Brian Wood

From Data to Insights: An Online Course for Learning the Multiparadigm Workflow

September 17, 2019 — Brian Wood, Lead Technical Writer, Document and Media Systems

From Data to Insights: An Online Course for Learning the Multiparadigm Workflow

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.

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Stephen Wolfram

The Ease of Wolfram|Alpha, the Power of Mathematica: Introducing Wolfram|Alpha Notebook Edition

September 12, 2019 — Stephen Wolfram

Wolfram|Alpha Notebook Edition

The Next Big Step for Wolfram|Alpha

Wolfram|Alpha has been a huge hit with students. Whether in college or high school, Wolfram|Alpha has become a ubiquitous way for students to get answers. But it’s a one-shot process: a student enters the question they want to ask (say in math) and Wolfram|Alpha gives them the (usually richly contextualized) answer. It’s incredibly useful—especially when coupled with its step-by-step solution capabilities.

But what if one doesn’t want just a one-shot answer? What if one wants to build up (or work through) a whole computation? Well, that’s what we created Mathematica and its whole notebook interface to do. And for more than 30 years that’s how countless inventions and discoveries have been made around the world. It’s also how generations of higher-level students have been taught.

But what about students who aren’t ready to use Mathematica yet? What if we could take the power of Mathematica (and what’s now the Wolfram Language), but combine it with the ease of Wolfram|Alpha?

Well, that’s what we’ve done in Wolfram|Alpha Notebook Edition.

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Daniel Lichtblau

Authorship Forensics from Twitter Data

September 5, 2019 — Daniel Lichtblau, Symbolic Algorithms Developer, Algorithms R&D

Authorship Forensics from Twitter Data

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.

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Jan Poeschko

Wolfram Cloud 1.50 and 1.51: Major New Releases Bring Cloud Another Step Closer to the Desktop

August 29, 2019 — Jan Poeschko, Cloud Development

Wolfram Cloud 1.50 and 1.51: Major New Releases Bring Cloud Another Step Closer to the Desktop

A couple weeks ago, we released Version 1.51 of the Wolfram Cloud. We’ve made quite a few significant functionality improvements even since 1.50—a major milestone from many months of hard work—as we continue to make cloud notebooks as easy and powerful to use as the notebooks on our desktop clients for Wolfram|One and Mathematica. You can read through everything that’s new in 1.51 in the detailed release notes. After working on this version through to its release, I’m excited to show off Wolfram Cloud 1.51—I’ve put together a few of the highlights and favorite new features for you here.

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Sjoerd Smit

Embracing Uncertainty: Better Model Selection with Bayesian Linear Regression

August 22, 2019 — Sjoerd Smit, Technical Consultant, European Sales

Embracing Uncertainty: Better Model Selection with Bayesian Linear Regression

Readers who follow the Mathematica Stack Exchange (which I highly recommend to any Wolfram Language user) may have seen this post recently, in which I showed a function I wrote to make Bayesian linear regression easy to do. After finishing that function, I have been playing around with it to get a better feel of what it can do, and how it compares against regular fitting algorithms such as those used by Fit. In this blog post, I don’t want to focus too much on the underlying technicalities (check out my previous blog post to learn more about Bayesian neural network regression); rather, I will show you some of the practical applications and interpretations of Bayesian regression, and share some of the surprising results you can get from it.

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Abrita Chakravarty

Designing the Wolfram U Data Science Course for Learning the Multiparadigm Workflow

August 15, 2019 — Abrita Chakravarty, Training and Development Specialist, Wolfram U

Designing the Wolfram U Data Science Course for Learning the Multiparadigm Workflow

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.

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Swede White

Spherical Aberration Optics Problem Finally Solved Using the Wolfram Language

August 13, 2019 — Swede White, Public Relations Manager

Spherical Aberration Optics Problem Finally Solved Using the Wolfram Language

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.

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Jesse Friedman

Taking the Cerne Abbas Walk: From Conceptual Art to Computational Art

August 8, 2019 — Jesse Friedman, Engine Connectivity Engineering

Taking the Cerne Abbas Walk

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.

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Bob Sandheinrich

The Ultimate Team Generator with the Wolfram Language

August 2, 2019 — Bob Sandheinrich, Development Manager, Document & Media Systems

The Ultimate Team Generator with the Wolfram Language

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.

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Keren Garcia

Building Uniform Polyhedra for Version 12

July 25, 2019 — Keren Garcia, Algorithms R&D

Building Uniform Polyhedra for Version 12

Since I started working at Wolfram, I’ve been a part of several different projects. For Version 12, my main focus was replicating models of the uniform polyhedra with the Wolfram Language to ensure that the data fulfilled certain criteria to make our models precise, including exact coordinates, consistent face orientation and a closed region in order to create a proper mesh model of each solid.

Working with visual models of polyhedra is one thing, but analyzing them mathematically proved to be much more challenging. Starting with reference models of the polyhedra, I found that the Wolfram Language made mathematical analysis of uniform polyhedra particularly efficient and easy.

But first, what really are polyhedra, and why should we care? With Version 12, we can explore what polyhedra are and how they’ve earned their continued place in our imaginations.

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