How IoT changes decision making, security and public policy – Erik Brynjolfsson

Professor of Information Technology, Director, The MIT Initiative on the Digital Economy


Professor of Information Technology,
Director, The MIT Initiative on the Digital Economy

We’re in the early stages of a management revolution. The upheaval is based on our unprecedented ability to collect, measure and digitally record information about human and systems activities, particularly with the finely tuned data sets available through IoT. One of the hallmarks of this new era is the acceleration of data-driven decision making within businesses, which has tripled in just five years, according to a recent study I conducted with Kristina McElheren, a professor at University of Toronto.

Accompanying the progress anticipated in this increasingly digital age, however, will be thorny challenges and broader issues for society at large. This is particularly true as organizations begin to feed the large data sets available from IoT into systems that use machine-learning algorithms—at which point they will begin making predictions and decisions in an increasingly automated way, and at large scale.

Machine-learning and artificial intelligence (AI) technologies have advanced greatly in recent years; the implications range much further than the attention they get for winning competitions with “Go” champions and chess masters. The real significance of these technologies will be found in their ability to automate and augment complex decision making.

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Using machine learning to increase meeting efficiency — Cynthia Rudin

MIT Sloan Asst. Prof. Cynthia Rudin

MIT Sloan Asst. Prof. Cynthia Rudin

Meetings play a big role in many people’s jobs. In the U.S. alone, an estimated 11 million meetings take place in a typical day. Managers can spend up to three-quarters of their time in meetings, and approximately 97% of workers say that collaboration is essential to do their best work.

As a result, meetings are tremendously important for businesses. Yet understanding meetings — much less finding ways to increase their productivity — is challenging for researchers because it requires an understanding of many social signals and complex interpersonal dynamics. Most of the work done in this area has been from the social sciences perspective using field work and surveys.

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