Overview
Title
To direct the use of artificial intelligence by National Oceanic and Atmospheric Administration to adapt to extreme weather, and for other purposes.
ELI5 AI
H.R. 2770 is a plan to help the National Oceanic and Atmospheric Administration (NOAA) use smart computers to better predict big storms and wildfires, and to work with schools and companies to make sure everyone can see the information they find.
Summary AI
H.R. 2770, introduced by Mr. Scott Franklin, is a bill to enhance the National Oceanic and Atmospheric Administration's (NOAA) ability to use artificial intelligence (AI) in adapting to extreme weather and wildfires. The bill proposes developing AI models for weather forecasting, improving data assimilation, and creating a fire environment modeling program. It encourages partnerships with private and academic institutions for innovation in weather and environmental forecasting and aims to ensure open access to data and models developed, with considerations for national security and intellectual property. Additionally, the bill emphasizes developing the federal workforce's expertise in AI applications for weather forecasting.
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Keywords AI
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AnalysisAI
General Summary of the Bill
The proposed legislation, known as the "Transformational Artificial Intelligence to Modernize the Economy against Extreme Weather and Wildfires Act" or "TAME Extreme Weather and Wildfires Act," aims to enhance the United States’ capability to forecast extreme weather and wildfire conditions using artificial intelligence (AI). The bill tasks the National Oceanic and Atmospheric Administration (NOAA) with spearheading efforts to improve weather prediction and information delivery. Key elements include developing AI-driven weather models, improving data assimilation techniques, providing technical support for such models, and fostering public-private partnerships to drive innovation in the field. The act also emphasizes developing the federal workforce’s expertise in AI applications for weather forecasting and ensuring public access to related data.
Summary of Significant Issues
Several significant issues emerge from the bill. First, the bill provides substantial discretionary power to the Under Secretary of Commerce for Oceans and Atmosphere, which might lead to inconsistent or biased implementation due to the lack of clear guidelines. Second, there are concerns about financial oversight. The absence of specific criteria for budget allocation or spending limits, particularly in sections focusing on innovation partnerships, could result in inefficiencies or wasteful expenditures. Third, issues regarding public data access are raised, as the Under Secretary has considerable discretion to limit data sharing under broad conditions, potentially restricting public access significantly. Additionally, the bill lacks well-defined success metrics, which may complicate assessing the effectiveness of these initiatives.
Impact on the Public
For the general public, the act holds the promise of significantly enhancing the accuracy and timeliness of weather and wildfire forecasts, potentially reducing the impact of natural disasters. With improved forecasts, communities might be better prepared for adverse weather conditions, potentially saving lives and reducing property damage. By increasing public access to weather data, the bill promises to empower citizens and stakeholders with more information to make informed decisions.
However, the broad discretion granted to the Under Secretary could result in inconsistent data accessibility, potentially limiting the benefits that might otherwise be realized. Additionally, without clear definitions and metrics, it may be challenging to gauge the bill’s success or hold responsible parties accountable for achieving its objectives.
Impact on Specific Stakeholders
For government agencies and federal employees, the bill proposes significant investments in workforce development related to AI applications for weather forecasting. This focus could advance federal expertise and capacity in an increasingly important field, benefiting public safety and emergency preparedness.
The private sector and academic institutions stand to benefit from the bill’s directives on forming partnerships to foster innovation. However, the lack of detailed criteria for these partnerships raises concerns about fairness and transparency, which could lead to criticisms of favoritism.
Additionally, intellectual property rights could become contentious, as the bill speaks to shared rights from research collaborations but lacks detailed guidance for managing these rights, potentially leading to legal disputes.
Overall, while the bill proposes progressive steps toward weather forecasting innovation, the execution of these measures will require careful oversight, transparent implementation, and well-defined accountability mechanisms to ensure broad public benefits and equitable participation from all stakeholders.
Issues
The bill's broad discretionary powers granted to the Under Secretary throughout various sections (Sections 3, 5, 9) may lead to inconsistent or potentially biased implementation. The language allowing action 'as considered appropriate by the Under Secretary' lacks clear guidelines, leading to a potential lack of transparency and accountability.
The absence of specific criteria for spending limits and budget allocation in Sections 3, 6, and 7 might lead to potential financial inefficiencies or wasteful spending. Especially concerning is Section 7 on 'Partnerships for transformational innovation' where there are no defined caps or limits, possibly resulting in unchecked expenditures.
Section 5's vagueness regarding the criteria for collaboration with academic and research institutions and the private sector may lead to perceived or actual favoritism, raising ethical concerns about fair competition and transparency in government collaborations.
Section 9's allowance for the Under Secretary to determine the extent of data accessibility without clear criteria might lead to unjustified limitations on public data access. This lack of transparency raises legal and ethical concerns about equitable data distribution and public access.
The potential impact on intellectual property rights as stated in Section 7, where shared rights from research and development activities might result in legal disputes. This raises concerns regarding how IP rights will be negotiated and managed between entities.
The lack of defined success metrics or specific implementation strategies across sections (such as Sections 3, 4, 7) may result in difficulty in evaluating the effectiveness of the initiatives and ensuring accountability, which is politically and financially significant to taxpayers and stakeholders.
The ambiguity in language throughout the bill, such as terms like 'novel structures', 'transformative innovation', and 'high-risk, high-return', seen in sections like 7 and 8, could lead to inconsistent interpretations, impacting the effectiveness and clarity of the bill’s objectives.
Sections
Sections are presented as they are annotated in the original legislative text. Any missing headers, numbers, or non-consecutive order is due to the original text.
1. Short title; table of contents Read Opens in new tab
Summary AI
The act, named the “TAME Extreme Weather and Wildfires Act,” includes a table of contents that outlines key sections, such as definitions, initiatives for earth system forecasting, advanced AI applications for weather prediction, and partnerships for innovation. It also mentions providing technical assistance for AI weather models and enhancing expertise in the federal workforce, along with ensuring data access.
2. Definitions Read Opens in new tab
Summary AI
The section provides definitions for various terms used in the Act, such as "artificial intelligence," which refers to a machine-based system that uses models to make decisions or predictions, and includes technologies like machine learning; "artificial intelligence weather model," a weather forecasting system using AI; "numerical weather model," which uses numerical computations for weather predictions; and other related terms like "curate," "observational data," "open license," "reforecast analysis," "synthetic data," "training dataset," "Under Secretary," and "weather enterprise."
3. Earth system forecasting and information delivery Read Opens in new tab
Summary AI
The bill section outlines a plan for the Under Secretary, alongside various governmental agencies and experts, to create comprehensive weather forecasting training datasets and explore artificial intelligence applications for improved weather prediction. It emphasizes the enhancement of public preparedness and resilience, maintaining support for traditional and advanced research in weather systems, and minimizing environmental impacts from AI usage.
4. Advanced artificial intelligence applications for weather and information delivery Read Opens in new tab
Summary AI
The Under Secretary is tasked with exploring advanced artificial intelligence applications to enhance weather forecasts and the delivery of information. This includes improving techniques for handling data, using AI models to quickly analyze weather patterns to ensure accuracy, and providing better decision-making support for communities based on weather predictions.
5. Technical assistance on use of artificial intelligence weather models Read Opens in new tab
Summary AI
The Under Secretary is tasked with regularly evaluating AI weather models and providing technical assistance, best practices, and support for their use with forecasters and emergency managers. Additionally, they are to create a framework for assessing weather models using reforecast analysis and may collaborate with several organizations to develop best practices. An independent study may also be commissioned to understand the impacts of AI weather models and enhance their integration into weather forecasting.
6. Fire environment modeling program Read Opens in new tab
Summary AI
The section outlines the creation of a Fire Environment Modeling Program that uses artificial intelligence to analyze data about wildfires. The program will help warn communities and firefighters, predict and detect wildfires, and forecast the movement and impact of these fires, and it involves acquiring and using a variety of data sources while minimizing environmental harm.
7. Partnerships for transformational innovation Read Opens in new tab
Summary AI
The section encourages partnerships with private and academic entities to innovate in weather forecasting and environmental predictions. It also suggests co-investment with these sectors to support high-risk research, share intellectual property, and collaborate on resources and results.
8. Federal Government workforce expertise Read Opens in new tab
Summary AI
The section outlines that the Under Secretary is responsible for developing a skilled workforce focused on using artificial intelligence for weather forecasting. Additionally, the Under Secretary is directed to collaborate with private sector experts to provide employees with training and resources to enhance weather prediction capabilities through AI technology.
9. Data access Read Opens in new tab
Summary AI
The section allows the Under Secretary to share data and code with the public for free and without restrictions but requires them to ensure this doesn't compromise national security, intellectual property rights, trade secrets, or any other legal agreements. It also clarifies that the law does not override other laws protecting U.S. national security.