DeepMind Sensor data captured from Google data centers yield a 40% increase in data center power usage efficiency and an overall site-wide 15% power usage efficiency gain using an AI program similar to an earlier game-like program of theirs that had learned how to play Atari games.
Facebook Artificial Intelligence Research laboratory open-sources the Torchnet project to package and optimize boiler plate deep learning code for reuse and plugin-ability.
Netflix's goal is to predict what you want to watch before you watch it. They do this by running a number of machine learning (ML) workflows every day. Meson is a workflow orchestration and scheduling framework that manages the lifecycle of all these machine learning pipelines that build, train and validate personalization algorithms to help with the video recommendations.
The machine learning and engineering communities weigh in on news of Google's new TensorFlow optimized processor, the TPU and possibly influence several industry leaders in the hardware space like Intel and Nvidia.
Today OpenAI, a non-profit artificial intelligence research company founded by InfoSys and Amazon Web Services, announced a beta for OpenAI Gym. Gym is a Python based toolkit for developing and comparing reinforcement learning (RL) algorithms offered under the MIT license.
Online harassment is a serious issue, one that the engineers and designers behind the keyboard don't always think about when building software. Machine learning is become more prevalent but as more technology companies take advantage of it, they risk alienating their users even more by presenting content that isn't actually relevant.
Late last month Google released an alpha version of their TensorFlow (TF) integrated cloud machine learning service as a response to a growing need to make their Tensor Flow library to run at scale on the Google Cloud Platform (GCP). Google describes several new feature sets around making TF usage scale by integrating several pieces of the GCP like Dataproc, a managed Hadoop and Spark service.
Since announcements late last year about Google open-sourcing TensorFlow, the company’s open-source library for machine learning, and previous coverage at InfoQ, the data-science community has had an opportunity to try out TensorFlow for their own projects.
Microsoft has recently announced its Azure IoT Hub offering has reached general availability (GA). This is a follow-up release to the public preview that Microsoft provided in October of last year. InfoQ previously covered the public preview announcement as part of the Azure Con event coverage.
Net Promoter Score (NPS) is a customer loyalty metric used to determine the likelihood that a customer will return to a company's website or use their service again. Airbnb uses NPS extensively in measuring the customer loyalty, as a more effective measurement to determine the likelihood that a customer will return to book again or recommend the company to their friends.
Riley Newman, head of data science at Airbnb, recently published an article describing how the Californian startup defines and uses data science. He explains that data can be seen as the voice of the customers, and data science as an act of interpretation. He also details several initiatives that have been particularly important for scaling data science.
Facebook recently announced open sourcing hardware design for its custom designed Open Rack compatible hardware. Attributing advances in Machine Learning and Artificial Intelligence to richer data sets and more powerful GPU-based systems, Facebook is unveiling its next generation systems code-named “Big Sur”, after the synonymous location in California.
About the same time Google announced open sourcing TensorFlow, Microsoft has pushed to GitHub DMTK, a Distributed Machine Learning Toolkit. While Google has released a one-machine version of TensorFlow, DMTK runs on a cluster of machines.
TensorFlow is a machine learning library created by the Brain Team researchers at Google and now open sourced under the Apache License 2.0. TensorFlow is detailed in the whitepaper TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. The source code can be found on Google Git.
In theory the operations team determines what the thresholds for warnings and alerts should be. But in practice, the operations team often have no idea what these values should be. Using machine learning techniques such as adaptive thresholds, Splunk ITSI solves this problem.