Envisioning City Spaces, Aligning DNA Sequences, Classifying Emotional Speech and More: Wolfram Community Highlights
October 10, 2019 — Chapin Langenheim, Editorial Project Coordinator, Project Management
In this roundup of our recent Wolfram Community favorites, our talented users explore different methods of accessing, interpreting and representing data—creating some eye-catching results that offer new ways of looking at the world. We’re also excited to showcase a few projects from alumni of our annual Wolfram High School Summer Camp and Wolfram Summer School. Check out the culmination of their hard work, as well as how Community members find clever solutions using the Wolfram Language.
How can you make teaching come alive and be more engaging? For many educators, the answer turns out to be not so much a single solution, but rather a set of tools that can vary according to subject and even by student. So today, I want to add something new to the pedagogical toolkit: Wolfram Virtual Labs.
Wolfram Virtual Labs are open educational resources in the form of interactive courseware that are used to explain different concepts in the classroom. Our ambition is to provide an easy way to study difficult concepts and promote student curiosity.
For this post, I spoke with Dr. Matteo Fasano about his experience with using Virtual Labs as a course complement in the masters’ courses in which he acts as a teaching assistant. He also told me why and how he supported the Wolfram MathCore group to develop the CollegeThermal Virtual Labs (now available) and how they can help teachers or instructors make learning more engaging.
October 4, 2019 — Brian Wood, Lead Technical Writer, Document and Media Systems
Robert Prince-Wright has been using Mathematica since its debut in 1988 to develop computational tools in education, business consulting and offshore engineering. We recently talked to Prince-Wright about his work developing simulation models for deepwater drilling equipment at safety and systems engineering company Berkeley & Imperial.
His latest work is cutting edge—but it’s only part of the story. Throughout his career, Prince-Wright has used Wolfram technologies for “modeling systems as varied as downhole wellbore trajectory, radionuclide dispersion and PID control of automation systems.” Read on to learn more about Prince-Wright’s accomplishments and discover why Wolfram technology is his go-to for developing unique computational solutions.
October 1, 2019 — Stephen Wolfram
The Story of Rule 30
How can something that simple produce something that complex? It’s been nearly 40 years since I first saw rule 30—but it still amazes me. Long ago it became my personal all-time favorite science discovery, and over the years it’s changed my whole worldview and led me to all sorts of science, technology, philosophy and more.
But even after all these years, there are still many basic things we don’t know about rule 30. And I’ve decided that it’s now time to do what I can to stimulate the process of finding more of them out. So as of today, I am offering $30,000 in prizes for the answers to three basic questions about rule 30.
September 24, 2019 — Suba Thomas, Software Engineer, Algorithms R&D
Real-time filters work like magic. Usually out of sight, they clean data to make it useful for the larger system they are part of, and sometimes even for human consumption. A fascinating thing about these filters is that they don’t have a big-picture perspective. They work wonders with only a small window into the data that is streaming in. On the other hand, if I had a stream of numbers flying across my screen, I would at the very least need to plot it to make sense of the data. These types of filters are very simple as well.
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 12, 2019 — Stephen Wolfram
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.
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 29, 2019 — Jan Poeschko, Cloud Development
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.
August 22, 2019 — Sjoerd Smit, Technical Consultant, European Sales
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.