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.
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.
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.
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.
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.
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.
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.
Drawing on Autopilot: Automated Plane (Geometry) Illustrations from The American Mathematical Monthly
Version 12 of the Wolfram Language introduces the functions GeometricScene, RandomInstance and FindGeometricConjectures for representing, drawing and reasoning about problems in plane geometry. In particular, abstract scene descriptions can be automatically supplied with coordinate values to produce diagrams satisfying the conditions of the scene. Let’s apply this functionality to some of the articles and problems about geometry appearing in the issues of The American Mathematical Monthly from February and March of 2019.
IntroductionIn the so-called "new SI," the updated version of the International System of Units that will define the seven base units (second, meter, kilogram, ampere, kelvin, mole and candela) and that goes into effect May 20 of 2019, all SI units will be definitionally based on exact values of fundamental constants of physics. And as a result, all the named units of the SI (newton, volt, ohm, pascal, etc.) will ultimately be expressible through fundamental constants. (Finally, fundamental physics will be literally ruling our daily life 😁.)
Here is how things will change from the evening of Monday, May 20, to the morning of Tuesday, May 21, of this year.