InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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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.
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TensorFlow Learns Cucumber Selection and Classification
Cucumber farmer with embedded systems engineering background teaches TensorFlow neural network to mimic his cucumber-farming family’s classification and selection skills for automation.
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Getting the Data Needed for Data Science
Data science is about the data that you need; deciding which data to collect, create, or keep is fundamental argues Lukas Vermeer, an experienced Data Science professional and Product Owner for Experimentation at Booking.com. True innovation starts with asking big questions, then it becomes apparent which data is needed to find the answers you seek.
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Google Launches Cloud Natural Language API
Google released their beta Cloud Natural Language API on July 20, joining the movement to make advances in natural language processing (NLP) from the small world of cutting-edge research and to the hands of everyday data scientists and software engineers. Google’s NLP API lets users take advantage of three core NLP features:
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DeepMind AI Program Increases Google Data Center Cooling Power Usage Efficiency by 40%
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.
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Facebook Open-Sources Deep Learning Project Torchnet
Facebook Artificial Intelligence Research laboratory open-sources the Torchnet project to package and optimize boiler plate deep learning code for reuse and plugin-ability.
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QCon San Francisco 2016 Trackhosts Confirmed
QCon San Francisco, the largest English speaking conference organized by InfoQ, returns to the Bay Area November 7-9 for its tenth successive year. There are 18 tracks at QCon San Francisco, each an individually curated full-day vertical conference focused on important topics for software developers.
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Meson Workflow Orchestration and Scheduling Framework for Netflix Recommendations
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.
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QCon San Francisco 2016 Tracks Announced and First Glimpse at Workshops
QCon San Francisco, the 10th annual bay area software conference that attracts attendees from all over the world, returns to the Fishermen's Wharf area of San Francisco November 7-9, 2016.
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Vert.x 3.3.0 Features Enhanced Networking Microservices, Testing and More
Vert.x core developer Clement Escoffier of RedHat explores key features of just released Vert.x 3.3.0 reactive toolkit.
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Apache TinkerPop Graduates to Top-Level Project
TinkerPop, a graph compute framework for OLTP and OLAP graph database and analytics processing graduated to top-level project with the Apache Software Foundation.
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Test Well and Prosper: The Great Java Unit-Testing Frameworks Debate
A recent post in Reddit sparked a debate between the traditional testing framework JUnit and upstart Spock with the central theme, “What’s wrong with JUnit?”
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Neha Narkhede: Large-Scale Stream Processing with Apache Kafka
In her presentation "Large-Scale Stream Processing with Apache Kafka" at QCon New York 2016, Neha Narkhede introduces Kafka Streams, a new feature of Kafka for processing streaming data. According to Narkhede stream processing has become popular because unbounded datasets can be found in many places. It is no longer a niche problem like, for example, machine learning.
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LinkedIn Details Production Kafka Debugging and Best Practices
LinkedIn’s Joel Koshy details their Kafka usage, debugging and monitoring two production incidents in using the core Kafka infrastructure concepts, semantics and behavioral patterns to plan for and detect similar problems in the future.
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Data Streaming Architecture with Apache Flink
Jamie Grier recently spoke at OSCON 2016 Conference about data streaming architecture using Apache Flink. He talked about the building blocks of data streaming applications and stateful stream processing with code examples of Flink applications and monitoring.