Applied AI

NIST’s multidisciplinary laboratories and varied fields are an ideal environment to develop and apply AI. AI approaches are increasingly an essential component in new project conception and execution. Various AI techniques are being used to support NIST scientists and engineers, drawing on ML and AI tools to gain a deeper understanding of and insight into their research. At the same time, NIST laboratory experiences with AI are assisting in better understanding AI’s capabilities and limitations.

Exploring Research Frontiers by Incorporating AI and ML

• NIST researchers are developing partnerships across the Institute and with academia, other government laboratories, and industrial entities. They are integrating AI into the design, planning, and optimization of NIST’s research efforts – including computer vision, engineering biology and biomanufacturing, image and video understanding, medical imaging, materials science, manufacturing, disaster resilience, energy efficiency, natural language processing, quantum science, robotics, and advanced communications technologies. Key focus areas include innovative measurements using AI/ML techniques, predictive systems using AI/ML models, and enabling and reducing the barriers to autonomous measurement platforms.

Producing Training Data, Algorithms, and Other Tools

• NIST’s technical staff is working on data characterization, key practices for documentation of datasets, and datasets that the broader community can use to test or train AI systems. The agency is expanding the availability of targeted test or training data, algorithms, and other tools for use in domain- specific applications including advanced materials, computer vision, design, industrial robotics, natural language processing, spectrum management, and video processing. 

Examples of NIST AI Applied Research Projects

• Subspace-informed Deep Learning Solutions for Nanoscale Microscopy
  NIST is developing AI and machine learning methods to improve the fidelity of novel nanoscale microscopy techniques that are under research by NIST physicists. (Read more)
   
• AI-based Measurements For Imaging
  NIST streamlines deriving measurements from terabyte-sized images and improves image-based measurement accuracy via web-based applications addressing image analyses and AI-based modeling. (Read more)
   
• Deep Learning for MRI Reconstruction and Analysis
  The goal of the project is to initiate the development of the metrology and standards infrastructure to ensure that medical DL-based systems are (1) trained on validated physics-based data and (2) provide reliability, accuracy, and explainability. (Read more)
   
• Imaging & Artificial Intelligence (AI) to Assess Tissue Quality
  We have collaborated with Peter Bajcsy (NIST/ITL) and Kapil Bharti (NIH/NEI) to develop an approach for non-invasively assessing quality of clinical-grade tissue-engineered human retinal pigment epithelium (RPE). The method uses quantitative brightfield absorbance imaging (QBAM) and AI. Absorbance imaging was implemented in Kapil Bharti’s Good Manufacturing Practice facility where we noninvasively collected brightfield images of the clinical-grade constructs. Constructs were imaged using absorbance imaging and traditional RPE potency test data were collected. The RPE potency tests were measurements of polarized secretion of vascular endothelial growth factor (VEGF) and measurements of trans-epithelial resistance (TER). Peter Bajcsy's team directed the image processing, image analysis and implementation of AI methods. AI algorithms were trained to predict potency (VEGF and TER) from the absorbance images. The trained AI algorithm made correct predictions for 35 of 36 test image data sets. This work demonstrates how a garden variety microscope, if used carefully, can make a precise, reproducible measurement of tissue quality.