Horst, J.
(2002),
A Native Intelligence Metric for Artificial Systems, Performance Metrics for Intelligent Systems, Workshop | | Proceedings of the Performance Metrics for Intelligent Systems (PerMIS) Workshop | NIST, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=824478
(Accessed October 10, 2024)
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
A Native Intelligence Metric for Artificial Systems
Published
Author(s)
Abstract
We define native intelligence as the specified complexity inherent in the information content of an artificial system. The artificial system is defined as a system that can be encoded in some general purpose language, expressed minimally as some finite length bit string, and decoded by a finite set of rules defined a priori. Using this definition of native intelligence, we employ a chance elimination argument in the literature to form a simple, but promising native intelligence metric. Several anticipated objections to this native intelligence metric are discussed.Proceedings Title
Performance Metrics for Intelligent Systems, Workshop | | Proceedings of the Performance Metrics for Intelligent Systems (PerMIS) Workshop | NIST
Conference Dates
August 13, 2002
Conference Title
Performance Metrics for Intelligent Systems
Pub Type
Conferences
Download Paper
Keywords
artificial systems, chance elimination, complexity theory, design inference, intelligent metric, linear systems, metrics, native intelligence, probability theory
Citation
Issues
If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.
Created August 1, 2002, Updated February 17, 2017