Artificial intelligence, machine learning, computer science and mathematics.
Researchers deployed an explainable AI system on a real self-driving car and found that its explanations helped people form a more accurate understanding of the vehicle’s behavior. The benefit was especially apparent when the car acted in unexpected ways.
AI-targeted shopping features were associated with impulse buying and unsustainable consumption in a study of 642 Vietnamese online shoppers. Trust in recommendations, perceived usefulness and emotional arousal were linked to stronger urges to buy, which in turn were associated with low-necessity purchases.
Researchers developed an AI system designed to detect small objects in complex aerial images from drones. On three image datasets, it improved detection measures over the comparison system while maintaining real-time processing at 68.7 frames per second.
A study proposes encoding each quantum bit into several physical qubits before measurement, then using a majority vote to decode the result. Simulations based on noise profiles from superconducting quantum platforms found that the approach suppressed measurement errors by several orders of magnitude with a modest linear increase in qubit use.
A meta-analysis found that fine-particle concentrations around cement plants had a weighted average of 20.76 micrograms per cubic metre, above the World Health Organization’s annual guideline of 5 micrograms per cubic metre. The researchers cautioned that the studies differed substantially, including in how long they sampled the air.
Researchers developed a drone-based system that recognized American Sign Language gestures with 98.63% accuracy on a combined dataset. The system is designed to support sign-to-speech and speech-to-sign communication where fixed cameras or other communication infrastructure are unavailable.
An AI system designed for damaged South Indian palm-leaf manuscripts reported 97.4% word accuracy and 98.6% character accuracy on an archival corpus of 1,250 folios. It also reported 99.4% accuracy in identifying authentic palm-leaf manuscripts.
People can interpret the same general statement in different ways, yet generic language may still pass on broad knowledge. Across three experiments, a model in which such statements have an uncertain threshold best explained participants’ varied interpretations of new generic claims.
Researchers developed an attack that generates backdoor triggers tailored to a shared AI model in federated learning. In experiments across multiple datasets, it improved the attack success rate by 60.95–80.19% over average baseline performance without increasing the malicious updates sent to the system.
An analysis of GitHub Copilot use found that open-source projects produced more code and involved more developers. The gains came with longer coordination time, especially for developers on the edges of project communities.
A systematic literature review catalogs unresolved technical challenges in developing artificial general intelligence, including limited common sense, unreliable responses and difficulty handling unfamiliar situations. It points to hybrid systems, embodied approaches and stronger reasoning as possible research directions, while emphasizing the need for better safety and verification.
A new open-source pipeline uses an existing AI system to segment far more of very large Earth-observation images without training data or manual threshold tuning. Across seven scenes, it increased coverage to 91–98% and joined objects split across image tiles.
A new preprint reports a complete computer-assisted verification of the Lonely Runner Conjecture for 14 runners, with 13 of them moving. The archive includes certificates, source code and an audit script; because it is a preprint, peer review may change the findings.
A survey of human-AI collaboration argues that more capable AI systems do not automatically produce reliable partnerships. It identifies human-centered design, governance and attention to high-stakes uses as central to making these systems trustworthy and beneficial.
Deep-learning tools for predicting RNA splicing can rely on genomic clues unrelated to splicing and miss important effects of RNA structure, a study finds. These weaknesses were linked to systematic errors, especially when the tools evaluated sequences that differed from those used for training.
