Today, artificial intelligence can describe images, recognize objects, and explain complex relationships. The pace of development is remarkable: So-called vision-language models (VLMs) combine text and image understanding in impressive ways. Yet, of all things, they struggle with a seemingly simple task—counting. Researchers at the Institute for Information Systems (iisys) at Hof University of Applied Sciences are now working to address this issue, with a paper posted to the arXiv preprint server.
Artificial intelligence systems based on neural networks—such as ChatGPT, Claude, DeepSeek or Gemini—are extraordinarily powerful, yet their internal workings remain largely a "black box." To better understand how these systems produce their responses, a group of physicists at Harvard University has developed a simplified mathematical model of learning in neural networks that can be analyzed mathematically using the tools of statistical physics.
Researchers from MIT and elsewhere have developed a more user-friendly and efficient method to help networking engineers identify potential system failures before they cause major problems, like a cloud service outage that leaves millions of users unable to access applications.
Over the past few decades, computer scientists have developed increasingly advanced artificial intelligence (AI) systems that can tackle some tasks exceedingly well. These include computer vision models, systems that can rapidly analyze images and categorize them, recognize objects and faces, or make other accurate predictions.
Most contemporary artificial intelligence (AI) systems learn to complete tasks via machine learning and deep learning. Machine learning is a computational approach that allows models to uncover patterns in data that are useful for making predictions. Deep learning, on the other hand, is a subset of machine learning that entails the use of multi-layered neural networks, which can autonomously extract features and learn complex patterns from unstructured data, sometimes with little or no human supervision.
In today's hospitals and clinics, a dermatologist may use an artificial intelligence model for classifying skin lesions to assess if the lesion is at risk of developing into a cancer or if it is benign. But if the model is biased toward certain skin tones, it could fail to identify a high-risk patient.
The new trend of "vibe coding" allows people to program software without writing a single line of code. Now, a new study by ETH Zurich published in the Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems has shown that users who want to develop apps and programs successfully with AI need not only a capacity for clear written expression, but also a basic knowledge of computer science.
Artificial intelligence that cannot explain how it makes decisions—often called "black box" AI—could soon be replaced by more transparent systems, research suggests. A study by Loughborough University, published in Physica D: Nonlinear Phenomena, outlines a new mathematical blueprint for building AI that can reveal how it learns, remembers, and makes decisions.
Paintings are often made up of thousands of tiny brushstrokes, each going in a certain direction, that are not easily observed by the viewer. A cross-disciplinary research team from the Penn State College of Information Sciences and Technology (IST) and Loughborough University in England has developed an image analysis method that helps to make the underlying brushstroke structure of paintings visible, giving new insight into how artists physically created their works.
Confidence is persuasive. In artificial intelligence systems, it is often misleading. Today's most capable reasoning models share a trait with the loudest voice in the room: They deliver every answer with the same unshakable certainty, whether they're right or guessing. Researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have now traced that overconfidence to a specific flaw in how these models are trained, and developed a method that fixes it without giving up any accuracy. The team's research is published on the arXiv preprint server.
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.
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.
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.
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.
What if ChatGPT answered with the name of a minister from a year ago when asked, "Who was the minister inaugurated last month?" This is a prime example of the limitations of AI that fails to properly reflect the latest information. A KAIST research team has developed a new evaluation technology that automatically reflects changing real-world information while catching "temporal errors" that may appear correct on the surface. This is expected to drastically improve AI reliability.
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