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  • CommAI, a Training and Testing AI System by Facebook

    Facebook recently announced CommAI-env, a platform for training and evaluating an AI system. Inspired by A roadmap towards Machine Intelligence the system aims for teaching intelligent agents general learning capabilities that would serve as the groundwork for further, more specialized training by human or machine level interaction. The article provides a high level overview of current state and..

  • How YouTube's Recommendation Algorithm Works

    In a recent paper published by Google, YouTube engineers analyzed in greater detail the inner workings of YouTube’s recommendation algorithm. The paper was presented on the 10th ACM Conference on Recommender Systems last week in Boston. In this news item we analyze how YouTube uses deep learning to operate one of the largest and most complex recommendation systems in industry.

  • IBM Creates Artificial Neurons from Phase Change Memory for Cognitive Computing

    A team of scientists at IBM Research in Zurich, have created an artificial version of neurons using phase-change materials to store and process data. These phase change based artificial neurons can be used to detect patterns and discover correlations in Big Data (real-time streams of event based data) and unsupervised machine learning at high speeds using very little energy.

  • Google Details New TensorFlow Optimized ASIC

    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.

  • Open Sourcing Artificial Intelligence Research

    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.

  • Collision: Online Harassment and Machine Learning

    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.

  • Google Cloud Machine Learning and Tensor Flow Alpha Release

    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.

  • Microsoft Azure IoT Hub Reaches General Availability

    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.

  • How Airbnb Uses Net Promoter Score to Predict Guest Rebooking

    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 on How Airbnb Uses Data Science

    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 Open Sourcing AI Hardware Design

    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.

  • DMTK, a Machine Learning Toolkit from Microsoft

    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.

  • Splunk ITSI: Adaptive Thresholds and Anomaly Detection

    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.

  • Splunk .conf 2015 Keynote

    Splunk opened their big data conference with an emphasis on “making machine data accessible, usable, and valuable to everyone”. This is a shift from their original focus: indexing arbitrary big data sources. Reasonably happy with their ability to process data, they want to ensure that developers, IT staff, and normal people have a way to actually use all of the data their company is collecting.

  • Microsoft Releases Azure Data Factory

    Any cloud provider that believes in data gravity is trying to make it easier to collect and store data in its facilities. To make data movement between cloud and on-premises endpoints easier, Microsoft recently announced the general availability of Azure Data Factory (ADF).

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