GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: In-pixel AI for future tracking detectors
3_agency: Department of Energy
3_abr: DOE
4_bureau: FNAL - Fermi National Accelerator (SC43 OIM)
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: AI algorithms are implemented in on-detector electronics in order to reduce data size and enable processing at high rates.
12_outputs: A recommendation of whether to save data based on AI classifier. Or, a fast inference of track parameters to be used for fast selection
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/1/2022
19_date_acq_dev_began: 10/1/2022
20_date_implemented: 10/1/2025
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: Accelerator operations machine data
34_data_docs: Documentation has been partially completed: Some documentation exists (detailing the composition and any statistical bias or measurement skew for training and evaluation purposes), but documentation took place within this use case's development.
35_demo_features: Other
37_custom_code: Yes
38_code_access: Yes – source code is publicly available.
39_code_link: https://github.com/smart-pix/smart-pixels-ml
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
63_stakeholder_consult: None of the above
2_use_case_name: SONIC: AI acceleration as a service
3_agency: Department of Energy
3_abr: DOE
4_bureau: FNAL - Fermi National Accelerator (SC43 OIM)
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: SONIC is used to accelerate AI workloads on coprocessors in scientific experiments.  This can accelerate sientific discovery and time to science thus enabling large cost savings and DOE scientific prestige.
12_outputs: It can be an AI algorithm from prediction to data compression to control (decision making).
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/1/2020
19_date_acq_dev_began: 10/1/2020
20_date_implemented: 10/1/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: research datasets from scientific experiments
34_data_docs: Documentation has been partially completed: Some documentation exists (detailing the composition and any statistical bias or measurement skew for training and evaluation purposes), but documentation took place within this use case's development.
35_demo_features: Other
37_custom_code: Yes
38_code_access: Yes – source code is publicly available.
39_code_link: https://github.com/fastmachinelearning/SonicCMS
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
63_stakeholder_consult: None of the above
2_use_case_name: READS: Real-time Edge AI for Distributed Systems
3_agency: Department of Energy
3_abr: DOE
4_bureau: FNAL - Fermi National Accelerator (SC43 OIM)
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: READS has two sub-projects. The first project created the means to stream live Main Injector and Recycler accelerator beam loss monitor data. This data is then fed to an AI model deployed on an FPGA so that it can infer, in realtime, the origin of beam loss, either Main Injector or Recycler, for each beam loss monitor in the tunnel enclosure. The second project aimed to improve upon traditional resonant beam extraction regulation techiniques using AI for use in the Fermilab Delivery Ring and Mu2e.
12_outputs: The ML outputs of the system are inferences as to the origin of beam loss in the Main Injector acclerator enclosure and also suggested regulation ramps to best improve the Spill Duty Factor in the Delivery Ring for Mu2e
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/1/2019
19_date_acq_dev_began: 6/1/2019
20_date_implemented: 10/1/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: research datasets from scientific experiments
34_data_docs: Documentation has been partially completed: Some documentation exists (detailing the composition and any statistical bias or measurement skew for training and evaluation purposes), but documentation took place within this use case's development.
35_demo_features: Other
37_custom_code: Yes
38_code_access: Yes – source code is publicly available.
39_code_link: https://github.com/fermilab-accelerator-ai/READS
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
63_stakeholder_consult: None of the above
2_use_case_name: Extreme data reduction for the edge
3_agency: Department of Energy
3_abr: DOE
4_bureau: FNAL - Fermi National Accelerator (SC43 OIM)
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: AI tools are developed for embedded inference in real-time processing systems for scientific experiments. This can accelerate sientific discovery and time to science thus enabling large cost savings and DOE scientific prestige.
12_outputs: It can be an AI algorithm from prediction to data compression to control (decision making).
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/1/2021
19_date_acq_dev_began: 6/1/2021
20_date_implemented: 10/1/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: research datasets from scientific experiments
34_data_docs: Documentation has been partially completed: Some documentation exists (detailing the composition and any statistical bias or measurement skew for training and evaluation purposes), but documentation took place within this use case's development.
35_demo_features: Other
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
63_stakeholder_consult: None of the above
2_use_case_name: Machine Learning for Accelerator Operations Using Big Data Analytics / L-CAPE
3_agency: Department of Energy
3_abr: DOE
4_bureau: FNAL - Fermi National Accelerator (SC43 OIM)
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: ML models are deployed for the FNAL's Linac.to detect, label andact upon faults.  The usage of ML will jimporve our fault labelingand detection.  This will allow for improved operatioal efficeincy, fault statistics, and preventitive maintenance. To my knowledge this is the first global accelerator operations ML system.
12_outputs: The ML outputs to a dashboard withfault labels and downtime predictiojns.The model will also try and predict dwwontime and possible actions.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/1/2020
19_date_acq_dev_began: 1/1/2020
20_date_implemented: 10/1/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: my own simulated data; research datasets from scientific experiments
34_data_docs: Documentation has been partially completed: Some documentation exists (detailing the composition and any statistical bias or measurement skew for training and evaluation purposes), but documentation took place within this use case's development.
35_demo_features: Other
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: No
53_real_world_testing: Performance evaluation in operational environment: The AI use case has been tested in an operational environment before being fully implemented as a solution, or has been tested in simulated or controlled environment using operational or synthetic data.
54_key_risks: None / under evaluation
55_independent_eval: Planned or in-progress
56_monitor_postdeploy: Intermittent and Manually Updated: A plan for monitoring the AI use case is in place, and requires data science teams to work with DevOps engineers to manually update models at scheduled intervals, and create metrics to detect data distribution shifts between the operational environment and the training data for the model. 
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
61_adverse_impact: No
63_stakeholder_consult: None of the above
65_appeal_process: Yes
67_opt_out: Yes
2_use_case_name: Soil Moisture Modeling
3_agency: Department of Energy
3_abr: DOE
4_bureau: LM HQ - Office of Legacy Management (LM)
8_topic_area: Science & Space
10_commercial_ai: Creating visual representations of data sets for reports and presentations using AI.
11_purpose_benefits: Predicts soil moisture contents without adding more monitoring wells.
12_outputs: Prediction
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
19_date_acq_dev_began: 10/3/2022
20_date_implemented: 3/1/2023
2_use_case_name: DOIChatGPT AI Chatbot
3_agency: Department of the Interior
3_abr: DOI
4_bureau: OCIO
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Create for DOI users a secure space to chat with a Large Language Model without leaving our security boundary.

This is a customized version of Microsofts Govchat repo deployed as an azure app service on the internal network with entra authentication, using DOIChatGPT APIM as the back end AI service provider.
12_outputs: See description 
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
31_data_catalog: Yes
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: DOIChatGPT API Management Instance
3_agency: Department of the Interior
3_abr: DOI
4_bureau: OCIO
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: An Azure OpenAI instance secured behind an Azure API Management instance hosted within our internal network. This will create a backbone of secure access to Azure OpenAI permitted by APIM subscription keys that also supports cost tracking per project key.

This is just a service layer to enable other project activities.
12_outputs: Outputs were not documented and DOI will update the use cases once additional data is collected from submitter 
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
20_date_implemented: 7/9/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
31_data_catalog: Yes
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: DOIChatGPT API Management Development Environment
3_agency: Department of the Interior
3_abr: DOI
4_bureau: OCIO
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits:  This will be a public facing version of the DOIChatGPT APIM and Azure OpenAI that is used for training and proof of concept validation (with sanitized data) prior to migrating to production DOIChatGPT APIM.
12_outputs: Outputs were not documented and DOI will update the use cases once additional data is collected from submitter 
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
20_date_implemented: 8/21/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
31_data_catalog: Yes
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: Cost Estimation Using EDL Database Data
3_agency: Department of the Interior
3_abr: DOI
4_bureau: OS - Office of the Secretary of the Interior
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits:  This use case aims to leverage AI/ML for estimating environmental remediation costs based on historical data within the EDL (Environmental Data Liability) database. The model would analyze archived and current records to provide estimates that aid budget planning and contractor evaluations.

Problem Being Solved: To enhance budget estimation accuracy for environmental remediation activities by comparing new sites with archived data of similar sites to estimate total cleanup costs.

Goal: The goal is to improve cost estimation accuracy, enhance contractor proposal evaluation, and aid in long-term financial planning.



12_outputs: Intended Use: The AI/ML tool would provide estimates to support the decision-making process for budget planning and contractor proposal assessments. This would act as a supplementary tool and not the primary decision source.
16_dev_stage: Initiated
17_impact_type: Neither
24_hisp_support: No
27_public_info: No
31_data_catalog: Yes
33_agency_data: Data Involved: Source: EDL Database; Types: Site type, phase of remediation, complexity, size, contaminants of concern, and cost information. Data characteristics: Structured data fields including categorical (e.g., site type, phase, and contaminants of concern) and numerical (e.g., costs, size estimates). Updates: periodic updates to reflect changes in cost due to inflation or other factors. Annual update for inflation.
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: Adobe Firefly Generative Images
3_agency: Department of the Interior
3_abr: DOI
4_bureau: NPS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits:   Adobe's Firefly generative image AI model was used within Photoshop to create artistic sketches of different nature scenes. These sketches were used in the StoryMap created by San Juan Island National Historical Park as artistic and narrative elements. The images were generated in Photoshop and then manually edited to ensure accuracy. Using this AI model created results that were quicker and cheaper compared to traditional artistic methods. The StoryMap with the sketches can be viewed here: https://arcg.is/1uCy0e
12_outputs: Imagery
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
22_dev_method: Data not reported by submitter and will be updated once additional information is collected
23_contract_piids: Data not reported by submitter and will be updated once additional information is collected
24_hisp_support: No
27_public_info: Yes
31_data_catalog: Yes
33_agency_data: Photographs taken by NPS staff were used to compare the accuracy of images output by the AI model
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: RAG knowledge base of agent procedures
3_agency: Department of the Interior
3_abr: DOI
4_bureau: OCIO
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Custom AI Solutions for Internal and Customer Support
Description:    We are addressing the challenge of providing quick, accurate, and consistent responses to both internal customer service agents and external customers. Currently, agents must manually search through extensive documentation, which can lead to delays in service response times, inconsistencies in information shared, and a potential for human error. Additionally, external customers often need direct access to information on our website but must wait for human support, which impacts customer satisfaction.
 
By implementing a customized AI chatbot, we aim to streamline access to knowledge for agents, reducing their workload and improving efficiency. Additionally, a customer-facing chatbot would offer external customers instant answers to common questions, further enhancing customer experience and reducing the volume of support inquiries handled by human agents.
12_outputs: Streamline access to knowledge for agents, reducing their workload and improving efficiency. Additionally, a customer-facing chatbot would offer external customers instant answers to common questions, further enhancing customer experience and reducing the volume of support inquiries handled by human agents.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
31_data_catalog: Yes
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: Use of AI to Enhance Flash Flood Forecast Tool
3_agency: Department of the Interior
3_abr: DOI
4_bureau: NPS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits:   University of Illinois created a model to predict rainfall on a watershed scale in Great Smoky Mountains National Park.  They are now working with IBM to create a system that will use this model to provide forecasts of flooding events with a goal of a 24+ hour lead time.  The developers are using AI to improve on the Quantitative Precipitation Forecast of the National Weather Service to thus improve on the accuracy of their tool.  The NPS is cooperating as an end-user of the flood forecast tool, but not as a developer or direct user of the AI.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
22_dev_method: Data not reported by submitter and will be updated once additional information is collected
23_contract_piids: Data not reported by submitter and will be updated once additional information is collected
24_hisp_support: No
31_data_catalog: Yes
33_agency_data: Data not reported by submitter and will be updated once additional information is collected
34_data_docs: Data not reported by submitter and will be updated once additional information is collected
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: Liable Party Research
3_agency: Department of the Interior
3_abr: DOI
4_bureau: ONRR
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Summarizing the key points of a lengthy report using AI.
11_purpose_benefits: Supervised machine learning model for scraping leasing documents for current liable party ownership.
12_outputs: Efficient liable party ownership.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
31_data_catalog: Yes
51_extension_request: No – Agency did not request an extension for this use case.