more exactly, we are looking for objects that are not part of an existing image catalog that would serve to train a machine learning algorithm.
“I expect that the algorithms used to analyze images shall generate a continuous flow of unexplained objects, described with a set of data from the observations, which will require classification by characteristics (size, shape, color, location, time, source, etc) .
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“Data scientists will be able to easily discover and select the data required to create and train new machine learning algorithms that will further reduce false positives and eventually deliver a ‘clean’ list of observations that cannot be explained as known objects.”
The news comes just weeks after Leob made claims that the ‘best chance’ humans have of proving alien life is to look for city lights on other planets.
The theoretical physicist took inspiration for his theory from an interview with International Space Station Commander Terry Virtz who observed the glow of artificial lights across the nightside of the Earth.
He wrote on Medium: “Our best chance for imaging city lights outside the solar system is around the nearest star to the Sun, Proxima Centauri, a red dwarf located 4.25 light-years away.”