Revolutions

AI, Machine Learning and Data Science Roundup: December 2018

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

PyTorch 1.0 has been released with faster performance, better support for distributed computing, a new C++ frontend, and a model-sharing repository.

Tensorflow 2.0 will include a built-in implementation of Keras for deeper integration and support of eager execution.

Industry News

The AI Now Institute has published the AI Now Report 2018, and makes 10 recommendations related to the social impact of AI technology.

Stanford's AI Index 2018 Annual Report documents the year's progress in AI teaching, research, and humanlike capabilities.

Tableau adds SSL support for connecting the R server for its R integration feature.

AWS Lambda adds support for Python 3.7, and offers a customization framework for supporting other programming languages.

New features in Amazon SageMaker: semantic segmentation, early stopping for automatic model tuning, and tools for tracking machine learning experiments.

H20.ai's machine learning platform now available as a hosted service on AWS.

Microsoft News

Microsoft calls for government regulation on the use of facial recognition technology.

The Microsoft Connect event on December 4 included several product announcements related to Machine Learning and AI, summarized below.

Azure Machine Learning service is now generally available. This service orchestrates the training and deployment of machine learning modules, using resources managed on-premises or in Azure. Capabilities include:

• Model training with open-source frameworks including scikit-learn, PyTorch and Tensorflow.
• A Python module for managing and orchestrating models and compute resources.
• Integration with developer tools including Jupyter Notebooks and Visual Studio Code.
• The ability to launch and redirect training to CPU and GPU-enabled resources: local, Azure virtual machines, and distributed clusters with auto-scaling capabilities.
• An AI-driven service for testing and optimizing multiple ML algorithms, and hyperparameter tuning for PyTorch and Tensorflow models.
• Production deployment for trained models via containers and the open ONNX standard.
• Pipelines to orchestrate the entire machine learning process from data to deployment.

Azure Notebooks, the hosted Jupyter Notebooks service, has been updated. New features include:

• A refreshed user interface, with improved project management and notifications.
• Upgrades to the free compute image, and the ability to use paid virtual machine (including GPU-class) instances to provide the compute kernel.
• Streamlined integration with Azure Authentication and Azure Machine Learning service.

ONNX Runtime, a cross-platform, high-performance engine for inferencing with trained ML models in the Open Neural Network Exchange (ONNX) representation, has been released as open source.

ML.NET 0.8, the open-source machine learning framework for .NET, is now available with new recommendation scenarios and a feature importance tool.

The Language Understanding service can now be deployed in a self-hosted container.

The Translator Text API now offers customized translation to support specialized expressions and vocabulary.

Neural Text to Speech, a preview feature of Cognitive Services Speech Service, synthesizes spoken voices indistinguishable from real people.

Azure Functions now supports serverless deployment of Python functions. This blog post provides detailed examples.

Azure SQL Database now provides integration with the R language (in preview).

AzureR: a new suite of packages to manage Azure services with R.

Learning resources

The book Bayesian Methods for Hackers is now available as a series of Jupyter Notebooks with code in Tensorflow.

A tutorial on causal inference in computing systems, presented by Amit Sharma and Emre Kicima at KDD 2018.

A guide to resources and tutorials for getting started with machine learning on Azure.

A tutorial on packaging Cognitive Services APIs into containers and running them on a Docker-enabled platform.

Pattern Recognition and Machine Learning, the 2006 book by Christopher M Bishop, is now available for free download.

A 4-part series on the history and recent advancements in neural word embeddings.

Applications

Using a computer vision model to measure building footprints in satellite images.

Using active learning to track the migration of endangered bird species.

Using deep learning to improve the effectiveness of diabetic retinopathy models.

Using computer vision to quantify fish stocks in Australia.

A collection of reference architectures for deploying machine learning and AI models to real-time applications and batch processes with Azure.

Find previous editions of the monthly AI roundup here.

AI, Machine Learning and Data Science Roundup: November 2018

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

torchdiffeq: A GPU-enabled PyTorch library for ordinary differential equation (ODE) solving.

Tensorflow 1.12 released.

Visual Studio Code adds support for Jupyter Notebooks, with the ability to import Notebooks into Code, and interact with cells via the iPython kernel.

Industry News

Amazon Comprehend, the AWS natural language processing service, can now be customized with user-defined entities and categories.

Google Cloud Platform introduces AI Hub, a place to publicly host and privately (within-enterprise) share and deploy pipelines, Jupyter notebooks, TensorFlow modules, and more.

A Chrome extension to open a Github-hosted Jupyter Notebook in Google Colab.

Facebook open-sources Horizon, a reinforcement learning platform that exports models in ONNX for deployment in large-scale production environments.

Google open-sources BERT (Bidirectional Encoder Representations), a system for pre-training natural language models with large corpuses of unannotated text.

Microsoft News

Microsoft has acquired XOXOCO, a startup focused on chatbots.

Microsoft Cognitive Services in Containers. Face detection and text analytics APIs in self-hosted containers that do not require private data to be sent to the cloud for analysis.

Named Entity Recognition comes to the Cognitive Services Text Analytics API, to identify persons, locations, organizations and other entities in unstructured text.

Azure Machine Learning Studio upgrades R support with the addition of the R 3.4 language engine.

New AI capabilities in preview for Power BI: AI model builder, Cognitive Services integration, and Azure Machine Learning interface. 

The Microsoft Data Science Virtual Machine now includes Catboost, the open-source gradient boosting on decision trees library.

AzureR, a new suite of packages for managing Azure services from R.

Learning resources

A guide to translate between different terms used for similar concepts in Statistics and Machine Learning, from CMU.

Best Practices for Using Machine Learning in Businesses: Szilard Pafka cuts through the hype in this Twitter-summary of his keynote presentation.

A comparison of automatic machine learning services: Google AutoML, Microsoft Automated ML, AutoKeras and auto-sklearn.

Using Docker to deploy an R API with the plumber package.

Applications

Google uses acoustic analysis to identify humpback whales by their call.

Uber's approach to evaluating experiments: look at the entire distribution of outcomes, because focusing on just the mean or median can be misleading.

T-mobile uses R in production to route customer service queries with natural language AI.

Find previous editions of the monthly AI roundup here.

AI, Machine Learning and Data Science Roundup: October 2018

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

PyTorch 1.0 is now in preview, and brings a streamlined workflow from model development to production. Relatedly, Sam Charrington compares the growing PyTorch ecosystem with that of Tensorflow.

Tensorflow 1.11 is released, with binaries for cuDNN 7.2 and TensorRT 4, and new functions for querying kernels.

fastai, a new open library for deep learning built on PyTorch, has been released by fast.ai. It provides a single, consistent API for many deep learning applications and data types.

The R packages keras and tensorflow now allow definition of models that take advantage of eager execution.

Anaconda can now install and manage Tensorflow as a conda package.

Industry News

PyXLL, a commercial Excel add-in for calling Python functions from Excel.

IBM releases AI Fairness 360, an open source toolkit for investigating and mitigating bias in machine learning models.

Google open sources a TensorFlow package for Active Question Answering, a reinforcement learning based method to train artificial agents to answer natural-language questions.

Google Cloud Platform adds PyTorch support to several services, including Tensorboard, Kubeflow and Deep Learning VM images.

RStudio Package Manager, a commercial product to help organizations manage R packages, has been released.

RStudio adds support for Stan, the Bayesian modeling language.

Databricks releases MLflow 0.7.0, featuring a new R client API that allows you to log parameters, code versions, metrics, and output files when running R code and visualize the results in MLflow.

Cloudera and Hortonworks are merging. Thomas Dinsmore assesses the impact on the machine learning ecosystem.

Dataiku 5 is released. The enterprise data platform adds support for containerized R and Python recipes, and integration with Tensorflow and Keras.

Microsoft News

Microsoft has opened a new AI research facility in Shanghai in conjunction with INESA, the China organization developing smart city solutions.

SQL Server 2019 will provide integration with "big data clusters": external SQL Server, Spark or HDFS containers managed in Kubernetes. For AI and ML workloads, you can run Spark or SQL Server's R, Python and Java extensions against the cluster data.

Azure Machine Learning Services now provides Python developer libraries for data preparation, experiment tracking and model management, training on GPU clusters, automated model search and hyperparameter optimization, deploying trained models as containers, and many other new capabilities.

Visual Studio Code Tools for AI has been updated to provide a convenient interface to Azure Machine Learning for users of the popular open-source editor.

Speech Service in Azure Cognitive Services is now generally available, and includes a new neural text-to-speech capability for humanlike synthesized spech.

Azure Databricks is now supported in more regions, offers GPU support for deep learning, and Databricks Delta is now available (in preview) for transactional data capabilities.

Microsoft Bot Framework SDK v4 is now available. The Ignite presentation "Creating Enterprise-Scale Intelligent Agents and Bots" provides an overview and several examples.

Cortana Skills Kit for Enterprise, a development platform based on Azure Bot Service and Language Understanding, is now in private preview.  

ONNX Runtime, a high-performance engine for executing trained models represented in the open ONNX format, is now in preview.

PyTorch is now supported in many Azure services, including Azure Machine Learning service, Data Science Virtual Machine, Azure Notebooks, and Visual Studio Code Tools for AI.

The Microsoft Infer.NET machine learning framework has been released as open source. An online book, Model-Based Machine Learning, describes its probabilistic approach with several in-depth examples.

Learning resources

Microsoft Learn, which provides free interactive training for the Azure platform, is now available. Modules include Deep Learning with PyTorch and Computer Vision with Tensorflow.

A tutorial on converting a PyTorch model to ONNX, the cross-platform model-sharing format.

Chromebook Data Science, a free course in Data Science and R from the Johns Hopkins Data Science lab that requires only a browser to take.

Applications

The Ethics Certification Program for Autonomous and Intelligent Systems, a new IEEE standard with the goal of advancing transparency and reducing algorithmic bias in AI systems.

The fast.ai research datasets collection, which includes MNIST, CIFAR 10 and Imagenet, is now available on AWS Open Data.

A comprehensive list of AI Ethics resources, published by fast.ai.

Snip Insights, a Microsoft AI Lab project for image analysis and text extraction from screenshots.

Azure Healthcare AI blueprint, a process to deploy a HIPAA and HITRUST compliant environment in Azure for managing and running healthcare AI experiments.

Find previous editions of the monthly AI roundup here

AI, Machine Learning and Data Science Roundup: September 2018

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

ONNX 1.3 released. The standard for representing predictive models adds a cross-platform API for executing graphs on optimized backends and hardware.

A new foundation supporting the development of scikit-learn, the Python machine learning library.

Google open sources "dopamine", a Tensorflow-based framework for reinforcement learning research.

Google adds a "What-If Tool" to Tensorboard, to interactively explore the robustness and algorithmic fairness of machine learning models.

Industry News

Google introduces Dataset Search, an index of public datasets from the environmental and social sciences, government, and journalism.

The 2018.3 update to the Alteryx analytics platform brings interactive graphics, and Spark and Jupyter Notebook integration.

AWS Deep Learning AMIs now provide Tensorflow 1.10 and PyTorch with CUDA 9.2 support.

Rigetti Computing launches the Quantum Cloud Service with a $1M prize for the first application demonstrating "quantum advantage".

Thomas Dinsmore's commentary on Forrester Wave rankings for "Multimodal Predictive Analytics and Machine Learning Platforms" and "Notebook-Based Predictive Analytics and Machine Learning Solutions".

Microsoft News

Microsoft acquires Lobe, a Bay Area startup that produced a drag-and-drop interface for building machine learning models.

NVIDIA GPU Cloud now provides ready-to-run containers with GPU-enabled deep learning frameworks for use on Azure.

Microsoft introduces Azure CycleCloud, a tool for creating, managing, operating, and optimizing burst, hybrid, and cloud-only HPC clusters. Schedulers including Slurm, Grid Engine and Condor are supported.

New features in Azure HDInsight: Spark 2.3.0 and Kafka 1.1 support; ML Services 9.3 integration, with updated R engine and new statistical and machine learning algorithms; Apache Phoenix, for SQL-like queries for data in HBase; Apache Zeppelin, web-based notebooks for querying Phoenix tables; and more.

Learning resources

A review of the algorithms behind AutoML systems for model selection and hyperparameter optimization, from the H2O blog.

Joel Grus's criticisms of Jupyter Notebooks as a platform for reproducible and production-ready computing. Yihui Xiu offers an alternative: RMarkdown.

A comprehensive introduction to mixed-effects models, and fitting them in Python.

A collection of videos, presentations and essays by Brandon Rohrer with approachable explanations of the inner workings of deep learning and machine learning algorithms.

A tutorial on using Azure Batch AI to parallelize forecasts of energy demand.

A meticulously-researched accounting of the resources (natural, technological, and human) that enable an Alexa voice query, presented as highly-detailed map.

A review of the book SQL Server 2017 Machine Learning Services with R.

Applications

Data scientists use text similarity analyses in R to try and identify the author of that anonymous NYT op-ed.

Sketch2Code, an application to translate hand drawings into HTML forms.

Measuring building footprints from satellite images, with semantic segmentation.

Near real-time fraud detection for mobile banking, with a classification model implemented in Azure Machine Learning.

Credit card fraud detection with an Autoencoder neural network, using the Azure Data Science VM.

Shell deploys machine learning and AI systems to avert equipment failures, autonomously direct drill-bits underground, and improve safety.

Find previous editions of the monthly AI roundup here

AI, Machine Learning and Data Science Roundup: August 2018

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

ONNX Model Zoo is now available, providing a library of pre-trained state-of-the-art models in deep learning in the ONNX format.

In the 2018 IEEE Spectrum Top Programming Language rankings, Python takes the top spot and R ranks #7.

Julia 1.0 has been released, marking the stabilization of the scientific computing language and promising forwards compatibility.

A retrospective on the R Project, now in its 25th year, from Significance Magazine.

Industry News

Google announces Cloud AutoML, a beta service to train vision, text categorization, or language translation models from provided data. The fast.ai blog evaluates the claim that AutoML works without the need for machine learning skills.

Google announces Edge TPU, a hardware chip and associated software to bring AI capabilities to edge devices.

Oracle open-sources GraphPipe, a network protocol designed to simplify and standardize transmission of machine learning data between remote processes.

AWS Deep Learning AMIs now include ONNX, making it easier to deploy exported deep learning models.

Amazon Rekognition is available in two new regions: Seoul and Mumbai. The video analysis service caused a stir when the ACLU reported it matching pictures of members of Congress to a database of criminal mugshots.

RStudio introduces Package Manager, a repository management server product to organize and centralize R packages.

Data from the O'Reilly Machine Learning Adoption Survey reveal that most machine learning models are built by in-house data science or product development teams, with only 3% adopting cloud-based ML service.

Anaconda Enterprise 5.2 adds capabilities for GPU-accelerated machine learning.

Microsoft News

TechNative's review of Microsoft's AI philosophy and technologies, and the roadmap ahead.

Microsoft R Open 3.5.1 is now available.

Power BI Desktop now offers Python integration (in preview).

AI for Accessibility, a $25M Microsoft program applying artificial intelligence to help people with disabilities.

Azure SQL Database now offers reserved capacity at a discounted rate, and reservation discounts for virtual machines now apply when using differing VM sizes within a group.

Learning resources

The Chartmaker Directory, an index of dozens of data visualization types with examples in more than 30 software tools.

An overview of the benefits and limitations of FPGUs compared to CPUs and GPUs for numeric computing.

A guide to deep learning applied to natural language processing, for those new to the field.

Containerized R and Python Web services with Docker and Microsoft ML Server.

Design, architecture, and implementation of an R-based recommendation system in Azure.

A guide to distributed deep learning in Spark, using Azure Batch and Azure HDInsight.

Google's Machine Learning Guides, with machine learning tips and a guide to text classification.

Field Guide to Machine Learning, a 6-part video series from Facebook Research.

What Data Scientists Really Do, according to a Harvard Business Review article.

Survey analytics company Crunch uses R to provide a data visualization service.

Finding optimal staff composition for a professional services company, with Azure Machine Learning.

Find previous editions of the monthly AI roundup here

AI, Machine Learning and Data Science Roundup: July 2018

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

A review of the current state of the Julia project, including performance comparisons with Go, Python and R. 

Python 3.7.0 has been released. Also, Guido van Rossum steps down as the project's "Benevolent Dictator for Life".

Tensorflow 1.9 released, featuring a new getting started guide for Keras.

Apache OpenNLP 1.9.0 released, adding several improvements to the natural language processing library.

The PYPL Popularity of Languages Index ranks Python as the 1st and R as the 7th most popular programming languages.

R 3.5.1 has been released.

Industry News

Google introduces Cirq, an open-source framework to simulate noisy intermediate-scale quantum computers.

Hortonworks announces new partnerships with Google, Microsoft and IBM to bring its big data platform to the cloud.

IBM plans to release an annotated dataset of 1 million images, with the goal of reducing bias in facial recognition systems.

Amazon Sagemaker enhances the DeepAR, BlazingText, and Linear Learner algorithms built into the machine learning framework.

A series on Data Ethics by Mike Loukides, Hilary Mason and DJ Patil. The most recent post proposes a checklist for ethical data science practices.

Apple has combined its various artificial intelligence divisions into a new structure led by recent hire John Giannandrea.

Microsoft News

Open data sets curated by Microsoft Research for computer vision, natural language processing and scientific analysis. 

Microsoft acquires Bonsai, a startup focused on using ML to train autonomous machines using simulated environments.

Microsoft President Brad Smith calls for public regulation and corporate responsibility in the use of facial recognition technology.

ML.NET 0.3 released adding ONNX support, LightGBM learners, and more to the .NET machine learning library.

Microsoft Speech Service SDK version 0.5.0 has been released, with support for long-running audio and automatic reconnections.

Azure HDInsight now supports Apache Spark 2.3, improving performance for Python-based interactions.

Azure Databricks now includes RStudio integration for R developers. The integration is described in detail at the Databricks blog.

The Microsoft Face API has significantly improved its ability to recognize gender across skin tones.

Learning resources

Andrey Kurenkov's essay on the limitations of reinforcement learning. A February 2018 blog post by Alex Irpan also provides some cautionary examples.

A Developer's Guide to Building AI Applications, a free e-book that provides a gentle introduction to the Microsoft AI Platform.

A tutorial on adding machine learning to mixed reality applications, using Azure ML Studio.

How to read and write Microsoft SQL Server data in Python using Pandas dataframes.

Object dectection via distributed deep learning on multiple GPUs, using Horovid on Azure Batch AI.

How Artificial Intelligence Could Prevent Natural Disasters, from Wired.

A neural network for colorizing video also finds application in object tracking.

Computer vision with an embedded learning library from Microsoft Research used to monitor endangered Purple Martin nests at Disney Animal World.

A diabetic retinopathy prediction application, based on image analysis with convolutional neural networks and implemented with Azure Machine Learning.

Fashion similarity matching with Siamese Networks.

Find previous editions of the monthly AI roundup here

AI, Machine Learning and Data Science Roundup: June 2018

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

Intel open-sources NLP Architect, a Python library for deep learning with natural language.

Gym Retro, an open source platform for reinforcement learning research on video games.

Facebook open-sources DensePose, a toolkit to transform 2D images of people into a 3-D surface map of the human body.

MLflow, an open source machine learning platform from Databricks, has been released.

Industry News

In a 12-minute documentary video and accompanying Wired article, Facebook describes how it uses Machine Learning to improve quality of the News Feed.

In the PYPL language rankings, Python ranks #1 in popularity and R is #7; both are rising.

Google announces its ethical principles for AI applications, and AI applications it will not pursue.

Wolfram Research launches the Wolfram Neural Network Repository, with implementations of around 70 neural net models.

Google Cloud introduces preemptive pricing for GPUs, with discounts compared to GPUs attached to non-preemptable VMs.

Conversational AIs conducting conversational full-duplex conversations: Microsoft Xiaoce and Google Duplex.

Microsoft News

Microsoft's head of AI research, Harry Shum, on "raising" ethical AI.

Microsoft has acquired Semantic Machines, a startup focused on conversational AI.

Microsoft is developing a bias-detection tool, with the goal of reducing discrimination in applied AI.

Microsoft's Bot Builder SDKv4 further simplifies the process of developing conversational bots.

Cognitive Services Labs, which offers previews of emerging Microsoft Cognitive Services technologies, adds labs for anomaly detection, ink analysis and more.

ML.NET 0.2, a cross-platform open source machine learning framework for .NET developers, has been released.

Microsoft R Open 3.5.0 has been released.

Azure Databricks now provides a machine-learning runtime and GPU support.

Learning resources

A tutorial on visualizing machine learning models with LIME, a package for R and Python.

A visual introduction to Machine Learning, Part II: Model Tuning and the Bias-Variance Tradeoff, with an in-depth and graphically elegant look at decision trees.

Materials for five new AI-oriented courses have been published to the LearnAI Materials site.

A Developer's Guide to Building AI Applications: a free e-book from O'Reilly and Microsoft.

Microsoft Professional Program for Artificial Intelligence, a free, self-paced on-line certification in AI skills.

The Azure AI Lab provides complete worked applications for generative image synthesis, cognitive search, automated drone flight, artistic style transfer and machine reading comprehension.

CVAE-GAN: a new generative algorithm for synthesizing novel realistic images, like faces.

Land cover mapping with aerial images, using deep learning and FPGAs.

Improving medical imaging diagnostics, using the new Azure Machine Learning Python package for Computer Vision.

Find previous editions of the monthly AI roundup here

AI, Machine Learning and Data Science Roundup: May 2018

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.

Project Jupyter has received the ACM Software System Award.

PyTorch 1.0 has been released, and deemed ready for both research and production for AI applications.

The ecosystem for ONNX (the open standard for exchange of neural network models) expands, with official support for Core ML, NVIDIA TensorRT 4, and the Snapdragon Neural Processing Engine.

A new Swift API for Tensorflow has been released, with compiler and language enhancements.

TensorFlow Probability, a probabilistic programming toolbox for machine learning.

Industry News

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 News

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.

New Python packages for machine learning in Azure: Forecasting, Computer Vision, and Text Analytics.

ML.net is a new open-source, cross-platform machine learning framework for developers. The technology behind AI features in Office and Windows has now been released as a project on Github.

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.

Learning resources

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.

AI, Machine Learning and Data Science Roundup: April 2018

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

An interface between R and Python: reticulate.

TensorFlow Hub: A library for reusable machine learning modules.

TensorFlow.js: Browser-based machine learning with WebGL acceleration. 

Download data from Kaggle with the Kaggle API.

Industry News

Tensorflow 1.7 supports the TensorRT library for faster computation on NVIDIA GPUs.

RStudio now provides a Tensorflow template in Paperspace for computation with NVIDIA GPUs.

Google Cloud Text-to-Speech provides natural speech in 32 voices and 12 languages.

Amazon Translate is now generally available.

Microsoft News

ZDNet reviews The Future Computed: "do read it to remind yourself how much preparation is required for the impact of AI".

Microsoft edges closer to quantum computer based on Majorana fermions (Bloomberg).

Microsoft’s Shared Innovation Initiative.

Azure Sphere: a new chip, Linux-based OS, and cloud services to secure IoT devices.

Microsoft’s Brainwave makes Bing’s AI over 10 times faster (Venturebeat).

Improvements for Python developers in the March 2018 release of Visual Studio Code.

A review of the Azure Data Science Virtual Machine with a focus on deep learning with GPUs.

Azure Media Analytics services: motion, face and text detection and semantic tagging for videos.

Learning resources

Training SqueezeNet in Azure with MXNet and the Data Science Virtual Machine.

Microsoft's Professional Program in AI, now available to the public as an EdX course

Run Python scripts on demand with Azure Container Instances.

How to train multiple models simultaneously with Azure Batch AI.

Scaling models to Kubernetes clusters with Azure ML Workbench.

A Beginner’s Guide to Quantum Computing and Q#.

A "weird" introduction to Deep Learning, by  Favio Vázquez.

Berkeley's Foundation of Data Science course now available online, for free.

Find previous editions of the monthly AI roundup here.

AI, Machine Learning and Data Science Roundup: March 2018

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.

R 3.4.4 released.

Google releases DeepLab on Github, a trained CNN model to assign semantic labels to every pixel of an image.

Google releases Lucid, a neural-network visualization library designed to help with the interpretability of vision systems.

Industry News

The Microsoft Cloud Service Map (PDF) lists AWS services with the equivalent Azure service. The site comparecloud.in does something similar for other cloud services as well.

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.

Hackernoon: Train Your Machine Learning Models on Google’s GPUs for Free — Forever.

A Preview of Bristlecone, Google’s New Quantum Processor.

Microsoft News

A podcast interview with Joseph Sirosh on the state of AI and Microsoft's Cloud AI services.

Announcing the AI Platform for Windows Developers, which will allow ONNX models to run natively on Windows-based devices. This blog post and video provides a detailed example.

New milestones for Microsoft AI services: Bing Entity Search and Custom Vision Service (preview) now in Azure Portal; Face API can now recognize up to a million distinct faces.

The Geo AI Data Science VM, including ESRI ArcGIS Pro and a land cover classification tutorial dataset, is now available in the Azure Marketplace.

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.

Learning resources

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.

Using deep learning to generate music: Part 1 and Part 2 on The AI Show on Channel 9.

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.

Find other editions of the monthly AI roundup here