Foresight and Understanding from Scientific Exposition (FUSE)
For information contact: email@example.com
Today, the identification and assessment of emerging technical capabilities is a time-consuming, domain-specific, and expert-intensive process. This demanding process is often carried out under severe time constraints on either too much or too little data, with limited reproducible auditing and bias controls, and with limited systematic validation against real-world activities. Furthermore, the increasing globalization of science and technology raises the potential for high-impact technical capabilities to emerge in increasingly diverse technical, socio-economic, and geographic areas.
Analysts and subject-matter experts need a reliable, evidence-based capability that allows them to dramatically accelerate the horizon-scanning process and reduce the labor involved to identify specific technical areas for in-depth review. It is essential that an automated capability can nominate both known and novel technical areas based on quantified indications of technical emergence with sufficient supporting evidence and arguments for that nomination. It is anticipated that FUSE technology will provide new analytic tools to help analysts maintain technical vigilance, across all disciplines and multiple languages, in the face of the exponentially growing flood of textual content.
The FUSE program seeks to develop automated methods that aid in the systematic, continuous, and comprehensive assessment of technical emergence using information found in published scientific, technical, and patent literature. A fundamental hypothesis of the FUSE program is that real-world processes of technical emergence leaves discernible traces in the public scientific and patent literature. FUSE is creating a system that can (1) process the massive, multi-discipline, growing, noisy, and multilingual body of scientific and patent literature from around the world; (2) automatically generate and prioritize technical terms within emerging technical areas, nominate those that exhibit technical emergence, and provide compelling evidence for the emergence; and (3) provide this capability for literature in the English and Chinese languages. Technology developed from the FUSE program would automatically nominate both known and novel technical terms based on quantified indicators of technical emergence with sufficient supporting evidence and arguments for that nomination. The FUSE program also addresses the vital challenge of validating such a system using real-world data.
Performers (Prime Contractors)
Columbia University; Raytheon BBN Technologies; SRI International
- Technical emergence
- Text analytics
- Knowledge discovery
- Big data
- Social network analysis
- Natural language processing
- Machine learning
To access FUSE program-related publications, please visit Google Scholar.