AI Predicts Outcomes of Human Rights Trials

University College London/UCL News, 10/24/2016

Artificial intelligence has recently been used to predict (past) judicial decisions in the European Court of Human Rights to a surprising degree of accuracy. This method could potentially be used to automatically identify cases that are likely to involve human rights violations, and is also an interesting example of how artificial intelligence can quantify and even predict human behavior based on pattern recognition.

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.

Can We Open the Black Box of AI?

Nature International Weekly Journal of Science, 10/5/2016

Scientists attempt to understand how computers think and learn in order to verify the reliability of large scale data analysis. This article covers several efforts in the last few years to understand how deep neural nets work. If scientists can understand how computers gather and interpret data in deep learning, these techniques can be used with more confidence, in day to day applications as well as in cutting edge scientific research.

Google Translate Gets a Deep-Learning Upgrade

IEEE Spectrum, 10/3/2016

Engineers at Google are upgrading their Google Translate service to use deep learning, which is an artificial intelligence technique. This is the first time this translation method has been used in a large production environment. The update greatly improves the accuracy of translations, increasing Google Translate’s ability to facilitate communication between speakers of different languages.

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.

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.

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.