September 18, 2015 — Jonathan Wallace, Manager, Marketing Communications
After the first Republican presidential debate, we showed you how the WordCloud function in the Wolfram Language can be used to create compelling visualizations of what the candidates said.
This time around, Alan Joyce and Vitaliy Kaurov have done an even cooler analysis over at Wolfram Community, delving further into what words were used most frequently and what subjects the candidates had in common—and how they set themselves apart.
For example, check out the words uniquely used by each candidate in Wednesday’s debate below.
August 13, 2015 — Jonathan Wallace, Manager, Marketing Communications
A few days ago, Fox News hosted the first presidential primary debate of 2016. The candidates met onstage, vying for support from the GOP electorate. Among the cacophony and crafty messaging, a truly artful winner has emerged: word clouds.
The WordCloud function (1 of 5000+ functions) in the Wolfram Language allows anyone to visualize words, sized by their frequency in a text. With a mere line of code, you can create a compelling word cloud graphic from data, text, or URLs.
But don’t take my word for it; let’s make the WordCloud function earn your support.
July 2, 2015 — Jenna Giuffrida, Content Administrator, Technical Communications and Strategy Group
We’re always on the lookout for new ideas and ways of using the Wolfram Language that our users produce and choose to write about in their books. In this quarter, we have applications that bridge the gap between art and geometry, and demonstrate intuitive data analysis. In addition to writing books, Wolfram welcomes authors to submit articles for publication in The Mathematica Journal, our very own in-house periodical.
April 21, 2015 — Jenna Giuffrida, Content Administrator, Technical Communications and Strategy Group
What do genealogy, linear algebra, and the Raspberry Pi have in common? Not much, but they come together in this diverse and engaging assortment of books by the international community of authors employing Wolfram technologies in their work.
April 14, 2015 — Alan Joyce, Director, Content Development
Wolfram|Alpha’s Facebook analytics ranks high among our all-time most popular features. By now, millions of people have used Wolfram|Alpha to analyze their own activity and generate detailed analyses of their Facebook friend networks. A few years ago, we took data generously contributed by thousands of “data donors” and used the Wolfram Language’s powerful tools for social network analysis, machine learning, and data visualization to uncover fascinating insights into the demographics and interests of Facebook users.
At the end of this month, however, Facebook will be deprecating the API we relied on to extract much of this information.
April 2, 2015 — Vitaliy Kaurov, Technical Communication & Strategy
You may have heard that on March 20 there was a solar eclipse. Depending on where you are geographically, a solar eclipse may or may not be visible. If it is visible, local media make a small hype of the event, telling people how and when to observe the event, what the weather conditions will be, and other relevant details. If the eclipse is not visible in your area, there is a high chance it will draw very little attention. But people on Wolfram Community come from all around the world, and all—novices and experienced users and developers—take part in these conversations. And it is a pleasure to witness how knowledge of the subject and of Wolfram technologies and data from different parts of the world are shared.
March 17, 2015 — Arnoud Buzing, Director of Quality and Release Management
Recently Stephen Wolfram announced the Wolfram Data Drop, which is a great new tool to upload any type of data from any type of device. I’ll show how you can use the Wolfram Data Drop with a weather station you build using some basic hardware and a few lines of code. Once completed, your device will take temperature measurements every second for 60 seconds, and upload their average value to the Wolfram Data Drop every minute. This will give you 60 data points per hour and 1,440 data points per day. With this data you can use Wolfram Programming Cloud to understand how the temperature changes over time. You can find the exact times in a given day when the temperature was the highest or lowest, when the temperature changed the fastest, and maybe even use the data to make predictions! Can you beat your local weather station and make a prediction that is better?
March 11, 2015 — Brett Champion, Manager, Visualization
A few years ago we created a timeline of the history of systematic data and computable knowledge, which you can look at online. I wrote the code that placed events along the timeline, and then our graphic designers did the real work in deciding where to put the labels, choosing fonts and colors, and doing all the other things that go into creating a production-quality poster.
Fast-forward a bit, and last year we added NumberLinePlot to the Wolfram Language to visualize points, intervals, and inequalities. Once people started seeing the number lines, we began getting requests for similar plots, but with dates and times, so we decided it was time to tackle TimelinePlot.
March 4, 2015 — Stephen Wolfram
Where should data from the Internet of Things go? We’ve got great technology in the Wolfram Language for interpreting, visualizing, analyzing, querying and otherwise doing interesting things with it. But the question is, how should the data from all those connected devices and everything else actually get to where good things can be done with it? Today we’re launching what I think is a great solution: the Wolfram Data Drop.
When I first started thinking about the Data Drop, I viewed it mainly as a convenience—a means to get data from here to there. But now that we’ve built the Data Drop, I’ve realized it’s much more than that. And in fact, it’s a major step in our continuing efforts to integrate computation and the real world.
So what is the Wolfram Data Drop? At a functional level, it’s a universal accumulator of data, set up to get—and organize—data coming from sensors, devices, programs, or for that matter, humans or anything else. And to store this data in the cloud in a way that makes it completely seamless to compute with.
February 27, 2015 — Vitaliy Kaurov, Technical Communication & Strategy
Martin Handford can spend weeks creating a single Where’s Waldo puzzle hiding a tiny red and white striped character wearing Lennon glasses and a bobble hat among an ocean of cartoon figures that are immersed in amusing activities. Finding Waldo is the puzzle’s objective, so hiding him well, perhaps, is even more challenging. Martin once said, “As I work my way through a picture, I add Wally when I come to what I feel is a good place to hide him.” Aware of this, Ben Blatt from Slate magazine wondered if it’s possible “to master Where’s Waldo by mapping Handford’s patterns?” Ben devised a simple trick to speed up a Waldo search. In a sense, it’s the same observation that allowed Jon McLoone to write an algorithm that can beat a human in a Rock-Paper-Scissors game. As Jon puts it, “we can rely on the fact that humans are not very good at being random.”