Bookmark and Share


Deep learning can be implemented at every stage of healthcare, creating tools that can raise the standard of care and quality of life.

The same framework that solves challenges for AVs is also capable of dramatically improving the safety of the road today via ADAS.

From enabling security throughout a city to the rapid identification and classification of medical imaging data, Intel accelerates IoT.

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.