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Category: robotics/AI – Page 8

A research team has developed a “next-generation AI electronic nose” capable of distinguishing scents like the human olfactory system does and analyzing them using artificial intelligence. This technology converts scent molecules into electrical signals and trains AI models on their unique patterns. It holds great promise for applications in personalized health care, the cosmetics industry, and environmental monitoring.

The study is published in the journal ACS Nano. The team was led by Professor Hyuk-jun Kwon of the Department of Electrical Engineering and Computer Science at DGIST, with integrated master’s and Ph.D. student Hyungtae Lim as first author.

While conventional electronic noses (e-noses) have already been deployed in areas such as and gas detection in industrial settings, they struggle to distinguish subtle differences between similar smells or analyze complex scent compositions. For instance, distinguishing among floral perfumes with similar notes or detecting the faint odor of fruit approaching spoilage remains challenging for current systems. This gap has driven demand for next-generation e-nose technologies with greater precision, sensitivity, and adaptability.

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For decades, neuroscientists have developed mathematical frameworks to explain how brain activity drives behavior in predictable, repetitive scenarios, such as while playing a game. These algorithms have not only described brain cell activity with remarkable precision but also helped develop artificial intelligence with superhuman achievements in specific tasks, such as playing Atari or Go.

Yet these frameworks fall short of capturing the essence of human and animal behavior: our extraordinary ability to generalize, infer and adapt. Our study, published in Nature late last year, provides insights into how in mice enable this more complex, intelligent behavior.

Unlike machines, humans and animals can flexibly navigate new challenges. Every day, we solve new problems by generalizing from our knowledge or drawing from our experiences. We cook new recipes, meet new people, take a new path—and we can imagine the aftermath of entirely novel choices.

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Breakthrough light-powered chip speeds up AI training and reduces energy consumption.

Engineers at Penn have developed the first programmable chip capable of training nonlinear neural networks using light—a major breakthrough that could significantly accelerate AI training, lower energy consumption, and potentially lead to fully light-powered computing systems.

Unlike conventional AI chips that rely on electricity, this new chip is photonic, meaning it performs calculations using beams of light. Published in Nature Photonics.

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Antimicrobial resistance (AMR) presents a serious challenge in today’s world. The use of antimicrobials (AMU) significantly contributes to the emergence and spread of resistant bacteria. Companion animals gain recognition as potential reservoirs and vectors for transmitting resistant microorganisms to both humans and other animals. The full extent of this transmission remains unclear, which is particularly concerning given the substantial and growing number of households with companion animals. This situation highlights critical knowledge gaps in our understanding of risk factors and transmission pathways for AMR transfer between companion animals and humans. Moreover, there’s a significant lack of information regarding AMU in everyday veterinary practices for companion animals. The exploration and development of alternative therapeutic approaches to antimicrobial treatments of companion animals also represents a research priority. To address these pressing issues, this Reprint aims to compile and disseminate crucial additional knowledge. It serves as a platform for relevant research studies and reviews, shedding light on the complex interplay between AMU, AMR, and the role of companion animals in this global health challenge. This Reprint is especially addressed to companion animal veterinary practitioners as well as all researchers working on the field of AMR in both animals and humans, from a One Health perspective.

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Converting sunlight into electricity is the task of photovoltaic solar cells, but nearly half the light that reaches a flat silicon solar cell surface is lost to reflection. While traditional antireflective coatings help, they only work within a narrow range of light frequency and incidence angles. A new study may have overcome this limit.

As reported in Advanced Photonics Nexus, researchers have proposed a new type of antireflective coating using a single, ultrathin layer of polycrystalline silicon nanostructures (a.k.a. a metasurface). Achieving minimal reflection across certain wavelengths and angles, the metasurface was reportedly developed by combining forward and inverse design techniques, enhanced by (AI).

The result is a coating that sharply reduces reflection across a wide range of wavelengths and angles, setting a new benchmark for performance with minimal material complexity.

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