Blog

Lytx: Using Data to Help Save Lives on Our Roadways

Lytx® — as in analytics — harnesses the power of video to transform transportation fleets with improved safety, efficiency, productivity and profitability. Lytx pioneered the video telematics category two decades ago when it launched its DriveCam® safety program, and remains the market leader today. With more than 500,000 subscriptions, Lytx protects over 3,000 commercial and government fleet clients and 850,000 drivers. The company has analyzed more than 80 billion miles of driving data — addin

Genius: Artificial Intelligence Transformation of Pro Sports

Genius Sports is a UK-based sports data company who is leveraging computer vision and deep learning to lead the digital transformation of sports intelligence, working on an international scale with top sports organizations such as MLB, PGA Tour, and the Premier League. This May, the NCAA announced its 10-year partnership with Genius, the goal of which is to develop and implement new data capture and analysis services, coaching insights, and real-time statistics that can be used to improve gamep

Product Updates: July 2018

Labelbox adds Auto Consensus, TensorFlow integration and usability features. We are very thankful to all of our passionate users who have helped us continuously improve Labelbox. Over the past few months, we’ve made a number of usability improvements. Now, we are taking a major step to help our users easily create and manage training data. Read on to learn about new features in Labelbox. Auto Consensus Auto Consensus allows you to quantitatively measure the quality of your training data — th

Condé Nast: AI for World-Class Media

Condé Nast is the parent company to over 20 media brands, including Wired, Vogue, Vanity Fair, and Condé Nast Traveler. It’s hard to imagine the extent and richness of a library that combines all the best of fashion, travel, and other media on the planet, let alone the task of managing it. However, one of Labelbox’s customers is doing just that. We spoke with Paul Fryzel, who is directly responsible for the ongoing research, design, and implementation of Condé Nast’s international media platfor

NTConcepts: Rapid training and deployment of an expert AI system

Challenge Developing expert AI systems using Machine Learning (ML) models depends on large amounts of accurate, consistent, and comprehensive training data. Raw data is labeled by experts in the domain of interest to create accurate training data. These labeling tasks must be scaled to produce large amounts of consistent and comprehensive training data. It is challenging, however, to implement a scalable data labeling infrastructure while still enabling rapid experimentation. The key challenge