Researchers have developed advanced explainable AI in a technical tour de force to decipher regulatory instructions encoded in DNA.
Deep learning-based system enables dermatologist-level identification of suspicious skin lesions from smartphone photos, allowing better screening.
A machine learning system learns on the job. By continuously adapting to new data inputs, this “liquid network” could aid decision-making in medical diagnosis.
Computer scientists use TACC systems to generate synthetic objects for robot training.
Deep-learning methods have the potential to offer substantially better results, generating superior representations for characterizing the human brain.
Experts working at the intersection of robotics, machine learning, and physics-based simulation share how computer simulation could accelerate the development of "smart robots" which "might interact with humans"
A device could help scientists better understand the health benefits of outdoor lighting and lead to wearables that could nudge users to get more outdoor time.
Researchers have developed a way for deep learning neural networks to rapidly estimate confidence levels in their output.
A computer vision technology has been put into a free mobile phone app for regular monitoring of glucose levels in people with diabetes.
An artificial intelligence-based detects early stages of Alzheimer’s through functional magnetic resonance imaging.
Researchers have created artificial intelligence algorithm that can automatically identify patients at high risk of intentional self-harm, based on the information in the clinical notes in the electronic health record.
AI experts report that they have successfully addressed a major obstacle to increasing AI capabilities.
Researchers have developed an algorithm that not only predicts hospital readmissions of heart failure patients, but also tells you why these occur.
Research shows how so-called “critical states” can be used to optimize artificial neural networks running on brain-inspired neuromorphic hardware.