3 min read

MariaDB MaxScale v2.2, Accenture AI testing services, Qualcomm’s AI engine, AllenNLP v0.4, and more in today’s top stories around machine learning, deep learning, and data science news.

1. MariaDB MaxScale 2.2, an advanced database proxy for MariaDB, is now generally available

MariaDB has announced the general availability of MariaDB MaxScale 2.2. MaxScale is an advanced database proxy for MariaDB. The version 2.2 hosts a variety of new features including

  • Replication cluster failover management
  • High availability of MaxScale
  • Security features for General Data Protection Regulation (GDPR) compliance, readiness of upcoming MariaDB Server 10.3
  • Improved management interface
  • Proxy Protocol, to ease out configuration and authorization of users by eliminating the need to duplicate them in both MariaDB MaxScale and MariaDB Server

To know the entire changes, have a look at the release notes.

2. Accenture announces new services for testing AI systems

Accenture launches new AI testing services. These services are built on a “Teach and Test” methodology designed to help companies build, monitor and measure reliable AI systems. The “Teach” phase emphasizes the choice of data, models, and algorithms that are used to train Machine Learning. In the “Test” phase, AI outputs are compared with the main performance indicators and analyzed for whether the system can explain how a decision or outcome was determined by using innovative techniques and Cloud-based tools to monitor the system. Accenture has used this methodology to train a conversational virtual agent for a financial services company’s website. The agent was trained 80 percent faster than previously possible and achieved an 85 percent accuracy rate on customer recommendations.

3. Qualcomm launches its new Artificial Intelligence Engine

To help developers provide better machine learning-based enhancements, Qualcomm has launched a new AI engine. The Qualcomm Artificial Intelligence Engine consists of several hardware and software components that can be used by app developers to provide “AI-powered user experiences”, with or without a network connection. Key features include:

  • Snapdragon Neural Processing Engine (NPE) software framework to accelerate AI user experiences on a device. The Snapdragon NPE supports Tensorflow, Caffe and Caffe2 frameworks, in addition to the Open Neural Network Exchange (ONNX) interchange format.
  • Support for the Android Neural Networks API, giving developers access to Snapdragon platforms directly through the Android operating system.
  • Hexagon Neural Network (NN) library allowing developers to run AI algorithms directly on the Hexagon Vector Processor.

4. Microsoft Azure Notebooks will now let users learn Data Science, free of charge

Microsoft has made it easier to create and share live, working code an easier process with its Microsoft Azure Notebooks service. This notebook is now available free of charge and allows data science enthusiasts to learn programming and data science outside of traditional schooling. Microsoft Azure Notebooks lets users get started quickly on tasks such as data visualization and prototyping, all within a web browser. It’s an implementation of the popular open-source Jupyter Notebooks service and is available to anyone who creates a free account.

5. AllenNLP, an open-source NLP research library built on PyTorch, releases its version 0.4

AllenNLP has released the version 0.4 of their NLP research library, which is built on PyTorch. The major changes include:

  • Inclusion of ELMo which produces contextualized word embeddings that greatly improve model performance.
  • Support for lazy datasets: Users can now stream data through the trainer with a lower memory footprint.
  • First-class support for models that operate on spans instead of tokens.
  • Support for programmatically importing additional dependencies.
  • A simple server to create a stand-alone web demo for a model.
  • Constrained decoding added to the ConditionalRandomField module (and to the corresponding NER tagger model)

Additional features and bug fixes are available in the GitHub repo.


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