This "roadmap" isn't a bill or a detailed policy proposal, but it offers a glimpse into the scale and scope lawmakers and stakeholders envision. However, with an election year approaching, the chances of this advancing are slim.

A final report issued by Sen. Chuck Schumer's (D-NY) office, the bipartisan group outlines the essential areas for investment to maintain U.S. competitiveness on the global stage.

Key highlights from the roadmap include:

Cross-government AI R&D initiative: This involves a coordinated effort among agencies like the DOE, NSF, NIST, NASA, and the Department of Commerce to format and share data in a manner conducive to AI research. Though it sounds straightforward, achieving this collaboration will be a complex, multi-year endeavour.

Support for AI hardware and software innovation: Funding semiconductor and architecture advancements through the CHIPS Act and other mechanisms to strengthen American AI capabilities.

iiExpansion of the National AI Research Resource: Additional funding and development are needed for this nascent initiative.

AI grand challenges: Competitions designed to stimulate groundbreaking advancements in science, engineering, medicine, and secure, efficient software and hardware design through AI.

Election cybersecurity and AI readiness: Efforts to mitigate AI-generated misinformation while safeguarding First Amendment rights, a task more challenging than it appears.

Modernizing federal government services: Updating IT infrastructure to incorporate modern data science and AI technologies, and deploying these technologies to identify inefficiencies in federal codes, regulations, and procurement programs. This is an ambitious goal for an AI project.

Defense-related AI initiatives: Evaluating and countering AI-enhanced Chemical, Biological, Radiological, and Nuclear (CBRN) threats with contributions from the DOD, DHS, DOE, and other agencies.

Addressing regulatory gaps in finance and housing: Investigating how AI-driven processes might further marginalize vulnerable populations and seeking solutions.

Regulating potentially harmful AI applications: Considering strict limitations or outright bans on certain AI uses, such as AI-driven social scoring systems

.Prohibiting harmful AI-generated content: Enacting legislation to ban AI-generated child sexual abuse material and other nonconsensual imagery.Enhancing healthcare AI evaluation: Providing NIH, HHS, and FDA with the necessary tools to effectively assess AI applications in healthcare.

Establishing AI transparency requirements: Developing a coherent approach to transparency for both private and public AI systems.

Improving content provenance information: Increasing the availability of data on what was used to train AI models and whether user interactions further train these models. AI developers may resist this until they can clean their training datasets.

Evaluating private versus open source AI: Assessing the risks and benefits of using private AI systems compared to open source alternatives, should scalable open source options become available.

The full report contains many more recommendations, although it lacks specific budget figures.

As the next few months will be dominated by election activities, this document primarily serves to establish general ideas rather than prompt immediate legislation. Many proposals will require extensive research and iterative processes before any laws or regulations are enacted.

The AI industry progresses much faster than the federal government, raising questions about how many of these priorities will remain relevant by the time Congress or the White House takes action.