March 14, 2019 — Shenghui Yang, Developer, Wolfram|Alpha Localization Systems
I approached my friend Frederick Wu and suggested that we should make a physical Wolfram Spikey Coin (not to be confused with a Wolfram Blockchain Token!) for the celebration of the 30th anniversary of Mathematica. Frederick is a long-term Mathematica user and coin collector, and together, we challenged ourselves to design our own commemorative coin for such a special event.
The iconic Spikey is a life-long companion of Mathematica, coined (no pun intended) in 1988 with the release of Version 1. Now, we’ve reached a time in which Wolfram technologies and different 3D printing processes happily marry together to make this project possible!
January 24, 2019 — Jacob Wells, Technical Specialist, European Sales
Do you select a bottle of wine based more on how fancy the sleeve is than its price point? If so, then you’re like me, and you may be looking to minimize the risk of wishful guesses. This article may provide a little rational weight to your purchasing decisions.
Due to my research using the Wolfram Language, I can now mention the fact that if you are spending less than
January 10, 2019 — Brian Wood, Lead Technical Marketing Writer, Document and Media Systems
So far in this series, I’ve covered the process of extracting, cleaning and structuring data from a website. So what does one do with a structured dataset? Continuing with the Election Atlas data from the previous post, this final entry will talk about how to store your scraped data permanently and deploy results to the web for universal access and sharing.
November 16, 2018 — Michael Trott, Chief Scientist
This morning, representatives of more than 100 countries agreed on a new definition of the base units for all weights and measures. Here’s a picture of the event that I took this morning at the Palais des Congrès in Versailles (down the street from the Château):
An important vote for the future weights and measures used in science, technology, commerce and even daily life happened here today. This morning’s agreement is the culmination of at least 230 years of wishing and labor by some of the world’s most famous scientists. The preface to the story entails Galileo and Kepler. Chapter one involves Laplace, Legendre and many other late-18th-century French scientists. Chapter two includes Arago and Gauss. Some of the main figures of chapter three (which I would call “The Rise of the Constants”) are Maxwell and Planck. And the final chapter (“Reign of the Constants”) begins today and builds on the work of contemporary Nobel laureates like Klaus von Klitzing, Bill Phillips and Brian Josephson.
I had the good fortune to witness today’s historic event in person.
October 25, 2018 — Christopher Carlson, Senior User Interface Developer, User Interfaces
Images and machine learning were the dominant themes of submissions to the One-Liner Competition held at this year’s Wolfram Technology Conference. The competition challenges attendees to show us the most astounding things they can accomplish with 128 or fewer characters—less than one tweet—of Wolfram Language code. And astound us they did. Read on to see how.
In past blog posts, we’ve talked about the Wolfram Language’s built-in, high-level functionality for 3D printing. Today we’re excited to share an example of how some more general functionality in the language is being used to push the boundaries of this technology. Specifically, we’ll look at how computation enables 3D printing of very intricate sugar structures, which can be used to artificially create physiological channel networks like blood vessels.
September 11, 2018 — Jon McLoone, Director, Technical Communication & Strategy
Having a really broad toolset and an open mind on how to approach data can lead to interesting insights that are missed when data is looked at only through the lens of statistics or machine learning. It’s something we at Wolfram Research call multiparadigm data science, which I use here for a small excursion through calculus, graph theory, signal processing, optimization and statistics to gain some interesting insights into the engineering of supersonic cars.
September 6, 2018 — Brian Wood, Lead Technical Marketing Writer, Document and Media Systems
In my previous post, I demonstrated the first step of a multiparadigm data science workflow: extracting data. Now it’s time to take a closer look at how the Wolfram Language can help make sense of that data by cleaning it, sorting it and structuring it for your workflow. I’ll discuss key Wolfram Language functions for making imported data easier to browse, query and compute with, as well as share some strategies for automating the process of importing and structuring data. Throughout this post, I’ll refer to the US Election Atlas website, which contains tables of US presidential election results for given years:
August 9, 2018 — Swede White, Lead Communications Strategist, Public Relations
Code for America’s National Day of Civic Hacking is coming up on August 11, 2018, which presents a nice opportunity for individuals and teams of all skill levels to participate in the Safe Drinking Water Data Challenge—a program Wolfram is supporting through free access to Wolfram|One and by hosting relevant structured datasets in the Wolfram Data Repository.
According to the state of California, some 200,000 residents of the state have unsafe drinking water coming out of their taps. While the Safe Drinking Water Data Challenge focuses on California, data science solutions could have impacts and applications for providing greater access to potable water in other areas with similar problems.
The goal of this post is to show how Wolfram technologies make it easy to grab data and ask questions of it, so we’ll be taking a multiparadigm approach and allowing our analysis to be driven by those questions in an exploratory analysis, a way to quickly get familiar with the data.
July 26, 2018 — Itai Seggev, Senior Kernel Developer, Algorithms R&D
One of the many beautiful aspects of mathematics is that often, things that look radically different are in fact the same—or at least share a common core. On their faces, algorithm analysis, function approximation and number theory seem radically different. After all, the first is about computer programs, the second is about smooth functions and the third is about whole numbers. However, they share a common toolset: asymptotic relations and the important concept of asymptotic scale.
By comparing the “important parts” of two functions—a common trick in mathematics—asymptotic analysis classifies functions based on the relative size of their absolute values near a particular point. Depending on the application, this comparison provides quantitative answers to questions such as “Which of these algorithms is fastest?” or “Is function a good approximation to function g?”. Version 11.3 of the Wolfram Language introduces six of these relations, summarized in the following table.