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Never mind reading generic guides or practicing with friends — Google is betting that algorithms can get you ready for a job interview. The company has launched an Interview Warmup tool that uses AI to help you prepare for interviews across various roles. The site asks typical questions (such as the classic “tell me a bit about yourself”) and analyzes your voiced or typed responses for areas of improvement. You’ll know when you overuse certain words, for instance, or if you need to spend more time talking about a given subject.

Interview Warmup is aimed at Google Career Certificates users hoping to land work, and most of its role-specific questions reflect this. There are general interview questions, though, and Google plans to expand the tool to help more candidates. The feature is currently only available in the US.

AI has increasingly been used in recruitment. To date, though, it has mainly served companies during their selection process, not the potential new hires. This isn’t going to level the playing field, but it might help you brush up on your interview skills.

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Papers referenced in the video:
A Physiology Clock for Human Aging (preprint)
https://www.biorxiv.org/content/10.1101/2022.04.14.488358v1

Continue reading “A Physiology Clock for Human Aging: BP and FEV1 As Top Predictors” »

Efficiently processing broadband signals using convolutional neural networks (CNNs) could enhance the performance of machine learning tools for a wide range of real-time applications, including image recognition, remote sensing and environmental monitoring. However, past studies suggest that performing broadband convolutional processing computations directly in sensors is challenging, particularly when using conventional complementary metal-oxide-semiconductor (CMOS) technology, which underpins the functioning of most existing transistors.

Researchers at Huazhong University of Science and Technology and Nanjing University have recently investigated the possibility of achieving the convolutional processing of broadband signals using an alternative platform, namely van der Waals heterostructures. Their paper, published in Nature Electronics, could ultimately inform the development of better performing image recognition algorithms.

“Our paper was inspired by some our previous research works,” Tianyou Zhai, Xing Zhou and Feng Miao, three of the researchers who carried out the study, told TechXplore. “In studies published in Advanced Materials and Advanced Functional Materials, we realized type-III and type-II band-alignments in different heterostructures. Furthermore, we published a paper in Science Advances, where we realized a reconfigurable neural network vision sensor based on WSe₂.”

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.