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 I’ve noted over the past month or so.
Open Source AI, ML & Data Science News
R 3.5.0 has been released, with significant performance improvements.
PyTorch 1.0 has been released, and deemed ready for both research and production for AI applications.
A new Swift API for Tensorflow has been released, with compiler and language enhancements.
TensorFlow Probability, a probabilistic programming toolbox for machine learning.
Impressive benchmarks for training Imagenet and CIFAR10 using AWS spot instances, with Pytorch and fastai.
Oracle acquires machine learning platform Datascience.com, a cloud workspace platform for data science projects and workloads.
Microsoft Build featured several announcements regarding AI and machine learning. You can watch the keynotes here.
Many new capabilities and features for Cognitive Services were announced at BUILD, including: Bing Custom Search, Custom Vision Service and Custom Decision Service; Microsoft’s Cognitive Services Labs (with previews of emerging Cognitive Services technologies); and Video Indexer.
Cognitive Search, a new Azure service providing an AI-first approach to content understanding.
Project Brainwave is now in preview, bringing hardware-accelerated inference for AI to Azure. Deep neural networks encoded by FPGAs (field-programmable gate arrays) can dramatically reduce the time to classify images (as just one application example). ResNet50 is available in Project Brainwave now (subject to quota approval).
Microsoft and Qualcomm have partnered on the Vision AI Developer Kit, now in preview.
Updates to the Bot Builder SDK and Bot Framework Emulator.
Microsoft R Open 3.4.4 is now available.
Transfer Learning for Text using Deep Learning Virtual Machine (DLVM). Code comparing the performance of eight machine-comprehension algorithms is also available in Github.
The TWIML AI podcast series on differential privacy, a technique for collecting data in such a way that it reduces the impact on privacy even in the event of a leak.
Building Neural Networks with TensorFlow and the Azure Data Science Virtual Machine. This hands-on lab walks through the process of building an image recognizer using transfer learning with MobilenetV1.
Full-integrated experience simplifying Language Understanding in conversational AI systems.
Deep Learning Image Segmentation for Ecommerce Catalogue Visual Search, with details on removing the background from a product image using GrabCut and Tiramisu.
How to Develop a Currency Detection Model using Azure Machine Learning, with details on how the real-time banknote recognition capability of the Seeing AI application was implemented in CoreML.
Deep Learning for Emojis with VS Code Tools for AI: semantic analysis of text with emojis.
A maze-solving Minecraft robot, implemented in R.
Find previous editions of the monthly AI roundup here.
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