September 17, 2020 — Hamza Alsamraee, Document & Media Systems
A few months before I accepted a Wolfram Research internship—around March—I was very fearful, and so was the majority of the world. We knew very little about the novel coronavirus, and the data was just not robust. In addition to the limited data we had, the scientific process necessarily takes time, so even that was not used to its full extent. In a world where not enough data can quickly become data overload, the question didn’t seem to be finding more data, but rather how can one extract useful and meaningful information from the available data?
A worldwide pandemic is definitely stressful, but a worldwide pandemic without accessible and computable information is much more so. Using Wolfram technologies in coordination with several internal teams, I created a Wolfram U course called COVID-19 Data Analysis and Visualization to try and cut through the informational fog and find some clarity. I saw this course as one that gives power to everyone to be able to look at data and gain insight. After all, data is knowledge, and knowledge is power.
September 15, 2020 — Cliff Hastings, Director, Sales & Strategic Initiatives
In March, my work life changed dramatically, as it did for many around the world. After working in an office environment for almost 25 years, I was told that I needed to work from home for the first time. So I took my laptop, power cord and extra battery home with the expectation that I’d be there for one to two weeks. The first couple of days were a mad dash with our IT department to ensure my VPN, softphone and main tools were set up correctly for remote work. They were great, and for the most part, I was working at 90% right away. The experience opened my eyes, as many of my sales employees work remotely, so I learned a lot about the positives (and, of course, the negatives) they often experience that I may not have appreciated in the past.
April 27, 2020 — Daniel Robinson, CBM Content Author, European Sales
How did the Department of Health and Social Care (DHSC) come up with their multi-phase response to tackle COVID-19? In this post, I investigate how the UK government’s original plan against the coronavirus aligns with the four-step computational thinking process. Teachers are welcome to use this post as a free resource.
Please note: where possible, I have taken data from before the DHSC’s plan was published.
The Computational Thinking Process
What is the computational thinking process? Simply put, it is a sequence of four steps that you can take in order to solve a problem. The aim is not just to obtain a solution, but to ensure that the right choices were made, the right tools were used and the right outcomes were achieved along the way. The steps are as follows: you define explicitly the problem you wish to solve, abstract it to a computational form, compute an answer, then interpret the result:
April 9, 2020 — Avery Davis, Public Relations Project Manager, Public Relations
When the world is in distress, Wolfram users turn to computation! Even in the midst of this global pandemic, Wolfram staff, friends and colleagues continue to show the power of computational curiosity. We’ve provided a centralized COVID-19 data and resources page, with ways to get free licenses for Wolfram technology through August, livestreamed multiparadigm explorations into the science and data behind the virus, computational explorations from Wolfram users and more. This resource will be continually updated, so make sure to check back often!
Our community of staff and users have been incredibly active, creating their own innovative resources and exploring available data from many different angles. Wolfram Community gathers talented and experienced data scientists, biologists, chemists, supply chain experts, epidemiologists, mathematicians, physicists and more. In recent weeks, we’ve seen a flurry of activity and exploration, a willingness to share ideas and information, and mutual encouragement from industry professionals and high-school students alike.
April 1, 2020 — Alec Titterton, CBM Content Development Manager, European Sales
With many schools transitioning to remote learning for the remainder of the school year, educators face the challenge of maintaining the same quality of education as in-person lessons. Here’s a collection of the resources offered by Wolfram Research and others to help educators in an e‑learning environment.
March 17, 2020 — Ishwarya Vardhani, Education Partnerships Manager, Partnerships
Communities the world over are bracing themselves for impact from the novel coronavirus COVID-19. Many school districts in particular have already suspended sessions for several weeks to come—and understandably, parents and educators feel anxious about navigating at-home learning (among the variety of other concerns brought about by a pandemic!).
Professionally, a large part of what I do at Wolfram involves working with educators, students and organizations, and empowering them with the technology to think computationally. I know of several parents with older kids who are now at home, enrolled in schools that are not completely prepared to provide online instruction. While the internet is awash with curricula, it can be a challenging task to assess the quality, relevance and usefulness of each course, given the amount of what is out there.
For decades now at Wolfram, we’ve been committed to the creation of cutting-edge technology and resources for classrooms. Let’s take a look at our wealth of free online resources for quality education while at home.
March 12, 2020 — Danielle Rommel, Director of Outreach and Communications
Given the current discussion of quarantines, sick leave and controlling community outbreaks, many people around the world have been prompted to wonder “Can my business run with a remote workforce?” or “Can my students learn and be productive without coming into the classroom?” The answer, we’ve found, is yes—and we’ve got decades of experience as a largely remote company to guide you, your school, government lab, university or company as you make the understandably challenging transition to working remotely.
February 6, 2020 — Brian Wood, Lead Technical Writer, Document and Media Systems
When 20 presidential candidates duke it out on the debate stage, who wins? Americans have been watching a crowded and contentious primary season for the 2020 Democratic nomination for president. After the debates, everyone’s talking about who got the most talk time or attention, which exchanges were most exciting or some other measure of who “won” the night—and who might ultimately clinch a victory at the caucuses. So I decided I’d do a little exploration of the debates using the entity framework, text analytics and graph capabilities of the Wolfram Language and see if I could come up with my own measure of a “win” for a debate, based on which candidate was most central to the conversation.
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.
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.