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The 2024 NIST Generative AI (GenAI) Pilot Study focuses on evaluating text-to-text (T2T) generation and discrimination tasks to assess the capabilities and limitations of generative AI models. The study aims to measure the effectiveness of AI-generated
This plan has been developed by the Department of Commerce in coordination with the Department of State and agencies across the U.S. Government. It reflects more than 65 comments received in response to a December 2023 Request for Information
This NIST Trustworthy and Responsible AI report provides a taxonomy of concepts and defines terminology in the field of adversarial machine learning (AML). The taxonomy is arranged in a conceptual hierarchy that includes key types of ML methods, life cycle
Peter Bajcsy, Brycie Wiseman, Michael Paul Majurski, Andras Vladar
The speed of in-line scanning electron microscope (SEM) measurements of linewidth, contact hole, and overlay is critically important for identifying the measurement area and generating indispensable process control information. Sample charging and damage
Eugene Song, Thomas Roth, David A. Wollman, Eoin Jordan, Martin Serrano, Amelie Gyrard
Modern edge artificial intelligence (AI) chipsets and edge-intelligence-enabled smart sensors frameworks support real-time data processing and event detection at the signal source. Beyond just measuring local conditions and transmitting corresponding
The rapid advancement of artificial intelligence (AI) has led to the emergence of several technologies, including Generative Adversarial Networks (GANs), deepfakes, generative AI, CGI, and anti-forensics techniques. These technologies pose a significant
This paper presents gFlashNet, a generic flashover prediction model, designed to address the limitations of existing models that are restricted to specific residential building layouts. The aim of this research is to improve the scalability and
Precisely modeling radio propagation in complex environments has been a significant challenge, especially with the advent of 5G and beyond networks, where managing massive antenna arrays demands more detailed information. Traditional methods, such as
This report examines the existing standards, tools, methods, and practices, as well as the potential development of further science-backed standards and techniques, for: authenticating content and tracking its provenance; labeling synthetic content, such
Wai Cheong Tam, Jian Chen, Wei Tang, Qi Tong, Hongqiang Fang, Anthony Putorti
This paper presents the development of a fast-responding and accurate detection model for early-stage thermal runaway of a lithium-ion battery utilizing acoustics and a deep learning paradigm. A series of single-cell lithium-ion battery tests is conducted
Theodore Jensen, Mary Frances Theofanos, Kristen K. Greene, Olivia Williams, Kurtis Goad, Janet Bih Fofang
The increasing prevalence of artificial intelligence (AI) will likely lead to new interactions and impacts for the general public. An understanding of people's perceptions of AI can be leveraged to design and deploy AI systems toward human needs and values
Nathan Dwarshuis, Nathanael Olson, Fritz Sedlazeck, Justin Wagner, Justin Zook
Despite the variety in sequencing platforms, mappers, and variant callers, no single pipeline is optimal across the entire human genome. Therefore, developers, clinicians, and researchers need to make tradeoffs when designing pipelines for their
Yan Lu, Zhuo Yang, Shengyen Li, Yaoyao Fiona Zhao, Jiarui Xie, Mutahar Safdar, Hyunwoong Ko
The key differentiation of digital twins from existing models-based engineering approaches lies in the continuous synchronization between physical and virtual twins through data exchange. The success of digital twins, whether operated automatically or with
In the U.S., commercial buildings are responsible for approximately 36 % of total energy consumption, and the heating, ventilation, and air-conditioning (HVAC) systems make up about 52 % of that total. Improving building operations can significantly reduce
Jesse Dunietz, Elham Tabassi, Mark Latonero, Kamie Roberts
Recognizing the importance of technical standards in shaping development and use of Artificial Intelligence (AI), the President's October 2023 Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence (EO 14110)
Chloe Autio, Reva Schwartz, Jesse Dunietz, Shomik Jain, Martin Stanley, Elham Tabassi, Patrick Hall, Kamie Roberts
This document is a cross-sectoral profile of and companion resource for the AI Risk Management Framework (AI RMF 1.0) for Generative AI, pursuant to President Biden's Executive Order (EO) 14110 on Safe, Secure, and Trustworthy Artificial Intelligence. The
Harold Booth, Murugiah Souppaya, Apostol Vassilev, Michael Ogata, Martin Stanley, Karen Scarfone
This document augments the secure software development practices and tasks defined in Secure Software Development Framework (SSDF) version 1.1 by adding practices, tasks, recommendations, considerations, notes, and informative references that are specific
Behzad Salimian Rizi, Afshin Faramarzi, Amanda Pertzborn, Mohammad Heidarinejad
In recent years, data-driven models have enabled accurate prediction of chiller power consumption and chiller coefficient of performance (COP). This study evaluates the usage of time series Extreme Gradient Boosting (XGBoost) models to predict chiller
This paper presents a network management AI assistant built with Large Language Models. It adapts at runtime to the network state and specific platform, leveraging techniques like prompt engineering, document retrieval, and Knowledge Graph integration. The
Additive manufacturing (AM) faces several challenges in achieving efficient and defect-free printing. Although traditional machine learning (ML) has proven effective in mitigating these challenges, it requires specialized models for solving specific
During Fiscal Year 2023 (FY 2023) – from October 1, 2022, through September 30, 2023 –the NIST Information Technology Laboratory (ITL) Cybersecurity and Privacy Program successfully responded to numerous challenges and opportunities in security and privacy
Nowrin Akter Surovi, Paul Witherell, Kumara Sundar, Vinay Saji Mathew
Additive Manufacturing (AM) is becoming increasingly popular in academia and industry due to its cost-effectiveness and time-saving benefits. However, AM faces several challenges that must be addressed to enhance its efficiency. While Machine Learning (ML)