AI use-case explorer
AI use cases
Search and filter every reported use case. Open a record for the agency's full description, governance answers and source provenance.
Columns (7)
| Use case | Agency | Stage | High-impact | Topic area | AI classification | Sourcing |
|---|---|---|---|---|---|---|
| Implement machine learning in NMIS to assist with mishap coding NASA-545 · OSMA: Office of the Chief Safety & Mission Assurance | NASA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Adaptive Problem Solving / Hyperparameter Optimization NASA-809 · JPL: Jet Propulsion Laboratory | NASA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Time Series Forecasting, Evaluation and Deployment (Time-FED) NASA-814 · JPL: Jet Propulsion Laboratory | NASA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| AI Risk-aware task and motion planning for snake-like robots in icy enviroments NASA-815 · JPL: Jet Propulsion Laboratory | NASA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Retrieving stratospheric NO2 profiles from OMPS Limb Profiler measurements NASA-850 · GSFC: Goddard Space Flight Center | NASA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Quantification of Uncertainty Analysis Toolkit (QUAnT) NASA-861 · GSFC: Goddard Space Flight Center | NASA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Variable Optimization FRB-0010 · Division of Supervision and Regulation | FRB | Pilot | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| Decision Tree for Deposits Data FRB-0029 · Division of Supervision and Regulation | FRB | Pilot | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| Novel Activities Call Report Classification FRB-0031 · Division of Supervision and Regulation | FRB | Pilot | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| Risk Rating Model FRB-0050 · Division of Supervision and Regulation | FRB | Pilot | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| Autocoding to Support Adverse Drug Event Surveillance R0662 · HHS/CDC | HHS | Pilot | Not high-impact | Administrative Functions | Classical / predictive ML | Developed in-house |
| Distiller SR: AI to screen research articles for Community Guide reviews R0665 · HHS/CDC | HHS | Pilot | Not high-impact | Administrative Functions | Classical / predictive ML | Developed in-house |
| Determining if multiple isolates share the same antimicrobial resistant plasmids using short read whole genome sequencing data R0703 · HHS/CDC | HHS | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Enhancing influenza A risk assessment rubrics: leveraging predictive correlates and machine learning from in vivo experiments. R0718 · HHS/CDC | HHS | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Drug Shortage Predictive Model R0840 · HHS/FDA/CDER | HHS | Pilot | Not high-impact | Health and Medical | Classical / predictive ML | Developed in-house |
| NICHD RPAB AI/ML NICHD Relevance Model R0901 · HHS/NIH | HHS | Pilot | Presumed high-impact, determined not | Administrative Functions | Classical / predictive ML | Developed in-house |
| New Offsite Models FDIC – 57 · Division of Risk Management Supervision | FDIC | Pilot | Not high-impact | Information Technology | Classical / predictive ML | Developed in-house |
| QCEW NAICS Autocoder DOL-39 · BLS | DOL | Pilot | Not high-impact | Other | Classical / predictive ML | Developed in-house |
| LCAT - Land Change Analysis Technique USDA-011 · Farm Production and Conservation | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Raster Tools USDA-017 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Genomic Analyses of Pathogen Subtypes USDA-043 · Food Safety | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Foodborne Illness Source Attribution USDA-044 · Food Safety | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Fire Resilient Landscapes USDA-074 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Spread and Balance Sample Design USDA-076 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Regression, Classification, Clustering with Hilbert Curves USDA-077 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| NWRC ND Field Station Birds and Agriculture Project USDA-100 · Marketing and Regulatory Programs | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| NWRC LSS Bioinformatic Coding USDA-101 · Marketing and Regulatory Programs | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| NWRC Rabies ML A.D. USDA-102 · Marketing and Regulatory Programs | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| NWRC Rabies ML M.H. USDA-103 · Marketing and Regulatory Programs | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| NWRC Pepin ML & AI USDA-104 · Marketing and Regulatory Programs | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| NWRC Genetics Metadata USDA-105 · Marketing and Regulatory Programs | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Digital Soil Mapping USDA-120 · Farm Production and Conservation | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Species Distribution Data analysis USDA-126 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Random Forest with Aquatic Effectiveness Monitoring USDA-131 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Forest fire effects remote sensing models USDA-132 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Yearly Incident Procurement Classification Report USDA-135 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Procurement and Financial Management | Classical / predictive ML | Developed in-house |
| Forest Mapping using G-LiHT airborne data in interior Alaska USDA-146 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Predicting fire severity potential in future wildfires in California USDA-151 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Energy and the Environment | Classical / predictive ML | Developed in-house |
| App for predicting soil temperature during prescribed burns USDA-154 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Wildfire effects and postfire forest dynamics USDA-155 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Long-term monitoring of northern spotted owl habitat and populations USDA-160 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Fire Containment Suitability (FireCON) USDA-162 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Emergency Management | Classical / predictive ML | Developed in-house |
| Potential Control Location Suitability USDA-163 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Emergency Management | Classical / predictive ML | Developed in-house |
| Alisha OCR Reader USDA-164 · Natural Resources and Environment | USDA | Pilot | Not high-impact | Emergency Management | Classical / predictive ML | Developed in-house |
| Machine Learning approach to predict the composition of seafloor massive sulfide deposits [2024 INV#WO0000000108420] DOI-0245 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Prioritized Constituents: Sediment [2024 INV#WO0000000109726] DOI-0237 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Data-Driven Streamflow Drought [2024 INV#WO0000000109714] DOI-0234 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| National-Extent Groundwater Quality Prediction for the National Water Census and Regional Integrated Water Availability Assessments [2024 INV#WO0000000109709] DOI-0233 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Vegetation mapping on the Hawaiian island of Lanai [2024 INV#WO0000000109501] DOI-0229 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Population and critical habitat modeling of overwintering monarch butterflies [2024 INV#WO0000000109410] DOI-0227 · USGS | DOI | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |