Bookmark and Share


Today, the most important area is the huge advances being made on almost a daily basis in neural networks and artificial intelligence.

CUDA Fortran compiler support enables scientific programmers using Fortran to take advantage of FP16 matrix operation acceleration.

Many AI applications have data pipelines that include several processing steps when executing inference operations.

One of the biggest challenges to AI can be eliciting high-performance deep learning inference that runs at real-world scale.

NVIDIA’s Turing GPUs introduced a new hardware functionality for computing optical flow between images with very high performance.

Data scientists need annotated data (and lots of it) to train the deep neural networks (DNNs) at the core of AI workflows.

Classification of astronomical sources in the night sky is important for helping us understand the properties of celestial systems.

RoadBotics works with more than 100 cities to use AI to detect potholes for better road maintenance.

Carter, built on a Segway RMP 210 robotic mobility platform, uses a lidar sensor and a stereoscopic camera to navigate the world around it.

From desktop computers to MRI scanners, diagnostic monitors and X-Ray machines, Intel has been at the forefront of healthcare transformation