This week’s list of data news highlights covers September 24-30, 2016 and includes articles about a new partnership to promote the development of smart cars in Europe and an algorithm-driven pizza restaurant.
1. Counting Calories with Computer Vision
Smartphone diet app Lose It has launched a feature called Snap It which uses computer vision algorithms to identify food items in user’s photos to help automatically count calories. Users can take a picture of their meal and Snap It will attempt to identify the food and provide nutritional information. Snap It is still in beta, but Lose It plans to improve its recognition ability over time as users take more photos of their food and validate Snap It’s suggestions.
2. Making Baseball Even More Data-Driven
A technology firm owned by U.S. Major League Baseball franchises called Major League Baseball Advanced Media (BAM) has developed a feature for its Statcast analytics tool that applies the data-driven approaches common to pitching and batting to fielding. Statcast uses a system of radar and cameras to create 3D models of the entire baseball field at a rate of 40,000 frames per second and converts these frames into machine-readable data that teams and sportscasters can use to comprehensively analyze players in the field.
3. Supporting Smarter Cars in the EU
A coalition of European automotive and telecommunications companies has formed a partnership called the European Automotive-Telecom Alliance to promote the development and deployment of connected and autonomous vehicles in Europe. The alliance, which includes companies such as Deutsche Telekom, Vodafone, BMW, and Renault, will focus initially on testing how connected and autonomous vehicles can improve traffic efficiency, serve as platforms for new kinds of logistics services, and enable new functions such as platooning, which can increase efficiency.
4. Cognitive Computing Takes on Third Grade Math
IBM has developed a new tool based on its Watson cognitive computing platform called Teacher Advisor to help third-grade math teachers in the United States develop personalized lesson plans. Teacher Advisor will analyze Common Core education standards, which sets targets for skills development, and student data to help teachers tailor instructional material for students in the same class but with varying skill levels, which can make traditional, static lesson plans ineffective. IBM will make Teacher Advisor freely available to third grade math teachers by the end of the year, and plans to expand the amount of subject areas and grade levels it can help with.
5. Translating Languages at Near-Human Levels
Google has developed an improved translation tool that uses deep learning to translate languages with near-human level fluency, with humans rating it between 64 and 87 percent better than Google’s previous system. Unlike the old system, which followed a specific methodology to translate text, the new deep learning system learns how to break down text and reproduce it in another language on its own. In a test, humans fluent in English and Spanish ranked 500 translated sentences from both Google’s new system and human translators on a 0 to 6 scale, with Google’s system receiving a score of 5.43, on average, compared to the human translation score of 5.55.
6. Algorithms Put an End to Soggy Pizza
A pizza shop in Silicon Valley called Zume has implemented an algorithm-driven system for cooking and delivering pizzas to ensure customers receive the freshest pizzas possible. Customers can order a pizza through a smartphone app, where a partially automated kitchen assembles the order and loads it into a truck with built-in ovens. Based on the customer’s location and order, the system will automatically plan a route for the truck driver and turn on the ovens to ensure the pizza finished cooking once the driver reaches the customer’s address.
7. Making it Easier to Standardize Open Data
The U.S. General Services Administration (GSA) has launched the Data Federation portal, a website that will serve as a repository of case studies highlighting how federal agencies should manage their data as well as encourage agencies at the local, state, and federal to adopt common data standards. GSA will use the Data Federation portal to host initiatives encouraging the public to use open data, and will also develop a toolkit of resources for agencies to develop effective data management strategies.
8. Subsidizing Smart Watches for Better Health Data
Health insurer Aetna has announced that it will subsidize the cost of the Apple Watch for some of its customers to encourage adoption of the wearable device that can serve as a fitness tracker and collect valuable biometric data. Aetna will also develop a series of apps for the Apple Watch that can help users better manage their health such as by providing medication adherence reminders and providing users with personalized recommendations after a new diagnosis.
9. Ramping Up Smart Cities in the United States
The White House has announced $80 million in funding and a series of new initiatives focusing on urban issues including energy, transportation, public safety, and city services, to accelerate the development of smart cities. The new efforts double the number of cities and communities participating the White House Smart Cities Initiative. Several federal agencies have announced new initiatives to support smart cities as well with tools, funding, and other resources. For example, the National Institute of Standards and Technology (NIST) has announced the formation of an international coalition to develop an Internet of Things-Enabled Smart City Framework to promote interoperability.
10. Putting AI on the Right Path
A group of major technology companies including Facebook, Google, Amazon, Microsoft, and IBM have launched the Partnership on Artificial Intelligence to Benefit People and Society to encourage AI research and development and promote beneficial uses of AI. Participating companies will adhere to eight tenets regarding AI, including educating and engaging the public about AI, researching its ethical, social, and economic implications, and fostering collaboration and openness between AI developers.
Image: Marco Virch.