Wolfram Blog

Creating Escher-Inspired Art with Mathematica

July 30, 2014 — Wolfram Blog

Kenzo Nakamura uses Mathematica to create Escher-inspired mathematical art. His trademark piece, Three-Circle Mandala, depicts a large circle covered by three smaller, repeating circles that form a Sierpinksi gasket.

When Nakamura began using Mathematica, he didn’t originally intend to use it for his artistic endeavors. He found the program by chance at a seminar while looking for the right tool to help him write his master’s thesis.

Now, in addition to using Mathematica for technical and operations research, Nakamura uses it to create Mathematica-derived visual illusions. Although his works are static drawings, their infinite properties create the illusion of movement.

Watch Nakamura discuss using Mathematica to create his drawings, and see a few of his creations.

(YouTube in Japanese)


Roger Germundsson
Jan Brugård

Announcing Wolfram SystemModeler 4

July 23, 2014
Roger Germundsson, Director of Research & Development
Jan Brugård, CEO, Wolfram MathCore

Today we are proud to announce the release of Wolfram SystemModeler 4.

Wolfram SystemModeler Logo

For SystemModeler 4, we have expanded the supported model libraries to cover many new areas. We’ve also improved workflows for everything from learning the software to developing models to analyzing and deploying them.

People have been using SystemModeler in an astonishing variety of areas. Many of those have been well supported by built-in libraries, but many are totally new domains where models typically need to be built from scratch.

For most applications, using existing model libraries gives a real boost to productivity, but developing a good library takes a lot of effort. There are many aspects to think of: the best structure for easy modeling, the right level of detail, the interfaces to other components, which components to include, documentation, etc. And you may very well have to refactor the library more than once before you’re done. Reusing components and interfaces from already tested and documented libraries not only speeds up development and learning, but also improves quality.

So we’ve made SystemModeler‘s already broad collection of built-in libraries even larger. For instance, we’ve added Digital, for digital electronics following the VHDL multivalued logic standard; QuasiStationary, for efficient approximate modeling of large analog circuits; and FundamentalWave, for modeling multiphase electrical machines. There are also many improvements to existing libraries, such as support for thermal ports in the Rotational and Translational mechanics libraries so that heat losses can be captured.


Posted in: SystemModeler

Wolfram Blog

How Citizen Computation Changes Democracy: Conrad Wolfram at TEDxHousesofParliament

July 22, 2014 — Wolfram Blog

Conrad Wolfram at TEDxHOP
Photography by Tracy Howl and Paul Clarke

Has our newfound massive availability of data improved decisions and lead to better democracy around the world? Most would say, “It’s highly questionable.”

Conrad Wolfram’s TEDx UK Parliament talk poses this question and explains how computation can be key to the answer, bridging the divide between availability and practical accessibility of data, individualized answers, and the democratization of new knowledge generation. This transformation will be critical not only to government efficiency and business effectiveness—but will fundamentally affect education, society, and democracy as a whole.

Wolfram|Alpha and Mathematica 10 demos feature throughout—including a live Wolfram Language generated tweet.

More about Wolfram’s solutions for your organization’s data »

Stephen Wolfram

Launching Mathematica 10—
with 700+ New Functions and a Crazy Amount of R&D

July 9, 2014 — Stephen Wolfram

We’ve got an incredible amount of new technology coming out this summer. Two weeks ago we launched Wolfram Programming Cloud. Today I’m pleased to announce the release of a major new version of Mathematica: Mathematica 10.

Wolfram Mathematica 10

We released Mathematica 1 just over 26 years ago—on June 23, 1988. And ever since we’ve been systematically making Mathematica ever bigger, stronger, broader and deeper. But Mathematica 10—released today—represents the single biggest jump in new functionality in the entire history of Mathematica.


Wolfram Blog

Hungry for More Pi?

July 2, 2014 — Wolfram Blog

We recently wrote some blog posts for our friends over at raspberrypi.org, sharing some of the cool things you can do with the Wolfram Language on the Raspberry Pi. Combined with the amazing projects and ideas being shared at Wolfram Community, we’re doing some seriously cool stuff on this little computer!


Posted in: Raspberry Pi

Etienne Bernard

World Cup Follow-Up: Update of Winning Probabilities and Betting Results

June 26, 2014 — Etienne Bernard

Find out Etienne’s initial predictions by visiting last week’s World Cup blog post.

The World Cup is half-way through: the group phase is over, and the knockout phase is beginning. Let’s update the winning probabilities for the remaining teams, and analyze how our classifier performed on the group-phase matches.

From the 32 initial teams, 16 are qualified for the knockout phase:

16 teams are qualified for the knockout phase


Posted in: Wolfram Language

Stephen Wolfram

Wolfram Programming Cloud Is Live!

June 23, 2014 — Stephen Wolfram

Twenty-six years ago today we launched Mathematica 1.0. And I am excited that today we have what I think is another historic moment: the launch of Wolfram Programming Cloud—the first in a sequence of products based on the new Wolfram Language.

Wolfram Programming Cloud

My goal with the Wolfram Language in general—and Wolfram Programming Cloud in particular—is to redefine the process of programming, and to automate as much as possible, so that once a human can express what they want to do with sufficient clarity, all the details of how it is done should be handled automatically.


Etienne Bernard

Predicting Who Will Win the World Cup with Wolfram Language

June 20, 2014 — Etienne Bernard

Check out Etienne’s updated predictions from Thursday, June 26 here.

The FIFA World Cup is underway. From June 12 to July 13, 32 national football teams play against each other to determine the FIFA world champion for the next four years. Who will succeed? Experts and fans all have their opinions, but is it possible to answer this question in a more scientific way? Football is an unpredictable sport: few goals are scored, the supposedly weaker team often manages to win, and referees make mistakes. Nevertheless, by investigating the data of past matches and using the new machine learning functions of the Wolfram Language Predict and Classify, we can attempt to predict the outcome of matches.

The first step is to gather data. FIFA results will soon be accessible from Wolfram|Alpha, but for now we have to do it the hard way: scrape the data from the web. Fortunately, many websites gather historical data (www.espn.co.uk, www.rsssf.com, www.11v11.com, etc.) and all the scraping and parsing can be done with Wolfram Language functions. We first stored web pages locally using URLSave and then imported these pages using Import[myfile,"XMLObject"] (and Import[myfile,"Hyperlinks"] for the links). Using XML objects allows us to keep the structure of the page, and the content can be parsed using Part and pattern-matching functions such as Cases. After the scraping, we cleaned and interpreted the data: for example, we had to infer the country from a large number of cities and used Interpreter to do so:

Spelling interpretation of Dhaka

From scraping various websites, we obtained a dataset of about 30,000 international matches of 203 teams from 1950 to 2014 and 75,000 players. Loaded into the Wolfram Language, its size is about 200MB of data. Here is a match and a player example stored in a Dataset:

Match and a player example stored in a dataset


Posted in: Wolfram Language

Wolfram Blog

Wolfram Technology Conference 2014: Register Now!

June 17, 2014 — Wolfram Blog

Stephen Wolfram speaking at 2013 WTCIt’s been a productive 2014 already here at Wolfram with tons of new technology being released and a whole new world of possibilities opening up. One great way to learn more about these accomplishments is to join us at the 2014 Wolfram Technology Conference.
The conference takes place Wednesday, October 22 through Friday, October 24, in Champaign, Illinois (our headquarters). This year’s talks will highlight the Wolfram Language and the thriving ecosystem growing around it, including the new Wolfram Programming Cloud, Mathematica,
Wolfram|Alpha, SystemModeler, and more.

At the conference, you’ll hear from Stephen Wolfram himself. Plus our top Wolfram developers will cover exciting new features in-depth, while industry experts will show you how you can use Wolfram technologies in your everyday work to accomplish more–and do so more efficiently.


Posted in: Wolfram News

Wolfram Blog

How the Wolfram Language Measures Up

June 4, 2014 — Wolfram Blog

Back in 2012, Jon McLoone wrote a program that analyzed the coding examples of over 500 programming languages that were compiled on the wiki site Rosetta Code. He compared the programming language of Mathematica (now officially named the Wolfram Language) to 14 of the most popular and relevant languages, and found that most programs can be written in the Wolfram Language with 1/2 to 1/10 as much code—even as tasks become larger and more complex.

We were curious to see how the Wolfram Language continues to stack up, since a lot has happened in the last two years. So we updated and re-ran Jon’s code, and, much to our excitement (though we really weren’t all that surprised), the Wolfram Language remains largely superior by all accounts!

Keep in mind that the programming tasks at Rosetta Code are the typical kinds of exercises that you can write in conventional programming languages: editing text, implementing quicksort, or solving the Towers of Hanoi. You wouldn’t even think of dashing off a program in C to do handwriting recognition, yet that’s a one-liner in the Wolfram Language. And since the Wolfram Language’s ultra-high-level constructs are designed to match the way people think about solving problems, writing programs in it is usually easier than in other languages. In spite of the Rosetta Code tasks being relatively low-level applications, the Wolfram Language still wins handily on code length compared to every other language.

Here’s the same graph as in Jon’s 2012 post comparing the Wolfram Language to C. Each point gives the character counts of the same task programmed in the Wolfram Language and C. Notice the Wolfram Language still remains shorter for almost every task, staying mostly underneath the dashed one-to-one line:

Wolfram Language versus C

The same holds true for Python:

Wolfram Language versus Python


Posted in: Wolfram Language