AI News, Radio Waves artificial intelligence

Mysterious signal from deep space is repeating in 16-day cycle

Fast radio bursts (FRBs) are perhaps the most mysterious anomaly in space, with many having unknown origins.

According to a new study, an FRB has been spotted coming from a galaxy 500 million light-years from Earth and it's repeating every 16 days.

Known as FRB 180916.J0158+65, this FRB sends out radio wave bursts for a period of four days, stops for a period of 12 days, then repeats itself.

“The differences between repeating and non-repeating fast radio bursts are thus less clear and we think that these events may not be linked to a particular type of galaxy or environment.

It's unclear what's causing the pattern to repeat, but the study's abstract suggests there is "a mechanism for periodic modulation either of the burst emission itself, or through external amplification or absorption, and disfavor models invoking purely sporadic processes."

It's possible the FRB could be orbiting a compact object, for example, a black hole, causing its pattern to repeat, the researchers added in the study.

Astronomers have found a deep space radio burst that pulses every 16 days - MIT Technology Review

A recently discovered fast radio burst turns out to be pulsing on a steady 16-day cycle, marking the first time scientists have been able to see a specific tempo from one of these mysterious signals.

The bursts arrived in four-day phases (sometimes with multiple bursts, sometimes without bursts) followed by 12 days of silence, indicating that the source producing the FRB operated on a regular 16-day cycle.

The plot thickens: The fact that the FRB has an overall 16-day cycle but the four-day window varies between zero signals and multiple signals suggests the source might be orbiting a massive object of some kind (such as low-mass black hole) that perhaps stimulates or eclipses emission of the signal based on the orbital period.

“Extraterrestrial Technosignatures” –AI of the Future Could Reveal the Incomprehensible

Colombano at NASA’s Ames Research Center not involved in the Ceres experiment, “may just be a tiny first step in a continuing evolution that may well produce forms of intelligence that are far superior to ours and no longer based on carbon “machinery.” The result of De la Torre’s intriguing visual experiment calls into question the application of artificial intelligence to the search for extra-terrestrial intelligence (SETI) where advanced and ancient technological civilizations may exist but be beyond our comprehension or ability to detect.

“We weren’t alone in this, some people seemed to discern a square shape in Vinalia Faculae, so we saw it as an opportunity to confront human intelligence with artificial intelligence in a cognitive task of visual perception, not just a routine task, but a challenging one with implications bearing on the search for extraterrestrial life (SETI), no longer based solely on radio waves,”

The team of this neuropsychologist from the University of Cadiz, who has already studied the problem of undetected non terrestrial intelligent signals (the cosmic gorilla effect), now brought together 163 volunteers with no training in astronomy to determine what they saw in the images of Occator.

These results, published in the Acta Astronautica journal, have allowed researchers to draw several conclusions: “On the one hand, despite being fashionable and having a multitude of applications, artificial intelligence could confuse us and tell us that it has detected impossible or false things,”

AI Might Make Us Mistakenly Think We’ve Found Aliens, Says Study

While some researchers search for biological signs of life beyond Earth, others are scouring the cosmos for technosignatures — evidence of the kind of technological activity we might expect from an advanced alien civilization.

De la Torre said in a news release, “not just a routine task, but a challenging one with implications bearing on the search for extraterrestrial life (SETI), no longer based solely on radio waves.” At the start of the study, which was published in the journal Acta Astronautica, the team asked 163 volunteers, none of whom had any astronomy training, to look at images of Occator and tell them what they saw.

“Both people and artificial intelligence detected a square structure in the images, but the AI also identified a triangle,” De la Torre said, “and when the triangular option was shown to humans, the percentage of persons claiming to see it also increased significantly.” In reality, the triangle shape was likely just a mix of shadows and light and not anything alien, according to De la Torre.

New Breakthrough in Coronavirus Research Uses GPU-Accelerated Software to Support Treatment Development

The article below is a guest post by Deepwave Digital, a technology company working to incorporate AI into radio frequency and wireless technology.

This network, the Citizens Broadband Radio Service (CBRS), will be the first autonomous spectrum sharing service provided by the telecommunications industry that leverages real time RF sensing.

When no priority users are present, the spectrum may be reallocated for commercial networks to provide new enterprise services or additional bandwidth to existing services.

For CBRS, the Deepwave team has implemented a deep neural network (DNN) on the AIR-T that is capable of detecting, classifying, and reporting the presence of priority users with extreme accuracy.

By leveraging the AIR-T and its AirStack development environment, the Deepwave team has demonstrated that enterprise-level signal processing solutions may be created, tested, and deployed on the Jetson product line and the AIR-T.

By GPU accelerating the popular SciPy Signal library, cuSignal demonstrates the capability for Python programmers to easily write GPU accelerated signal processing applications, making it even easier for DSP engineers to leverage the GPU.

Deepwave is currently evaluating cuSignal for inclusion in future releases and comparing it against our traditional workflow of using CUDA, cuFFT, and other software libraries directly.