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Researchers have developed an algorithm that can identify the basic needs of users from the text and images they share on social networks. The experts hope this tool will help psychologists to diagnose possible mental health problems. The study suggests that Spanish-speaking users are more likely to mention relationship problems when feeling depressed than English speakers.

We spend a substantial amount of our time sharing images, videos or thoughts on social networks such as Instagram, Facebook and Twitter. Now, a group of researchers from the Universitat Oberta de Catalunya (UOC) has developed an algorithm that aims to help psychologists diagnose possible mental health problems through the content people post on these platforms.

According to William Glasser’s Choice Theory, there are five basic needs that are central to all human behavior: Survival, Power, Freedom, Belonging and Fun. These needs even have an influence on the images we choose to upload to our Instagram page. “How we present ourselves on social media can provide useful information about behaviors, personalities, perspectives, motives and needs,” explained Mohammad Mahdi Dehshibi, who led this study within the AI for Human Well-being (AIWELL) group, which belongs to the Faculty of Computer Science, Multimedia and Telecommunications at the UOC.

Modern life can be full of baffling encounters with artificial intelligence—think misunderstandings with customer service chatbots or algorithmically misplaced hair metal in your Spotify playlist. These AI systems can’t effectively work with people because they have no idea that humans can behave in seemingly irrational ways, says Mustafa Mert Çelikok. He’s a Ph.D. student studying human-AI interaction, with the idea of taking the strengths and weaknesses of both sides and blending them into a superior decision-maker.

In the AI world, one example of such a hybrid is a “centaur.” It’s not a mythological horse–human, but a human-AI team. Centaurs appeared in chess in the late 1990s, when artificial intelligence systems became advanced enough to beat human champions. In place of a “human versus machine” matchup, centaur or cyborg chess involves one or more computer chess programs and human players on each side.

“This is the Formula 1 of chess,” says Çelikok. “Grandmasters have been defeated. Super AIs have been defeated. And grandmasters playing with powerful AIs have also lost.” As it turns out, novice players paired with AIs are the most successful. “Novices don’t have strong opinions” and can form effective decision-making partnerships with their AI teammates, while “grandmasters think they know better than AIs and override them when they disagree—that’s their downfall,” observes Çelikok.

A team of researchers and engineers at Canadian company Xanadu Quantum Technologies Inc., working with the National Institute of Standards and Technology in the U.S., has developed a programmable, scalable photonic quantum chip that can execute multiple algorithms. In their paper published in the journal Nature, the group describes how they made their chip, its characteristics and how it can be used. Ulrik Andersen with the Technical University of Denmark has published a News & Views piece in the same journal issue outlining current research on quantum computers and the work by the team in Canada.

Scientists around the world are working to build a truly useful quantum computer that can perform calculations that would take traditional computers millions of years to carry out. To date, most such efforts have been focused on two main architectures—those based on superconducting electrical circuits and those based on trapped-ion technology. Both have their advantages and disadvantages, and both must operate in a supercooled environment, making them difficult to scale up. Receiving less attention is work using a photonics-based approach to building a quantum computer. Such an approach has been seen as less feasible because of the problems inherent in generating quantum states and also of transforming such states on demand. One big advantage photonics-based systems would have over the other two architectures is that they would not have to be chilled—they could work at room temperature.

In this new effort, the group at Xanadu has overcome some of the problems associated with photonics-based systems and created a working programmable photonic quantum chip that can execute multiple algorithms and can also be scaled up. They have named it the X8 photonic quantum processing unit. During operation, the chip is connected to what the team at Xanadu describe as a “squeezed light” source—infrared laser pulses working with microscopic resonators. This is because the new system performs continuous variable quantum computing rather than using single-photon generators.

A team of Cornell University engineers developed a new microscopy technique that’s powerful enough to spot an individual atom in three dimensions — and create an image so clear that the only blurriness comes from the movement of that atom itself.

The technique, which according to the study published Thursday in the journal Science relies on an electron microscope coupled with sophisticated 3D reconstruction algorithms, doesn’t just set a new record in atom resolution. The researchers even say this might be as good as microscopy gets.

“This doesn’t just set a new record,” lead author and Cornell engineer David Muller said in a press release. “It’s reached a regime which is effectively going to be an ultimate limit for resolution. We basically can now figure out where the atoms are in a very easy way. This opens up a whole lot of new measurement possibilities of things we’ve wanted to do for a very long time.”

The famed Navier-Stokes equations can lead to cases where more than one result is possible, but only in an extremely narrow set of situations.

Summary

The human brain has the, remarkable ability to learn patterns from small amounts of data and then recognize novel instances of those patterns despite distortion and noise. Although advances in machine learning algorithms have been weakly informed by the brain since the 1940’s, they do not yet rival human performance.

Algorithm set for deployment in Japan could identify giant temblors faster and more reliably.

Two minutes after the world’s biggest tectonic plate shuddered off the coast of Japan, the country’s meteorological agency issued its final warning to about 50 million residents: A magnitude 8.1 earthquake had generated a tsunami that was headed for shore. But it wasn’t until hours after the waves arrived that experts gauged the true size of the 11 March 2011 Tohoku quake. Ultimately, it rang in at a magnitude 9—releasing more than 22 times the energy experts predicted and leaving at least 18,000 dead, some in areas that never received the alert. Now, scientists have found a way to get more accurate size estimates faster, by using computer algorithms to identify the wake from gravitational waves that shoot from the fault at the speed of light.

“This is a completely new [way to recognize] large-magnitude earthquakes,” says Richard Allen, a seismologist at the University of California, Berkeley, who was not involved in the study. “If we were to implement this algorithm, we’d have that much more confidence that this is a really big earthquake, and we could push that alert out over a much larger area sooner.”

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The manipulation of electromagnetic waves and information has become an important part of our everyday lives. Intelligent metasurfaces have emerged as smart platforms for automating the control of wave-information-matter interactions without manual intervention. They evolved from engineered composite materials, including metamaterials and metasurfaces. As a society, we have seen significant progress in the development of metamaterials and metasurfaces of various forms and properties.

In a paper published in the journal eLight on May 6, 2022, Professor Tie Jun Cui of Southeast University and Professor Lianlin Li of Peking University led a research team to review intelligent metasurfaces. “Intelligent metasurfaces: Control, Communication and Computing” investigated the development of intelligent metasurfaces with an eye for the future.

This field has refreshed human insights into many fundamental laws. They have unlocked many novel devices and systems, like cloaking, tunneling, and holograms. Conventional structure-alone or passive metasurfaces has moved towards intelligent metasurfaces by integrating algorithms and nonlinear materials (or active devices).

The latest “machine scientist” algorithms can take in data on dark matter, dividing cells, turbulence, and other situations too complicated for humans to understand and provide an equation capturing the essence of what’s going on.

Despite rediscovering Kepler’s third law and other textbook classics, BACON remained something of a curiosity in an era of limited computing power. Researchers still had to analyze most data sets by hand, or eventually with Excel-like software that found the best fit for a simple data set when given a specific class of equation. The notion that an algorithm could find the correct model for describing any data set lay dormant until 2009, when Lipson and Michael Schmidt, roboticists then at Cornell University, developed an algorithm called Eureqa.

Their main goal had been to build a machine that could boil down expansive data sets with column after column of variables to an equation involving the few variables that actually matter. “The equation might end up having four variables, but you don’t know in advance which ones,” Lipson said. “You throw at it everything and the kitchen sink. Maybe the weather is important. Maybe the number of dentists per square mile is important.”

Continue reading “‘Machine Scientists’ Distill the Laws of Physics From Raw Data” »