New York State has put out a call for volunteers with technology expertise to create “technology SWAT teams” to boost the state’s response to COVID-19. 

The official site for the COVID-19 Technology SWAT Team, as it’s called, is light on details but broad in scope. The state seeks volunteers with a wide range of tech skills: “professionals with experience in product management, software development / engineering, hardware deployment and end-user support, data science, operations management, design, or other similar areas.”

[ Also on InfoWorld: Kaggle calls data scientists to action on COVID-19 ]

While individuals can and are encouraged

TensorFlow remains the dominant AI modeling framework. Most AI (artificial intelligence) developers continue to use it as their primary open source tool or alongside PyTorch, in which they develop most of their ML (machine learning), deep learning, and NLP (natural language processing) models.

In the most recent O’Reilly survey on AI adoption in the enterprise, more than half of the responding data scientists cited TensorFlow as their primary tool. This finding is making me rethink my speculation, published just last month, that TensorFlow’s dominance among working data scientists may be waning. Neverthless, PyTorch remains a strong second choice,

We are all familiar with this chart by now.

KNIME

With the increasingly rapid spread of COVID-19 all around the world, we have read and heard much about how contagious it is; its impact on China, Iran, South Korea, Italy, and elsewhere; and the severe containment measures adopted. And we have seen many versions of this chart, monitoring the spread of the disease daily by counting the cumulative number of confirmed cases, deaths, and recoveries all over the world.

An informative breakdown of

By and large, if you need a database, you can reach for one of the big names—MySQL/MariaDB, PostgreSQL, SQLite, MongoDB—and get to work. But sometimes the one-size-fits-all approach doesn’t fit all. Every now and then your use case falls down between barstools, and you need to reach for something more specialized. Here are nine offbeat databases that run the gamut from in-memory analytics to key-value stores and time-series systems.

DuckDB

The phrase “SQL OLAP system” generally conjures images of data-crunching monoliths or sprawling data warehouse clusters. DuckDB is to analytical databases what SQLlite

The ggplot2 data visualization R package is extremely powerful and flexible. However, it’s not always easy to remember how to do every task – especially if you’re not a frequent user. How do you change the size of a graph title? How do you remove legend titles? My usual solution is to save RStudio code snippets for things I have trouble remembering. But there’s also a package that can help: ggeasy.

As the name says, the goal of ggeasy is to, well, make ggplot2 easy – or at least easier. It has what some people may find

Explainable machine learning is a sub-discipline of artificial intelligence (AI) and machine learning that attempts to summarize how machine learning systems make decisions. Summarizing how machine learning systems make decisions can be helpful for a lot of reasons, like finding data-driven insights, uncovering problems in machine learning systems, facilitating regulatory compliance, and enabling users to appeal — or operators to override — inevitable wrong decisions.

Of course all that sounds great, but explainable machine learning is not yet a perfect science. The reality is there are two major issues with explainable machine learning to keep in mind:

1. Some “black-box” machine