Revolutions

AI, Machine Learning and Data Science Roundup: January 2020

A 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 recently.

Open Source AI, ML & Data Science News

Pandas 1.0.0 is released, a milestone for the ubiquitous Python data frame package.

Tensorflow 2.1.0 is released, the last Tensorflow release to support Python 2.

Karate Club, a library of state-of-the-art methods for unsupervised learning on graph structured data, built on the NetworkX Python package.

OpenAI announces it is standardizing on PyTorch, a move likely to affect future releases of deep learning models released by the research institution.

sparklyr is now a Linux Foundation incubation project, and version 1.1 of the Spark-and-R interface is now available.

DiCE, an open-source Python library for Diverse Counterfactual Explanations for machine learning models, from Microsoft Research.

Industry News

GCP releases Auto Data Exploration and Feature Recommendation Tool, to speed up the process of preparing data for machine learning.

Google Dataset Search, a search engine for finding public datasets, is now generally available.

Metaflow, a "human-centric" framework for data science in Python, released as open source by Netflix and AWS.

RStudio reorganizes as a Public Benefit Corporation, with a charter to create free and open-source software for data science, scientific research, and technical communication.

Facebook open sources VizSeq, a Python toolkit that simplifies visual analysis on a wide variety of text generation tasks.

Open Data on AWS, a new service to share data and include it in the Registry of Open Data on AWS.

Microsoft News

Microsoft launches AI for Health, a $40M five-year program that provides grants, data science experts, technology, and other resources to tackle health issues with AI.

The Python extension for Visual Studio Code adds kernel selection for Notebooks, auto-activation of environments in the terminal, and other improvements.

Azure Machine Learning service now supports VMs with single root input/output virtualization and InfiniBand, to speed up the process of training large deep learning models like BERT.

Microsoft Video Indexer now supports multi-language speech transcription, and extraction of high-resolution key frames for use with Custom Vision.

Microsoft Translator API now supports custom translations, to incorporate domain-specific vocabulary and idioms.

InterpretML, an open-source Python package that implements Explainable Boosting Machine to train interpretable machine learning models and explain black-box systems, is now available as an Alpha release.

fairlearn 0.4.1, the latest release of the Python package for assessing the fairness and mitigating the observed unfairness of AI systems, is now available.

Learning resources

Computer Vision Recipes, a repository of examples and best practice guidelines for building computer vision systems. Includes Jupyter notebooks and utility functions based on PyTorch for several scenarios.

NLP Recipes, a repository of Natural Language Processing best practices and examples. Includes Jupyter notebooks implementing state-of-the-art methods and common scenarios that are popular among researchers and practitioners working on problems involving text and language.

The AI Now Institute 2019 report offers 12 recommendations on what policymakers, advocates and researchers can do to address the use of AI in ways that widen inequality.

Stanford's AI Index 2019 report tracks data, metrics and trends related to Artificial Intelligence research, applications, and impact.

Mathematics for Machine Learning, a book (by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong) to teach the mathematical concepts underlying AI implementations. (PDF available online.)

How to select algorithms for Azure Machine Learning, a guide to selecting machine learning methods by problem type and other requirements.

A tutorial on Automated Machine Learning for time series forecasting, with Azure Machine Learning.

People+AI Guidebook, resources for designing human-centered AI products from Google.

Data Project Checklist, a questionnaire to guide setting up a new data science project from Fast AI's Jeremy Howard.

PyTorch: An Imperative Style, High-Performance Deep Learning Library, the first full research paper on the popular framework.

Applications

AI Dungeon 2, a free-form text adventure game for mobile and browsers. Based on GPT-2, the interactive story can go in just about any direction.

Generative music playing in the lobby of a NYC hotel (created by Musician Björk in partnership with Microsoft) reacts to weather and migrating flocks of birds detected in a rooftop camera.

Facebook develops an AI system based on neural machine translation that can solve mathematical equations.

DialoGPT, a large-scale pretrained model from Microsoft Research that generates human-quality conversational responses.

VisualizeMNIST, a browser-based tool to interactively visualize the layers of a simple digit recognition model.

Find previous editions of the AI roundup here.

AI, Machine Learning and Data Science Roundup: November 2019

A 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 recently.

Open Source AI, ML & Data Science News

Python 3.8 is now available. From now on, new versions of Python will be released on a 12-month cycle, in October of each year.

Python takes the #2 spot in Github's annual ranking of programming language popularity, displacing Java and behind JavaScript.

PyTorch 1.3 is now available, with improved performance, deployment to mobile devices, "Captum" model interpretability tools, and Cloud TPU support.

The Gradient documents the growing dominance of PyTorch, particularly in research.

Keras Tuner, hyperparameter optimization for Keras, is now available on PyPI.

ONNX, the open exchange format for deep learning models, is now a Linux Foundation project.

AI Inclusive, a newly-formed worldwide organization to promote diversity in the AI community.

Industry News

Databricks announces the MLflow Model Registry, to share and collaborate on machine learning models with MLflow.

Flyte, Lyft's cloud-native machine learning and data processing platform, has been released as open source.

RStudio introduces Package Manager, a commercial RStudio extension to help organizations manage binary R packages on Linux systems.

Exploratory, a new commercial tool for data science and data exploration, built on R.

GCP releases Explainable AI, a new tool to help humans understand how a machine learning model reaches its conclusions.

Google proposes Model Cards, a standardized way of sharing information about ML models, based on this paper.

GCP AutoML Translation is now generally available, and the GCP Translation API is now available in Basic and Advanced editions.

GCP Cloud AutoML is now integrated with the Kaggle data science competition platform.

Amazon Rekognition adds Custom Labels, allowing users to train the image classification service to recognize new objects with as few as 10 training images per label.

Amazon Sagemaker can now use hundreds of free and paid machine learning models offered in Amazon Marketplace.

The AWS Step Functions Data Science SDK, for building machine learning workflows in Python running on AWS infrastructure, is now available.

Microsoft News

Azure Machine Learning service has released several major updates, including:

• a new web-based "studio" user interface (shown in this video),
• a new drag-and-drop workflow designer (video),
• a simple UI to apply automated machine learning to a dataset (video)
• built-in Notebook support,
• creation of end-to-end machine learning pipelines for training and deployment (video),
• auto-scaling compute instances for training and model deployment (video),
• a new Model Interpretability toolkit (video),
• a new asynchronous batch inferencing capability (video),
• integration with Azure Open Datasets (video),
• integration with Visual Studio Code,
• expanded PyTorch support, and
• new R SDK support (video).

Visual Studio Code adds several improvements for Python developers, including support for interacting with and editing Jupyter notebooks.

ONNX Runtime 1.0 is now generally available, for embedded inference of machine learning models in the open ONNX format.

Many new capabilities have been added to Cognitive Services, including:

• Personalizer, a new service to optimize user interfaces with reinforcement learning;
• Form Recognizer, a new service to extract structured data from printed or handwritten forms;
• A new Custom Neural Voice capability, to build a unique voice from a few minutes of training audio;
• Microsoft Video Indexer adds multi-language speech identification and transcription;
• Several services, including Face API, Custom Vision, and Translator Text are new available to Azure Free accounts

Bot Framework SDK v4 is now available, and a new Bot Framework Composer has been released on Github for visual editing of conversation flows.

SandDance, Microsoft's interactive visual exploration tool, is now available as open source.

Learning resources

An essay about the root causes of problems with diversity in NLP models: for example, "hers" not being recognized as a pronoun. 

Videos from the Artificial Intelligence and Machine Learning Path, a series of six application-oriented talks presented at Microsoft Ignite.

A guide to getting started with PyTorch, using Google Colab's Free GPU offer.

Public weather and climate datasets, provided by Google.

Applications

The Relightables: capture humans in a custom light stage, drop video into a 3-D scene with realistic lighting.

How Tesla builds and deploys its driving automation models with PyTorch (presentation at PyTorch DevCon).

OpenAI has released the full GPT-2 language generation model.

Spleeter, a pre-trained PyTorch model to separate a music track into vocal and instrument audio files.

Detectron2, a PyTorch reimplementation of Facebook's popular object-detection and image-segmentation library.

Find previous editions of the AI roundup here.

AI, Machine Learning and Data Science Roundup: September/October 2019

A 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 recently.

Open Source AI, ML & Data Science News

Tensorflow 2.0.0 has been released. This major update makes many changes to improve simplicity and ease of use. Tensorflow 1.x models will need to be upgraded for Tensorflow 2.0.

Keras 2.3.0 has been released, the first release of the high-level deep learning framework to support Tensorflow 2.0.

spaCy v2.2 has been released, with retrained natural language models and a new data augmentation system.

greta: an R package to fit complex Bayesian models using Tensorflow as the optimization engine.

Julia 1.2.0 has been released. This minor release of the scientific computing language includes no breaking changes.

Industry News

Facebook and Microsoft launch a research program to detect deepfakes, and Google releases a library of deepfake videos for research.

Facebook open sources Hydra, a framework for elegantly configuring complex Python applications.

Hyperparameter tuning has been added to Facebook's fastText text classifier library.

Google contributes MLIR, the compiler framework for Tensorflow graphs, to the LLVM Foundation.

Google open-sources differential-privacy, a C++ library of algorithms to produce aggregate statistics from sensitive datasets.

Microsoft News

The Vision AI Developer Kit, a Qualcomm IoT camera coupled with an Azure toolkit for AI vision applications, is now generally available.

PyTorch 1.2 is now supported in Azure: Azure ML Service, Azure Notebooks and Data Science Virtual Machine.

Azure Video Indexer adds support for detecting animated characters and multilingual identification and transcription.

Jupyter Notebooks are now generally available in Azure Cosmos DB, supporting all data models including Cassandra, MongoDB, SQL, Gremlin and Spark.

Twelve Cognitive Services are now included in Azure Free,  including Custom Vision, Text Analytics, Translator and Personalizer.

Machine Learning Notebooks for Azure ML Service, including a suite of examples of automated machine learning.

Visual Studio expands testing and debugging support for Python.

Python support in Azure Functions is now generally available.

Learning resources

Python for Beginners, a series of instructional videos and associated code samples from Microsoft.

Natural Language Processing Best Practices & Examples, a collection of Jupyter notebooks and utility functions for text classification, entity recognition and more, from Microsoft.

The CodeSearchNet Corpus, an open database of six million code samples released by Github, with the aim of improving semantic analysis of code and documentation.

Excavating AI, a philosophical essay on the issues related to image selection and labelling in training datasets for computer vision.

Video: How Backpropagation Works, part of Brendan Rohrer's End-to-End Machine Learning School series.

Recorded talks from the useR!2019 conference are available to view on the R Consortium channel.

A paper describes how AI agents may learn "superstitions" from reinforcement learning, recalling Skinner's superstitious pigeons.

Reference implementation for deploying ONNX models with Azure IoT edge, as demonstrated in this episode of the IoT Show.

Applications

Write with Transformer, a handy website from Hugging Face where you can generate text from powerful natural language models.

A new paper describes a method to generate a 3-D "Ken Burns effect" zoom from a single image.

An OpenAI project finds virtual agents learning unexpected strategies in a game of hide-and-seek.

CTRL: the Control Transformer Language model can be used to attribute the source of text, or generate synthetic text on a variety of themes. This new language model from Salesforce claims to be the largest released to date.

Startup Airdoc is using computer vision to detect chronic diseases including diabetes and hypertension from retinal scans of patients in China.

Fashion service Stitch Fix provides a machine-learning based service to recommend items that coordinate with a piece of clothing.

Find previous editions of the AI roundup here.

AI, Machine Learning and Data Science Roundup: July/August 2019

A mostly 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

StanfordNLP: a pure-Python package for grammatical analysis of sentences in over 50 languages.

New projects added to the PyTorch ecosystem: Skorch (scikit-learn compatibility), botorch (Bayesian optimization), and many others.

PyTorch-Transformers, a library of pretrained NLP models (BERT, GPT-2 and more) from HuggingFace.

R 3.6.1 is released.

Announcing mlr3, a new machine-learning framework for R.

Dash for R: a framework for building interactive web applications on both Python and R models.

R now supports the Apache Arrow data interchange format via the arrow package on CRAN.

A Nature article about Julia, the scientific computing language.

Industry News

Ethical Aspects of Autonomous and Intelligent Systems,  the position statement on AI ethics from the IEEE.

ParlAI, a Python framework and repository of data sets and reference models which Facebook is using to address weaknesses in chatbots today.

Facebook releases ELI5, a set of scripts to download paired questions and answers from the ELI5 subreddit, along with models for question-answering research.

Facebook open sources CraftAssist, a platform for creating AI assistants that can manipulate the Minecraft world via chat conversations.

Lyft releases the Level 5 Dataset, the largest publicly-released dataset for autonomous driving models.

Uber releases the Plato Research Dialogue System, a framework for creating, training, and evaluating conversational AI agents.

Uber releases Ludwig v0.2. This update to the code-free deep learning toolbox adds support for Comet.ml and BERT models, and a deployment server.

EfficientNet-EdgeTPU, a family of image classification models optimized to run on Google's low-power Edge TPU  chips.

The What-If Tool is now available within GCP AI Platform, to visualize the impact of variables on Tensorflow model outputs.

IBM introduces AI Explainability 360, a suite of open-source tools for machine learning interpretability.

Microsoft News

Microsoft invests $1B in Open AI to spur research in general artificial intelligence.

AI for Cultural Heritage, a new pillar in Microsoft's $125M AI for Good portfolio.

VS Code adds support for debugging Python cells in Jupyter Notebooks.

Azure Form Recognizer (preview) adds the ability to extract structured data from scanned receipts.

Cognitive Services Text Analytics can now provide sentiment scores for sentences and entire documents, and with improved accuracy.

Video Indexer now allows the language model to be customized, so that specialized vocabulary (jargon) can be automatically detected and transcribed.

Microsoft Machine Learning Server 9.4 is now available, with updates to the open-source engines for operationalizing R and Python.

AzureR, a family of packages for creating, managing and monitoring Azure services from the R language.

Azure Machine Learning Services adds a command-line interface complementing the existing Python SDK, to support MLOps workflows via the CLI.

Microsoft open-sources scripts and notebooks to pre-train and finetune the BERT natural language model with domain-specific texts.

Learning resources

A beginner's introduction to recurrent neural networks from Victor Zhou, with a from-scratch implementation of a sentiment analysis RNN in Python.

Two new, free courses from fast.ai: Deep Learning from the Foundations and A Code-First Introduction to Natural Language Processing.

Statistics with Julia, a draft (but comprehensive) book on statistical analysis in the Julia language.

Keynote presentations from the useR!2019 conference. Contributed presentations from the annual R conference coming soon.

Model interpretability with automated machine learning in Azure Machine Learning service.

Tutorial: Deploying Azure ML Service models to Azure Functions for inference.

A comparison of pre-trained text-recognition services from Amazon, Google and Microsoft.

What's the difference between deep learning, machine learning, and AI?

A tutorial on pre-training BERT models with Google Cloud TPUs.

Debugging ML models: dealing with information leakage and data bias.

Applications

Stitch Fix has built a centralized experimentation platform, to standardize and automate the process of A/B testing.

IMT Atlantique, a Microsoft AI for Earth grantee, uses satellite remote sensing data to monitor changes in ocean levels.

How AI is helping track endangered species.

The Climate Modeling Alliance uses Julia and CUDA to simulate climate models.

Parrotron: a Google Research project to facilitate understanding of speech-impaired people by devices and other people.

Find previous editions of the monthly AI roundup here.

AI, Machine Learning and Data Science Roundup: June 2019

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 2.0 beta is now available, featuring first-class Keras support and with eager execution enabled by default.

MLflow 1.0, the open source platform for managing end-to-end machine learning lifecycles from Databricks, is now available.

Industry News

Facebook open sources PyRobot, a framework that enables AI researchers and students to control a physical robot with a few lines of Python code.

Databricks Connect, a new universal Spark client library with bindings in Python, Scala, Java and R to manage data and compute in hosted Databricks environments.

Google introduces Deep Learning Containers (beta), which come with a Jupyter environment pre-configured to interact with the GCP AI platform.

GCP AI Platform Notebooks, Google's managed JupyterLab service, now support the R language (beta).

Amazon releases GluonTS, an open-source Python toolkit for building deep-learning based time series models.

Microsoft News

Microsoft introduces Immersive Reader, a new Azure Cognitive Service that allows developers to provide assisted reading experiences to non-native speakers and people with dyslexia, ADHD, or visual impairment.

Microsoft open-sources TensorWatch, a Jupyter-based debugging and visualization tool to observe machine learning and deep learning training in process.

Azure Machine Learning Service introduces new time series forecasting capabilities.

Power BI adds AI capabilities: cognitive text and image analysis, and consumption of models from Azure ML Services.

InterpretML, a new open-source Python package for training interpretable models and explaining black-box systems from Microsoft Research.

MLOps, an extension to Azure DevOps for orchestration and management of models in Azure ML Service, such as this Video Anomaly Detection example.

Learning resources

ONNX.JS: demos with code of running GPU-accelerated inference on ONNX models in the browser.

Tutorial: using the new Automated Machine Learning web user interface in the Azure portal.

Video recordings from the 2019 New York R Conference are available to view.

Feature Engineering and Selection, a new book Max Kuhn and Kjell Johnson with implementations in R.

An overview of datatable, Python's version of the R package for efficient, multithreaded processing of out-of-memory datasets.

Learn R and Python in Parallel, an online book by Nailong Zhang useful for learning one language based on your knowledge of the other.

Mastering Shiny, a new book in progress by Joe Cheng, developer of the interactive UI framework for R.

Exploring Data with R, an introduction to R from MSDN Magazine.

Applications

A new paper by leading researchers suggests 10 domains where AI could be applied to address the threat of climate disruption.

Uber uses causal inference in product development, operations analysis and improving user experiences.

The Future Computed: AI and Manufacturing, a 135-page Microsoft e-book featuring applications of AI in manufacturing.

Try out GauGAN, NVIDIA's style transfer algorithm that converts a crude finger-painting into a realistic landscape.

MASS, a new pre-training method that outperforms BERT and GPT in generating realistic natural language text.

FUNIT (Few-Shot Unsupervised Image-to-Image Translation), a NVIDIA research project used to convert images of one animal (or even a human face) to other breeds/species.

Neural Code Search, a Facebook model for using natural language search to find relevant computer code.

Google Research Football, an open-source reinforcement learning simulator to teach an AI agent to play a computer soccer game.

The Dalí Museum in Florida creates an interactive simulation of the iconic artist from historical footage (video).

Editor's note: The monthly roundup will return in August. Find previous editions of the monthly AI roundup here.

AI, Machine Learning and Data Science Roundup: May 2019

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 1.1 is now available, with new support for Tensorboard and improvements to distributed training and JIT compilation.

JupyterHub 1.0 is released, a milestone for the multi-user Jupyter Notebook server.

matplotlib 3.1 is released, adding several improvements to the Python data visualization library.

Python is now included in Windows 10, with updates available via the Microsoft Store.

FastBert, a simple PyTorch interface for training text classifiers based on the popular language representation model BERT, is released.

torchvision 0.3, the PyTorch library of datasets and tools for computer vision, adds new models for semantic segmentation and object detection.

ML.NET 1.0, the open-source cross-platform machine learning framework for .NET developers, is now available.

ONNX Runtime 0.4 is released, adding support for Intel and NVIDIA accelerators to further reduce latency for deployed neural networks.

R 3.6.0 is released, with many new capabilities and improved memory usage and performance.

Industry News

The Wolfram Engine, featured in Mathematica and including a suite of algorithms for visualization, machine learning, NLP and more, is now available free for development (license required for production).  

Facebook releases Pythia, a new open-source deep learning framework based on PyTorch, for multitasking in the vision and language domain.

Google Cloud AutoML Natural Language provides an interactive UI to classify content and build a predictive ML model without coding.

Google Cloud TPU Pods, cloud-based clusters that can include more than 1,000 TPU chips as an "ML supercomputer", are now publicly available in beta. NVIDIA T4 GPUs are also now generally available in GCP.

Snips open-sources Tract, an embedded neural network inference engine designed for wake-word detection by virtual assistant devices.

Hewlett Packard Enterprises announced plans to acquire Cray, the supercomputer company.

Intel announces several open-source initiatives for AI and cloud technologies, including a Deep Learning Reference Stack optimized for Intel chipsets.

Microsoft News

Azure Machine Learning Service adds new MLOps capabilities, providing version control, audit trails, and packaging, deployment and monitoring support for machine learning models via an Azure DevOps extension. Also, model deployment to FPGA is now generally available in Azure (and additionally in preview for Databox Edge).

Further updates to Azure Machine Learning Service are now in preview, including a new drag-and-drop visual interface, a new form-based UI for automated machine learning, model interpretability, and hosted Python notebooks.

Azure Cognitive Services has added many new capabilities, highlights of which include:

• A new category of service: Decision. The services include Content Moderator, Anomaly Detector and a new service called Personalizer (in preview).
• Conversation Transcription, a new Speech service that can discriminate and transcribe audio from multiple speakers in real time.
• Ink Recognizer, a new Vision service that allows developers to add digital handwriting recognition to apps.
• Additional Cognitive Services offering container support: Anomaly Detector, Speech-to-Text and Text-to-Speech.
• Azure Search, which includes many cognitive skills including image recognition and language comprehension, is now generally available.
• Form Recognizer, a new knowledge discovery service that extracts key-value pairs and tables from forms in scanned and PDF documents, now in preview.

Visual Studio Code adds Remote Development, allowing use of remote Python workspaces over SSH, in Docker containers, and in Windows Subsystem for Linux. Other recent Python support improvements include Intellisense in the console and enhancements to the Python Language Server.

Azure Data Explorer now supports queries with custom Python code, as well as integration with Spark.

Azure SQL Database Edge, a small-footprint data engine with support for Python, R and Spark and optimized for edge devices and time-series data, is now in private preview.

Microsoft announces an end-to-end toolchain for autonomous systems (in preview), which developers can use to simulate and build robots and other AI-driven autonomous devices.

Microsoft R Open 3.5.3 is released; new R packages AzureKusto (interface to Data Explorer) and AzureGraph (interface to the Microsoft Graph), are available.

Learning resources

A beginner's tutorial on training a convolutional neural network, using only Python and numpy.

Rules of Machine Learning: Google's list of best practices for developers looking to create applications with machine learning capabilities. 

ODSC suggests 25 public data sets to get started with machine learning, spanning text, images, and tabular data.

Foundations of Data Science, a free book by Avrim Blum, John Hopcroft and Ravi Kannan with a focus on matrix decompositions and associated ML techniques.

Azure Open Datasets, a collection of curated public datasets easily accessible to Azure ML services.

Open Images v5, a large collection of annotated images including segmentation masks for 2.8 million objects in 350 categories, has been released by Google.

Microsoft Learn now offers several modules with free training for AI engineers and data scientists.

Six principles behind health data-driven organizations, a useful resource on building a data science culture by Francesca Lazzeri.

Applications

How Python is used at Netflix for personalization, machine learning, experimentation, statistical analysis and more.

Google develops a method to infer depth maps for video by using "mannequin challenge" videos as training data.

Samsung researchers develop a method of animating a single photograph of a person as a realistic talking head (video).

Google's "Translatotron", a speech-to-speech translation model that translates speech audio into a second language while retaining the original speaking voice.

AI-based applications help children with disabilities bridge language gaps.

Facebook researchers develop a method to synthesize full-body video of a person performing actions animated in real time under joystick control, based only on real source video.

LaLiga and BMW are using the Azure Bot Framework SDK to deliver specialized personal assistant applications.

Find previous editions of the monthly AI roundup here.

AI, Machine Learning and Data Science Roundup: April 2019

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

Facebook open-sources PyTorch-BigGraph for producing embeddings for graphs where the model is too large to fit in memory. Also published: an embeddings graph for 50 million Wikipedia concepts.

Visual Studio Code expands Python support, including a new variable explorer and data viewer, improved debugging capabilities, and real-time collaboration via Live Share.

Pyright, a static type-checker for Python, available as a command-line tool and a VS Code extension.

Frequently Asked Questions about Tensorflow 2.0, now in alpha testing.

VoTT, an open source annotation and labeling tool for image and video assets, has been updated to version 2.

Industry News

Google announces AI Platform (beta), an integrated platform for developers to create AI capabilities and deploy them to GCP or on premises.

New enterprise AI solutions from Google (in beta): Document Understanding AI, Contact Center AI, and Recommendations AI for retail.

Google made a number of other of AI-related announcements at Google Next, including new ML capabilities in BigQuery, AutoML Tables to generate an ML model predicting a data column with one click, updated pre-trained models for vision and natural language services, general availability of Cloud TPU v3, and more.

NVIDIA Tesla G4 GPUs are now available in Google Colab for faster training with larger models.

Microsoft News

Microsoft commits to host the world's leading environmental data sets on Azure as part of a broader sustainability initiative.

Microsoft's research in Machine Teaching: the process of augmenting models with human input rather than using data alone.

Anomaly Detector, a new Azure Cognitive Service to detect unusual values in time series data, is now in preview.

Azure Custom Vision, the transfer learning service for image classification with user-provided labels, is now generally available.

Azure Video Indexer can now be trained to recognize specific people in video from user-provided photographs.

Azure Cognitive Services are now accessible in Apache Spark workloads, via Spark ML pipelines.

You can now deploy Tensorflow models to Azure Data Box Edge with Azure ML Service and ONNX, for local inference on high-performance FPGA chips.

Azure HDInsight now supports Apache Hadoop 3.0, and updated versions of Hive, HBase, and Phoenix.

Managed MLflow is now generally available in Azure Databricks, to track experiments and manage models and projects. Microsoft has also joined the open-source MLflow project.

PowerBI now offers AutoML, to enable business analysts to build machine learning models from data without coding.

Learning resources

Advanced Natural Language Processing with spaCy, a free online course from Ines Montani, a core developer of the Python package. These tutorial notebooks on sentiment analysis by Ben Trevett also make use of spaCy.

The Economist reworks some of their worst published charts, providing a lesson in better data visualization.

Discriminating Systems: Gender, Race and Power in AI: Results from a year-long study on diversity in the AI sector from the AI Now Institute.

A tutorial on creating a serverless HTTP endpoint with Python and Azure Functions using Visual Studio Code.

Tutorial: incrporating predictions from an R model in Power BI report.

AWS Machine Learning University: free access to the curriculum used to teach Amazon staff about ML on AWS.

An introduction to reinforcement learning, with AWS RoboMaker.

Applications

Ganvatar: an impressive demonstration of synthesizing faces along semantic vector axes (age, gender, happiness).

Using the time series and outlier detection features of the Kusto Query Language to detect cyber threats with Azure Sentinel.

Forecasting orange juice sales in a grocery chain (Jupyter Notebook), using automated machine learning in Azure ML Service.

Seek, a smartphone app that uses computer vision to identify plant and animal species in real time.

Find previous editions of the monthly AI roundup here.

AI, Machine Learning and Data Science Roundup: March 2019

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.

AI, Machine Learning and Data Science Roundup: February 2019

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, the open interchange format for AI models, updates to version 1.4.0 with support for models larger than 2Gb.

PT-BERT, a PyTorch implementation of Google's BERT language representation model, adds new pre-trained language models: GPT and Transformer-XL.

Stanford University has released StanfordNLP, a natural language analysis package for Python with pre-trained models for 53 languages.

The Python Foundation releases Python 3.7 on the Windows 10 App Store.

Industry News

Amazon offers recommendations to policymakers on the use of facial recognition technology and calls for regulation of its use.

AWS open-sources the Neo-AI project, a machine learning compiler and runtime that tunes Tensorflow, PyTorch, ONNX, MXNet and XGBoost models for performance on edge devices. 

Google introduces FEAST, an open-source "feature store" for managing and discovering features in machine learning models.

Google releases Natural Questions, a corpus of 300,000 questions and human-annotated answers, for question-answering research.

Uber open-sources Ludwig, a platform for training and testing deep learning models without programming, simply by specifying input and output variables in data.

Microsoft News

Microsoft has joined the SciKit-learn Consortium as a Platinum member.

Azure Data Explorer, a high-performance service for real-time analysis of streaming data, is now generally available.

HDInsight Tools for VSCode, providing a lightweight code editor for HDInsight PySpark and Hive batch jobs, is now available.

Azure Cognitive Services can now be used with a unified API key, and in more regions and with new certifications including HIPAA BAA.

An in-depth InfoWorld review of Azure ML services.

Learning resources

Papers with Code, a repository of machine learning papers with associated implementation code and evaluation metrics.

Practical Deep Learning for Coders, the popular free course from fast.ai, has been updated with all-new content. Microsoft Azure and GCP provide pre-configured environments with the required tools, data and materials.

Recommenders Hub: an open source GitHub repository containing Microsoft’s best practices and state-of-the-art algorithms for building recommendation systems.

Presentation videos and workshop materials from the recent RStudio conference are available.

Tutorial: running trained deep learning models on Apple devices, by converting CoreML exported from PyTorch into ONNX.

Tutorials from the MIT Deep Learning courses: Deep learning basics, Driving scene segmentation, DeepTraffic deep reinforcement learning.

Reference Architecture: Batch scoring of Spark models on Azure Databricks.

Applications

Facebook develops a predictive model for global electricity availability, publishes the output and some code.

The British Broadcasting Corporation releases an R package and a guide for data journalists on creating data visualizations in the BBC News style.

TWIMLAI's podcast series on Microsoft's AI for Earth , AI for Accessibility and AI for Humanitarian Action programs.

A profile of Heather Dowdy, the head of Microsoft's AI for Accessibility program.

Find previous editions of the monthly AI roundup here.

AI, Machine Learning and Data Science Roundup: January 2019

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

Preview of Tensorflow 2.0 (the public preview is expected "early this year").

pcLasso, an R package implementing a new method for supervised learning described by co-author Rob Tibshirani as "principal components regression meets the lasso".

R 3.5.2 has been released.

Industry News

A retrospective of Google Research activities in AI in 2018.

Google Cloud Platform now supports R jobs (via SparkR) in Cloud DataProc.

GCP App Engine support for Python 3.7 now generally available.

Amazon SageMaker now comes preconfigured with support for SciKit-learn.

Microsoft News

Microsoft Professional Program for Data Analysis, a new online course and certification. Other MPP tracks include Data Science and Artificial Intelligence.

[video] A single API key can now be used to access Cognitive Services APIs for language, vision and search.

AzureR, a suite of packages for interfacing with storage, virtual machines, containers and other Azure services from the R language, is now available on CRAN.

Learning resources

E-book by TWIML AI host Sam Charrington: Kubernetes for Machine Learning, Deep Learning and AI (requires free sign-up).

E-book by Patrick Hall and Navdeep Gill: Introduction to Machine Learning Interpretability (requires free registration).

An in-depth introduction to convolutional neural networks, from Ars Technica: How computers got shockingly good at recognizing images.

An on-line course from Databricks, Deep Learning Fundamental Series, with a focus on Keras and TensorFlow.

Getting Started with TensorFlow Probability, from R, a blog post from RStudio.

What can Neural Networks Learn?, an approachable look at the inner workings of neural network classifiers from Brandon Rohrer.

A blog post describing the computational graph concepts behind TensorFlow, including an illustrative implementation of core TensorFlow operations in numpy.

Applications

Using computer vision to monitor shelf stocking policies, a three-part series on complex image classification: Part 1, Part 2, Part 3.

[video] Building a pet breed identification application via transfer learning with Azure ML Service and Python. (Github repo here.)

Find previous editions of the monthly AI roundup here.