April 16, 2015 — Stephen Wolfram
The Wolfram Cloud Needs to Be Perfect
The Wolfram Cloud is coming out of beta soon (yay!), and right now I’m spending much of my time working to make it as good as possible (and, by the way, it’s getting to be really great!). Mostly I concentrate on defining high-level function and strategy. But I like to understand things at every level, and as a CEO, one’s ultimately responsible for everything. And at the beginning of March I found myself diving deep into something I never expected…
Here’s the story. As a serious production system that lots of people will use to do things like run businesses, the Wolfram Cloud should be as fast as possible. Our metrics were saying that typical speeds were good, but subjectively when I used it something felt wrong. Sometimes it was plenty fast, but sometimes it seemed way too slow.
We’ve got excellent software engineers, but months were going by, and things didn’t seem to be changing. Meanwhile, we’d just released the Wolfram Data Drop. So I thought, why don’t I just run some tests myself, maybe collecting data in our nice new Wolfram Data Drop?
A great thing about the Wolfram Language is how friendly it is for busy people: even if you only have time to dash off a few lines of code, you can get real things done. And in this case, I only had to run three lines of code to find a problem.
First, I deployed a web API for a trivial Wolfram Language program to the Wolfram Cloud:
April 14, 2015 — Alan Joyce, Director, Content Development
Wolfram|Alpha’s Facebook analytics ranks high among our all-time most popular features. By now, millions of people have used Wolfram|Alpha to analyze their own activity and generate detailed analyses of their Facebook friend networks. A few years ago, we took data generously contributed by thousands of “data donors” and used the Wolfram Language’s powerful tools for social network analysis, machine learning, and data visualization to uncover fascinating insights into the demographics and interests of Facebook users.
At the end of this month, however, Facebook will be deprecating the API we relied on to extract much of this information.
April 10, 2015 — Jeremy Michelson, Manager of Data and Semantics Engineering
The Wolfram Language provides tools for programmatic handling of free-form input. For example, Interpreter, which was introduced in Version 10.0, converts snippets of text into computable Wolfram Language expressions. In smart form fields, this functionality can automatically translate input like “forty-two” into a Wolfram Language expression like “42.”
But what does it take to perform more complicated operations or customize responses and actions? For that you need a grammar. The grammar indicates the structure that should be matched and the action that should be taken using information extracted from the match.
A grammar gives you natural language control over your computer so that you can process language snippets to yield functions that perform commands. For example, telling your computer to “open a website” requires mapping snippets like “open” and “a website” to the Open command and the URL of a website.
An important emerging standard has been rapidly adopted by industry: the Functional Mock-up Interface (FMI). It’s an independent standard allowing model exchange between different tools. We introduced FMI export with Version 4.0 of SystemModeler. Exporting your model as a Functional Mock-up Unit (FMU) serves many purposes. First and foremost, it can be used in other tools and programming languages. It also protects your intellectual property by compiling the model code to a binary, which is useful when exchanging models with customers and collaborators. Now with Version 4.1 of SystemModeler, we are happy to announce that we also support FMI import.
April 2, 2015 — Vitaliy Kaurov, Technical Communication & Strategy
You may have heard that on March 20 there was a solar eclipse. Depending on where you are geographically, a solar eclipse may or may not be visible. If it is visible, local media make a small hype of the event, telling people how and when to observe the event, what the weather conditions will be, and other relevant details. If the eclipse is not visible in your area, there is a high chance it will draw very little attention. But people on Wolfram Community come from all around the world, and all—novices and experienced users and developers—take part in these conversations. And it is a pleasure to witness how knowledge of the subject and of Wolfram technologies and data from different parts of the world are shared.
March 31, 2015 — Danielle Rommel, Events Manager
Are you a student and a technology junkie? If so, keep reading! The Wolfram Student Ambassador Program allows exemplary students the opportunity to further their tech career by acting as the face of Wolfram at their universities (plus earn some great swag, opportunities, and prizes).
For this pilot program, we are searching for one representative each from colleges and universities all around North America. We are looking for the top tier of technical talent, the peak of perfection, the coolest of coders. The ideal candidate will have 10—14 hours to dedicate to the program each month. They are already a leader on campus, charismatic and loved by all, and with an undying passion for Wolfram technologies.
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.