Computer Science · Artificial Intelligence
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.
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 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.
Dissolved oxygen is vital for aquatic life, but it can change for many interacting reasons. In Iran’s Javeh Reservoir, researchers used several AI models to predict dissolved oxygen and then used explainability tools to identify the main drivers, including air temperature and certain water inflow and outflow conditions.
