December 12, 2019 — Ishwarya Vardhani, Education Partnerships Manager, Partnerships
Happy Hour of Code! There’s no better reason to start learning or continue honing your programming skills than the Hour of Code, an annual celebration of computer science during Computer Science Education Week. While we like to think that every hour is a great hour to code, we look forward to the Hour of Code event as an opportunity to come together and share some of our best Wolfram Language resources for students. Since its 2013 launch, the Hour of Code has been an immense success, introducing valuable programming skills to millions of students. So with this year’s Hour already underway, let’s take a look at the ways you can get started!
November 13, 2019 — Jesse Friedman, Software Engineer, Engine Connectivity Engineering
Two weeks ago, I had the pleasure of returning as a commentator for the fourth annual Livecoding Championship, a special event held during the 2019 Wolfram Technology Conference. We had such an incredible turnout this year, with 27 total participants and 14 earning at least one point! Conference attendees and Wolfram staff competed for the title of Livecoding Champion, with seven questions (plus one tiebreaker!) challenging their speed, agility and knowledge of the Wolfram Language. It was a high-spirited battle for first place, and while I had prepared “answer key” solutions in advance, I always look forward to the creativity and cleverness that competitors demonstrate in their wide range of approaches to each question.
By popular request, in addition to revisiting the questions, I’ll walk you through how competitors reached their solutions and earned their points, as a kind of “study guide” for next year’s aspiring champions. So hold on to your keyboards—we’re going in!
November 7, 2019 — Paritosh Mokhasi, Kernel Developer, Algorithms R&D
My student days learning fluid dynamics were all about studying complicated equations and various methods of simplifying and manipulating these equations to get some kind of a result. Unfortunately, this left very little to the imagination when it came to getting an intuitive feel for how a fluid would behave in different situations. When I took my first experimental fluid dynamics course, I got to see how one would use different visualization techniques to understand qualitatively the behavior of the flow. These visualizations gave me a way of creatively looking at a flow, and, as an added bonus, they looked stunning. All these experiments and visualizations were being carried out inside a wind tunnel.
October 24, 2019 — Stephen Wolfram
Wolfram Notebooks on the Web
We’ve been working towards it for many years, but now it’s finally here: an incredibly smooth workflow for publishing Wolfram Notebooks to the web—that makes possible a new level of interactive publishing and computation-enabled communication.
You create a Wolfram Notebook—using all the power of the Wolfram Language and the Wolfram Notebook system—on the desktop or in the cloud. Then you just press a button to publish it to the Wolfram Cloud—and immediately anyone anywhere can both read and interact with it on the web.
It’s an unprecedentedly easy way to get rich, interactive, computational content onto the web. And—together with the power of the Wolfram Language as a computational language—it promises to usher in a new era of computational communication, and to be a crucial driver for the development of “computational X” fields.
September 17, 2019 — Brian Wood, Lead Technical Writer, Document and Media Systems
Our interactive Multiparadigm Data Science (MPDS) course has been up at Wolfram U for over a month now, and we’re pretty satisfied with the results so far. Hundreds of people have started the course—including students from our first Data Science Boot Camp, who joined us at Wolfram headquarters for a three-week training camp. Thanks to the success of the boot camp, we have also had several projects submitted for advanced MPDS certification, which will soon be available within the interactive course.
But what exactly does it mean to be a practitioner of MPDS? And how might the multiparadigm approach improve my computational projects? To find out, I decided to try this free course for myself.
September 5, 2019 — Daniel Lichtblau, Symbolic Algorithms Developer, Algorithms R&D
A Year Ago Today
On September 5 of last year, The New York Times took the unusual step of publishing an op-ed anonymously. It began “I Am Part of the Resistance inside the Trump Administration,” and quickly became known as the “Resistance” op-ed. From the start, there was wide‐ranging speculation as to who might have been the author(s); to this day, that has not been settled. (Spoiler alert: it will not be settled in this blog post, either. But that’s getting ahead of things.) When I learned of this op-ed, the first thing that came to mind, of course, was, “I wonder if authorship attribution software could….” This was followed by, “Well, of course it could. If given the right training data.” When time permitted, I had a look on the internet into where one might find training data, and for that matter who were the people to consider for the pool of candidate authors. I found at least a couple of blog posts that mentioned the possibility of using tweets from administration officials. One gave a preliminary analysis (with President Trump himself receiving the highest score, though by a narrow margin—go figure). It even provided a means of downloading a dataset that the poster had gone to some work to cull from the Twitter site.
The code from that blog was in a language/script in which I am not fluent. My coauthor on two authorship attribution papers (and other work), Catalin Stoean, was able to download the data successfully. I first did some quick validation (to be seen) and got solid results. Upon setting the software loose on the op-ed in question, a clear winner emerged. So for a short time I “knew” who wrote that piece. Except. I decided more serious testing was required.
August 15, 2019 — Abrita Chakravarty, Training and Development Specialist, Wolfram U
A few weeks back, we announced Wolfram U’s latest open online course: Multiparadigm Data Science (MPDS). This course gives a hands-on introduction to basic concepts of data science through a multiparadigm approach—using various types of data, modern analytical techniques, automated machine learning and a range of interfaces for communicating your data science results. Our goal is to increase your understanding of data science while allowing you to take advantage of multiparadigm insights—whether you’re a newcomer working on a simple problem or an expert using well-established methods.
As the content creator and instructor, I’d like to provide some background on myself and my approach to the MPDS course. Beyond doing data science, I’ve found that multiparadigm principles make both teaching and learning more effective. In this post, I’ll give insight to the design of the course—the main goals, what topics are included and how to use the built-in interactivity to get the most out of your experience.
August 13, 2019 — Swede White, Public Relations Manager
Solving a 2,000-Year-Old Mystery
It’s not every day that a 2,000-year-old optics problem is solved. However, Rafael G. González-Acuña, a doctoral student at Tecnológico de Monterrey, set his sights on solving such a problem—spherical aberration in lenses. How can light rays focus on a single point, taking into account differing refraction? It was a problem that, according to Christiaan Huygens back in 1690, even Isaac Newton and Gottfried Leibniz couldn’t sort out, and was formulated two millennia ago in Greek mathematician Diocles’s work, On Burning Mirrors.
But González-Acuña and his colleagues realized that today, they had the use of the Wolfram Language and its computational tools to solve this age-old problem. The result? A breakthrough publication that outlines an analytical solution to why and how lensed images are sharper in the center than at the edges, with 99.999999999% accuracy simulating 500 light beams.
As it happens, González-Acuña was recently at the Wolfram Summer School, and we had the opportunity to ask him a little bit about his work.
August 8, 2019 — Jesse Friedman, Software Engineer, Engine Connectivity Engineering
Cerne Abbas Walk is an artwork by Richard Long, in the collection of the Tate Modern in London and on display at the time of this writing. Several of Long’s works involve geographic representations of his walks, some abstract and some concrete. Cerne Abbas Walk is described by the artist as “a six-day walk over all roads, lanes and double tracks inside a six-mile-wide circle centred on the Giant of Cerne Abbas.” The Tate catalog notes that “the map shows his route, retracing and re-crossing many roads to stay within a predetermined circle.”
The Giant in question is a 180-foot-high chalk figure carved into a hill near the village of Cerne Abbas in South West England. Some archaeologists believe it to be of Iron Age pedigree, some think it to date from the Roman or subsequent Saxon periods and yet others find the bulk of evidence to indicate a 17th-century origin as a political satire. (I find the last theory to be both the most amusing and the most convincing.)
I found the geographic premise of Cerne Abbas Walk intriguing, so I decided to replicate it computationally.
August 2, 2019 — Bob Sandheinrich, Development Manager, Document & Media Systems
Every summer, I play in a recreational Ultimate Frisbee league—just “Ultimate” to those who play. It’s a fun, relaxed, coed league where I tend to win more friends than games.
The league is organized by volunteers, and one year, my friend and teammate Nate was volunteered to coordinate it. A couple weeks before the start of the season, Nate came to me with some desperation in his voice over making the teams. The league allows each player to request to play with up to eight other players—disparagingly referred to as their “baggage.” And Nate discovered that with over 100 players in a league, each one requesting a different combination of teammates, creating teams that would please everyone seemed to become more complicated by the minute.
Luckily for him, the Wolfram Language has a suite of graph and network tools for things like social media. I recognized that this seemingly overwhelming problem was actually a fairly simple graph problem. I asked Nate for the data, spent an evening working in a notebook and sent him the teams that night.