March 24, 2015 — Mariusz Jankowski
Recently, during a particularly severe patch of winter weather and much too much shoveling of snow off my driveway, I decided, with help from the Wolfram Language, to bring back memories of fairer weather by looking at commuting to work on a bicycle.
This past year, I finally succumbed to the increasingly common practice of recording personal activity data. Over the last few years, I’d noted that my rides had become shorter and easier as the season progressed, so I was mildly interested in verifying this improvement in personal fitness. Using nothing more than a smart phone and a suitable application, I recorded 27 rides between home and work, and then used the Wolfram Language to read, analyze, and visualize the results.
Here is a Google Earth image showing my morning bike route covering a distance of a little under 11 miles, running from east to west.
March 17, 2015 — Arnoud Buzing, Director of Quality and Release Management
Recently Stephen Wolfram announced the Wolfram Data Drop, which is a great new tool to upload any type of data from any type of device. I’ll show how you can use the Wolfram Data Drop with a weather station you build using some basic hardware and a few lines of code. Once completed, your device will take temperature measurements every second for 60 seconds, and upload their average value to the Wolfram Data Drop every minute. This will give you 60 data points per hour and 1,440 data points per day. With this data you can use Wolfram Programming Cloud to understand how the temperature changes over time. You can find the exact times in a given day when the temperature was the highest or lowest, when the temperature changed the fastest, and maybe even use the data to make predictions! Can you beat your local weather station and make a prediction that is better?
March 12, 2015 — Stephen Wolfram
Pictures from Pi Day now added »
Between Mathematica and Wolfram|Alpha, I’m pretty sure our company has delivered more π to the world than any other organization in history. So of course we have to do something special for Pi Day of the Century.
March 4, 2015 — Stephen Wolfram
Where should data from the Internet of Things go? We’ve got great technology in the Wolfram Language for interpreting, visualizing, analyzing, querying and otherwise doing interesting things with it. But the question is, how should the data from all those connected devices and everything else actually get to where good things can be done with it? Today we’re launching what I think is a great solution: the Wolfram Data Drop.
When I first started thinking about the Data Drop, I viewed it mainly as a convenience—a means to get data from here to there. But now that we’ve built the Data Drop, I’ve realized it’s much more than that. And in fact, it’s a major step in our continuing efforts to integrate computation and the real world.
So what is the Wolfram Data Drop? At a functional level, it’s a universal accumulator of data, set up to get—and organize—data coming from sensors, devices, programs, or for that matter, humans or anything else. And to store this data in the cloud in a way that makes it completely seamless to compute with.
February 27, 2015 — Vitaliy Kaurov, Technical Communication & Strategy
Martin Handford can spend weeks creating a single Where’s Waldo puzzle hiding a tiny red and white striped character wearing Lennon glasses and a bobble hat among an ocean of cartoon figures that are immersed in amusing activities. Finding Waldo is the puzzle’s objective, so hiding him well, perhaps, is even more challenging. Martin once said, “As I work my way through a picture, I add Wally when I come to what I feel is a good place to hide him.” Aware of this, Ben Blatt from Slate magazine wondered if it’s possible “to master Where’s Waldo by mapping Handford’s patterns?” Ben devised a simple trick to speed up a Waldo search. In a sense, it’s the same observation that allowed Jon McLoone to write an algorithm that can beat a human in a Rock-Paper-Scissors game. As Jon puts it, “we can rely on the fact that humans are not very good at being random.”
January 30, 2015 — Jenna Giuffrida, Content Administrator, Technical Communications and Strategy Group
This weekend marks the culmination of blood, sweat, and, oh yes, tears (Deflategate, anyone?) from months of struggle: Super Bowl XLIX.
For those of you who are interested, Wolfram|Alpha possesses a wealth of sports stats so that you can get all the cold, hard facts about the Patriots and the Seahawks.
December 15, 2014 — Wolfram Blog Team
It’s the holiday season, and Wolfram is gearing up for bright lights and winter weather by holding a new Tweet-a-Program challenge. To help us celebrate the holidays, tweet your best holiday ornament-themed lines of Wolfram Language code. As with our other challenges, we’ll use the Wolfram Language to randomly select winning tweets (along with a few of our favorites) to pin, retweet, and share with our followers. If you’re a lucky winner, we’ll send you a free Wolfram T-shirt!
If you need some help getting into the holiday spirit, check out these examples:
December 3, 2014 — Adriana O'Brien, Business Development, Partnerships
Get ready, get set… code! It’s the time of year to get thinking about programming with the Hour of Code.
For many years, Wolfram Research has promoted and supported initiatives that encourage computation, programming, and STEM education, and we are always thrilled when efforts are taken by others to do the same. Code.org, in conjunction with Computer Science Education Week, is sponsoring an event to encourage educators and organizations across the country to dedicate a single hour to coding. This hour gives kids (and adults, too!) a taste of what it means to study computer science—and how it can actually be a creative, fun, and fulfilling process. Millions of students participated in the Hour of Code in past years, and instructors are looking for more engaging activities for their students to try. Enter the Wolfram Language.
December 1, 2014 — Piotr Wendykier, Mathematica Algorithm R&D
Can computers learn to paint like Van Gogh? To some extent—definitely yes! For that, akin to human imitation artists, an algorithm should first be fed the original artists’ creations, and then it will be able to generate a machine take on them. How well? Please judge for yourself.
Recently the Department of Engineering at the University of Cambridge announced the winners of the annual photography competition, “The Art of Engineering: Images from the Frontiers of Technology.” The second prize went to Yarin Gal, a PhD student in the Machine Learning group, for his extrapolation of Van Gogh’s painting Starry Night, shown above. Readers can view this and similar computer-extended images at Gal’s website Extrapolated Art. An inpainting algorithm called PatchMatch was used to create the machine art, and in this post I will show how one can obtain similar effects using the Wolfram Language.
November 26, 2014 — Rita Crook, Marketing Projects Manager
The Imitation Game, a movie portraying Alan Turing’s life (who would have celebrated his 100th birthday on Mathematica‘s 23rd birthday—read our blog post), was released this week, which we’ve been looking forward to. Turing machines were one of the focal points of the movie, and we launched a prize in 2007 to determine whether the 2,3 Turing machine was universal.
So of course, Cumberbatch’s promotional video where he impersonates other beloved actors reached us as well, which got me wondering, could Mathematica‘s machine learning capabilities recognize his voice, or could he fool a computer too?