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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November 1, 2018 — Jesse Friedman, Intern, Document and Media Systems

For the third year in a row, the annual Wolfram Technology Conference played host to a new kind of esport—the Livecoding Championship. Expert programmers competed to solve challenges with the Wolfram Language, with the goal of winning the championship tournament belt and exclusive bragging rights.

Wolfie with tournament belt

This year I had the honor of composing the competition questions, in addition to serving as live commentator alongside trusty co-commentator (and Wolfram’s lead communications strategist) Swede White. You can view the entire recorded livestream of the event here—popcorn not included.

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October 25, 2018 — Christopher Carlson, Senior User Interface Developer, User Interfaces

Images and machine learning were the dominant themes of submissions to the One-Liner Competition held at this year’s Wolfram Technology Conference. The competition challenges attendees to show us the most astounding things they can accomplish with 128 or fewer characters—less than one tweet—of Wolfram Language code. And astound us they did. Read on to see how.

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October 11, 2018 — Daniel Lichtblau, Symbolic Algorithms Developer, Algorithms R&D

Hero

Between October 1787 and April 1788, a series of essays was published under the pseudonym of “Publius.” Altogether, 77 appeared in four New York City periodicals, and a collection containing these and eight more appeared in book form as The Federalist soon after. As of the twentieth century, these are known collectively as The Federalist Papers. The aim of these essays, in brief, was to explain the proposed Constitution and influence the citizens of the day in favor of ratification thereof. The authors were Alexander Hamilton, James Madison and John Jay.

On July 11, 1804, Alexander Hamilton was mortally wounded by Aaron Burr, in a duel beneath the New Jersey Palisades in Weehawken (a town better known in modern times for its tunnels to Manhattan and Alameda). Hamilton died the next day. Soon after, a list he had drafted became public, claiming authorship of more than sixty essays. James Madison publicized his claims to authorship only after his term as president had come to an end, many years after Hamilton’s death. Their lists overlapped, in that essays 49–58 and 62–63 were claimed by both men. Three essays were claimed by each to have been collaborative works, and essays 2–5 and 64 were written by Jay (intervening illness being the cause of the gap). Herein we refer to the 12 claimed by both men as “the disputed essays.”

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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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July 24, 2018 — Jon McLoone, Director, Technical Communication & Strategy

Hero

A couple of weeks ago I shared a package for controlling the Raspberry Pi version of Minecraft from Mathematica (either on the Pi or from another computer). You can control the Minecraft API from lots of languages, but the Wolfram Language is very well aligned to this task—both because the rich, literate, multiparadigm style of the language makes it great for learning coding, and because its high-level data and computation features let you get exciting results very quickly.

Today, I wanted to share four fun Minecraft project ideas that I had, together with simple code for achieving them. There are also some ideas for taking the projects further.

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July 5, 2018 — Jon McLoone, Director, Technical Communication & Strategy

The standard Raspbian software on the Raspberry Pi comes with a basic implementation of Minecraft and a full implementation of the Wolfram Language. Combining the two provides a fun playground for learning coding. If you are a gamer, you can use the richness of the Wolfram Language to programmatically generate all kinds of interesting structures in the game world, or to add new capabilities to the game. If you are a coder, then you can consider Minecraft just as a fun 3D rendering engine for the output of your code.

Minecraft

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June 26, 2018 — Brian Wood, Lead Technical Marketing Writer, Document and Media Systems

In the past few decades, the process of redistricting has moved squarely into the computational realm, and with it the political practice of gerrymandering. But how can one solve the problem of equal representation mathematically? And what can be done to test the fairness of districts? In this post I’ll take a deeper dive with the Wolfram Language—using data exploration with Import and Association, built-in knowledge through the Entity framework and various GeoGraphics visualizations to better understand how redistricting works, where issues can arise and how to identify the effects of gerrymandering.

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