Google set out to determine what makes a manager great at Google. But first, a research team tried to prove the opposite: that managers actually don’t matter, that the quality of a manager didn’t impact a team’s performance. This hypothesis was based on an early belief held by some of Google’s leaders and engineers that managers are, at best, a necessary evil, and at worst, a layer of bureaucracy.
The team defined manager quality based on two quantitative measures: manager performance ratings and manager feedback from Google’s annual employee survey. This data quickly revealed that managers did matter: teams with great managers were happier and more productive.
But knowing that managers mattered didn’t explain what made managers great. So the team asked employees about their managers. By going through the comments from the annual employee survey and performance evaluations, the team found ten common behaviors across high-scoring managers. The researchers also conducted double blind interviews with a group of the best and worst managers to find illustrative examples of what these two groups were doing differently.
Perspective is an API that makes it easier to host better conversations. The API uses machine learning models to score the perceived impact a comment might have on a conversation. Developers and publishers can use this score to give realtime feedback to commenters or help moderators do their job, or allow readers to more easily find relevant information, as illustrated in two experiments below. Our first model identifies whether a comment could be perceived as “toxic” to a discussion.
TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.