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.
August 15, 2019 — Abrita Chakravarty, Training and Development Specialist, Wolfram U
A few weeks back, we announced Wolfram U’s latest open online course: Multiparadigm Data Science (MPDS). This course gives a hands-on introduction to basic concepts of data science through a multiparadigm approach—using various types of data, modern analytical techniques, automated machine learning and a range of interfaces for communicating your data science results. Our goal is to increase your understanding of data science while allowing you to take advantage of multiparadigm insights—whether you’re a newcomer working on a simple problem or an expert using well-established methods.
As the content creator and instructor, I’d like to provide some background on myself and my approach to the MPDS course. Beyond doing data science, I’ve found that multiparadigm principles make both teaching and learning more effective. In this post, I’ll give insight to the design of the course—the main goals, what topics are included and how to use the built-in interactivity to get the most out of your experience.
August 13, 2019 — Swede White, Public Relations Manager
Solving a 2,000-Year-Old Mystery
It’s not every day that a 2,000-year-old optics problem is solved. However, Rafael G. González-Acuña, a doctoral student at Tecnológico de Monterrey, set his sights on solving such a problem—spherical aberration in lenses. How can light rays focus on a single point, taking into account differing refraction? It was a problem that, according to Christiaan Huygens back in 1690, even Isaac Newton and Gottfried Leibniz couldn’t sort out, and was formulated two millennia ago in Greek mathematician Diocles’s work, On Burning Mirrors.
But González-Acuña and his colleagues realized that today, they had the use of the Wolfram Language and its computational tools to solve this age-old problem. The result? A breakthrough publication that outlines an analytical solution to why and how lensed images are sharper in the center than at the edges, with 99.999999999% accuracy simulating 500 light beams.
As it happens, González-Acuña was recently at the Wolfram Summer School, and we had the opportunity to ask him a little bit about his work.
August 8, 2019 — Jesse Friedman, Engine Connectivity Engineering
Cerne Abbas Walk is an artwork by Richard Long, in the collection of the Tate Modern in London and on display at the time of this writing. Several of Long’s works involve geographic representations of his walks, some abstract and some concrete. Cerne Abbas Walk is described by the artist as “a six-day walk over all roads, lanes and double tracks inside a six-mile-wide circle centred on the Giant of Cerne Abbas.” The Tate catalog notes that “the map shows his route, retracing and re-crossing many roads to stay within a predetermined circle.”
The Giant in question is a 180-foot-high chalk figure carved into a hill near the village of Cerne Abbas in South West England. Some archaeologists believe it to be of Iron Age pedigree, some think it to date from the Roman or subsequent Saxon periods and yet others find the bulk of evidence to indicate a 17th-century origin as a political satire. (I find the last theory to be both the most amusing and the most convincing.)
I found the geographic premise of Cerne Abbas Walk intriguing, so I decided to replicate it computationally.
August 2, 2019 — Bob Sandheinrich, Development Manager, Document & Media Systems
Every summer, I play in a recreational Ultimate Frisbee league—just “Ultimate” to those who play. It’s a fun, relaxed, coed league where I tend to win more friends than games.
The league is organized by volunteers, and one year, my friend and teammate Nate was volunteered to coordinate it. A couple weeks before the start of the season, Nate came to me with some desperation in his voice over making the teams. The league allows each player to request to play with up to eight other players—disparagingly referred to as their “baggage.” And Nate discovered that with over 100 players in a league, each one requesting a different combination of teammates, creating teams that would please everyone seemed to become more complicated by the minute.
Luckily for him, the Wolfram Language has a suite of graph and network tools for things like social media. I recognized that this seemingly overwhelming problem was actually a fairly simple graph problem. I asked Nate for the data, spent an evening working in a notebook and sent him the teams that night.
July 25, 2019 — Keren Garcia, Algorithms R&D
Since I started working at Wolfram, I’ve been a part of several different projects. For Version 12, my main focus was replicating models of the uniform polyhedra with the Wolfram Language to ensure that the data fulfilled certain criteria to make our models precise, including exact coordinates, consistent face orientation and a closed region in order to create a proper mesh model of each solid.
Working with visual models of polyhedra is one thing, but analyzing them mathematically proved to be much more challenging. Starting with reference models of the polyhedra, I found that the Wolfram Language made mathematical analysis of uniform polyhedra particularly efficient and easy.
But first, what really are polyhedra, and why should we care? With Version 12, we can explore what polyhedra are and how they’ve earned their continued place in our imaginations.
July 19, 2019 — Jamie Peterson, Technical Programs Manager, Wolfram U
Wolfram U’s latest interactive course, Multiparadigm Data Science, gives a comprehensive overview of Multiparadigm Data Science (MPDS) through a series of videos, quizzes and live computations, all running from the Wolfram Cloud. Using real-world examples, this free course provides an introduction to MPDS, strategies for improving your process and building your ideal toolkit, and the Wolfram Language functionality that makes it easy to implement.
July 11, 2019 — Jacob Wells, Technical Specialist, European Sales
With the recent announcement of the all-new Raspberry Pi 4, we are proud to announce that our latest development, Version 12 of Mathematica and the Wolfram Language, is available for you to use when you get your hands on the Raspberry Pi 4.
Mathematica 12 is a major milestone in our journey that has spanned 30 years, significantly extending the reach of Mathematica and introducing a whole array of new features, including significant expansion of numerical, mathematic and geometric computation, audio and signal processing, text and language processing, machine learning, neural networks and much more. Version 12 gives Mathematica users new levels of power and effectiveness. With thousands of different updates across the system, and 278 new functions in 103 areas, there is so much to explore.
July 2, 2019 — Jon McLoone, Director, Technical Communication & Strategy
This week, I won some money applying a mathematical strategy to a completely unpredictable gambling game. But before I explain how, I need to give some background on last-mover advantage.
Some time ago, I briefly considered doing some analysis of the dice game Yahtzee. But I was put off by the discovery that several papers (including this one) had already enumerated the entire game state graph to create a strategy for maximizing the expected value of the score (which is 254.59).
However, maximizing the expected value of the score only solves the solo Yahtzee game. In a competitive game, and in many other games, we are not actually trying to maximize our score—we are trying to win, and these are not always the same thing.
June 25, 2019 — Chapin Langenheim, Editorial Project Coordinator, Project Management
Wolfram Community is our favorite, continually growing forum to share and show support for projects using the Wolfram Language, connect with other Mathematica aficionados and find solutions for coding questions. It’s also a great platform for sharing computational innovations that can benefit your local community—or beyond. We’ve collected some of the exciting ways Wolfram Community members have been giving back through Wolfram technology—check them out!