Private Medical Data Is For Sale – and It’s Driving a Business Worth Billions

The Guardian, 1/10/017

Private medical data is a multi-million dollar industry that is growing rapidly, according to Adam Tanner at Harvard’s Institute for Quantitative Social Science. When medical data is initially sold to big data miners, it may be referred to only by unidentifiable numbers. However, data miners can re-identify patients by cross-referencing the medical data with data collected from other sources.

Big Data Analytics — Nostradamus of the 21st Century

Griffith University, 11/30/2016

Researchers at Griffith University successfully predicted the winner of the 2016 presidential election, including the outcomes in 49 out of 50 states, using data collected from social media interactions. The prediction ran contrary to general expectations based on polling, suggesting that more accurate election predictions can be obtained by analyzing social media interactions — which requires large-scale data analytics.

Meeting of the Minds for Machine Intelligence

MIT News Office, 11/22/2016

Industry leaders and computer scientists are pushing for more use of machine intelligence so that machines can aid doctors, business corporations, investors and many more entities in decision making. The article discusses the potential rewards of using machine intelligence to solve real-world problems, for example, whether machine learning can help to better quantify uncertainty when trying to predict outcomes in various fields.

Want to Avoid Vendor Lock-In? Software-Defined Storage Could Be the Answer

Computing, 11/28/2016

At a Computing IT Leaders Forum, panelists discussed the potential of software-defined storage (SDS) to help companies avoid vendor lock-in and allow more flexibility in planning — an issue given how quickly data storage technology changes. The article also mentions how SDS provides an alternative to cloud storage, as the latter raises concerns about data security.

Machine-Learning Algorithm Can Show Whether State Secrets Are Properly Classified

MIT Technology Review, 11/14/2016

Researchers from Columbia University and a Brazilian think tank have developed a machine-learning algorithm to predict whether now-declassified U.S. State Department cables from the 1970’s were unclassified, limited official use, confidential, or secret, based on contents and metadata such as sender and recipient. The study provides insight into how the information was classified, but also carries potential national security implications by highlighting trends in erroneous information classification — and systematic gaps where secret cables should have been declassified.

Quantifying Urban Revitalization

MIT News, 10/24/2016

Researchers have developed a system that estimates how safe visitors will perceive an area to be based on photographs of the area. The researchers began with a crowdsourcing effort to build an initial database of images and safety ratings of the area in the image. Over 1.4 million responses have then been used to train a machine-learning algorithm to identify these aspects automatically.

Big Data Help CIA Predict Social Unrest 5 Days Before It Begins

Tech Times, 10/7/2016

The CIA and their new Directorate of Digital Innovation are working on “anticipatory intelligence” to predict future events. The Deputy Director says that they can sometimes forecast outbreaks of unrest up to five days ahead. These predictions are made by using classified information as well as open source data; commentators speculate that much of the data comes from massive social media surveillance.