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As Google looks to maintain pace in AI with the rest of the tech giants, it’s consolidating its AI research divisions.

Today Google announced Google DeepMind, a new unit made up of the DeepMind team and the Google Brain team from Google Research. In a blog post, DeepMind co-founder and CEO Demis Hassabis said that Google DeepMind will work “in close collaboration… cross the Google product areas” to “deliver AI research and products.”

As a part of Google DeepMind’s formation, Google says that it’ll create a new scientific board to oversee research progress and the direction of the unit, which will be led by Koray Kavukcuoglu, VP of research at DeepMind. Eli Collins, VP of product at Google Research, will join Google DeepMind as VP of product, while Google Brain lead Zoubin Ghahramani will become a member of the Google DeepMind research leadership team, reporting to Kavukcuoglu.

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The People’s Liberation Army (PLA) has successfully tested long-range artillery that can hit a bullseye from almost 10 miles away, says report.

According to the South China Morning Post (SCMP), China has tested an AI-powered, long-range artillery piece that is so accurate, it could hit a single person from 9.95 miles (16 km) away.

Images released from testing by the People’s Liberation Army (PLA) show shells hitting target boards in the bullseye, according to the traditionally state-friendly SCMP. The precision attained in the tests, which exceeded expectations, was significantly higher than that of any other big guns currently in service.

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One of the most difficult issues in space exploration is extending the lifespan of satellites. Even if the satellite’s system is operational, it can run out of fuel and become defunct.

Enter Orbit Fab. This startup is working on an innovative solution to make satellites reusable by developing gas stations in space to refuel them. We had written about them in 2021.

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Graphene is found to exhibit a magnetoresistance dwarfing that of all known materials at room temperature—a behavior that may lead to new magnetic sensors and help decipher the physics of strange metals.

One might expect that, two decades after its discovery, graphene would have exhausted its potential for surprises. But the thinnest, strongest, most conductive of all materials has now added another record to its tally. A collaboration that includes graphene’s codiscoverer and Nobel laureate Andre Geim of the University of Manchester, UK, reports that graphene can have a room-temperature magnetoresistance—a magnetic-field-induced change in electrical resistivity—that’s 100 times larger than that of any known material [1]. Graphene’s giant magnetoresistance could lead to novel magnetic-field sensors but also offer an experimental window into exotic quantum regimes of electrical conduction that might be related to the mysterious “strange metals.”

Magnetoresistance, which occurs both in bulk materials and multilayer structures, found a killer app in magnetic-field sensors such as those used to read data from magnetic memories. Researchers have long been interested in the limits of this phenomenon, which has led to discoveries of “giant,” “colossal,” and “extraordinary” forms of magnetoresistance. The associated materials exhibit resistivity changes of up to 1,000,000% when exposed to magnetic fields of several teslas (T). The largest effects, however, require extremely low temperatures that can only be reached with impractical liquid-helium cooling systems.

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Autism spectrum disorder (ASD) is a developmental disorder associated with difficulties in interacting with others, repetitive behaviors, restricted interests and other symptoms that can impact academic or professional performance. People diagnosed with ASD can present varying symptoms that differ in both their behavioral manifestations and intensity.

As a result, some often require far more support than others to complete their studies, learn new skills and lead a fulfilling life. Neuroscientists have been investigating the high variability of ASD for several decades, with the hope that this will aid the development of more effective therapeutic strategies tailored around the unique experiences of different patients.

Researchers at Weill Cornell Medicine have recently used machine learning to investigate the molecular and neural mechanisms that could underlie these differences among individuals diagnosed with ASD. Their paper, published in Nature Neuroscience, identifies different subgroups of ASD associated with distinct functional connections in the brain and symptomatology, which could be related to the expression of different ASD-related genes.

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The DarkSide experiment is an ambitious research effort aimed at detecting dark matter particle interactions in liquid argon using a dual-phase physics detector located at the underground Gran Sasso National Laboratory. These interactions could be observed by minimizing background signals, and this could be possible thanks to the remarkable discrimination power of the scintillation pulse of liquefied argon in the DarkSide-50 detector, which can separate nuclear recoil events associated with these interactions from more than 100 million electronic recoil events linked to radioactive background.

The large team of researchers involved in the DarkSide experiment has recently been using the detector to search for lighter particles. The results of a new search for dark matter–nucleon interactions, published in Physical Review Letters, allowed them to set new constraints for sub-GeV/c2 dark matter.

“The DarkSide-50 experiment was designed as a test for the use of from underground sources, naturally depleted in the radioactive 39 Ar, for very large scale dark matter searches,” Cristiano Galbiati a Researcher at Princeton University and the Gran Sasso Science Institute, told Phys.org. “It is remarkable to see how a group of young researchers within the collaboration was able to exploit the apparatus to extract the best limit for dark matter searches that were not part of the original scope of the experiment. If anything, the ingenuity and resolve of this group should be credited for this important result.”

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