January 16, 2020 — Jamie Peterson, Technical Programs Manager, Wolfram U
Looking to fulfill your New Year’s resolution of learning new data science skills? Join Wolfram U for Wolfram Technology in Action: Data Science, a three-part web series demonstrating a range of data science applications in the Wolfram Language. These 90-minute sessions feature recorded talks from the 2019 Wolfram Technology Conference, along with live presentations by Wolfram staff scientists, application developers, software engineers and Wolfram Language users who apply the technology every day to their business operations and research.
Newcomers to Wolfram technology are welcome, as are longtime users wanting to see the latest functionality in the language.
January 14, 2020 — Jeffrey Bryant, Research Programmer, Wolfram|Alpha Scientific Content
Yellowstone National Park has long been known for its active geysers. These geysers are a surface indication of subterranean volcanic activity in the park. In fact, Yellowstone is actually the location of the Yellowstone Caldera, a supervolcano: a volcano with an exceptionally large magma reservoir. The park has had a history of many explosive eruptions over the last two million years or so.
I’ve found that the United States Geological Survey (USGS) maintains data on the various volcanic calderas and related features, which makes it perfect for computational exploration with the Wolfram Language. This data is in the form of SHP files and related data stored as a ZIP archive. Thanks to the detail of this available data, we can use the Wolfram Language and, in particular, GeoGraphics to get a better picture of what this data is telling us.
December 18, 2019 — Daniel Bigham, Business Systems R&D
When people think about Wolfram technology, corporate enterprise resource management (ERP) isn’t the first thing that comes to mind. It certainly wasn’t our first thought when we started searching for a new solution to manage our own accounting, customer service, licensing and HR needs. But after looking at the current ERP offerings, we found that none of the existing buy-in options did what we wanted.
So we thought, why not build our own?
The resulting project has been a revelation. Not only have we built something to our taste, but something fundamentally different: a new architecture, new interfaces, a new approach. Using Wolfram technology has not only made development easier; it has given us a revolutionary new perspective. By leveraging our uniquely powerful technology stack—and integrating it tightly with the existing infrastructure—we’re redefining what an ERP system can be.
December 10, 2019 — Jon McLoone, Director, Technical Communication & Strategy
Much effort and money are spent trying to analyze whether political messages resonate with the electorate. With the UK in its final days before a general election, I thought I would see if I could gain such insight with minimal effort.
My approach is simple: track the sentiment of tweets that mention each party. Since the Wolfram Language has a built-in sentiment classifier and connections to external services, we can analyze these messages with only a few lines of code.
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 10, 2019 — Chapin Langenheim, Editor & Web 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.
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 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 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.