Uber uses a master algorithm to determine how much money its drivers make--and women are ending up with less. The gap: In a study released today of over 1.8 million drivers on the platform, women were found to earn $1.24 per hour less than men. Women also earned $130 less per week on average, in part because they tend to drive fewer hours. All of these are variables in the formula Uber uses to calculate driver wages, and the study showed they all tilted in men's favor (the study claims men earn $21.28 an hour, on average). Women also have higher turnover on the platform, and more experienced drivers tend to get higher pay.

While "Big Data" tools such as Spark and MapReduce may offer a resilient way to spread a job out across multiple nodes in such a way that the work can tolerate the failure of a few nodes, some deep learning jobs require that each node stay running until the end of the job, car-sharing service Uber has found. To work with this requirement, Uber has turned to gang scheduling, an optimization algorithm long-known in the field of supercomputing. Gang scheduling ensures that a cluster computing job will run only if all the nodes can be run at the same time, explained Min Cai, Uber staff engineer, during a presentation at MesosCon in Los Angeles last week. Cai was one of the Uber engineers who implemented the gang scheduling algo in an open source framework, called Horovod, for running Google's TensorFlow machine learning software across multiple nodes. Uber uses the software to run training models for deep learning tasks running hundreds of GPUs, for research into guidance for self-driving cars, image classification, and fraud detection.