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 12, 2018
Patrik Ekenberg, Applications Engineer, Wolfram MathCore
Anna Palmer, Intern, Wolfram MathCore

Life science teaches us to answer everything from “How can vaccines be used to indirectly protect people who haven’t been immunized?” to “Why are variations in eye color almost exclusively present among humans and domesticated animals?” You can now learn to answer these questions by using modeling with Wolfram’s virtual labs. Virtual labs are interactive course materials that are used to make teaching come alive, provide an easy way to study different concepts and promote student curiosity.

Cat with different eye colors

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June 26, 2018 — Brian Wood, Lead Technical Marketing Writer, Technical Communications and Strategy Group

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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May 24, 2018 — Carlo Giacometti, Kernel Developer, Algorithms R&D


Recognizing words is one of the simplest tasks a human can do, yet it has proven extremely difficult for machines to achieve similar levels of performance. Things have changed dramatically with the ubiquity of machine learning and neural networks, though: the performance achieved by modern techniques is dramatically higher compared with the results from just a few years ago. In this post, I’m excited to show a reduced but practical and educational version of the speech recognition problem—the assumption is that we’ll consider only a limited set of words. This has two main advantages: first of all, we have easy access to a dataset through the Wolfram Data Repository (the Spoken Digit Commands dataset), and, maybe most importantly, all of the classifiers/networks I’ll present can be trained in a reasonable time on a laptop.

It’s been about two years since the initial introduction of the Audio object into the Wolfram Language, and we are thrilled to see so many interesting applications of it. One of the main additions to Version 11.3 of the Wolfram Language was tight integration of Audio objects into our machine learning and neural net framework, and this will be a cornerstone in all of the examples I’ll be showing today.

Without further ado, let’s squeeze out as much information as possible from the Spoken Digit Commands dataset!

Spoken Digit Commands dataset

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March 2, 2018 — Brian Wood, Lead Technical Marketing Writer, Technical Communications and Strategy Group

Do you want to do more with data available on the web? Meaningful data exploration requires computation—and the Wolfram Language is well suited to the tasks of acquiring and organizing data. I’ll walk through the process of importing information from a webpage into a Wolfram Notebook and extracting specific parts for basic computation. Throughout this post, I’ll be referring to this website hosted by the National Weather Service, which gives 7-day forecasts for locations in the western US:

NOAA website image

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January 18, 2018
Eila Stiegler, Quality Analysis Manager, Wolfram|Alpha Quality Analysis
Jeffrey Bryant, Research Programmer, Wolfram|Alpha Scientific Content

I love to run. A lot. And many of my coworkers do too. You can find us everywhere, and all the time: on roads, in parks, on hills and mountains, and even running up and down parking decks, a flat lander’s version of hills. And if there is a marathon to be run, we’ll be there as well. With all of the internal interest in running marathons, Wolfram Research created this Marathon Viewer as a sponsorship project for the Christie Clinic Illinois Marathon.

Marathon Viewer website

Here are four of us, shown as dots, participating in the 2017 Illinois Marathon:

How did the above animation and the in-depth look at our performance come about? Read on to find out.

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January 4, 2018 — Michael Gammon, Blog Administrator, Document and Media Systems

Release features and logogram analysis

Whew! So much has happened in a year. Consider this number: we added 230 new functions to the Wolfram Language in 2017! The Wolfram Blog traces the path of our company’s technological advancement, so let’s take a look back at 2017 for the blog’s year in review.

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December 7, 2017 — Jon McLoone, Director, Technical Communication & Strategy

Computation is no longer the preserve of science and engineering, so I thought I would share a simple computational literary analysis that I did with my daughter.

Shell Lord of the Flies

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November 30, 2017 — Vitaliy Kaurov, Academic Director, Wolfram Science and Innovation Initiatives

When Does a Word Become a Word?

“A shot of expresso, please.” “You mean ‘espresso,’ don’t you?” A baffled customer, a smug barista—media is abuzz with one version or another of this story. But the real question is not whether “expresso” is a correct spelling, but rather how spellings evolve and enter dictionaries. Lexicographers do not directly decide that; the data does. Long and frequent usage may qualify a word for endorsement. Moreover, I believe the emergent proliferation of computational approaches can help to form an even deeper insight into the language. The tale of expresso is a thriller from a computational perspective.

X in expresso data analysis poster

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June 29, 2017 — Swede White, Lead Communications Strategist, Public Relations

Revolutionary social networks lead image

As the Fourth of July approaches, many in America will celebrate 241 years since the founders of the United States of America signed the Declaration of Independence, their very own disruptive, revolutionary startup. Prior to independence, colonists would celebrate the birth of the king. However, after the Revolutionary War broke out in April of 1775, some colonists began holding mock funerals of King George III. Additionally, bonfires, celebratory cannon and musket fire and parades were common, along with public readings of the Declaration of Independence. There was also rum.

Today, we often celebrate with BBQ, fireworks and a host of other festivities. As an aspiring data nerd and a sociologist, I thought I would use the Wolfram Language to explore the Declaration of Independence using some basic natural language processing.

Using metadata, I’ll also explore a political network of colonists with particular attention paid to Paul Revere, using built-in Wolfram Language functions and network science to uncover some hidden truths about colonial Boston and its key players leading up to the signing of the Declaration of Independence.

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