October 30, 2013 — Allison Taylor, Public Relations
I was lucky enough in college to be able to double-major in physics and film/media. One of the coolest connections that formed from these completely opposite subjects was the use of Mathematica. What started out as just a computational tool for all the work in my physics classes turned into an experimental playground for the digital animation I was creating in my film classes.
Mathematica is an ideal program to model the true science of motion. And as you’ll come to see, it looks complicated, but is actually quite simple!
Let’s start with understanding some basic human anatomy (or zombie anatomy, since this post is technically about zombies):
October 8, 2013 — Jason Martinez, Research Programmer
Recently the author of xkcd, Randall Munroe, was asked the question of how long it would be necessary for someone to fall in order to jump out of an airplane, fill a large balloon with helium while falling, and land safely. Randall unfortunately ran into some difficulties with completing his calculation, including getting his IP address banned by Wolfram|Alpha. (No worries: we received his request and have already fixed that.)
August 15, 2013 — Michael Trott, Chief Scientist
This blog post is the continuation of my last two posts (1, 2) about formulas for curves. So far, we have discussed how to make plane curves that are sketches of animals, faces, fictional characters, and more. In this post, we will discuss the constructions of some filled curves (laminae).
July 19, 2013 — Michael Trott, Chief Scientist
In my last blog post, I discussed how to construct closed-form trigonometric formulas for sketches of people’s faces. Using similar techniques, Wolfram|Alpha has recently added a collection of hundreds of such closed-form curves for faces, shapes, animals, logos, and signatures.
May 17, 2013 — Michael Trott, Chief Scientist
Here at Wolfram Research and at Wolfram|Alpha we love mathematics and computations. Our favorite topics are algorithms, followed by formulas and equations. For instance, Mathematica can calculate millions of (more precisely, for all practical purposes, infinitely many) integrals, and Wolfram|Alpha knows hundreds of thousands of mathematical formulas (from Euler’s formula and BBP-type formulas for pi to complicated definite integrals containing sin(x)) and plenty of physics formulas (e.g from Poiseuille’s law to the classical mechanics solutions of a point particle in a rectangle to the inverse-distance potential in 4D in hyperspherical coordinates), as well as lesser-known formulas, such as formulas for the shaking frequency of a wet dog, the maximal height of a sandcastle, or the cooking time of a turkey.
Recently we added formulas for a variety of shapes and forms, and the Wolfram|Alpha Blog showed some examples of shapes that were represented through mathematical equations and inequalities. These included fictional character curves:
April 12, 2013 — Vitaliy Kaurov, Technical Communication & Strategy
What does programming have to do with a passion for the arts and history? Well, if you turn education into a game and add a bit of coding, then you can easily end up in the realm of a modern paradigm called, fancily, “gamification.” Though gamification is a very wide concept based on game use in non-game contexts (design, security, marketing, even protein folding, you name it), at heart it is very simple: play, have fun, and get things done. I may have oversimplified things here for the sake of a rhyme, but if you bear with my lengthy prelude, we may just see a simple case of turning passion into software.
My obsession with diagrams and simple line drawings began almost unnoticeably in the winter of 2003 in New York City after attending an exhibition at The Metropolitan Museum of Art: “the first comprehensive survey of Leonardo da Vinci’s drawings ever presented in America.” You may think it’d be a drag—crowds marching very slowly in a single long line coiling through the exhibition hallways. But perception of time transforms when you stare at 500-year-old craft. I think it was then that it started to dawn on me what special value a first sketch has. A first act when an idea, something very subjective, evasive, living solely inside one’s mind, materializes as a solid reality, now perceivable by another human being. Imagine it happened ages ago. Wouldn’t you be curious what was going on at that moment in time, what got frozen in this piece of craft in front of you?
February 28, 2013 — Christopher Carlson, Senior User Interface Developer, User Interfaces
The National Museum of Mathematics, which opened in Manhattan in December, doesn’t have a logo. It has an infinite family of logos. And the logos the museum uses for official business are not created by design professionals. They’re designed by the museum’s visitors. The logo is itself an exhibit in the museum.
The museum’s unique meta-logo was conceived and implemented at Wolfram Research. When I say “implemented,” I don’t mean just “calculated” or “rendered,” but actually “programmed.” This is a logo that requires an implementation.
February 4, 2013 — Oleksandr Pavlyk, Manager of Probability and Statistics, Mathematica Algorithm R&D
On January 23, 1913 of the Julian calendar, Andrey A. Markov presented for the Royal Academy of Sciences in St. Petersburg his analysis of Pushkin’s Eugene Onegin. He found that the sequence of consonants and vowels in the text could be well described as a random sequence, where the likely category of a letter depended only on the category of the previous or previous two letters.
At the time, the Russian Empire was using the Julian calendar. The 100th anniversary of the celebrated presentation is actually February 5, 2013, in the now used Gregorian calendar.
To perform his analysis, Markov invented what are now known as “Markov chains,” which can be represented as probabilistic state diagrams where the transitions between states are labeled with the probabilities of their occurrences.
December 20, 2012 — Paul-Jean Letourneau, Senior Data Scientist, Wolfram Research
This year is the 100th birthday of Alan Turing, so at the 2012 Wolfram Science Summer School we decided to turn a group of 40 unassuming nerds into ferocious hunters. No, we didn’t teach our geeks to take down big game. These are laptop warriors. And their prey? Turing machines!
In this blog post, I’m going to teach you to be a fellow hunter-gatherer in the computational universe. Your mission, should you choose to accept it, is to FIND YOUR FAVORITE TURING MACHINE.
First, I’ll show you how a Turing machine works, using pretty pictures that even my grandmother could understand. Then I’ll show you some of the awesome Turing machines that our summer school students found using Mathematica. And I’ll describe how I did an über-search through 373 million Turing machines using my Linux server back home, and had it send me email whenever it found an interesting one, for two weeks straight. I’ll keep the code to a minimum here, but you can find it all in the attached Mathematica notebook.
Excited? Primed for the hunt? Let me break it down for you.
The rules of Turing machines are actually super simple. There’s a row of cells called the tape:
November 14, 2012 — Jon McLoone, International Business & Strategic Development
Update: See our latest post on How the Wolfram Language Measures Up.
I stumbled upon a nice project called Rosetta Code. Their stated aim is “to present solutions to the same task in as many different languages as possible, to demonstrate how languages are similar and different, and to aid a person with a grounding in one approach to a problem in learning another.”
After amusing myself by contributing a few solutions (Flood filling, Mean angle, and Sum digits of an integer being some of mine), I realized that the data hidden in the site provided an opportunity to quantify a claim that I have often made over the years—that Mathematica code tends to be shorter than equivalent code in other languages. This is due to both its high-level nature and built-in computational knowledge.
Here is what I found.
Mathematica code is typically less than a third of the length of the same tasks written in other languages, and often much better.