Intel open-sources BigDL, a distributed deep learning library that runs on Apache Spark. It leverages existing Spark clusters to run deep learning computations and simplifies the data loading from big datasets stored in Hadoop.
At the AWS re:Invent conference, Amazon launched Rekognition, a managed service for Image Recognition and Analysis, powered by Deep Learning. The capabilities that Rekognition provides include Object and Scene detection, Facial Analysis, Face Comparison and Facial Recognition. The service attempts to extract meaning from visual content for the 1.2 Trillion pictures captured annually.
Instacart is an online delivery service for groceries under one hour. Customers order the items on the website or using the mobile app, and a group of Instacart’s shoppers go to local stores, purchase the items and deliver them to the customer. InfoQ interviewed Mathieu Ripert, data scientist at Instacart, to find out how machine learning is leveraged to guarantee a better customer experience.
“Fast and Probably Good Seedings for k-Means” by Olivier Bachem et al. was presented on 2016’s Neural Information Processing Systems (NIPS) conference and describes AFK-MC2, an alternative method to generate initial seedings for k-Means clustering algorithm that is several orders of magnitude faster than the state of art method k-Means++.
Using Neural Networks for sequence prediction is a well-known Computer Science problem with a vast array of applications in speech recognition, machine translation, language modeling and other fields. FB AI Research scientists designed adaptive softmax, an approximation algorithm tailored for GPUs which can be used to efficiently train neural networks over vocabularies of a billion words & beyond.
Amazon's Werner Vogels announces MXNet as the deep learning toolkit of choice for internal adoption, and extends AWS commitment to open-source MXNet ecosystem development.
Logz.io offers a hosted service which performs intelligent log analysis by using machine learning to derive insights from human interactions with log data that includes discussions on tech forums and public code repositories.
Apache Spark integration with deep learning library TensorFlow, online learning using Structured Streaming and GPU hardware acceleration were the highlights of Spark Summit EU 2016 held last week in Brussels.
Google details a graph streaming algorithm for constant runtime over large graphs of varying complexity space and predictor outputs.
Microsoft recently released two new data science tools for interactive data exploration: modeling and reporting. These tools can be reused by data science teams with data specific tasks in their projects. The goal is to ensure consistency and completeness of data science tasks across different projects in the organization.
As TensorFlow becomes more widely adopted in the machine learning and data science domains, existing machine learning models and engines are being ported from existing frameworks to TensorFlow for improved performance, furthering the adoption and success of the open-sourced project.
Ocado Technology uses TensorFlow to categorize customer emails for automated support queue categorization and prioritization for the goals of quick response time and avoiding impersonal support bots often used with large customer volumes and finite support resources.
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..
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.
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.