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]
Helping resolve quantum computers’ memory problemon April 4, 2026 at 10:00 am
A major problem with quantum computers is memory, as the information they contain can be quickly lost. Quantum computers are not yet fully reliable—they are far too unstable. However, all around the world, people are trying to improve them—some of whom are based in Norway.A major problem with quantum computers is memory, as the information they contain can be quickly lost. Quantum computers are not yet fully reliable—they are far too unstable. However, all around the world, people are trying to improve them—some of whom are based in Norway.[#item_full_content]
One of the most influential scientific and philosophical viewpoints is “More is Different,” introduced in 1972 by Nobel Prize–winning physicist Philip W. Anderson, highlighting the limitations of the reductionist approach. The emergent properties cannot be derived from the fundamental laws that govern their elementary particles. The generalization of this approach suggests a hierarchical structure of science, where explainable properties of small-scale systems cannot necessarily predict the emerging phenomena on larger scales of similar systems. Its interdisciplinary perspective covers chemistry, molecular biology, cell biology, and social sciences besides physics.One of the most influential scientific and philosophical viewpoints is “More is Different,” introduced in 1972 by Nobel Prize–winning physicist Philip W. Anderson, highlighting the limitations of the reductionist approach. The emergent properties cannot be derived from the fundamental laws that govern their elementary particles. The generalization of this approach suggests a hierarchical structure of science, where explainable properties of small-scale systems cannot necessarily predict the emerging phenomena on larger scales of similar systems. Its interdisciplinary perspective covers chemistry, molecular biology, cell biology, and social sciences besides physics.[#item_full_content]
Artificial intelligence is increasingly being used to help optimize decision-making in high-stakes settings. For instance, an autonomous system can identify a power distribution strategy that minimizes costs while keeping voltages stable.Artificial intelligence is increasingly being used to help optimize decision-making in high-stakes settings. For instance, an autonomous system can identify a power distribution strategy that minimizes costs while keeping voltages stable.[#item_full_content]
Fair decisions, clear reasons: Creating fuzzy AI with fairness built in from the starton April 2, 2026 at 11:40 am
Although AI is not intentionally biased, it can inherit biases from the data fed into it, learning and repeating them until the system becomes inherently unfair. This is complicated by the problem of identifying where the AI system introduced the bias, as most AI systems display their final decision without showing the steps that made it. Unfair patterns may go unnoticed simply because they are hard to identify.Although AI is not intentionally biased, it can inherit biases from the data fed into it, learning and repeating them until the system becomes inherently unfair. This is complicated by the problem of identifying where the AI system introduced the bias, as most AI systems display their final decision without showing the steps that made it. Unfair patterns may go unnoticed simply because they are hard to identify.[#item_full_content]
Your call center rep is emotionally exhausted—their computer may know when to helpon April 2, 2026 at 9:40 am
When a customer calls to complain about a billing error or a delayed package, the person on the other end of the line is doing more than answering questions.When a customer calls to complain about a billing error or a delayed package, the person on the other end of the line is doing more than answering questions.[#item_full_content]
The number of scientific papers is growing so rapidly that scientists are no longer able to keep track of all of them, even in their own research area. Researchers from the Karlsruhe Institute of Technology (KIT), in collaboration with scientific partners, have shown how new research ideas can still be obtained from this wealth of information. Using artificial intelligence (AI), they systematically analyzed materials science publications to identify potential new avenues of research. Their results have been published in Nature Machine Intelligence.The number of scientific papers is growing so rapidly that scientists are no longer able to keep track of all of them, even in their own research area. Researchers from the Karlsruhe Institute of Technology (KIT), in collaboration with scientific partners, have shown how new research ideas can still be obtained from this wealth of information. Using artificial intelligence (AI), they systematically analyzed materials science publications to identify potential new avenues of research. Their results have been published in Nature Machine Intelligence.[#item_full_content]
Nano- and microplastics are increasingly being detected in the human body. However, their detection remains challenging, often relying on invasive techniques and specialized equipment. Researchers at the Institute of Computer Science at the University of Tartu are developing a device that can measure plastic in the human body. Their research is published in the journal Proceedings of the 27th International Workshop on Mobile Computing Systems and Applications.Nano- and microplastics are increasingly being detected in the human body. However, their detection remains challenging, often relying on invasive techniques and specialized equipment. Researchers at the Institute of Computer Science at the University of Tartu are developing a device that can measure plastic in the human body. Their research is published in the journal Proceedings of the 27th International Workshop on Mobile Computing Systems and Applications.[#item_full_content]
