At a 1984 Academy symposium titled, “Computer Culture: The Scientific, Intellectual, and Social Impact of the Computer,” physicist Heinz R. Pagels declared: “the computer revolution is not just new hardware and software in the home and office but is also changing our world view.”
Pagels and his colleagues stood on a technological threshold. More than three decades later, the era of big data may be the worldview ushered in by the revolution in computer science—“the integration of human culture and computer culture” that Pagels predicted.
Scientists have always collected and analyzed data. But since the 1980s, the language has evolved from “data sets” to “massive data sets” to, simply, “big data.” And collecting, manipulating and making sense of big data has become an essential tool for researchers across many disciplines—for making discoveries in astronomy, preventing and treating disease, modeling ecological systems, and more.
In this new world of data, computer scientists have created machine learning—a subfield focused on mathematical algorithms that discover knowledge within specific data sets, and then "learn" from the data in an iterative fashion in order to make predictions. Applications like search engines, credit card fraud detectors, and stock market analysis rely on machine learning.
To build a community of scientists in machine learning—and to provide a neutral ground for scientists in industry, government, and academia to exchange ideas—the Academy formed the Machine Learning Discussion Group, which launched its annual Machine Learning Symposium in 2006. Every year since, researchers have met to probe both theoretical issues and more practical uses of machine learning in areas like speech recognition and predicting elections.
Much of machine learning, as well as other innovations in computing, addresses health data. Participants at a 2002 Academy workshop focused on the Applications of Bioinformatics in Cancer Detection were discussing what was then leading-edge technology. And more recently, in 2015, professionals from science, engineering, analytics, health care, business, and government gathered for a conference on “Mobile Health: The Power of Wearables, Sensors, and Apps to Transform Clinical Trials.”
Yet, enthusiasm for the problem-solving potential of health, census, and other data has been tempered by the challenges of preserving the privacy of individuals who contribute the data. The Academy helped raise the visibility of these issues in 2011, when it presented one of its prestigious Blavatnik Awards for young scientists to Johannes Gehrke, PhD, a pioneer in data privacy.
Since putting an early stake in the ground with the 1984 “Computer Culture” meeting, the Academy has continued to convene forward-looking symposia in this area, bringing together experts, innovators, students, professionals, and others who have helped set the agenda for the computer revolution. Today this outreach also extends to students as young as elementary school who learn to code by participating in Academy education programs.
Back in 1984, Pagels could not foresee the computer revolution in detail. But his commitment to “bring it forth for the good of all humanity” continues to guide the Academy. “It is a task,” wrote Pagels, “that instills a sense of responsibility, excitement, promise, and hope.”