GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: MLNav (Machine Learning Navigation)
3_agency: National Aeronautics and Space Administration
3_abr: NASA
4_bureau: Jet Propulsion Laboratory
8_topic_area: Deep Learning
10_commercial_ai: None of the above.
11_purpose_benefits: Accelerates path planning of rovers and other types of vehicles through ML-based heuristics, while guaranteeing safety through conventional, model-based collision checking. Integrated with M2020's Enhanced AutoNav (ENav) and tested with the real terrain data from Mars on ENav simulator. Uses U-net for the ML-based heuristics, trained by simulation-generated terrain data.
12_outputs: Path planning recommendations for Mars2020 Rover
16_dev_stage: In production
17_impact_type: Neither
18_date_initiated: 10/1/2019
19_date_acq_dev_began: 9/30/2021
20_date_implemented: 7/1/2020
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
27_public_info: No
28_iqa_compliance: NASA's data stewardship best practices are in effect under guidance from the Chief Data Officer.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
31_question_type: Blank/Not Provided
33_agency_data: Simulated terrain data for Mars
34_data_docs: Yes
37_custom_code: Yes
38_code_access: Yes
40_has_ato: No.  Engineering Review and Resease instead.
41_system_name: Mars2020 Rover
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
49_existing_reuse: No
50_internal_review: Yes
2_use_case_name: Perseverance Rover on Mars - Terrain Relative Navigation
3_agency: National Aeronautics and Space Administration
3_abr: NASA
4_bureau: Jet Propulsion Laboratory
8_topic_area: Deep Learning
10_commercial_ai: None of the above.
11_purpose_benefits: 3D machine vision via dual cameras to inform convolutional neural networks for rover navigation path planning.  Physics / momentum - based Terrain Relative Navigation (TRN).  Enhanced AutoNavigation (Enav) plans feasible paths using multiple techniques, to include random forests.  Approximate Clearance Evaluation (ACE) assesses obstacles to deterimine if the suspension of the rover can clear them (drive over them) or needs to route around them.  Additional, more complex AIML techniques are being planned and tested for future rovers at JPL.  TRN was also used to provide precision landing for the Perseverance Rover in the entry, descent, and landing process to get the rover to the surface of Mars.
12_outputs: Real-time terrain-relative navigation recommendations.
16_dev_stage: In production
17_impact_type: Neither
18_date_initiated: 1/1/2004
19_date_acq_dev_began: 10/1/2014
20_date_implemented: 2/18/2021
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
27_public_info: No
28_iqa_compliance: NASA's data stewardship best practices are in effect under guidance from the Chief Data Officer.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
31_question_type: Blank/Not Provided
33_agency_data: Simulated terrain data for Mars
34_data_docs: Yes
37_custom_code: Yes
38_code_access: Yes
40_has_ato: No.  Engineering Review and Resease instead.
41_system_name: Mars2020 Rover
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
49_existing_reuse: Built on prior TRN work
50_internal_review: Yes
2_use_case_name: SPOC (Soil Property and Object Classification)
3_agency: National Aeronautics and Space Administration
3_abr: NASA
4_bureau: Jet Propulsion Laboratory
8_topic_area: Deep Learning
10_commercial_ai: None of the above.
11_purpose_benefits: Using a convolutional neural network (CNN), SPOC (Soil Property and Object Classification) takes rover images and classifies the terrain type (e.g., sand, soil) from visual appearance. This ability enables rover to drive more safely. It is trained by labeled images from MER (Mars Exploration Rover), MSL (Mars Science Laboratory), and Mars 2020 rovers, annotated by tens of thousands of citizen scientsts through the AI4Mars project. SPOC deployed on MSL's ground operation system and onboard test on M2020 is being considered.  SPOC is one of many inputs to navigation.
12_outputs: Terrain image classifications such as soil, sand, rock, etc.
16_dev_stage: In production
17_impact_type: Neither
18_date_initiated: 1/1/2016
19_date_acq_dev_began: 1/1/2022
20_date_implemented: 7/1/2020
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
27_public_info: No
28_iqa_compliance: NASA's data stewardship best practices are in effect under guidance from the Chief Data Officer.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
31_question_type: Blank/Not Provided
33_agency_data: As an embedded system on the Mars Rover, ATO, PII, SAOP, HISP, etc. do not apply
34_data_docs: Yes
37_custom_code: Yes
38_code_access: Yes
39_code_link: https://data.nasa.gov/Space-Science/AI4MARS-A-Dataset-for-Terrain-Aware-Autonomous-Dri/cykx-2qix
40_has_ato: No.  Engineering Review and Resease instead.
41_system_name: Multiple approved space-based systems
42_dev_tools_wait: Less than 1 Year
43_infra_provisioned: No
45_compute_request: Yes
49_existing_reuse: Reused for many scientific missions
50_internal_review: Yes
2_use_case_name: Volcano SensorWeb
3_agency: National Aeronautics and Space Administration
3_abr: NASA
4_bureau: Jet Propulsion Laboratory
8_topic_area: AIML Platform/Environment
10_commercial_ai: None of the above.
11_purpose_benefits: The Sensor Web Project uses a network of sensors linked by software and the internet to an autonomous satellite observation response capability. This system of systems is designed with a flexible, modular, architecture to facilitate expansion in sensors, customization of trigger conditions, and customization of responses.  This system has been used to implement a global surveillance program to study volcanos. We have also run sensorweb tests to study flooding, cryosphere events, and atmospheric phenomena.  Specifically, in our application, we use low resolution, high coverage sensors to trigger observations by high resolution instruments. Note that there are many other rationales to network sensors into a sensorweb. For example automated response might enable observation using complementary instruments such as imaging radar, infra-red, visible, etc. Or automated response might be used to apply more assets to increase the frequency of observation to improve the temporal resolution of available data.  Our sensorweb project is being used to monitor the Earth's 50 most active volcanos. We have also run sensorweb experiments to monitor flooding, wildfires, and cryospheric events (snowfall and melt, lake freezing and thawing, sea ice formation and breakup.)
12_outputs: Detection and classification of volcano-related features.
16_dev_stage: In production
17_impact_type: Neither
18_date_initiated: 1/1/2003
19_date_acq_dev_began: 1/1/2003
20_date_implemented: 1/1/2003
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
27_public_info: No
28_iqa_compliance: NASA's data stewardship best practices are in effect under guidance from the Chief Data Officer.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
31_question_type: Blank/Not Provided
33_agency_data: Terrestrial and space-based remote sensing satellite images to train the model
34_data_docs: Yes
37_custom_code: Yes
38_code_access: Yes
40_has_ato: No.  Engineering Review and Resease instead.
41_system_name: Deployed on multiple remote sensing spacecraft
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
49_existing_reuse: This capability has been re-used among many missions
50_internal_review: Yes
2_use_case_name: NASA OCIO STI Concept Tagging Service
3_agency: National Aeronautics and Space Administration
3_abr: NASA
4_bureau: Langley Research Center
8_topic_area: Natural Language Processing
10_commercial_ai: None of the above.
11_purpose_benefits: An API (application program interface) for exposing topic models created with the STI (Scientific & Technical Information) concept training repository.
12_outputs: API standards
16_dev_stage: In production
17_impact_type: Neither
18_date_initiated: 1/1/2021
19_date_acq_dev_began: 1/1/2021
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
27_public_info: No
28_iqa_compliance: NASA's data stewardship best practices are in effect under guidance from the Chief Data Officer.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
31_question_type: Blank/Not Provided
34_data_docs: Yes
37_custom_code: Yes
38_code_access: Yes
40_has_ato: No.  Engineering Review and Resease instead.
41_system_name: STI
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
49_existing_reuse: This project re-used open source and prior NLP work
50_internal_review: Yes
2_use_case_name: Autonomous WAiting Room Evaluation (AWARE)
3_agency: National Aeronautics and Space Administration
3_abr: NASA
4_bureau: Langley Research Center
8_topic_area: Deep Learning
10_commercial_ai: None of the above.
11_purpose_benefits: Using an existing security camera and YOLO Machine Learning model to detect and count number of people waiting for service at Langley's Badge & Pass Office. When a predetermined threshold of people is exceeded, automated texts and emails are sent to request additional help at the service counters.
12_outputs: Count of current customers in waiting area
16_dev_stage: In production
17_impact_type: Neither
18_date_initiated: 1/1/2023
19_date_acq_dev_began: 7/1/2023
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: NASA's data stewardship best practices are in effect under guidance from the Chief Data Officer.
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
31_question_type: Blank/Not Provided
34_data_docs: No
37_custom_code: Yes
38_code_access: Yes
40_has_ato: Yes
41_system_name: AWARE
42_dev_tools_wait: Less than 1 Year
43_infra_provisioned: No
45_compute_request: No
49_existing_reuse: This project re-used prior work in identifying vehicles, objects or animals in a NASA environment
50_internal_review: Yes
2_use_case_name: Onboard Planner for Mars2020 Rover (Perseverance)
3_agency: National Aeronautics and Space Administration
3_abr: NASA
4_bureau: Jet Propulsion Laboratory
8_topic_area: Other
10_commercial_ai: None of the above.
11_purpose_benefits: The M2020 onboard scheduler incrementally constructs a feasible schedule by iterating through activities in priority-first order. When considering each activity it computes the valid time intervals for placement, taking into account preheating, maintenance heating, and wake/sleep of the rover as required. After an activity is placed (other than a preheat/maintenance or wake/sleep), the activity is never reconsidered by the scheduler for deletion or moving. Therefore the scheduler can be considered non backtracking, and only searches in the sense that it computes valid timeline intervals for legal activity placement.  Meta Search: Because the onboard scheduler will be invoked many times in a given sol (Martian Day) with a range of possible contexts (due to execution variations), its non backtracking nature leaves its vulnerable to brittleness. In order to mitigate this potential brittleness, the Copilot systems perform a monte carlo based stochastic analysis to set meta parameters of the scheduler - primarily activity priority but also potentially preferred time and temporal constraints.      Also: Research, experiments, and engineering to empower future rovers with onboard autonomy; planning, scheduling & execution; path planning; onboard science; image processing; terrain classification; fault diagnosis; and location estimation.  This is a multi-faceted effort and includes experimentation and demonstrations on-site at JPL's simulated mars navigation yard.
12_outputs: Planning and scheduling recommendations
16_dev_stage: In mission
17_impact_type: Neither
18_date_initiated: 1/1/2016
19_date_acq_dev_began: 1/1/2018
20_date_implemented: 7/1/2020
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
27_public_info: No
28_iqa_compliance: NASA's data stewardship best practices are in effect under guidance from the Chief Data Officer.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
31_question_type: Blank/Not Provided
33_agency_data: As a Mission-embedded system on Mars Rover, ATO, public dissemination, SAOP, HISP, etc. do not apply
34_data_docs: Yes
37_custom_code: Yes
38_code_access: Yes
40_has_ato: No.  Engineering Review and Resease instead.
41_system_name: Multiple approved space-based systems
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
49_existing_reuse: This work built-upon prior rovers
50_internal_review: Yes
2_use_case_name: SensorWeb: Volcano, Flood, Wildfire, and others.
3_agency: National Aeronautics and Space Administration
3_abr: NASA
4_bureau: Jet Propulsion Laboratory
8_topic_area: Statistical Methods
10_commercial_ai: None of the above.
11_purpose_benefits: The Sensor Web Project uses a network of sensors linked by software and the internet to an autonomous satellite observation response capability. This system of systems is designed with a flexible, modular, architecture to facilitate expansion in sensors, customization of trigger conditions, and customization of responses.  This system has been used to implement a global surveillance program to study volcanos. We have also run sensorweb tests to study flooding, cryosphere events, and atmospheric phenomena.  Specifically, in our application, we use low resolution, high coverage sensors to trigger observations by high resolution instruments. Note that there are many other rationales to network sensors into a sensorweb. For example automated response might enable observation using complementary instruments such as imaging radar, infra-red, visible, etc. Or automated response might be used to apply more assets to increase the frequency of observation to improve the temporal resolution of available data.  Our sensorweb project is being used to monitor the Earth's 50 most active volcanos. We have also run sensorweb experiments to monitor flooding, wildfires, and cryospheric events (snowfall and melt, lake freezing and thawing, sea ice formation and breakup.)
12_outputs: Identification and labelling of terrain, climate and weather features
16_dev_stage: In mission
17_impact_type: Neither
18_date_initiated: 1/1/2003
19_date_acq_dev_began: 1/1/2003
20_date_implemented: 1/1/2003
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
27_public_info: No
28_iqa_compliance: NASA's data stewardship best practices are in effect under guidance from the Chief Data Officer.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
31_question_type: Blank/Not Provided
33_agency_data: Remote sensing data from many scientific instruments
34_data_docs: Yes
37_custom_code: Yes
38_code_access: Yes
40_has_ato: No.  Engineering Review and Resease instead.
41_system_name: Multiple approved space-based systems
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
49_existing_reuse: This capability has been re-used among many missions
50_internal_review: Yes
2_use_case_name: Global, Seasonal Mars Frost Maps
3_agency: National Aeronautics and Space Administration
3_abr: NASA
4_bureau: Jet Propulsion Laboratory
8_topic_area: Deep Learning
10_commercial_ai: None of the above.
11_purpose_benefits: Global frost Martian maps dervied from five remote sensing datasets and processed with tools like CNNs and other data science techniques. Publicly available on JMARS
12_outputs: Frost maps
16_dev_stage: In production
17_impact_type: Neither
18_date_initiated: 5/1/2021
19_date_acq_dev_began: 10/1/2021
20_date_implemented: 11/1/2024
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: NASA's data stewardship best practices are in effect under guidance from the Chief Data Officer.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
31_question_type: Blank/Not Provided
33_agency_data: HiRISE, CTX, THEMIS, MCS, CRISM
34_data_docs: Yes
37_custom_code: Yes
38_code_access: No – agency does not have access to source code.
40_has_ato: No.  Engineering Review and Resease instead.
41_system_name: Multiple approved space-based systems
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
49_existing_reuse: This capability has been re-used among many missions
50_internal_review: Yes
2_use_case_name: Purchase Card Management System (PCMS)
3_agency: National Aeronautics and Space Administration
3_abr: NASA
4_bureau: Jet Propulsion Laboratory
8_topic_area: NLP
10_commercial_ai: None of the above.
11_purpose_benefits: Purchase card application uses ML model to suggest if a purchase may be a taggable asset or a chemical.
12_outputs: Metadata tagging recommendation
16_dev_stage: In production
17_impact_type: Neither
18_date_initiated: 11/13/2018
19_date_acq_dev_began: 11/13/2018
20_date_implemented: 11/15/2018
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
27_public_info: No
28_iqa_compliance: NASA's data stewardship best practices are in effect under guidance from the Chief Data Officer.
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
31_question_type: Blank/Not Provided
33_agency_data: The training data set relies on historical records related to the description of purchased items along with the corresponding Asset-Tag/Chemical flag to determine the suggested item categorization
34_data_docs: Yes
37_custom_code: Yes
38_code_access: Yes
40_has_ato: No.  Engineering Review and Resease instead.
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
49_existing_reuse: We may share this technique with other relevant use cases
50_internal_review: Yes
2_use_case_name: New Technology and Software Reporting (NTR)
3_agency: National Aeronautics and Space Administration
3_abr: NASA
4_bureau: Jet Propulsion Laboratory
8_topic_area: NLP
10_commercial_ai: None of the above.
11_purpose_benefits: NTR application uses ML model to suggest a Technology Category (e.g. Aerospace, Robotics) for the new technology being reported
12_outputs: Category classification
16_dev_stage: In production
17_impact_type: Neither
18_date_initiated: 5/9/2024
19_date_acq_dev_began: 7/2/2024
20_date_implemented: 8/22/2024
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
27_public_info: No
28_iqa_compliance: NASA's data stewardship best practices are in effect under guidance from the Chief Data Officer.
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
31_question_type: Blank/Not Provided
33_agency_data: The training data set relies on historical records related to the description of the New Technolgy Reported along with the corresponding Technology Category field to determine the suggested categorization
34_data_docs: Yes
37_custom_code: Yes
38_code_access: Yes
40_has_ato: No.  Engineering Review and Resease instead.
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
49_existing_reuse: We may share this technique with other relevant use cases
50_internal_review: Yes
2_use_case_name: ServiceNow GenAI "Now Assist"
3_agency: National Science Foundation
3_abr: NSF
4_bureau: OCIO
8_topic_area: Mission-Enabling
10_commercial_ai: Collaborating in real-time using AI-assisted tools in word processors.
11_purpose_benefits: ServiceNow GenAI is a FedRAMP High certified system to accelerate digital transformation using Now Assist. Automatically generate content such as responses, work notes, and knowledgebase articles related to customer service experience. Increase productivity and speed to delivery with intelligent recommendations. Resolve issues swiftly with chatbots that understand human language.
12_outputs: Increase productivity and speed to delivery with intelligent recommendations. Resolve issues swiftly with chatbots that understand human language.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
2_use_case_name: Budget Area Topic Classification Tool
3_agency: National Science Foundation
3_abr: NSF
4_bureau: CISE/OAD
8_topic_area: Mission-Enabling
10_commercial_ai: Summarizing the key points of a lengthy report using AI.
11_purpose_benefits: The Budget Area Topic Classification Tool uses a pre-trained BERT model ('bert-base-uncased') to classify proposals into 1 of 8 budget areas: Advanced Manufacturing, AI, Advanced Wireless, Clean Energy, Microelectronics & Semiconductors, Quantum, or Trustworthy AI. This approach is semi-supervised in nature as the training data is comprised of pre-tagged CISE proposals from prior years. Upon training the model to learn the multi-label structure of the underlying data, the model's accuracy and discriminatory power was tested using a hold-out sample (i.e. 80/20 split). 
12_outputs: The model outputs a numerical vector of probabilities representing the likelihood that a given proposal belongs to 1 or more categories. A cut-off value can then be set for determining final classification for a given proposal.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
2_use_case_name: AI voice over
3_agency: National Science Foundation
3_abr: NSF
4_bureau: OLPA
8_topic_area: Mission-Enabling
10_commercial_ai: Transcribing and summarizing a recorded meeting or interview using AI.
11_purpose_benefits: Commercial-off-the-shelf (COTS) - The AI system was purchased pre-built from a third-party, Wellsaid has been used to generate placeholder voice over, which will be replaced later by real voice over, so it doesnt cause much AI policy concerns.


12_outputs: allowing the agency to skip design and development by federal or contract personnel.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Adobe Photoshop
3_agency: National Science Foundation
3_abr: NSF
4_bureau: OLPA
8_topic_area: Mission-Enabling
10_commercial_ai: Creating visually appealing presentations using AI-driven design suggestions.
11_purpose_benefits: OLPA's Creative Services team will use Photoshop's AI-powered tools 
12_outputs: to refine edits of photos and graphics, particularly the retouching of portraits.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
2_use_case_name: Resubmit Checker 
3_agency: National Science Foundation
3_abr: NSF
4_bureau: EDU/DRL
8_topic_area: Mission-Enabling
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: The resubmit checker takes a list of proposal IDs and returns a list of similar proposals submitted to NSF in the past, scored by their similarity. 
12_outputs: This is useful for identifying resubmitted proposals for several purposes: evaluating whether to return without review for lack of revision, ensuring continuity of merit review, and the analysis of broader resubmit trends.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither