February 14, 2019 — Toni Schindler, Consultant, Wolfram|Alpha Scientific Content

This post discusses new Wolfram Language features from the upcoming release of Version 12. Copyable input expressions and a downloadable notebook version of this post will be available when Version 12 is released.

Imagine you could import any website to obtain meaningful data for further processing, like creating a diagram, highlighting places on a map or integrating with other data sources. What if you could query data on the web knowing only one simple query language? That’s the vision of the semantic web. The semantic web is based on standards like the Resource Description Framework (RDF) and SPARQL (a query language for RDF). The upcoming release of Version 12 of the Wolfram Language introduces experimental support for interacting with the semantic web: you will be able to Import and Export a variety of RDF data formats as well as query remote SPARQL endpoints and in-memory data using either a query string or a symbolic representation of SPARQL.

Computational Musicology Using Wikidata and MusicBrainz

Image Map

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January 24, 2019 — Jacob Wells, Technical Specialist, European Sales

Do you select a bottle of wine based more on how fancy the sleeve is than its price point? If so, then you’re like me, and you may be looking to minimize the risk of wishful guesses. This article may provide a little rational weight to your purchasing decisions.

Due to my research using the Wolfram Language, I can now mention the fact that if you are spending less than $40 on a random bottle of wine, you have a less than 0.1% chance of finding a 95+-rated wine. I could also perhaps reel off some flavors and characteristics of wines from Tuscany, for example—cherry, fruit, spice and tannins. My aim is to show you how I took a passing idea of mine and brought it to fruition using the Wolfram Language.

How I became a wine expert using the Wolfram Language

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January 3, 2019 — Wolfram Blog Team

Mark Greenberg is a retired educator and contributor to the Tech-Based Teaching blog, which explores the intersections between computational thinking, edtech and learning. He recounts his experience adapting old game code using the Wolfram Language and deployment through the Wolfram Cloud.

Chicken Scratch is an academic trivia game that I originally coded about 20 years ago. At the time I was the Academic Decathlon coach of a large urban high school, and I needed a fun way for my students to remember thousands of factoids for the Academic Decathlon competitions. The game turned out to be beneficial to our team, and so popular that other teams asked to buy it from us. I refreshed the questions each year and continued holding Chicken Scratch tournaments at the next two schools I worked in.

Chicken Scratch

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December 28, 2018 — Stephen Wolfram

Wolfram’s Spikey logo—a flattened rhombic hexecontahedron

Spikeys Everywhere

We call it “Spikey”, and in my life today, it’s everywhere:

Stephen Wolfram surrounded by Spikeys

It comes from a 3D object—a polyhedron that’s called a rhombic hexecontahedron:

3D rhombic hexecontahedron

But what is its story, and how did we come to adopt it as our symbol?

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November 13, 2018 — Jesika Brooks, Blog Editor - EduTech, Public Relations

This post was initially published on Tech-Based Teaching, a blog about computational thinking, educational technology and the spaces in between. Rather than prioritizing a single discipline, Tech-Based Teaching aims to show how edtech can cultivate learning for all students. Past posts have explored the value of writing in math class, the whys and hows of distant reading and the role of tech in libraries.



It’s November, also known as National Novel Writing Month (NaNoWriMo). This annual celebration of all things writerly is the perfect excuse for would-be authors to sit down and start writing. For educators and librarians, NaNoWriMo is a great time to weave creative writing into curricula, be it through short fiction activities, campus groups or library meet-ups.

During NaNoWriMo, authors are typically categorized into two distinct types: pantsers, who “write by the seat of their pants,” and plotters, who are meticulous in their planning. While plotters are likely writing from preplanned outlines, pantsers may need some inspiration.

That’s where Wolfram|Alpha comes in handy.

Wolfram|Alpha

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September 20, 2018
Greg Hurst, Consultant, Wolfram|Alpha Math Content
Matt Gelber, Postdoctoral Researcher, University of Illinois at Urbana-Champaign

In past blog posts, we’ve talked about the Wolfram Language’s built-in, high-level functionality for 3D printing. Today we’re excited to share an example of how some more general functionality in the language is being used to push the boundaries of this technology. Specifically, we’ll look at how computation enables 3D printing of very intricate sugar structures, which can be used to artificially create physiological channel networks like blood vessels.

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September 11, 2018 — Jon McLoone, Director, Technical Communication & Strategy

Having a really broad toolset and an open mind on how to approach data can lead to interesting insights that are missed when data is looked at only through the lens of statistics or machine learning. It’s something we at Wolfram Research call multiparadigm data science, which I use here for a small excursion through calculus, graph theory, signal processing, optimization and statistics to gain some interesting insights into the engineering of supersonic cars.

Car gauges

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August 23, 2018 — Brian Wood, Lead Technical Marketing Writer, Document and Media Systems

As the technology manager for Assured Flow Solutions, Andrew Yule has long relied on the Wolfram Language as his go-to tool for petroleum production analytics, from quick computations to large-scale modeling and analysis. “I haven’t come across something yet that the Wolfram Language hasn’t been able to help me do,” he says. So when Yule set out to consolidate all of his team’s algorithms and data into one system, the Wolfram Language seemed like the obvious choice.

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August 16, 2018 — Erez Kaminski, Wolfram Technology Specialist, Wolfram Technology Group

For the past two years, FOALE AEROSPACE has been on an exhilarating journey to create an innovative machine learning–based system designed to help prevent airplane crashes, using what might be the most understated machine for the task—the Raspberry Pi. The system is marketed as a DIY kit for aircraft hobbyists, but the ideas it’s based upon can be applied to larger aircraft (and even spacecraft!).

FOALE AEROSPACE is the brainchild of astronaut Dr. Mike Foale and his daughter Jenna Foale. Mike is a man of many talents (pilot, astrophysicist, entrepreneur) and has spent an amazing 374 days in space! Together with Jenna (who is currently finishing her PhD in computational fluid dynamics), he was able to build a complex machine learning system at minimal cost. All their development work was done in-house, mainly using the Wolfram Language running on the desktop and a Raspberry Pi. FOALE AEROSPACE’s system, which it calls the Solar Pilot Guard (SPG), is a solar-charged probe that identifies and helps prevent loss-of-control (LOC) events during airplane flight. Using sensors to detect changes in the acceleration and air pressure, the system calculates the probability of each data point (an instance in time) to be in-family (normal flight) or out-of-family (non-normal flight/possible LOC event), and issues the pilot voice commands over a Bluetooth speaker. The system uses classical functions to interpolate the dynamic pressure changes around the airplane axes; then, through several layers of Wolfram’s automatic machine learning framework, it assesses when LOC is imminent and instructs the user on the proper countermeasures they should take.

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August 9, 2018 — Swede White, Lead Communications Strategist, Public Relations

Code for America’s National Day of Civic Hacking is coming up on August 11, 2018, which presents a nice opportunity for individuals and teams of all skill levels to participate in the Safe Drinking Water Data Challenge—a program Wolfram is supporting through free access to Wolfram|One and by hosting relevant structured datasets in the Wolfram Data Repository.

According to the state of California, some 200,000 residents of the state have unsafe drinking water coming out of their taps. While the Safe Drinking Water Data Challenge focuses on California, data science solutions could have impacts and applications for providing greater access to potable water in other areas with similar problems.

The goal of this post is to show how Wolfram technologies make it easy to grab data and ask questions of it, so we’ll be taking a multiparadigm approach and allowing our analysis to be driven by those questions in an exploratory analysis, a way to quickly get familiar with the data.

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