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Artificial intelligence

ֱ 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/ֱ

August 6, 2020 | AI Kickoff Webinar 
This webinar kicks off a ֱ initiative involving private and public sector organizations and individuals in discussions about building blocks for trustworthy AI systems and the associated measurements, methods, standards, and tools to implement those building blocks when developing, using, and overseeing AI systems. ֱ’s effort will be informed by a series of workshops that will follow this initial session.
 
August 18, 2020 | Bias in AI Workshop
This workshop focuses on collectively facilitating the development of a shared understanding of bias in AI, what it is, and how to measure it. This online event will consist of collaborative panels and breakout sessions and will bring together experts from the public and private sectors to engage in important discussions about bias in 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

ֱ 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.

ֱ's Role

Interagency Engagement

ֱ participates in interagency efforts to further innovation in AI. ֱ 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 ֱ’s Information Technology Laboratory, serves on the Machine Learning and AI Subcommittee. 

A February 11, 2019,  tasks ֱ 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: /topics/artificial-intelligence/ai-standards.

Research

ֱ 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 that ensure innovation, public trust and confidence in systems that use AI technologies. In addition, ֱ 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 ֱ AI program has two major goals: 

  1. Advancing application of AI to ֱ metrology problems by bolstering AI expertise at ֱ and enabling ֱ 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 ֱ to share their knowledge and experience and to provide technical support.

ֱ and Updates

ֱ 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

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 ֱ-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

International Workshop on Deep Video Understanding

Author(s)
Keith Curtis, George M. Awad, Shahzad K. Rajput, Ian M. Soboroff
This is the introduction paper to the International Workshop on Deep Video Understanding. In recent years, a growing trend towards working on understanding

Software

Nestor

The Nestor Graphical User Interface (GUI) is a free toolkit that helps maintainers annotate their Maintenance Work Order (MWO) data through a process called

Awards