Deep learning (DL), a transformative branch of machine learning and more broadly artificial intelligence (AI), is poised to transform every business segment and industry. Breakthroughs in DL hardware and software, as well as a massive expansion in DL-based capabilities and solutions from the edge to the cloud will continue over the next several years.
Deep learning (DL) will continue to make significant progress in technical capabilities and scope of deployment across all aspects of life, including revolutionizing healthcare, retail, manufacturing, autonomous vehicles, security and fraud prevention, and data analytics. However, to build the future of AI, it is necessary to define a set of goals and expectations that will drive a new generation of technologies.
The rapid growth of deep learning (DL) is incredible. Progressing and enabling innovation at a breathtaking clip, DL is expected to drive technological progress and industry transformation for years to come.
How ethical facial recognition can identify security threats in real time with Oosto (formally AnyVision).
Complex workloads demand complex methodologies. When the measure of a business’s success is how efficient and quick its processes are, AI seems to be the answer.
In this tutorial, we will show step-by-step how to quantize ONNX models with Intel® Neural Compressor.
Artificial intelligence (AI) is a powerful tool to transform vast amounts of manufacturing data into insights that can improve the manufacturing process. To maximize success, Intel has fine-tuned its approach to AI in manufacturing by focusing on those applications that demonstrate substantial business value, practical feasibility, and quick time to value.
Intel® Optane™ persistent memory can help unify data access methods for temporal caching to improve data-centric workload performance.
Discover how to use the Neural Network Compression Framework of the OpenVINOTM toolkit for 8-bit quantization in PyTorch
How Intel’s AI tech innovation can be applied to solve healthcare challenges.
