Category Archives: @NIST

Loading
loading...

AI Risk Management Framework

 

We list notable NIST projects or efforts related to LLMs, based on available information from NIST’s publications and initiatives. These projects emphasize NIST’s role in advancing measurement science, standards, and guidelines for trustworthy AI systems, including LLMs. Note that some projects are specific studies, while others are broader programs that encompass LLMs.
  • Evaluating LLMs for Real-World Vulnerability Repair in C/C++ Code
    NIST conducted a study to evaluate the capability of advanced LLMs, such as ChatGPT-4 and Claude, in repairing memory corruption vulnerabilities in real-world C/C++ code. The project curated 223 code snippets with vulnerabilities like memory leaks and buffer errors, assessing LLMs’ proficiency in generating localized fixes. This work highlights LLMs’ potential in automated code repair and identifies limitations in handling complex vulnerabilities.
  • Translating Natural Language Specifications into Access Control Policies
    This project explores the use of LLMs for automated translation and information extraction of access control policies from natural language sources. By leveraging prompt engineering techniques, NIST demonstrated improved efficiency and accuracy in converting human-readable requirements into machine-interpretable policies, advancing automation in security systems.
  • Assessing Risks and Impacts of AI (ARIA) Program
    NIST’s ARIA program evaluates the societal risks and impacts of AI systems, including LLMs, in realistic settings. The program includes a testing, evaluation, validation, and verification (TEVV) framework to understand LLM capabilities, such as controlled access to privileged information, and their broader societal effects. This initiative aims to establish guidelines for safe AI deployment.
  • AI Risk Management Framework (AI RMF)
    NIST developed the AI RMF to guide the responsible use of AI, including LLMs. This framework provides a structured approach to managing risks associated with AI systems, offering tools and benchmarks for governance, risk assessment, and operationalizing trustworthy AI across various sectors. It’s widely applied in LLM-related projects.
  • AI Standards “Zero Drafts” Pilot Project
    Launched to accelerate AI innovation, this project focuses on developing AI standards, including those relevant to LLMs, through an open and collaborative process. It aims to create flexible guidelines that evolve with LLM advancements, encouraging input from stakeholders to ensure robust standards.
  • Technical Language Processing (TLP) Tutorial
    NIST collaborated on a TLP tutorial at the 15th Annual Conference of the Prognostics and Health Management Society to foster awareness and education on processing large volumes of text using machine learning, including LLMs. The project explored how LLMs can assist in content analysis and topic modeling for research and engineering applications.
  • Evaluation of LLM Security Against Data Extraction Attacks
    NIST investigated vulnerabilities in LLMs, such as training data extraction attacks, using the example of GPT-2 (a predecessor to modern LLMs). This project, referencing techniques developed by Carlini et al., aims to understand and mitigate privacy risks in LLMs, contributing to safer model deployment.
  • Fundamental Research on AI Measurements
    As part of NIST’s AI portfolio, this project conducts fundamental research to establish scientific foundations for measuring LLM performance, risks, and interactions. It includes developing evaluation metrics, benchmarks, and standards to ensure LLMs are reliable and trustworthy in diverse applications.
  • Adversarial Machine Learning (AML) Taxonomy for LLMs
    NIST developed a taxonomy of adversarial machine learning attacks, including those targeting LLMs, such as evasion, data poisoning, privacy, and abuse attacks. This project standardizes terminology and provides guidance to enhance LLM security against malicious manipulations, benefiting both cybersecurity and AI communities.
  • Use-Inspired AI Research for LLM Applications
    NIST’s AI portfolio includes use-inspired research to advance LLM applications across government agencies and industries. This project develops guidelines and tools to operationalize LLMs responsibly, focusing on practical implementations like text summarization, translation, and question-answering systems.

Remarks:

  • These projects reflect NIST’s focus on evaluating, standardizing, and securing LLMs rather than developing LLMs themselves. NIST’s role is to provide frameworks, guidelines, and evaluations to ensure trustworthy AI.
  • Some projects, like ARIA and AI RMF, are broad programs that encompass LLMs among other AI systems, but they include specific LLM-related evaluations or applications.

 

Why You Need Standards

Department of Justice Antitrust Case Filings

When we talk about standards in our personal lives, we might think about the quality we expect in things such as restaurants and first dates. But the standards that exist in science and technology have an even greater impact on our lives. Technical standards keep us safe, enable technology to advance, and help businesses succeed. They quietly make the modern world tick and prevent technological problems that you might not realize could even happen…”

Technical Requirements for Weighing & Measuring Devices

Innovation and Competitiveness in Artificial Intelligence

The International Trade Administration (ITA) of the U.S. Department of Commerce (DOC) is requesting public comments to gain insights on the current global artificial intelligence (AI) market. Responses will provide clarity about stakeholder concerns regarding international AI policies, regulations, and other measures which may impact U.S. exports of AI technologies. Additionally, the request for information (RFI) includes inquiries related to AI standards development. ANSI encourages relevant stakeholders to respond by ITA’s deadline of October 17, 2022.

Fueling U.S. Innovation and Competitiveness in AI: Respond to International Trade Administration’s Request for Information

Commerce Department Launches the National Artificial Intelligence Advisory Committee

 

Standard Reference Material

Metrology is the scientific discipline that deals with measurement, including both the theoretical and practical aspects of measurement. It is a broad field that encompasses many different areas, including length, mass, time, temperature, and electrical and optical measurements.  The goal of metrology is to establish a system of measurement that is accurate, reliable, and consistent. This involves the development of standards and calibration methods that enable precise and traceable measurements to be made.

The International System of Units is the most widely used system of units today and is based on a set of seven base units, which are defined in terms of physical constants or other fundamental quantities.  Another important aspect of metrology is the development and use of measurement instruments and techniques. These instruments and techniques must be designed to minimize errors and uncertainties in measurements, and they must be calibrated against recognized standards to ensure accuracy and traceability.

Metrology also involves the development of statistical methods for analyzing and interpreting measurement data. These methods are used to quantify the uncertainty associated with measurement results and to determine the reliability of those results.

National Institute for Standards & Technology

Federal Participation in Consensus Standards

ARCHIVE: UM Welcomes ANSI 2015

Artificial Intelligence Standards

U.S. Artificial Intelligence Safety Institute

ANSI Response to NIST “A Plan for Global Engagement on AI Standards”

On April 29, 2024 NIST released a draft plan for global engagement on AI standards.

Comments are due by June 2. More information is available here.

 

Request for Information Related to NIST’s Assignments

Under Sections 4.1, 4.5 and 11 of the Executive Order Concerning Artificial Intelligence 

The National Institute of Standards and Technology seeks information to assist in carrying out several of its responsibilities under the Executive order on Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence issued on October 30, 2023. Among other things, the E.O. directs NIST to undertake an initiative for evaluating and auditing capabilities relating to Artificial Intelligence (AI) technologies and to develop a variety of guidelines, including for conducting AI red-teaming tests to enable deployment of safe, secure, and trustworthy systems.

Regulations.GOV Filing: NIST-2023-0009-0001_content

Browse Posted Comments (72 as of February 2, 2024 | 12:00 EST)

Standards Michigan Public Comment

 


Unleashing American Innovation

Federal Agency Conformity Assessment

Time & Frequency Services

Technical Requirements for Weighing & Measuring Devices

Why You Need Standards

Summer Internship Research Fellowship

A Study of Children’s Password Practices

Human Factors Using Elevators in Emergency Evacuation

Cloud Computing Paradigm

What is time?

Readings / Radio Controlled Clocks

Standard Reference Material

What is time?

“What then is time? If no one asks me, I know what it is.

If I wish to explain it to him who asks, I do not know.”

Saint Augustine (“Confessions” Book XI)

 

When did time zones become a thing?

Readings / Radio Controlled Clocks

Layout mode
Predefined Skins
Custom Colors
Choose your skin color
Patterns Background
Images Background
error: Content is protected !!
Skip to content