HarmonyGNN boosts graph AI accuracy on four tough benchmarks by up to 9.6%on April 13, 2026 at 5:20 pm

Researchers have demonstrated a new training technique that significantly improves the accuracy of graph neural networks (GNNs)—AI systems used in applications from drug discovery to weather forecasting. GNNs are AI systems designed to perform tasks where the input data is presented in the form of graphs. Graphs, in this context, refer largely to data structures where data points (called nodes) are connected by lines (called edges). The edges indicate some sort of relationship between the nodes. Edges can be used to connect nodes that are similar (called homophily)—but can also connect nodes that are dissimilar (called heterophily).Researchers have demonstrated a new training technique that significantly improves the accuracy of graph neural networks (GNNs)—AI systems used in applications from drug discovery to weather forecasting. GNNs are AI systems designed to perform tasks where the input data is presented in the form of graphs. Graphs, in this context, refer largely to data structures where data points (called nodes) are connected by lines (called edges). The edges indicate some sort of relationship between the nodes. Edges can be used to connect nodes that are similar (called homophily)—but can also connect nodes that are dissimilar (called heterophily).[#item_full_content]

Revealing the hidden logic behind AI’s judgments of peopleon April 13, 2026 at 5:00 pm

In a world where artificial intelligence is quietly shaping who gets hired, who receives loans, and even how medical decisions are made, a new question is emerging: How does AI judge us? A new study by Prof. Yaniv Dover and Valeria Lerman from Hebrew University suggests the answer is both reassuring and deeply unsettling. The study is published in the journal Proceedings of the Royal Society A Mathematical Physical and Engineering Science.In a world where artificial intelligence is quietly shaping who gets hired, who receives loans, and even how medical decisions are made, a new question is emerging: How does AI judge us? A new study by Prof. Yaniv Dover and Valeria Lerman from Hebrew University suggests the answer is both reassuring and deeply unsettling. The study is published in the journal Proceedings of the Royal Society A Mathematical Physical and Engineering Science.[#item_full_content]

Mechanical computers use springs and bolts to count, sort odd-even pushes and remember forceon April 13, 2026 at 1:40 pm

Published in Nature Communications, researchers from St. Olaf College and Syracuse University built a computer made entirely of mechanical components that can perform simple computations without electricity or batteries.Published in Nature Communications, researchers from St. Olaf College and Syracuse University built a computer made entirely of mechanical components that can perform simple computations without electricity or batteries.[#item_full_content]

Online data is generally pretty secure. Assuming everyone is careful with passwords and other protections, you can think of it as being locked in a vault so strong that even all the world’s supercomputers, working together for 10,000 years, could not crack it.Online data is generally pretty secure. Assuming everyone is careful with passwords and other protections, you can think of it as being locked in a vault so strong that even all the world’s supercomputers, working together for 10,000 years, could not crack it.[#item_full_content]

Virtual reality tools have untapped potential to elicit positive emotions for use in education, health care, architecture and psychological therapy, according to a recent study from Murdoch University that looked at four core visual factors and associated sub-factors and how they contribute to realism and emotional engagement in virtual reality environments.Virtual reality tools have untapped potential to elicit positive emotions for use in education, health care, architecture and psychological therapy, according to a recent study from Murdoch University that looked at four core visual factors and associated sub-factors and how they contribute to realism and emotional engagement in virtual reality environments.[#item_full_content]

It wasn’t long ago that news headlines claimed that AI might soon assist radiologists in interpreting X-rays of broken bones and analyzing mammograms. We are still far from the destination, as a new study has brought to light the mirage effect, where AI creates detailed descriptions of images that do not exist.It wasn’t long ago that news headlines claimed that AI might soon assist radiologists in interpreting X-rays of broken bones and analyzing mammograms. We are still far from the destination, as a new study has brought to light the mirage effect, where AI creates detailed descriptions of images that do not exist.[#item_full_content]

There is an exciting future on the horizon—one in which your thoughts could directly control electronic devices you use every day. In many ways, that future is already here, enabled by neural interfaces—engineered devices designed to exchange information with the body’s nervous system. From consumer wearables to clinical devices, electronics controlled by neural interfaces are making their way into the marketplace and medical practice. These technologies are demonstrating potential for augmenting, and even restoring, human capabilities in profound ways.There is an exciting future on the horizon—one in which your thoughts could directly control electronic devices you use every day. In many ways, that future is already here, enabled by neural interfaces—engineered devices designed to exchange information with the body’s nervous system. From consumer wearables to clinical devices, electronics controlled by neural interfaces are making their way into the marketplace and medical practice. These technologies are demonstrating potential for augmenting, and even restoring, human capabilities in profound ways.[#item_full_content]

In a recent paper, SFI Complexity Postdoctoral Fellow Yuanzhao Zhang and co-author William Gilpin show that a deceptively simple forecasting strategy can outperform several leading machine learning forecasting models.In a recent paper, SFI Complexity Postdoctoral Fellow Yuanzhao Zhang and co-author William Gilpin show that a deceptively simple forecasting strategy can outperform several leading machine learning forecasting models.[#item_full_content]

Training a large artificial intelligence model is expensive, not just in dollars, but in time, energy, and computational resources. Traditionally, obtaining a smaller, faster model either requires training a massive one first and then trimming it down, or training a small one from scratch and accepting weaker performance.Training a large artificial intelligence model is expensive, not just in dollars, but in time, energy, and computational resources. Traditionally, obtaining a smaller, faster model either requires training a massive one first and then trimming it down, or training a small one from scratch and accepting weaker performance.[#item_full_content]

Researchers at Skoltech have proposed a new approach to training neural networks for wave propagation in absorbing media. The method significantly improves the accuracy and stability of solutions and accelerates model training in the design of laser fusion systems, high-power laser facilities, and optical schemes with plasma elements, where the calculation of wave propagation and laser-plasma interaction consumes a significant portion of computational resources.Researchers at Skoltech have proposed a new approach to training neural networks for wave propagation in absorbing media. The method significantly improves the accuracy and stability of solutions and accelerates model training in the design of laser fusion systems, high-power laser facilities, and optical schemes with plasma elements, where the calculation of wave propagation and laser-plasma interaction consumes a significant portion of computational resources.[#item_full_content]

Hirebucket

FREE
VIEW