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.”
February 9, 2015 — Jenna Giuffrida, Content Administrator, Technical Communications and Strategy Group
We are once again thrilled by the wide variety of topics covered by authors around the world using Wolfram technologies to write their books and explore their disciplines. These latest additions range from covering the basics for students to working within specialties like continuum mechanics.
November 4, 2014 — Vitaliy Kaurov, Technical Communication & Strategy
Data is critical for an objective outlook, but bare data is not a forecast. Scientific models are necessary to predict pandemics, terrorist attacks, natural disasters, market crashes, and other complex aspects of our world. One of the tools for combating the ongoing and tragic Ebola outbreak is to make computer models of the virus’s possible spread. By understanding where and how quickly the outbreak is likely to appear, policy makers can put into place effective measures to slow transmissions and ultimately bring the epidemic to a halt. Our goal here is to show how to set up a mathematical model that depicts a global spread of a pandemic, using real-world data. The model would apply to any pandemic, but we will sometimes mention and use current Ebola outbreak data to put the simulation into perspective. The results should not be taken as a realistic quantitative projection of current Ebola pandemic.
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 7, 2014 — Wolfram Blog Team
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 Team
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!