Organizations across industries are realizing the value of natural language processing (NLP) and are implementing it to deliver better digital products, services, and customer tools, and to increase operational efficiencies or gain novel capabilities. As one of the fastest-growing areas of AI R&D across FSI, health care, and retail, NLP enables organizations to now search, query, detect anomalies, understand relationships, and parse trillions of transactions in real-time. Though advances in language representation models yield impressive accuracy for these use cases, teams still struggle to realize value from development, testing, and production systems. We examine leveraging GPU-optimized capabilities and pre-trained AI models, determining when to leverage task-specific or customized models, and discuss state-of-the-art benchmarks for layered and mixed-modal text analysis applications while accelerating deployment via TensorRT, NVIDIA Triton Inference Server, and cloud-based methods.
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Abstract: Developing State-of-the-art Computer Vision Solutions for Federal Applications Speaker: Dr. Michael Segala, Ph.D. Originally Prepared For: NVIDIA GTC20 Reducing manual processes and automatically identifying critical features in images and videos is transforming the industry and the federal and public sectors. Utilizing state-of-the-art computer vision has become the vital backbone in examining large volumes of […]
Learn about the developing explainable AI (XAI) systems within major deep learning initiatives for federal government applications. With organizations such as the Defense Advanced Research Projects Agency, the National Institute of Standards and Technology, and the National Security Commission on Artificial Intelligence (NSCAI) naming it a frontier in the next decade of AI research, we […]
Transforming unstructured clinical data by AI-based decision tools allows embedding models in nearly every patient care, device, and clinical trial information pathway. Such systems remove the need for human-driven data cleaning and parsing, and allow for fully automated solutions to segment medical images, analyze samples, and retrieve critical EMR-level information. Focusing on these approaches for […]