Weekly News U.S. Supreme Court

Published on August 8th, 2014 | by Travis Korte

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10 Bits: The Data News Hot List

This week’s list of data news highlights covers August 2-8 and includes articles about a computer model to predict supreme court decisions and a collaboration between two major health insurers to create a data exchange system.

1. Model Can Predict Supreme Court Outcomes

A team of scholars has developed a computer model that forecasts U.S. Supreme Court decisions with 70 percent accuracy. The model was built from data on 7,700 cases and more than 68,000 justice votes. The model takes into account information on the case’s topic, what lower court it originated in, and other data. In the future, the model’s creators hope to pit it against human predictors to improve its accuracy and possibly deploy similar models for lower courts.

2. Insurers to Collaborate on Health Information Exchange

Insurers Blue Shield of California and Wellpoint Inc.’s Anthem Blue Cross announced this week that they would collaborate on a health information exchange covering about nine million plan members. The initiative, called California Integrated Data Exchange, will be an independent nonprofit organization that gives participating doctors and hospitals access to the trove of information in exchange for sharing their own patients’ records. California’s top health official said she would be willing to explore the possibility of including information from the state’s Medicaid participants in the exchange in the future.

3. IBM Creates Computer Chip Inspired by Brain

This week, IBM researchers detailed a new computer processor that is inspired by the architecture of the brain. The processor, called TrueNorth, manipulates information using an interconnected network of transistors that behave like neurons. Its creators hope their device, which is about as complex as a bee’s brain, will lend itself to artificial intelligence applications.

4. Connected Baby Monitor Comes on Market

A connected baby activity tracker from San Francisco-based startup Sproutling is now available for pre-order. The tracker, which uses sensors to measure babies’ heart rate, temperature, mood, and position while they sleep, comes with a mobile app from which parents can monitor their children remotely and get alerts if something is wrong. The company is also hoping parents will let the company anonymously share their babies’ data, both to improve Sproutling’s models but also to contribute to research in the field of neonatal health.

5. Using Analytics to Reduce Child Abuse Risk

The Florida Department of Children and Families (DCF) is hoping to use predictive analytics to reduce child abuse risk. Earlier this year, the DCF released a report determining key risk factors for child fatalities and concluded that the agency should develop a child welfare analytics system that incorporates a wider set of data. Some suggestions for data to include in the predictive models include information on financial hardship, substance abuse in the family, or household members with a history of violence.

6. Deploying the Internet of Things in Factories

General Electric (GE) is using Internet of Things sensors to gather data on its factory floors and optimize its production. The sensors collect granular information on temperature and humidity conditions, machine performance, and product quality. In June, the company announced it would commit $400 million to build a “brilliant factory” in which machine parts report information continuously and operators can predictively schedule maintenance before anything breaks. The efforts are part of an initiative the company calls “the industrial Internet,” for which it set aside $1 billion in funding in 2011.

7. Algorithm Extracts Sound from Silent Video

Researchers from MIT, Microsoft, and Adobe have developed an algorithm that can reconstruct sounds from vibrations in objects. Specifically, they set out to recover intelligible speech from tiny vibrations in objects—such as a potato chip bag, a cup of water, and the leaves of a potted plant—that were filmed through soundproof glass. The algorithm’s creators hope it can one day be applied in law enforcement and forensics contexts.

8. Splunk Project Analyzes Public Comments

Data software company Splunk launched its eRegulations Insights Project this week to sort through all the public comments left on government filing repository regulations.gov. The company has found a number of insights so far, including the fact that a third of the public comments made since 2012 focused on three issues: the Affordable Care Act, the Keystone Pipeline, and political contributions from nonprofits. The recently released FCC Open Internet comments were not included in the project, as they are housed on a separate system.

9. UK Court Says Freedom of Information Includes Choice of Format

The UK’s second-highest court ruled this week that individuals seeking government data through freedom of information requests have a choice of file format. The case, in which a Buckinghamshire man fought a protracted battle with that city for student test results data, hinged on provisions in the country’s freedom of information laws that let requesters specify whether they wanted the information in hard copy or electronic format. The judge found that the provisions naturally extend themselves to electronic file formats.

10. Spotify is Using Deep Learning to Make its Recommendations Better

Music streaming company Spotify is using deep learning, a branch of machine learning inspired by neurons in the brain, to improve its song recommendation algorithms. Deep learning is helping Spotify analyze the acoustic content of songs in order to recommend music that sounds similar to what its users already like, rather than simply recommending songs that listeners with similar taste have liked. The company also hopes this approach will help it filter out sonic mismatches from its current recommendations.

Photo: Flickr User Richard Gillin

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About the Author

Travis Korte is a research analyst at the Center for Data Innovation specializing in data science applications and open data. He has a background in journalism, computer science and statistics. Prior to joining the Center for Data Innovation, he launched the Science vertical of The Huffington Post and served as its Associate Editor, covering a wide range of science and technology topics. He has worked on data science projects with HuffPost and other organizations. Before this, he graduated with highest honors from the University of California, Berkeley, having studied critical theory and completed coursework in computer science and economics. His research interests are in computational social science and using data to engage with complex social systems. You can follow him on Twitter @traviskorte.



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