From this we can see that all the agencies that we have inferred information about have a reasonable mix of initiatives in the POC stage, in development and in use. The outlier in this case is the Department of Commerce, and all their initiatives are currently marked as in-use. We took a second manual pass over the data, and read the summaries ourselves to establish whether Claude’s inferences were correct and that all of the DOC’s AI/ML initiatives were live, and there were no other statuses – we believe that Claude’s inferences were correct, if one were to solely go by the Summary description that the DOC provided. Again, the takeaway here should be that the DOC, like NASA, has only provided information about AI/ML initiatives that are live, and nothing about what is currently in the pipeline and being worked on.

AI/ML Techniques and Approaches used across agencies

We also wanted to take a look at the approaches that were being used in AI/ML initiatives across US government agencies, and this too required some level of cleansing on the provided data. We faced three major challenges. The first challenge was that there isn’t an agreed upon taxonomy of classification for AI and ML approaches, therefore there isn’t a standard way of comparing the qualitative aspects of work being done in one agency vs another. The second challenge was that many projects and initiatives used multiple approaches, and the format of the data provided didn’t lend itself to separating those out (but we did make the effort to do so). The third challenge was that information on techniques was missing for the majority (395 out of 710) of the projects. We again used the summary field to determine what the techniques might be.

The most common technique mentioned (in over 100 projects) in the CSV is “machine learning” – which by itself is too broad to convey much meaningful information.

In specifically named techniques, the most common one by far was Natural Language Processing (NLP). Agencies as diverse as the DHS, USDA and the DOE are relying on NLP approaches for a variety of tasks.

A close second is Computer Vision/Image Analysis for object recognition and image segmentation/classification. Several of these are combined with autonomous systems and robotics with the intent to create observation and measurement platforms that operate without human intervention, e.g. in the field of weather measurements and wildlife/ocean conservation efforts.

A third category of projects is classification and anomaly detection. Federal government agencies routinely handle large volumes of data, most of it unstructured. Agency teams have recognized the opportunity that machine learning presents for parsing that data efficiently, and improving the effectiveness of government services, e.g. both the USCIS and DOL are using this technique to help streamline and improve efficiencies in their case management systems.

On the other hand, generative AI (GenAI) techniques and approaches were few and far between. There is one mention of the term “LLM”, one mention of ChatGPT and three mentions of Generative Adversarial Networks (GANs). Our assessment is that this is more an artifact of data lag rather than an indication that government agencies are not taking advantage of GenAI. In the next iteration of this inventory, we expect to see GenAI being used broadly across the government.

Looking at Use Cases

Next we took a high level view of how some key agencies are using AI/ML. While these are early days yet, it is possible to pick up insights.

The Department of Homeland Security (DHS) is using AI/machine across Customs and Border Protection (CBP), Cybersecurity and Infrastructure Security Agency (CISA), and Immigration and Customs Enforcement (ICE). Common use cases include:

• Object detection/image recognition for security purposes (e.g. detecting anomalies in X-ray images, vehicle detection)
• Natural language processing for analyzing unstructured data (e.g. processing text from docket comments, case reports)

The Department of Health and Human Services (HHS) has several efforts around using natural language processing and machine learning for healthcare and biomedical applications, led by agencies like:

• Centers for Disease Control (CDC) – Detecting health conditions from medical images, analyzing social media data.
• Food and Drug Administration (FDA) – Adverse event analysis, review of drug applications.
• National Institutes of Health (NIH) – Predictive models for disease progression, mining scientific literature

The Department of Veterans Affairs (VA) is applying AI for:

• Analyzing medical records/data for risk prediction (e.g. suicide risk, disease progression)
• Computer-aided detection/diagnosis from medical images and sensor data
• Clinical decision support systems

A notable VA project is “Predictor profiles of opioid use disorder and overdose” which uses machine learning models to evaluate risk factors.

Other Departments like USDA, DOT, EPA are utilizing AI/ML techniques like computer vision, natural language processing and predictive modeling:

• Crop/vegetation mapping from satellite imagery (USDA)
• Analyzing regulatory comments from public (EPA)
• Predicting air transportation delays (DOT)

Source Code

OMB’s M-24-10 memo clearly states in Section 4-d-i that

1. Sharing and Releasing AI Code and Models: Agencies must proactively share their custom-developed code — including models and model weights — for AI applications in active use and must release and maintain that code as open source software on a public repository unless:
2. the sharing of the code is restricted by law or regulation, including patent or intellectual property law, the Export Asset Regulations, the International Traffic in Arms Regulations, and Federal laws and regulations governing classified information;
3. the sharing of the code would create an identifiable risk to national security, confidentiality of Government information, individual privacy, or the rights or safety of the public;
4. the agency is prevented by a contractual obligation from doing so; or
5. the sharing of the code would create an identifiable risk to agency mission, programs, or operations, or to the stability, security, or integrity of an agency’s systems or personnel.

Agencies should prioritize sharing custom-developed code, such as commonly used packages or functions, that has the greatest potential for re-use by other agencies or the public.

We see that only 17 of the projects had any information about source code. We presume that the remaining projects are open source, and have their source code available, and that link was not included in the provided inventory. We do not have a straightforward way of verifying this assumption.

For the 17 projects that do have their source code links in the CSV, Github links are provided, which is welcome.

Intersection with FedRAMP

One area of interest to implementers would be other federal regulations that apply to these initiatives. The most common of these would be FedRAMP, which applies to any commercial cloud based use case where federal government data is going to be stored on or otherwise transmitted into commercial public cloud systems. This would cover cloud providers such as Amazon AWS, Microsoft Azure, Google’s GCP and Oracle’s OCI and also cloud hosted applications and services such as OpenAI’s ChatGPT service.

The inventory does not provide any direct information about such specifics for any use case. We can however look at the description of the use cases and infer any cloud usage from there.

• The Department of Labor (DOL) has several projects utilizing cloud based commercial off the shelf NLP models for language translation, claims document processing and website chatbots.
• The United States Treasury has similar use cases, and is using Egain’s Intent Engine and Amazon Translate.
• The National Archives and Records Administration is using AI tools from commercial cloud providers to remove PII from records and documents that it is archiving.
• The General Services Administration is using ServiceNow Virtual Agent and OneReach AI.
• The Veterans Administration is using AiCure, a commercial application, to monitor prescription medication adherence and Medtronic’s GI Genius to aid in detection of colon polyps.
• The department of Health and Human services is using AI in several innovative ways. For example, utilizing Raisonance AI’s product that detects respiratory issues via a smartphone that analyzes the sound signature of a person’s cough, and the VisualDx app that assists physicians in visual diagnosis.

This is not an exhaustive list.

Summary

We are seeing widespread adoption of AI and Machine Learning (AI/ML) techniques across government agencies. Natural Language Processing (NLP) and Computer Vision/Neural Networks stand out as the most heavily utilized techniques. Convolutional Neural Networks (CNNs) demonstrate particular popularity in projects involving image analysis and computer vision, highlighting their effectiveness in these domains. Generative AI techniques have a small presence, but we expect that to exponentially increase.

Overall, our analysis recognizes the deep role of AI and Machine Learning within government operations.  The people working in US government agencies recognize the significant benefits in terms of efficiency, data-driven insights, and enhanced decision-making processes that we have available to us from AI and machine learning, and we’re seeing evidence of that thanks to the transparency that OMBs memo M-24-10 has mandated.