A monthly roundup of news about Artificial Intelligence, Machine Learning and Data Science. This is an eclectic collection of interesting blog posts, software announcements and data applications from Microsoft and elsewhere that I've noted over the past month or so.
Open Source AI, ML & Data Science News
TensorFlow Privacy: a Python library for training machine learning models with differential privacy, for use with sensitive data to generate models that don't learn details about specific people.
Tensorflow Federated, an open-source library for Federated Learning, enabling many participating clients to train shared ML models while keeping their data local.
R 3.5.3 has been released.
NNI, an open source AutoML toolkit for neural architecture search and hyper-parameter tuning, from Microsoft Research.
Industry News
Open AI has published a paper describing GPT-2, an unsupervised language model that can generate paragraphs of coherent text that could be mistaken for human writing. Only a scaled-down version has been released, for fear of abuse.
Mozilla releases Common Voices, a public domain dataset of 1,400 hours of recorded voice data in 18 languages.
GCP's Deep Learning Virtual Machine now incorporates RAPIDS, the open-source GPU-acceleration library for Python machine learning.
Google introduces GPipe, an open-source library for training large-scale deep neural networks on devices with otherwise insufficient resources.
Determined AI, platform-agnostic software for automated machine learning and infrastructure management, has been announced.
Microsoft News
Azure Machine Learning Service now incorporates RAPIDS, the open source NVIDIA library that provides accelerated GPU support for Pandas dataframes and scikit-learn machine learning algorithms.
ONNX Runtime adds the NVIDIA TensorRT execution provider, for improved inferencing support on NVIDIA GPU devices.
The Intel Optimized Data Science Data Science Virtual Machine, providing up to 10x performance increase for CPU-based deep learning workloads, is now available on Azure Marketplace.
New Python features in VS Code include validation of breakpoint targets, a new test explorer, and the ability to run selected code without the need to define code cells.
New enhancements for Cognitive Services Computer Vision: bounding boxes for detected objects, and more types of objects detected (including thousands of brand logos).
Azure Cognitive Services containers for Text Analytics and Language Understanding containers are now supported on edge devices with Azure IoT Edge.
MMLSpark 0.16 is released, with improvements to machine learning methods for Apache Spark including Azure Search integration and the Smart Adaptive Recommender.
Microsoft Research releases MT-DNN as a PyTorch package, an architecture for natural language understandings that combines elements of MTL and BERT.
Learning resources
Microsoft launches AI Business School, a collection of learning modules aimed at business leaders on AI strategy, technology and responsible impact.
A high-level article on how and why transfer learning works, and the connection with embeddings.
The Quartz AI Studio, a task-oriented resource for data journalists, provides useful guides for text and image analysis for general practitioners as well.
Causal Inference: The Mixtape, a freely-licensed book by Scott Cunningham that comes with its own playlist.
An architecture for using R at scale on GCP, by Mark Edmondson.
The Python Data Science Handbook (by Jake VanderPlas) in Jupyter Notebook form. Also available in Azure Notebooks and Google Colab.
Datasets for Machine Learning: A list of the biggest datasets from across the web.
Applications
SPADE, a method for synthesizing photo-realistic images from a simple sketch.
Seeing AI now includes a feature to help vision-impaired users explore photographs by touch, and is now supported on iPad devices.
Google deploys a compact RNN transducer to mobile phones that can transcribe speech on-device and streams output letter-by-letter, and a quasi-recurrent neural network for handwriting transcription.
NVIDIA introduces the Kaldi ASR Framework for high-speed speech transcription.
Find previous editions of the monthly AI roundup here.
Thank you for sharing this. This post covers a large number of essential topics and link to the useful resources. Really glad to find this out, made my work much easier!
Regards
Park Menk
Posted by: Park Menk | April 06, 2019 at 12:46
Thanks for sharing the information regarding AI, machine learning & data science.It is a good collection of interesting blog posts, software announcements and data applications from Microsoft.
Posted by: Andy | April 15, 2019 at 04:16
No more updates? Come back, we miss you.
Posted by: Leonardo | April 17, 2019 at 07:35
@Leonardo, I'm working on the April issue as we speak. Should be out in the next couple of days.
Posted by: David Smith | April 18, 2019 at 10:47