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]

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]