DARPA Goes “Meta” with Machine Learning for Machine Learning

DARPA goes meta with machine learning for #machinelearning.  #modeling #datascience #bigdata

  • Because the process to build empirical models is so manual, their relative sophistication and value is often limited.
  • To free researchers from the tedium and limits of having to design their own empirical models, DARPA today launched its Data-Driven Discovery of Models (D3M) program.
  • The goal of D3M is to help overcome the data-science expertise gap by enabling non-experts to construct complex empirical models through automation of large parts of the model-creation process.
  • What’s missing are empirical models of complex processes that influence the behavior and impact of those data elements.
  • In a world in which scientists, policymakers and others are awash in data, the inability to construct reliable models that can deliver insights from that raw information has become an acute limitation for planners.

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@DARPA: “DARPA goes meta with machine learning for #machinelearning. #modeling #datascience #bigdata”


Popular search engines are great at finding answers for point-of-fact questions like the elevation of Mount Everest or current movies running at local theaters. They are not, however, very good at answering what-if or predictive questions—questions that depend on multiple variables, such as “What influences the stock market?” or “What are the major drivers of environmental stability?” In many cases that shortcoming is not for lack of relevant data. Rather, what’s missing are empirical models of complex processes that influence the behavior and impact of those data elements. In a world in which scientists, policymakers and others are awash in data, the inability to construct reliable models that can deliver insights from that raw information has become an acute limitation for planners.


DARPA Goes “Meta” with Machine Learning for Machine Learning