InfoQ Homepage Machine Learning Content on InfoQ
-
Teaching Machines to Understand Emotions with Sentiment Analysis
Sentiment analysis teaches computers to recognise the human emotions present in text. The fundamental trade-off in sentiment analysis is between simplicity and accuracy. Approaches vary from using a list of words associated with emotions, to deep learning with techniques like word embeddings, neural networks and attention mechanisms.
-
Google's Cloud TPU V2 and V3 Pods Are Now Publicly Available in Beta
Recently, Google announced that its second- and third-generation Cloud Tensor Processing Units (TPU) Pods — its scalable cloud-based supercomputers with up to 1,000 of its custom TPU — are now publicly available in beta. With these Pods, Machine Learning (ML) researchers, engineers, and data scientists can speed up the time needed to train and deploy machine learning models.
-
Databricks MLflow Integration Now Generally Available
Databricks recently made MLflow integration with Databrick notebooks generally available for its data engineering and higher subscription tiers. The integration combines the features of MLflow with those of Databrick notebooks and jobs. MLflow provides the following three main capabilities: experiment tracking, projects, and MLflow models.
-
Microsoft Launches Several New Machine Learning Services and Extends Its Cognitive Services
Before its Build Developer Conference, Microsoft released several new Machine Learning services and Cognitive Services updates, ranging from no-code tools to hosted notebooks, with several new APIs and other services in-between.
-
Making Robots More Intelligent, Microsoft Releases Autonomous Systems Platform
At the recent Build conference in Seattle, Microsoft announced, in limited preview, an end-to-end toolchain to help developers and organizations build autonomous systems for their industries. The platform includes machine teaching tools and simulation technologies that enable intelligent robotic systems to complete tasks like running autonomous forklifts and robotic inspection platforms.
-
ML.NET, an Open Source Machine Learning Framework for the .NET Ecosystem: Pranav Rastogi Q&A
Earlier this month Microsoft released the first major version of ML.NET, an open source machine learning (ML) framework for the .NET ecosystem. ML.NET allows the development of custom ML models using either C# or F#. These models can be used in scenarios involving sentiment analysis, fraud and spam detection, product and movie recommendation, image classification, and more.
-
Microsoft Open-Sources Approximate Nearest Neighbor Search Algorithm Powering Bing
Microsoft's latest contribution to open source, Space Partition Tree And Graph (SPTAG), is an implementation of the approximate nearest neighbor search (NNS) algorithm that is used in Microsoft Bing search engine.
-
Google Releases Google-Landmarks-V2, a Large-Scale Dataset for Landmark Recognition & Retrieval
Google has released Google-Landmarks-v2, an improved dataset for Landmark Recognition & Retrieval, along with Detect-to-Retrieve, a Tensorflow codebase for large-scale instance-level image recognition. Two companion Kaggle competitions based on Google-Landmarks-v2 were also launched. With over 200,000 landmarks in 5 million images, it is the largest landmark dataset ever published.
-
Amazon Updates SageMaker Ground Truth with New Labeling Features, Vendor Support and Availability
Amazon announced that SageMaker Ground Truth now offers simplified labeling workflows, support for additional labeling vendors, and is available in the Asia Pacific (Sydney) AWS region – bringing the total to six supported AWS regions in the Americas, Europe, and Asia.
-
Google Launches AI Platform - an End-to-End Platform to Build, Run, and Manage ML Projects
Google has recently launched AI Platform, an end-to-end platform to build, test, and deploy machine learning models. It brings together a host of products and services to help businesses solve complex challenges using AI in a way that is easier and collaborative.
-
Google Scales Weak Supervision to Overcome Labeled Dataset Problem
Google recognizes that the need for labeled data in machine learning (ML) is a significant bottleneck and recently adapted the open-source Snorkel framework to overcome the problem at scale. Google enhanced Snorkel by integrating it with Tensorflow, using the file system for sharing data instead of a database, and creating separate executables for labeling functions.
-
Teaching the Computer to Play the Chrome Dinosaur Game with TensorFlow.js Machine Learning Library
A simple, yet entertaining and useful for educational purposes application of machine learning, was recently made available on Fritz's HeartBeat Medium publication. Google's machine learning TensorFlow.js library is leveraged in the browser to teach the computer to play the Chrome Dinosaur Game.
-
Salesforce Adds Intelligence to its Einstein Services Offering
In a recent press release, Salesforce announced additions to their Einstein platform that target bringing AI solutions to Salesforce developers and admins using a low code, point and click configuration-based solution. The recent additions to the platform include Einstein Translation and Einstein Optical Character Recognition (OCR).
-
How the Sequence of Characters in a Name Can Predict Race and Ethnicity
Gaurav Sood, Principal Data Scientist at Microsoft, recently spoke at the AnacondaCon 2019 Conference on how to use the sequence of characters in a person's name to predict that person's race and ethnicity, using machine learning techniques.
-
AWS Releases Enhancements to AI Services for NLP, Speech-to-Text Transcription, and Image Detection
Amazon Web Services (AWS) released new features for three of its AI services: Amazon Comprehend, Amazon Rekognition, and Amazon Transcribe.