March 27, 2015 — Tim Shedelbower, Visualization Developer
The first gauge I remember was a blue wrist watch I received from my parents as a child. Their hope was probably to correct my tardiness, but it proved valuable for more important tasks such as timing bicycle races. Today digital gauges help us analyze a variety of data on smart phones and laptops. Battery level, signal strength, network speed, and temperature are some of the common data elements constantly monitored.
Today we are proud to announce the release of Wolfram SystemModeler 4.1. We will present some of the news in blog posts, beginning with this one, in which we will highlight the new reliability functionality.
We will illustrate this with an example, and you can try it out by downloading a trial version of SystemModeler and this example model, and a trial of the Wolfram Hydraulic library.
Most people probably have experiences with things they bought and liked, but that then suddenly failed for some reason. During the last few years we have both experienced this problem, including a complete engine breakdown in Johan’s car (the engine had to be replaced), and Jan’s receiver, which suddenly went completely silent (the receiver had to be sent in for repair and have its network chip replaced).
In both cases it caused problems for the customers (us) as well as for the producer. These are just a couple of examples, and we’re sure you have your own.
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 20, 2015 — Alan Joyce, Director, Content Development
Since the inception of Wolfram|Alpha, Wikipedia has held a special place in its development pipeline. We usually use it not as a primary source for data, but rather as an essential resource for improving our natural language understanding, particularly for mining the common and colloquial ways people refer to entities and concepts in various domains.
We’ve developed a lot of internal tools to help us analyze and extract information from Wikipedia over the years, but now we’ve also added a Wikipedia “integrated service” to the latest version of the Wolfram Language—making it incredibly easy for anyone to incorporate Wiki content into Wolfram Language workflows.
March 19, 2015 — Todd Rowland, Academic Director, Wolfram Science Summer School
Imagine people from all around the world, young and old, neuroscientists and quantum physicists, Arduino hackers and music composers, gathered with Stephen Wolfram and his team in one place to discover new science and technology.
That’s the Wolfram Science Summer School, which for the last decade or so has been my favorite time of the year. When it was founded in 2003, the school’s focus was on Stephen Wolfram’s A New Kind of Science, but its scope has expanded to include what is now called Wolfram Science. Stephen Wolfram explained in a blog post last year how this school is like entrepreneurship science. It’s not about doing the same old stuff, as you might get in a typical academic environment.
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 11, 2015 — Brett Champion, Manager, Visualization
A few years ago we created a timeline of the history of systematic data and computable knowledge, which you can look at online. I wrote the code that placed events along the timeline, and then our graphic designers did the real work in deciding where to put the labels, choosing fonts and colors, and doing all the other things that go into creating a production-quality poster.
Fast-forward a bit, and last year we added NumberLinePlot to the Wolfram Language to visualize points, intervals, and inequalities. Once people started seeing the number lines, we began getting requests for similar plots, but with dates and times, so we decided it was time to tackle TimelinePlot.
March 9, 2015 — Adriana O'Brien, Business Development, Partnerships
Today we’re excited to announce that the Wolfram Demonstrations Project has crossed the 10,000 Demonstrations mark and is now supporting the latest versions of the Wolfram Language and CDF Player. Launched in 2007, the Demonstrations Project is the largest open web repository of peer-reviewed interactive knowledge apps. With examples ranging from elementary math to medical image processing, the site fulfills a need for professionally vetted, sophisticated, and easy-to-use resources for students, educators, publishers, and anyone looking to communicate technical concepts with graphic clarity.
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.