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Artificial intelligence

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Displaying 1 - 25 of 279

Photopolymer Additive Manufacturing 2025 Workshop Report: Building a Unified Vision from Research to Regulation

June 10, 2026
Author(s)
Callie Higgins, Jason Killgore, Mike Idacavage, Vince Anewenter, Mickey Fortune, Gary Cohen, Perri Katzman, Jessica Hemond, Spencer Loveless, Michael Gould
The third biannual Photopolymer Additive Manufacturing Alliance Workshop was held on September 15-16, 2025, at the University of Colorado Boulder to continue its mission of advancing photopolymer additive manufacturing (PAM). Building on the 2023 PAMA

A generative pre-trained transformer with Kerr-soliton attention

May 22, 2026
Author(s)
Lindell Williams, Yan Jin, Scott Papp
Artificial intelligence systems, particularly through generative pre-trained transformers (GPTs), have enabled capability-rich language models, but their operation incurs substantial costs in digital computation, memory, and data movement. Attention is a

Robust AI Security and Alignment: A Sisyphean Endeavor?

May 14, 2026
Author(s)
Apostol Vassilev
This article establishes information-theoretic limitations for robustness of artificial intelligence (AI) security and alignment. Knowing these limitations and preparing for their challenges is essential for responsible adoption of AI. Broader implications

Nondestructive characterization of laser-cooled atoms using machine learning

May 13, 2026
Author(s)
Ian Spielman, Justyna Zwolak, Michael Doris, Dario D'Amato, Guilherme de Sousa, Brady Egleston
We develop machine learning techniques for estimating physical properties of laser-cooled potassium-39 atoms in a magneto-optical trap using only the scattered light---i.e., fluorescence---that is intrinsic to the cooling process. In-situ snap-shot images

Challenges to the monitoring of deployed AI systems: Center for AI Standards and Innovation

March 6, 2026
Author(s)
Anita Rao, Andrew Keller, Neha Kalra, Ryan Steed, Kweku Kwegyir-Aggrey, Kevin Klyman, Diane Staheli, Amanda Bergman
As artificial intelligence (AI) systems are increasingly integrated into commercial and government applications, there is a growing need to monitor these systems in real-world settings. Although pre-deployment evaluations are valuable for assessing AI

Machine Learning Based Forecasting for Building Fires

January 9, 2026
Author(s)
Wai Cheong Tam, Hongqiang Fang, Yifei Ding
The fast-evolving conditions of rapid fire progressions demand swift and informed decision-making from firefighters. This paper presents a series of research efforts to develop artificial intelligent-driven technologies that provide real-time, actionable

AI-Driven Antimicrobial Peptide Characterization Unveils Novel Motifs for Drug Design

December 29, 2025
Author(s)
Sarala Padi, Kinjal Mondal, David Hoogerheide, Frank N. Heinrich, Ella Mihailescu, Antonio Cardone, Jeffery Klauda
Antibiotics have been effectively developed to target and kill bacteria; however, the growing challenge of antimicrobial resistance (AR) has complicated the treatment of certain infections. To address this pressing issue, researchers are investigating

An Improved Robotic Workcell for Operational Technology (OT) Research

December 18, 2025
Author(s)
Timothy Zimmerman, Michael Pease, Michael Dawson
The National Institute of Standards and Technology (NIST) has constructed a discrete manufacturing workcell to support its operational technology (OT) and critical infrastructure research. This work is an improvement on the "Collaborative Robotics Testbed"

2025 NIST GenAI Text Challenge Evaluation Plan

September 18, 2025
Author(s)
Seungmin Seo, Hariharan Iyer, Yooyoung Lee
The NIST Generative AI (GenAI) Text Challenge is an evaluation program designed to probe the capabilities and limitations of generative AI models from three complementary perspectives: Generator, Prompter, and Discriminator. The challenge will focus on two

EVALUATING IDENTITY LEAKAGE IN SPEAKER DE-IDENTIFICATION SYSTEMS

August 21, 2025
Author(s)
Seungmin Seo, Oleg Aulov, Afzal Godil, Kevin Mangold
Speaker de-identification aims to conceal a speaker's identity while preserving intelligibility of the underlying speech. We introduce a benchmark that quantifies residual identity leak- age with three complementary error rates: equal error rate (EER)

From Traditional Topic Models to LLM Topic Models: Can Large Language Models Replace Traditional Topic Models?

August 1, 2025
Author(s)
Zongxia Li, Lorena Calvo Bartolome, Alexander Hoyle, Daniel Stephens, Paiheng Xu, Alden Dima, Jordan Boyd-Graber, Juan Fung
A common use of NLP is to facilitate the understanding of large document collections, with models based on Large Language Models (LLMs) replacing probabilistic topic models. Yet the effectiveness of LLM-based approaches in real-world applications remains

2025 NIST GenAI (Pilot): Code Challenge Evaluation Plan

July 16, 2025
Author(s)
Peter Fontana, Yooyoung Lee, Hariharan Iyer, Sonika Sharma
We are launching a pilot for measuring and evaluating unit tests generated by Artificial Intelligence (AI) for testing elementary python code. This pilot will provide an environment that will facilitate the development and improvement of the abilities of

Quasi-Deterministic Channel Propagation Model for Human Sensing: Gesture Recognition Use Case

July 9, 2025
Author(s)
Jack Chuang, Raied Caromi, Jelena Senic, Samuel Berweger, Neeraj Varshney, Jian Wang, Anuraag Bodi, Camillo Gentile, Nada Golmie
We describe a quasi-determinstic channel propagation model for human gesture recognition reduced from real-time measurements with our context aware channel sounder, considering four human subjects and 20 distinct body motions, for a total of 120,000

2024 NIST GenAI (Pilot Study): Text-to-Text Evaluation Overview and Results

June 25, 2025
Author(s)
Hariharan Iyer, Seungmin Seo, Lukas Diduch, Kay Peterson, George Awad, Yooyoung Lee
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 and AI detectors. The study aims to measure the effectiveness of

Hallucination Detection in Large Language Models Using Diversion Decoding

June 24, 2025
Author(s)
Basel Abdeen, S M Tahmid Siddiqui, Meah Tahmeed Ahmed, Anoop Singhal, Latifur Khan, Punya Modi, Ehab Al-Shaer
Large language models (LLMs) have emerged as a powerful tool for retrieving knowledge through seamless, human-like interactions. Despite their advanced text generation capabilities, LLMs exhibit hallucination tendencies, where they generate factually
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