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?
For most of us, taking bad pictures is incredibly easy. Band-Aid or remedy, digital post-processing can involve altering the photographed scene itself. Say you’re trekking through the mountains taking photos of the horizon, or you’re walking down the street and catch a beautiful perspective of the city, or it’s finally the right time to put the new, expensive phone camera to good use and capture the magic of this riverside… Just why do all the pictures look so bad? They’re all foggy! It’s not that you’re a bad photographer—OK, maybe you are—but that you’ve stumbled on a characteristic problem in outdoor photography: haze.
What is haze? Technically, haze is scattered light, photons bumped around by the molecules in the air and deprived of their original color, which they got by bouncing off the objects you are trying to see. The problem gets worse with distance: the more the light has to travel, the more it gets scattered around, and the more the scene takes that foggy appearance.
What can we do? What can possibly help our poor photographer? Science, of course.
Wolfram recently attended and sponsored the 2014 IEEE International Conference on Image Processing (ICIP), which ended October 30 in Paris. It was a good occasion to review the previous years’ best papers at the conference, and we noticed an interesting take on the haze problem proposed by Chen Feng, Shaojie Zhuo, Xiaopeng Zhang, Liang Shen, and Sabine Süsstrunk . Let’s give their method a try and implement their “dehazing” algorithm.
The core idea behind the paper is to leverage the different susceptibilities of the light being scattered, which depend on the wavelength of the light. Light with a larger wavelength, such as red light, is more likely to travel around the dust, the smog, and all the other particles present in the air than shorter wavelength colors, like green or blue. Therefore, the red channel in an image carries better information about the non-hazy content of the scene.
But what if we could go even further? What prevents us from using the part of the spectrum slightly beyond the visible light? Nothing really—save for the fact we need an infrared camera.
Provided we are well equipped, we can then use the four channels of data (near infrared, red, green, and blue) to estimate the haze color and distribution and proceed to remove it from our image.
November 17, 2014 — Wolfram Blog Team
This week Wolfram will be celebrating Benoit Mandelbrot‘s birthday and his contributions to mathematics by holding a Tweet-a-Program challenge. In honor of Mandelbrot, tweet us your favorite fractal-themed lines of Wolfram Language code. Then, 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 win, we’ll send you a free Wolfram T-shirt!
In Tweet-a-Program’s first few exciting months, we’ve already seen a number of awesome fractal examples like these:
November 10, 2014 — Christopher Carlson, Senior User Interface Developer, User Interfaces
This year’s Wolfram Technology Conference once again included the One-Liner Competition, an opportunity for some of the world’s most talented Wolfram Language developers to show us the amazing things you can do with tiny pieces of Wolfram Language code.
In previous years, One-Liner submissions were allowed 140 characters and 2D typesetting constructs. This year, in the spirit of Tweet-a-Program, we limited entries to 128-character, tweetable Wolfram Language programs. That’s right: we challenged them to write a useful or entertaining program that fits in a single tweet.
And the participants rose to the occasion. Entries were blind-judged by a panel of Wolfram Research developers, who awarded two honorable mentions and first, second, and third prizes.
One honorable mention went to Michael Sollami for his “Mariner Valley Flyby,” which takes you on a flight through the terrain of the Mariner Valley on Mars. The judges were greatly impressed by the idea and the effect. Unfortunately, a small glitch in the program is visible at the start of the output, due to an error in the code. Since Michael’s submission is right up against the 128-character limit, it would have taken some clever tweaking to fix it.