Because of a plethora of data from sensor networks, Internet of Things devices and big data resources combined with a dearth of data scientists to effectively mold that data, we are leaving many important applications – from intelligence to science and workforce management – on the table.
It is a situation the researchers at DARPA want to remedy with a new program called Data-Driven Discovery of Models (D3M). The goal of D3M is to develop algorithms and software to help overcome the data-science expertise gap by facilitating non-experts to construct complex empirical models through automation of large parts of the model-creation process. If successful, researchers using D3M tools will effectively have access to an army of “virtual data scientists,” DARPA stated.
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This army of virtual data scientists is needed because some experts project deficits of 140,000 to 190,000 data scientists worldwide in 2016 alone, and increasing shortfalls in coming years. Also, because the process to build empirical models is so manual, their relative sophistication and value is often limited, DARPA stated.
“We have an urgent need to develop machine-based modeling for users with no data-science background. We believe it’s possible to automate certain aspects of data science, and specifically to have machines learn from prior example how to construct new models,” said Wade Shen, program manager in DARPA’s Information Innovation Office in a statement.
Specifically, the agency says D3M aims to develop automated model discovery systems that lets users with subject matter expertise but no data science background create empirical models of real, complex processes.
“This capability will enable subject matter experts to create empirical models without the need for data scientists, and will increase the productivity of expert data scientists via automation. The automated model discovery systems developed by the D3M Program will be tested on real-world problems that will progressively get harder during the course of the program. Toward the end of the program, D3M will target problems that are both unsolved and underspecified in terms of data and instances of outcomes available for modeling.”
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