October 16, 2014 — Jenna Giuffrida, Content Administrator, Technical Communications and Strategy Group
Summer has drawn to a close, and so too have our annual internships. Each year Wolfram welcomes a new group of interns to work on an exciting array of projects ranging all the way from Bell polynomials to food science. It was a season for learning, growth, and making strides across disciplinary and academic divides. The Wolfram interns are an invaluable part of our team, and they couldn’t wait to tell us all about their time here. Here are just a few examples of the work that was done.
October 13, 2014 — Conrad Wolfram, Director of Strategic & International Development
I’m usually going on about “computation,” or in education, “maths.” But I’ve come to appreciate just how much of computation’s utility in modern life centres around data (rather than, say, algebraic modelling).
Clearly data science is a major, growing, and vital field—one that’s relatively new in its current incarnation. It’s been born and is driven forward by new technology and our ability to collect, store, transmit, and “process” ever larger quantities of data.
But “processing” has often failed to elucidate what’s important in the data. We need answers, not just analytics; we need decisions, not just big data.
Computation in all its forms is a key to getting decisions from data. And funnily enough, it’s not only for analytics that computation’s used, but for enabling human language data interrogation, interactive deployment, and many other examples—crucial usability, not only raw computational power.
It’s to bring all these aspects together that we’re hosting a 1-day summit in London next month entitled “Master Your Data with [the Latest, Most Powerful!] Computation,” that’s with my [ ] editorial.
October 7, 2014 — Wolfram Blog
In honor of World Space Week and this year’s theme of satellite navigation, “Space: Guiding Your Way,” we’re issuing a Tweet-a-Program Code Challenge focused on anything to do with space and getting there. You tweet us your “space-iest” line(s) of Wolfram Language code, and then we’ll use the Wolfram Language to randomly select three winning tweets (plus a few favorites) to shower with retweets, pin or post to our wall, and receive a free Wolfram T-shirt!
Any space-themed submissions tweeted to us @wolframtap all day Thursday and Friday (12am PDT Thursday, October 9 through 11:59pm PDT Friday, October 10) will be eligible to win. To not waste needed code space, no hashtag is required with your original submission, but we encourage you to share your results by retweeting them with hashtag #wsw2014 and #tapspaceweek.
In addition to satellite path tracking and real-time analysis, the Wolfram Language gives you access to all sorts of entities, formulas, and other functionality for astronomical computation and coding—from supernovas, comets, and constellations to the Sun, deep space, and other galaxies.
Maybe you want to remix the planets and their colors, as Stephen Wolfram did in one of his first Tweet-a-Program tweets:
August 19, 2014 — Michael Trott, Chief Scientist
In today’s blog post, we will use some of the new features of the Wolfram Language, such as language processing, geometric regions, map-making capabilities, and deploying forms to analyze and visualize the distribution of beer breweries and whiskey distilleries in the US. In particular, we want to answer the core question: for which fraction of the US is the nearest brewery further away than the nearest distillery?
Disclaimer: you may read, carry out, and modify inputs in this blog post independent of your age. Hands-on taste tests might require a certain minimal legal age (check your countries’ and states’ laws).
We start by importing two images from Wikipedia to set the theme; later we will use them on maps.
July 22, 2014 — Wolfram Blog
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.
February 21, 2014 — Wolfram Blog Team
Editorial note: This post was written by Paul-Jean Letourneau as a follow-up to his post Mathematica Gets Big Data with HadoopLink.
I’ve blogged in the past about some of the cool genomics features in Wolfram|Alpha. You can even search the human genome for DNA sequences you’re interested in. Biologists often need to search for the locations of DNA fragments they find in the lab, in order to know what animal the fragment belongs to, or what chromosome it’s from. Let’s use HadoopLink to build a genome search engine!
July 15, 2013 — Matthias Odisio, Mathematica Algorithm R&D
Or: How I Learned to Watch the Best Movies in the Best Way
I remember when I lived across the street from an art movie theater called Le Club, looking at the movie posters on my way back home was often enough to get me in the ticket line. The director or main actors would ring a bell, or a close friend had recommended the title. Sometimes the poster alone would be appealing enough to lure me in. Even today there are still occasions when I make decisions from limited visual information, like when flipping through movie kiosks, TV guides, or a stack of DVDs written in languages I can’t read.
So how can Mathematica help? We’ll take a look at the top 250 movies rated on IMDb. Based on their posters and genres, how can one create a program that suggests which movies to see? What is the best way to see the most popular movies in sequence?
May 22, 2013 — Jon McLoone, International Business & Strategic Development
The benefits of linking from Mathematica to other languages and tools differ from case to case. But unusually, in the case of the new RLink in Mathematica 9, I think the benefits have very little to do with R, the language. The real benefit, I believe, is in the connection it makes to the R community.
When we first added the MathLink libraries for C, there were real benefits in farming out intensive numerical work (though Mathematica performance improvements over the years and development of the compiler have greatly reduced the occasions where that would be worth the effort). Creating an Excel link added an alternative interface paradigm to Mathematica that wasn’t available in the Mathematica front end. But in the case of R, it isn’t immediately obvious that it does many things that you can’t already do in Mathematica or many that it does significantly better.
However, with RLink I now have immediate access to the work of the R community through the add-on libraries that they have created to extend R into their field. A great zoo of these free libraries fill out thousands of niches–sometimes popular, sometimes obscure–but lots of them. There are over 4,000 right here and more elsewhere. At a stroke, all of them are made immediately available to the Mathematica environment, interpreted through the R language runtime.
May 9, 2013 — Matthias Odisio, Mathematica Algorithm R&D
Detecting skin in images can be quite useful: it is one of the primary steps for various sophisticated systems aimed at detecting people, recognizing gestures, detecting faces, content-based filtering, and more. In spite of this host of applications, when I decided to develop a skin detector, my main motivation lay elsewhere. The research and development department I work in at Wolfram Research just underwent a gentle reorganization. With my colleagues who work on probability and statistics becoming closer neighbors, I felt like developing a small application that would make use of both Mathematica‘s image processing and statistics features; skin detection just came to my mind.
Skin tones and appearances vary, and so do flavors of skin detectors. The detector I wanted to develop is based on probabilistic models of pixel colors. For each pixel of an image given as input, the skin detector provides a probability that the pixel color belongs to a skin region.