When sequestration hit Wright Patterson Air Force Base in 2013, hard decisions had to be made. Beginning in March of that year, 42 percent of the $37.2 billion in defense spending cuts impacted areas like procurement, research and development, along with testing and evaluation. Leadership had to make hard decisions, with long-term consequences. Six years later, an emphasis on efficiency and innovation are forcing hard questions about the best investment areas for the Air Force’s technology portfolio, and Air Force leadership is looking for ways to make smart decisions.
Machine Learning (ML) is defined as the practice of using algorithms to parse data, learn from it and then forecast future trends for that topic. Robert D. Hof writes for the Harvard Review, “Machine intelligence is starting to transform everything from communications and computing to medicine, manufacturing, and transportation”. Wright Brothers Institute’s Competitive and Integrated Intelligence (CI2) team has added a cutting-edge capability to their suite of services that combines machine learning tools, a rich set of databases and deep analytical expertise that quickly cuts through the noise in big data to deliver high value answers to complex challenges. This capability will help Air Force decision makers use data to make smart choices, in less time.
“Instead of looking at just enough data to support a hypothesis, machine learning does inductive discovery— it looks at what the data is telling me, even things I didn’t think about.”
- Rich Clayton, vice president of product strategy, Oracle Analytics.
Collaboration between the right data, the right tools and the right analysis must be established in order to provide the ideal convergence of insights. Most of these components are offered commercially, but only provide one or two of the services. WBI’s three-pronged approach leads the industry by providing all of these capabilities in one deliverable.
WBI’s CI2 team combines their established relationship with AFRL scientists and engineers ,their experience and in-depth knowledge of how the Air Force operates, and their multi-lens intelligence framework with machine learning based technology landscape tools to turn data into insights.
These data driven insights will help AFRL make decisions about research priorities, hiring personnel, product commercialization, non-traditional partnerships and portfolio investment. Not only does machine learning enable the rapid identification and forecasting of science and technology trends that can help determine where to spend precious research dollars, it is also the foundation for insights that can help prepare AFRL for an uncertain future, while ensuring that they are providing the best capabilities to the warfighter.
Unique characteristics:
Analysts understanding of R&D topics, Air Force terminology and the technology market provides unique insights
Ability to leverage outside data sets, via the ML tool
https://www.simplilearn.com/data-science-vs-data-analytics-vs-machine-learning-article