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Liquid droplets trained to play tic-tac-toe

Artificial intelligence and high-performance computing are driving up the demand for massive sources of energy. But neuromorphic computing, which aims to mimic the structure and function of the human brain, could present a new paradigm for energy-efficient computing.

To this end, researchers at Lawrence Livermore National Laboratory (LLNL) created a droplet-based platform that uses ions to perform simple neuromorphic computations. Using its ability to retain , the team trained the droplet system to recognize handwritten digits and play tic-tac-toe. The work was published in Science Advances.

The authors were inspired by the , which computes with ions instead of electrons. Ions move through fluids, and moving them may require less energy than moving electrons in solid-state devices.

Stitched for strength: The physics of jamming in stiff, knitted fabrics

School of Physics Associate Professor Elisabetta Matsumoto is unearthing the secrets of the centuries-old practice of knitting through experiments, models, and simulations. Her goal? Leveraging knitting for breakthroughs in advanced manufacturing—including more sustainable textiles, wearable electronics, and soft robotics.

Matsumoto, who is also a principal investigator at the International Institute for Sustainability with Knotted Chiral Meta Matter (WPI-SKCM2) at Hiroshima University, is the corresponding author on a new study exploring the physics of ‘’—a phenomenon when soft or stretchy materials become rigid under low stress but soften under higher tension.

The study, “Pulling Apart the Mechanisms That Lead to Jammed Knitted Fabrics,” is published in Physical Review E, and also includes Georgia Tech Matsumoto Group graduate students Sarah Gonzalez and Alexander Cachine in addition to former postdoctoral fellow Michael Dimitriyev, who is now an assistant professor at Texas A&M University.

Microsoft in talks to maintain access to OpenAI’s tech beyond AGI milestone

Microsoft is reportedly in advanced talks with OpenAI for a new agreement that would give it ongoing access to the startup’s technology even if OpenAI achieves what it defines as AGI, or advanced general intelligence. If the deal goes through, it would clear a key hurdle in OpenAI’s transition toward becoming a fully commercial enterprise.

The companies have been negotiating regularly, and they could come to an agreement in a few weeks, Bloomberg reports, citing three anonymous sources. The report cited some of the sources as saying that while the talks have been positive, other roadblocks could emerge in the form of regulatory scrutiny and Elon Musk’s lawsuit to block OpenAI’s for-profit transition.

OpenAI is currently structured as a mission-driven nonprofit that oversees a capped for-profit company — a setup that’s meant to limit how fully it can commercialize or raise money. That structure hasn’t stopped it from raising billions and operating like a traditional tech company, but OpenAI still wants to shake off its constraints.

From AI to Organoids: How Growing Brain-like Structures are Advancing Machine Learning

Artificial Intelligence (AI) is usually built with silicon chips and code. But scientists are now exploring something very different. In 2025, they are growing brain organoids, which are small, living structures made from human stem cells. These organoids act like simple versions of the human brain. They form real neural connections and send electrical signals. They even show signs of learning and memory.

By linking organoids with AI systems, researchers are beginning to explore new computational approaches. Recent studies have shown that organoids possess the ability to recognize speech, detect patterns, and respond to input. Living brain tissue may help create AI models that learn and adapt faster than traditional machines. Early results indicate that organoid-based systems could offer a more flexible and energy-efficient form of intelligence.

Brain Organoids and the Emergence of Organoid Intelligence.

EpInflammAge: Epigenetic-Inflammatory Clock for Disease-Associated Biological Aging Based on Deep Learning

We present EpInflammAge, an explainable deep learning tool that integrates epigenetic and inflammatory markers to create a highly accurate, disease-sensitive biological age predictor. This novel approach bridges two key hallmarks of aging—epigenetic alterations and immunosenescence. First, epigenetic and inflammatory data from the same participants was used for AI models predicting levels of 24 cytokines from blood DNA methylation. Second, open-source epigenetic data (25 thousand samples) was used for generating synthetic inflammatory biomarkers and training an age estimation model. Using state-of-the-art deep neural networks optimized for tabular data analysis, EpInflammAge achieves competitive performance metrics against 34 epigenetic clock models, including an overall mean absolute error of 7 years and a Pearson correlation coefficient of 0.85 in healthy controls, while demonstrating robust sensitivity across multiple disease categories. Explainable AI revealed the contribution of each feature to the age prediction. The sensitivity to multiple diseases due to combining inflammatory and epigenetic profiles is promising for both research and clinical applications. EpInflammAge is released as an easy-to-use web tool that generates the age estimates and levels of inflammatory parameters for methylation data, with the detailed report on the contribution of input variables to the model output for each sample.

A qualitative systematic review on AI empowered self-regulated learning in higher education

npj Science of Learning — A qualitative systematic review on AI empowered self-regulated learning (SRL) in higher education. Aiming to synthesize empirical studies, we employed a qualitative approach to scrutinize AI’s role in supporting SRL processes. Through a meticulous selection process adhering to PRISMA guidelines, we identified 14 distinct studies that leveraged AI applications, including chatbots, adaptive feedback systems, serious games, and e-textbooks, to support student autonomy. Our findings reveal a nuanced landscape where AI demonstrates potential in facilitating SRL’s forethought, performance, and reflection phases, yet also highlights whether the agency is human-centered or AI-centered leading to variations in the SRL model. This review underscores the imperative for balanced AI integration, ensuring technological advantages are harnessed without undermining student self-efficacy. The implications suggest a future where AI is a thoughtfully woven thread in the SRL fabric of higher education, calling for further research to optimize this synergy.

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