December 23, 2015 — Kathryn Cramer, Technical Communications and Strategy Group
With some impressive new features, new forums, and many new members, Wolfram Community has had a great year. As we approach the end of 2015, we wanted to share a few highlights from the last few months’ excellent posts on the Wolfram Community site.
Interested in drones? Check out these posts.
Connecting ROS to the Wolfram Language, Or Controlling a Parrot ArDrone 2.0 from Mathematica, by Loris Gliner, a student in aeronautical engineering.
Loris Gliner used his time in the Wolfram mentorship program to work out how to connect the Wolfram Language to the Linux Robot Operating System. He includes code examples and a video showing the flight of a Parrot ArDrone 2.0 controlled via the Wolfram Language.
December 4, 2015 — Bernat Espigulé-Pons, Consultant, Technical Communications and Strategy Group
About a year ago, I decided to record every single move I make using Runkeeper, and now I want to make some visualizations of my activity throughout the whole year. This is a fairly straightforward project where I will download the data from Runkeeper, then use the Wolfram Language to process, analyze, and visualize my activities. I will show how to create animations like this one that superimposes 24 minutes of all my activities recorded in Barcelona:
November 18, 2015 — Michael Trott, Chief Scientist
Paintings of the great masters are among the most beautiful human artifacts ever produced. They are treasured and admired, carefully preserved, sold for hundreds of millions of dollars, and, perhaps not coincidentally, are the prime target of art thieves. Their composition, colors, details, and themes can fascinate us for hours. But what about their outer shape—the ratio of a painting’s height to its width?
In 1876, the German scientist Gustav Theodor Fechner studied human responses to rectangular shapes, concluding that rectangles with an aspect ratio equal to the golden ratio are most pleasing to the human eye. To validate his experimental observations, Fechner also analyzed the aspect ratios of more than ten thousand paintings.
We can find out more about Fechner with the following piece of code:
September 17, 2015 — Dana Flinn, Project Administrator, Public Relations
If you’ve ever hit a roadblock while learning to code, then you know the frustration of trying to find the best resource to help you out. We have good news. We are happy to announce that Christopher Wolfram, son of Wolfram Research’s founder, Stephen Wolfram, will be live-coding on Livecoding.tv. This new Y Combinator–backed coding platform brings programmers together to watch live streams of people coding real products.
Enhance your coding skills and learn directly from someone with the knowledge and expertise that results from working directly with Stephen Wolfram. Christopher’s presentation will focus on education analytics; users who tune in will see a firsthand demonstration of how to interact with datasets and visualizations in the Wolfram Language. The live streaming is scheduled for Tuesday, September 22 at 7pm CDT.
July 21, 2015 — Emily Suess, Technical Writer, Technical Communications and Strategy Group
Wolfram Community connects you with users from around the world who are doing fun, innovative, and useful things with the Wolfram Language. From game theory and connected devices to astronomy and design, here are a few posts you won’t want to miss.
Are you familiar with the Reddit 60-second button? The Reddit experiment was a countdown that would vanish if it ever reached zero. Clicking a button gave the countdown another 60 seconds. One Community post brings Wolfram Language visualization and analysis to Reddit’s experiment, which has sparked questions spanning game theory, community psychology, and statistics. David Gathercole started by importing a dataset from April 3 to May 20 into Mathematica and charted some interesting findings. See what he discovered and contribute your own ideas.
May 13, 2015 — Stephen Wolfram
“What is this a picture of?” Humans can usually answer such questions instantly, but in the past it’s always seemed out of reach for computers to do this. For nearly 40 years I’ve been sure computers would eventually get there—but I’ve wondered when.
I’ve built systems that give computers all sorts of intelligence, much of it far beyond the human level. And for a long time we’ve been integrating all that intelligence into the Wolfram Language.
Now I’m excited to be able to say that we’ve reached a milestone: there’s finally a function called ImageIdentify built into the Wolfram Language that lets you ask, “What is this a picture of?”—and get an answer.
And today we’re launching the Wolfram Language Image Identification Project on the web to let anyone easily take any picture (drag it from a web page, snap it on your phone, or load it from a file) and see what ImageIdentify thinks it is:
April 28, 2015 — Stephen Wolfram
My goal with the Wolfram Language is to take programming to a new level. And over the past year we’ve been rolling out ways to use and deploy the language in many places—desktop, cloud, mobile, embedded, etc. So what about wearables? And in particular, what about the Apple Watch? A few days ago I decided to explore what could be done. So I cleared my schedule for the day, and started writing code.
My idea was to write code with our standard Wolfram Programming Cloud, but instead of producing a web app or web API, to produce an app for the Apple Watch. And conveniently enough, a preliminary version of our Wolfram Cloud app just became available in the App Store—letting me deploy from the Wolfram Cloud to both mobile devices and the watch.
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.”
August 1, 2014 — Arnoud Buzing, Director of Quality and Release Management
Today I’m happy to announce an update for Mathematica and the Wolfram Language for the Raspberry Pi that brings those new features to the Raspberry Pi. To get the new version of the Wolfram Language, simply run this command in a terminal on your Raspberry Pi:
sudo apt-get update && sudo apt-get install wolfram-engine
This new version will also be pre-installed in the next release of NOOBS, the easy setup system for the Raspberry Pi.