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String theory remains elusive as a 'provable' phenomenon. But a team of physicists has now taken a significant step forward in validating string theory by using an innovative mathematical method that points to its 'inevitability.'
When analyzing artworks, understanding the visual clarity of compositions is crucial. Inspired by digital artists, researchers have created a metric to quantify clarity in digital images. As a result, scientists can accurately capture changes in structure during artistic processes and physical transformations. This new metric can improve analysis and decision-making across the scientific and creative domains, potentially transforming how we understand and evaluate the structure of images. It has been tested on digital artworks and physical systems.
Researchers developed an AI debiasing technique that improves the fairness of a machine-learning model by boosting its performance for subgroups that are underrepresented in its training data, while maintaining its overall accuracy.
A study by a team of scientists finds that AI systems are also prone to social identity biases, revealing fundamental group prejudices that reach beyond those tied to gender, race, or religion.
A new study highlights a hidden challenge of using AI in medical imaging research -- the phenomenon of highly accurate yet potentially misleading results known as 'shortcut learning.' The researchers analyzed thousands of knee X-rays and found that AI models can 'predict' unrelated and implausible traits such as whether patients abstained from eating refried beans or beer. While these predictions have no medical basis, the models achieved high levels of accuracy by exploiting subtle and unintended patterns in the data.
Mathematicians have applied statistical mechanics to climate change detection and attribution for first time. They have shown how to separate the 'signal' of human-made climate change from the 'noise' of natural climate fluctuations. This allows for a dramatic improvement in the ability to detect climate change and early warnings of climatic tipping points.
Pretrained large-scale AI models need to 'forget' specific information for privacy and computational efficiency, but no methods exist for doing so in black-box vision-language models, where internal details are inaccessible. Now, researchers addressed this issue through a strategy based on latent context sharing, successfully getting an image classifier to forget multiple classes it was trained on. Their findings could expand the use cases of large-scale AI models while safeguarding end users' privacy.
To help improve punctuality by understanding how delays propagate and identifying critical trains, researchers have developed a new network-based method.
Modeling how cars deform in a crash, how spacecraft responds to extreme environments, or how bridges resist stress could be made thousands of times faster thanks to new artificial intelligence that enables personal computers to solve massive math problems that generally require supercomputers.
A pioneering artificial intelligence (AI) powered model able to understand the sequences and structure patterns that make up the genetic 'language' of plants, has been launched by a research collaboration.
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