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Artificial intelligence

NIST contributes to the research, standards and data required to realize the full promise of artificial intelligence (AI) as an enabler of American innovation across industry and economic sectors.

Illustration that shows an outline of a face and then icons to represent different areas of AI including heart (health), lock (cyber), windmills (energy), steering wheel (cars) and manufacturing arm
Credit: N. Hanacek/NIST

January 26-28, 2020 | Explainable AI Workshop 
NIST will hold a virtual workshop on Explainable Artificial Intelligence (AI). Explainable AI is a key element of trustworthy AI and there is significant interest in explainable AI from stakeholders, communities, and areas across this multidisciplinary field. As part of NIST’s efforts to provide foundational tools, guidance, and best practices for AI-related research, we released a draft whitepaper, Four Principles of Explainable Artificial Intelligence, for public comment. Inspired by comments received, this workshop will delve further into developing an understanding of explainable AI.

Why is Artificial Intelligence (AI) important?

Artificial Intelligence (AI) is rapidly transforming our world. Remarkable surges in AI capabilities have led to a number of innovations including autonomous vehicles and connected Internet of Things devices in our homes. AI is even contributing to the development of a brain-controlled robotic arm that can help a paralyzed person feel again through complex direct human-brain interfaces. These new AI-enabled systems are revolutionizing everything from commerce and healthcare to transportation and cybersecurity.

AI has the potential to impact nearly all aspects of our society, including our economy, but the development and use of the new technologies it brings are not without technical challenges and risks. AI must be developed in a trustworthy manner to ensure reliability, safety and accuracy.

Cultivating Trust in AI Technologies

NIST has a long-standing reputation for cultivating trust in technology by participating in the development of standards and metrics that strengthen measurement science and make technology more secure, usable, interoperable and reliable. This work is critical in the AI space to ensure public trust of rapidly evolving technologies, so that we can benefit from all that this field has to promise. 

AI systems typically make decisions based on data-driven models created by machine learning, or the system’s ability to detect and derive patterns. As the technology advances, we will need to develop rigorous scientific testing that ensures secure, trustworthy and safe AI. We also need to develop a broad spectrum of standards for AI data, performance, interoperability, usability, security and privacy.

NIST's Role

Interagency Engagement

NIST participates in interagency efforts to further innovation in AI. NIST Director and Undersecretary of Commerce for Standards and Technology Walter Copan serves on the White House Select Committee on Artificial Intelligence. Charles Romine, Director of NIST’s Information Technology Laboratory, serves on the Machine Learning and AI Subcommittee. 

A February 11, 2019, Executive Order on Maintaining American Leadership in Artificial Intelligence tasks NIST with developing “a plan for Federal engagement in the development of technical standards and related tools in support of reliable, robust, and trustworthy systems that use AI technologies.” For more information, see: https://www.nist.gov/topics/artificial-intelligence/ai-standards.

Research

NIST research in AI is focused on how to measure and enhance the security and trustworthiness of AI systems. This includes participation in the development of international standards that ensure innovation, public trust and confidence in systems that use AI technologies. In addition, NIST is applying AI to measurement problems to gain deeper insight into the research itself as well as to better understand AI’s capabilities and limitations. 

The NIST AI program has two major goals: 

  1. Advancing application of AI to NIST metrology problems by bolstering AI expertise at NIST and enabling NIST scientists to draw routinely on machine learning and AI tools to gain deeper insight into their research; and 
  2. Fundamental research to measure and enhance the security and explainability of AI systems. 

The recently launched AI Visiting Fellow program brings nationally recognized leaders in AI and machine learning to NIST to share their knowledge and experience and to provide technical support.

News and Updates

NIST AI System Discovers New Material

When the words “artificial intelligence” (AI) come to mind, your first thoughts may be of super-smart computers, or robots that perform tasks without needing

NIST Asks A.I. to Explain Itself

It’s a question that many of us encounter in childhood: “Why did you do that?” As artificial intelligence (AI) begins making more consequential decisions that

Events

Explainable AI Workshop

Tue, Jan 26 - Thu, Jan 28 2021
January 26, 2021 | Explainable AI Workshop January 27 and January 28, 2021 | Breakout Sessions January 28, 2021 |

Projects and Programs

JARVIS-ML

JARVIS-ML is a repository of machine learning (ML) model parameters, descriptors, and ML related input and target data. JARVIS-ML is a part of the NIST-JARVIS

Neuromorphic Device Measurements

Neuromorphic computing is a radical new approach to information processing for artificial intelligence where, instead of using digital electronics, inspiration

Temporal Computing

The human brain does some types of information processing, like speech recognition, image recognition, or video processing, much more efficiently than can be

Publications

Technical Language Processing: Unlocking Maintenance Knowledge

Author(s)
Michael P. Brundage, Thurston B. Sexton, Melinda Hodkiewicz, Alden A. Dima, Sarah Lukens
Out-of-the-box natural-language processing (NLP) pipelines need re-imagining to understand and meet the requirements of the engineering sector. Text-based

Six-sigma Quality Management of Additive Manufacturing

Author(s)
Yan Lu, Hui Yang, Paul W. Witherell
Quality is a key determinant in deploying new processes, products or services, and influences the adoption of emerging manufacturing technologies. The advent of

Software

Nestor

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Awards