TL;DR:
- GAO is developing an AI model for querying extensive reports, aiding Congress.
- Dual objectives: audit AI use in government and enhance internal operations.
- GAO’s Innovation Lab focuses on foundational large language model (LLM).
- Aim to master generative AI for judicious applications and oversight.
- Internal infrastructure ensures secure and ethical AI utilization.
- Ongoing project for 8-10 weeks, selecting pre-trained models and calibrating parameters.
- Emphasis on transparent AI decision-making, avoiding external data sources.
- Exploring AI to summarize committee hearings and analyze public comments.
- Close to deploying AI, pending cybersecurity authorization and testing.
- User engagement is crucial and plans to involve the entire GAO organization.
Main AI News:
In the realm of government accountability, the Government Accountability Office (GAO) is taking strides to harness the potential of artificial intelligence (AI). GAO Comptroller General Gene Dodaro recently addressed the House Administration’s Subcommittee on Modernization, shedding light on their AI endeavors. Dodaro highlighted the agency’s dual objective: to scrutinize AI applications in government and to leverage this technology to enhance their own operations.
To fulfill these objectives, GAO’s Innovation Lab has embarked on a transformative journey, welcoming a formidable large language model (LLM) into its arsenal. This AI model is poised to revolutionize how GAO interacts with its extensive trove of reports, enabling more sophisticated and insightful queries. Once perfected, this potent tool will be made available to Congress, empowering lawmakers with unprecedented access to vital information.
Yet, the true potential of this AI model remains undefined, deliberately so. In an interview with FedScoop, GAO’s Chief Data Scientist and Director of the Innovation Lab, Taka Ariga, described the ongoing AI project as an “experimentation phase.” The overarching goal is to establish a foundational LLM that can serve as the bedrock for myriad use cases.
While GAO aims to employ this AI marvel to enhance both its own operations and the work of Congress, it is equally invested in mastering the nuances of generative AI. This knowledge will enable them to discern the most judicious applications and bolster their oversight of the government’s increasing reliance on this cutting-edge technology.
To strike a harmonious balance among these varied interests, Ariga elucidated GAO’s strategy. The agency is crafting an internal infrastructure that provides a secure haven for its staff to interact with generative AI and explore customized use cases. The paramount concern is to maximize productivity gains while adhering to stringent security and ethical standards.
This ambitious AI initiative has been underway for approximately eight to 10 weeks, primarily focusing on selecting the most suitable pre-trained models for their specific objectives. Ariga revealed the next phase involves calibrating parameters and adapting these models to GAO’s unique content.
One vital aspect of their endeavor is to ensure that the AI model does not derive answers from external sources like Wikipedia, avoiding the pitfalls of an “auto-magical” interface devoid of transparency. Ariga emphasized the importance of a step-by-step, rational process underpinning the AI’s decision-making, fostering a deep understanding of its outputs.
Among the intriguing applications under consideration is the use of generative AI to interface with Congress.gov via an application programming interface (API). This would involve summarizing committee hearings and correlating them with GAO’s own reports, providing a comprehensive perspective on key topics.
GAO’s explorations into generative AI extend further to analyzing public comments on Regulation.gov to distill common themes and insights. Ariga highlighted their openness to diverse hypotheses, emphasizing a commitment to not restrict possibilities based on past considerations by the Innovation Lab.
While GAO is inching closer to deploying this technology, there are procedural steps yet to be navigated, including cybersecurity authorization and comprehensive testing. Throughout this journey, Ariga stressed the importance of engaging with end users, starting with a select group and gradually expanding the circle. Ultimately, GAO envisions affording its entire organization the opportunity to experience the unique capabilities of a GAO-specific large language model. This transformation promises to usher in a new era of efficiency and insight for the Government Accountability Office and its stakeholders.
Conclusion:
GAO’s strategic foray into AI signifies a pivotal moment in government accountability. By leveraging advanced AI capabilities for report querying and fostering transparency in decision-making, GAO is poised to enhance its effectiveness significantly. This move underscores the increasing importance of AI in the public sector, setting a precedent for organizations and markets alike to explore AI’s transformative potential in fostering accountability and efficiency.
Source
GAO is developing an AI model to enhance its operations and assist Congress by querying its vast report catalog
TL;DR:
Main AI News:
In the realm of government accountability, the Government Accountability Office (GAO) is taking strides to harness the potential of artificial intelligence (AI). GAO Comptroller General Gene Dodaro recently addressed the House Administration’s Subcommittee on Modernization, shedding light on their AI endeavors. Dodaro highlighted the agency’s dual objective: to scrutinize AI applications in government and to leverage this technology to enhance their own operations.
To fulfill these objectives, GAO’s Innovation Lab has embarked on a transformative journey, welcoming a formidable large language model (LLM) into its arsenal. This AI model is poised to revolutionize how GAO interacts with its extensive trove of reports, enabling more sophisticated and insightful queries. Once perfected, this potent tool will be made available to Congress, empowering lawmakers with unprecedented access to vital information.
Yet, the true potential of this AI model remains undefined, deliberately so. In an interview with FedScoop, GAO’s Chief Data Scientist and Director of the Innovation Lab, Taka Ariga, described the ongoing AI project as an “experimentation phase.” The overarching goal is to establish a foundational LLM that can serve as the bedrock for myriad use cases.
While GAO aims to employ this AI marvel to enhance both its own operations and the work of Congress, it is equally invested in mastering the nuances of generative AI. This knowledge will enable them to discern the most judicious applications and bolster their oversight of the government’s increasing reliance on this cutting-edge technology.
To strike a harmonious balance among these varied interests, Ariga elucidated GAO’s strategy. The agency is crafting an internal infrastructure that provides a secure haven for its staff to interact with generative AI and explore customized use cases. The paramount concern is to maximize productivity gains while adhering to stringent security and ethical standards.
This ambitious AI initiative has been underway for approximately eight to 10 weeks, primarily focusing on selecting the most suitable pre-trained models for their specific objectives. Ariga revealed the next phase involves calibrating parameters and adapting these models to GAO’s unique content.
One vital aspect of their endeavor is to ensure that the AI model does not derive answers from external sources like Wikipedia, avoiding the pitfalls of an “auto-magical” interface devoid of transparency. Ariga emphasized the importance of a step-by-step, rational process underpinning the AI’s decision-making, fostering a deep understanding of its outputs.
Among the intriguing applications under consideration is the use of generative AI to interface with Congress.gov via an application programming interface (API). This would involve summarizing committee hearings and correlating them with GAO’s own reports, providing a comprehensive perspective on key topics.
GAO’s explorations into generative AI extend further to analyzing public comments on Regulation.gov to distill common themes and insights. Ariga highlighted their openness to diverse hypotheses, emphasizing a commitment to not restrict possibilities based on past considerations by the Innovation Lab.
While GAO is inching closer to deploying this technology, there are procedural steps yet to be navigated, including cybersecurity authorization and comprehensive testing. Throughout this journey, Ariga stressed the importance of engaging with end users, starting with a select group and gradually expanding the circle. Ultimately, GAO envisions affording its entire organization the opportunity to experience the unique capabilities of a GAO-specific large language model. This transformation promises to usher in a new era of efficiency and insight for the Government Accountability Office and its stakeholders.
Conclusion:
GAO’s strategic foray into AI signifies a pivotal moment in government accountability. By leveraging advanced AI capabilities for report querying and fostering transparency in decision-making, GAO is poised to enhance its effectiveness significantly. This move underscores the increasing importance of AI in the public sector, setting a precedent for organizations and markets alike to explore AI’s transformative potential in fostering accountability and efficiency.
Source