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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

Deep learning networks can be trained with lower precision for high throughput by halving storage requirements and memory traffic on tensors

Reducing model precision is an efficient way to accelerate inference on processors that support low precision math.

As cities expand and competition for space increases, managing vital green spaces is becoming increasingly important.