InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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AWS Releases Enhancements to Its AI Services
Amazon Web Services (AWS) released new features for three of its AI services: Amazon Comprehend, Amazon Rekognition, and Amazon Transcribe.
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Google Expands ML Kit, Adds Smart Reply and Language Identification
In a recent Android blog post, Google announced the release of two new Natural Language Processing (NLP) features for ML Kit, including Language Identification and Smart Reply. In both cases, Google is providing domain-independent APIs that help developers analyze and generate text, speak and other types of Natural Language text.
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JEP 230: A New Microbenchmark Suite for JDK 12
The OpenJDK Microbenchmark Suite (JEP 230), based on the Java Microbenchmark Harness (JMH), is a new feature in the release of JDK 12. Claes Redestad, principal member of technical staff at Oracle, spoke to InfoQ about the new Microbenchmark Suite.
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NSFW.js: Machine Learning Applied to Indecent Content Detection
With the beta-released NSFW.js, developers can now include in their applications a client-side filter for indecent content. NSFW.js classifies images into one of five categories: Drawing, Hentai, Neutral, Porn, Sexy. Under some benchmarks, NSFW categorizes images with a 90% accuracy rate.
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Google Open-Sources GPipe Library for Faster Training of Large Deep-Learning Models
Google AI is open-sourcing GPipe, a TensorFlow library for accelerating the training of large deep-learning models.
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Prashanth Southekal on Applied Machine Learning
Prashanth Southekal, managing principal at DBP Institute, hosted a workshop last month at Enterprise Data World 2019 Conference, on applied machine learning techniques and when to use different ML algorithms.
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Bringing Intelligence to Enterprise Content Management, Google Releases Document Understanding AI
At the recent Google Cloud Next Conference, Google announced a new beta machine learning service, called Document Understanding AI. The service targets Enterprise Content Management (ECM) workloads by allowing customers to organize, classify and extract key value pairs from unstructured content, in the enterprise, using Artificial Intelligence (AI) and Machine Learning (ML).
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Amazon Expands Its Machine Learning Offering with AWS Deep Learning Containers
Recently, Amazon introduced AWS Deep Learning Containers (AWS DL Containers), which are Docker images pre-installed with deep learning frameworks allowing customers to deploy custom machine learning environments quickly.
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Facebook Open-Sources PyTorch-BigGraph Embedding Framework
Facebook AI Research is open-sourcing PyTorch-BigGraph, a distributed system that can learn embeddings for graphs with billions of nodes.
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Convolutional Neural Network Deep Learning Techniques for Crowd Counting
Deep learning techniques like Convolutional Neural Networks (CNNs) are a better choice for crowd-counting use cases, compared to traditional detection or regression based models. Ganes Kesari, co-founder and head of analytics at Gramener, spoke last week at the AnacondaCon 2019 Conference on how to count things using artificial intelligence (AI) models.
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DeepMind's AI Defeats Top StarCraft Players
DeepMind's AlphaStar AI defeated two top professional StarCraft players 5-0.
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A Framework for High-Value Big Data
Asha Saxena recently spoke at the Enterprise Data World 2019 Conference about the value big data analytics initiatives bring to the organizations. Saxena proposed a big data framework that can help with organizational maturity and internal competencies.
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Microsoft Expands the Availability of Its Cognitive Services: Anomaly Detector and Custom Vision
Microsoft recently announced the public preview of Anomaly Detector and general availability of Custom Vision. With both services, Microsoft further expands its Cognitive Services offering for its customers.
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Introducing TensorFlow Privacy, a New Machine Learning Library for Protecting Sensitive Data
In a recent blog post, TensorFlow announced TensorFlow Privacy, an open source library that allows researchers and developers to build machine learning models that have strong privacy. Using this library ensures user data are not remembered through the training process based upon strong mathematical guarantees.
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Q&A on Condé Nast's Natural Language Processor and Content Analysis
Beginning in 2015, Condé Nast created a natural-language-processing and content-analysis engine to improve the metadata around content created across their 22 brands. The new system has led to a 30% increase in click-through rates. InfoQ spoke with Antonino Rau, a software engineer and technology manager at Condé Nast US about the evolution of their NLP-as-a-service system named HAL.
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