Data science and AI have been the force behind the rise of Python adoption .

According to the State of the Octoverse report, Python has beaten out Java to become the second most used language after JavaScript.

The growth of Python can be correlated with a wide range of tools and frameworks. The most notable ones are the surge of Jupyter Notebook, TensorFlow, and NLTK. Jupyter Notebook has risen over 100% YoY for last thee years. Besides, TensorFlow and NLTK were among the most popular projects, thereby increasing the number of contribution from Python users; TensorFlow was fifth in the number of contribution with 9.9k.

Here’s an interesting project to use AI to deal with nuclear waste.

The researchers leveraged physics-informed generative adversarial networks (“GANs”).

Nuclear waste storage sites are a subject of intense controversy and debate; nobody wants the radioactive remnants in their backyard. Now, a collaboration between Berkeley Lab, Pacific Northwest National University (PNNL), Brown University and Nvidia has yielded new insights for nuclear waste remediation using the joint power of supercomputing […]

The new Azure Machine Learning studio is an immersive web experience for managing the end-to-end lifecycle.

The new web experience brings all of the data science capabilities for data scientists and engineers, across diverse skill levels from no code authoring, to code-first experiences, and their ML assets together in a single web pane to streamline machine learning.

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Most of the time, your apps are running happily and healthily.

However, when things go wrong, you can use Genie and Navigator in App Service Diagnostics to figure out what’s wrong.

Jen Lee joins Scott Hanselman to show how the App Service Diagnostics interactive interface (Genie) guides you intelligently through the self-troubleshooting experience. Navigator, enables you to find potentially breaking changes to your app and its dependencies.

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If you have large data sets that seem too big to map, then watch this IoT show to learn how to cluster data in Azure Maps so your users can rapidly extract insights from very large data sets.

Ricky Brundritt, Principal Technical Program Manager, Azure Maps, takes you on a historical journey from grid-based clustering to radius-based clustering. You’ll learn how the power of the open source community has contributed to the supercluster library which Azure Maps leverages extensively. Watch Ricky demo and step through Azure Maps code for clustering using large data sets of shipwrecks and earthquakes. Learn how to use cluster aggregates to perform calculations based on properties of the children of each cluster. Ricky wraps up with a demo visualizing clustered map data in the form of pie charts—again, to enable your users to extract insights quickly.

Access demo source code here: https://aka.ms/AzureMapsSamples

Face-recognition technology is quickly becoming more common. Should we be concerned?

It’s being used to unlock phones, clear customs, identify immigrants and solve crimes. In the Video Op-Ed above, Clare Garvie demands the United States government hit pause on face recognition. She argues that while this convenient technology may seem benign to those who feel they have nothing to hide, face recognition is something we should all fear. Police databases now feature the faces of nearly half of Americans — most of whom have no idea their image is there. The invasive technology violates citizens’ constitutional rights and is subject to an alarming level of manipulation and bias.

Microgravity can be used to unlock old materials and make new ones in ways that can’t be replicated on Earth. Private companies know this, and are leading the charge toward the next gold rush. But can they turn low Earth orbit into a home for the next industrial revolution?