Daily AI News

MIT CSAIL researchers developed SustainaPrint, a system that reinforces only the weakest zones of eco-friendly 3D prints, achieving strong results with less plastic.

System developed at MIT could provide realistic predictions for a wide variety of reactions, while maintaining real-world physical constraints.

Artificially created data offer benefits from cost savings to privacy preservation, but their limitations require careful planning and evaluation, Kalyan Veeramachaneni says.

Professor Caroline Uhler discusses her work at the Schmidt Center, thorny problems in math, and the ongoing quest to understand some of the most complex interactions in biology.

VaxSeer uses machine learning to predict virus evolution and antigenicity, aiming to make vaccine selection more accurate and less reliant on guesswork.

While superconducting qubits are great at fast calculations, they struggle to store information for long periods. A team at Caltech has now developed a clever solution: converting quantum information into sound waves. By using a tiny device that acts like a miniature tuning fork, the researchers were able to extend quantum memory lifetimes up to 30 times longer than before. This breakthrough could pave the way toward practical, scalable quantum computers that can both compute and remember.

Researchers discovered that bees use flight movements to sharpen brain signals, enabling them to recognize patterns with remarkable accuracy. A digital model of their brain shows that this movement-based perception could revolutionize AI and robotics by emphasizing efficiency over massive computing power.

A research team has created a quantum logic gate that uses fewer qubits by encoding them with the powerful GKP error-correction code. By entangling quantum vibrations inside a single atom, they achieved a milestone that could transform how quantum computers scale.

Researchers have unveiled a new quantum material that could make quantum computers much more stable by using magnetism to protect delicate qubits from environmental disturbances. Unlike traditional approaches that rely on rare spin-orbit interactions, this method uses magnetic interactions—common in many materials—to create robust topological excitations. Combined with a new computational tool for finding such materials, this breakthrough could pave the way for practical, disturbance-resistant quantum computers.