Southampton to Help Develop Software Which Could Transform Ship Maintenance

University of Southampton, 9/15/2016

Researchers are planning to develop software that can monitor and gather data on the performance and efficiency of a marine vessel. Through the use of machine learning, this data would then be applied to create an ideal balance between efficient performance and ship integrity, lowering maintenance and operational costs.

How Big Data and Algorithms Are Slashing the Cost of Fixing Flint’s Water Crisis

The Conversation, 09/08/2016

Beyond tech companies such as Amazon and Google, big data has a significant effect on science, engineering, and even plumbing. As the government in Flint, Michigan has worked to correct the dangerous levels of lead contamination reported earlier this year, researchers at the University of Michigan have aggregated and analyzed data for homes in the area. Their analysis is providing new insights into how the government can best direct their efforts, to reduce costs and increase impact.

Big Pixel Initiative Develops Remote Sensing Analysis to Help Map Global Urbanization

UC San Diego News Center, 9/14/2016

An interdisciplinary research team developed a large dataset of 21,030 high-resolution satellite images of India labeled for whether they show built-up areas. The dataset can be used as input to algorithms for detecting urban areas; for example, the team used it to map urbanization in India. This allows scientists and governments to study changes in urbanization and industrialization over time.

A Beauty Contest Was Judged by AI and the Robots Didn’t Like Dark Skin

The Guardian, 9/8/2016

Beauty.AI developed a set of algorithms to judge photos according to five factors in human standards of beauty; it disproportionately chose photos of white people. The article discusses the potential consequences of emergent bias in algorithms and/or datasets in general, including more consequential examples like predictive policing.

Inferring Urban Travel Patterns From Cellphone Data

MIT News, 8/29/2016

Researchers are using data on the locations people make calls from to model the movement patterns of Boston commuters; the system may replace or supplement surveys of residents. The article discusses the benefits of gathering and processing more data more quickly and cheaply, though students may be able to identify some disadvantages of using call data.

As FBI Warns Election Sites Got Hacked, All Eyes Are on Russia

Wired, 8/29/2016

Hackers have broken into the Illinois and Arizona state boards of elections’ records, following hacks of the Democratic National Committee and the Clinton campaign in the last couple of months. This highlights concerns about the security of voter records and even ballot integrity, including effects on U.S. citizens’ confidence in election results.

How an Algorithm Learned to Identify Depressed Individuals by Studying Their Instagram Photos

MIT Technology Review, 8/19/2016

Researchers have developed a machine-learning algorithm that achieves 70% recall in identifying depressed individuals by characteristics of their (pre-diagnosis) Instagram photo posts. This is a great example of a medical development with great potential for benefit (early diagnosis and treatment) that also raises serious concerns (privacy, misuse of the information, misprediction). It’s also an example of Mechanical Turk being used as a research platform.

Paraplegics Take a Step to Regain Movement

Duke Today, 8/10/2016

Patients using brain-machine interfaces to control robotic prosthetics, along with virtual-reality devices that simulated moving their own limbs, were unexpectedly able to regain some actual control of their own limbs, apparently reengaging their spinal cord nerves. Includes a short description of computational modeling of how the brain controls movement.

Protecting Privacy in Genomic Databases

MIT News, 8/9/2016

Researchers at MIT and Indiana Univerity are developing differential privacy techniques that add a small amount of random noise to queries on large genetic databases. This means databases can be made more openly available without (as much) risking the privacy of the individuals whose genetic data is in them. Includes a description of the potential privacy attacks being mitigated, and notes the scientific consequences of having to worry about privacy-compromising attacks on health databases.