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.
Tensorflow 2.0 beta combination with Keras support is an interesting combination in AI, very worth experimenting with.
Posted by: toan chuc cao thu | July 01, 2019 at 22:40
really really helpful and informative. Thanks
Posted by: Asad Ali | July 06, 2019 at 01:05