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.
Cucumber farmer with embedded systems engineering background teaches TensorFlow neural network to mimic his cucumber-farming family’s classification and selection skills for automation.
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.
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.