Foresight and Understanding from Scientific Exposition (FUSE)

Technical emergence refers to the process whereby innovative ideas, capabilities, applications, and even entirely new fields of study arise, are tested, mature, and, if conditions are favorable, make a significant impact. Those able to “scan the horizon” for the early signs of technical emergence, and take advantage of the resulting capabilities and applications, can gain a significant competitive edge.

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 (SMEs) 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 this 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.

In FUSE, the real-world concept of a scientific or technical area or domain of inquiry will be represented by a Related Document Group (RDG). This set of documents serves as a proxy for one or more related technical concepts, capabilities, applications, fields of study, etc., that have developed over the span of time defined by the documents’ publication dates. An RDG can represent a narrowly circumscribed topic or domain of technical activity (e.g., support vector machines applied to face recognition), or something more general (e.g., artificial intelligence or computer science) or more exotic, such as a body of multi-disciplinary scientific work sharing both methods and applications. The sizes of RDGs will range from a few tens of documents to many thousands of documents.

The FUSE Program seeks to develop automated methods that aid in the systematic, continuous, and comprehensive assessment of technical emergence using information found in the published scientific, technical, and patent literature. FUSE envisions a system that could (1) process the massive, multi-discipline, growing, noisy, and multilingual body of full-text scientific, technical, and patent literature from around the world; (2) automatically generate and prioritize RDGs, nominate those that exhibit technical emergence, and provide compelling evidence for that emergence; and (3) provide this capability for literatures in English and at least two non-English languages. The FUSE Program will also address the vital challenge of validating such a system, using real world data.


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