This is the first edition of a monthly roundup of news about Artificial Intelligence, Machine Learning and Data Science. This is an eclectic collection of interesting blog posts, software announcements, applications and events I’ve noted over the past month or so.
Open Source AI, ML & Data Science News
Comparative performance benchmarks of deep-learning frameworks (including Tensorflow and PyTorch) on GPU architectures.
Tensorflow 1.6.0 released, now supports CUDA 9.0 and cdDNN 7.
Python 2.0 end-of-life date confirmed as January 1, 2020.
Google releases Lucid, a neural-network visualization library designed to help with the interpretability of vision systems.
Analysis of the 2018 Gartner Magic Quadrant for Data Science and Machine Learning Platforms, and SAS’s future prospects, from Thomas W Dinsmore.
A NYT feature article on conversational bots: To Give A.I. the Gift of Gab, Silicon Valley Needs to Offend You.
A podcast interview with Joseph Sirosh on the state of AI and Microsoft’s Cloud AI services.
Microsoft ML Server 9.3 and Microsoft R Client 3.4.3 have been released.
Auto insurer Progressive sells policies with Flo, a chatbot in Facebook Messenger built with Azure Bot Service.
A podcast interview about Project InnerEye, an innovative machine learning tool that helps radiologists identify and analyze 3-D images of cancerous tumors. Details on how to implement a similar application can be found in the blog post, Using Microsoft AI to Build a Lung-Disease Prediction Model Using Chest X-Ray Images.
Using AI to automatically redact faces in video.
Microsoft Research has developed a system to translate news articles from Chinese to English with the same accuracy as human translators.
An overview of LUIS, Microsoft’s cloud-based service for developing language- and speech-based applications.
The A-Z of Machine Learning / Deep Learning algorithms: a Twitter thread in 26 parts.
What to Use When: a guide to navigating the Microsoft AI landscape.
Tutorial: using the Data Science Virtual Machine and transfer learning to create a dogs-vs-cats image classifier.
An introduction to Docker for data scientists, from the Microsoft Machine Learning blog.
A new e-book: SQL Server 2017 Machine Learning Services with R.
An 8-step, 5-minute tutorial for setting up a cluster in Azure for use with the sparklyr package for R.