Lifeboat Foundation

Using artificial intelligence, physicists have compressed a daunting quantum problem that until now required 100,000 equations into a bite-size task of as few as four equations—all without sacrificing accuracy. The work, published in the September 23 issue of Physical Review Letters, could revolutionize how scientists investigate systems containing many interacting electrons. Moreover, if scalable to other problems, the approach could potentially aid in the design of materials with sought-after properties such as superconductivity or utility for clean energy generation.

“We start with this huge object of all these coupled-together differential equations; then we’re using machine learning to turn it into something so small you can count it on your fingers,” says study lead author Domenico Di Sante, a visiting research fellow at the Flatiron Institute’s Center for Computational Quantum Physics (CCQ) in New York City and an assistant professor at the University of Bologna in Italy.

The formidable problem concerns how electrons behave as they move on a gridlike lattice. When two electrons occupy the same lattice site, they interact. This setup, known as the Hubbard model, is an idealization of several important classes of materials and enables scientists to learn how electron behavior gives rise to sought-after phases of matter, such as superconductivity, in which electrons flow through a material without resistance. The model also serves as a testing ground for new methods before they’re unleashed on more complex quantum systems.

Saildrone/NOAA

Recently, the collaboration between the U.S. National Oceanic and Atmospheric Administration (NOAA) and Saildrone, a company that develops sailing drones, did exactly that. They sent a robot into Hurricane Fiona, the tropical storm that has deluged Puerto Rico and is now headed towards Canada’s east coast, Mashable reported.

Associate Professor Miriam Klein-Flügge and colleagues looked at brain connectivity and mental health data from nearly 500 people. In particular, they looked at the connectivity of the amygdala – a brain region well known for its importance in emotion and reward processing. The researchers used functional magnetic resonance imaging to consider seven small subdivisions of the amygdala and their associated networks rather than combining the whole region together as previous studies have done.

The team also adopted a more precise approach to the data on mental wellbeing, looking at a large group of healthy people and using questionnaires that captured information about wellbeing in the social, emotional, sleep, and anger domains. This generated more precise data than many investigations which still use broad diagnoses such as depression or anxiety, which involve many different symptoms.

The paper, published in Nature Human Behaviour, shows how the improved level of detail about both brain connectivity and wellbeing made it possible to characterise the exact brain networks that relate to these distinct aspects of mental health. The brain connections that mattered most for discerning whether an individual was struggling with sleep problems, for example, looked very different from those that carried information about their social wellbeing.

Room service is unlikely to be part of the package, but Hilton has nevertheless agreed to help Voyager Space design the crew suites for a new space station.

Disperse, a U.K.-based construction tech company that offers an artificial intelligence (AI)-powered platform to help project managers track work and capture data from building sites, has raised $16 million in funding.

Founded out of London in 2015, Disperse effectively creates a digital version of an entire construction site, including visual snapshots that track the progress of work to help all stakeholders — regardless of where they’re based — keep up with things. For this, Disperse sends someone around a site at regular intervals with a standard 360° camera, and the resulting imagery is fed directly into the Disperse platform which processes the visuals and applies computer vision techniques to figure out what’s happening.

For example, this can help to show the state of a project at a given moment in time, and solve disputes should they arise in terms of determining whether a job was completed as it should’ve been. It also automatically spotlights potential problems or bottlenecks while they can still be resolved.

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Massachusetts Institute of Technology (MIT) engineers have developed a battery-free, wireless underwater camera that’s powered by sound waves.

Scientists estimate that more than 95 percent of Earth’s oceans have never been observed, which means we have seen less of our planet’s ocean than we have the far side of the moon or the surface of Mars.

The high cost of powering an underwater camera for a long time, by tethering it to a research vessel or sending a ship to recharge its batteries, is a steep challenge preventing widespread undersea exploration.

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Preview of NASA’s Double Asteroid Redirection Test, or DART, the first asteroid deflection mission.

Worm-hole generators by the pound mass: https://greengregs.com/

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Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Watch here.

The circuits are found in several forms and in different locations. Some offer faster creation of new AI models. They use multiple processing circuits in parallel to churn through millions, billions or even more data elements, searching for patterns and signals. These are used in the lab at the beginning of the process by AI scientists looking for the best algorithms to understand the data.