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.

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March 27, 2015 — Tim Shedelbower, Visualization Developer

Array of gauges

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.

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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.

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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.

Printed poster timeline

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.

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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.

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.

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February 5, 2015 — Emily Suess, Technical Writer, Technical Communications and Strategy Group

As Valentine’s Day approaches, Wolfram is holding a Tweet-a-Program challenge. To help us celebrate the romantic holiday, tweet us your best Valentine-themed Wolfram Language code. As with our other challenges, we’ll pin, retweet, and share your submissions with our followers—and we’ll use the Wolfram Language to randomly select winning tweets, along with one or two of our favorites. If you’re a lucky winner, we’ll send you a Wolfram T-shirt!

Submissions aren’t limited to heart-themed programs, but check out these examples if you need a little inspiration:

Wolfram Tweet-a-Program ContourPlot3D

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January 15, 2015 — Oleksandr Pavlyk, Kernel Technology

January 16, 2015, marks the 360th birthday anniversary of Jacob Bernoulli (also James, or Jacques).

Input 1 through Output 4

Jacob Bernoulli was the first mathematician in the Bernoulli family, which produced many notable mathematicians of the seventeenth and eighteenth centuries.

Jacob Bernoulli’s mathematical legacy is rich. He introduced Bernoulli numbers, solved the Bernoulli differential equation, studied the Bernoulli trials process, proved the Bernoulli inequality, discovered the number e, and demonstrated the weak law of large numbers (Bernoulli’s theorem).

Jacob Bernoulli's mathematical achievements

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January 6, 2015 — Mikael Forsgren, Wolfram MathCore

Mathematical modeling is not just used for understanding and designing new products and drugs; modeling can also be used in health care, and in the future, your doctor might examine your liver with a mathematical model just like the one researchers at AstraZeneca have developed.


The liver is a vital organ, and currently there isn’t really a way to compensate for loss of liver function in the long term. The liver performs a wide range of functions, including detoxification, protein synthesis, and secretion of compounds necessary for digestion, just to mention a few. In the US and Europe, up to 15 % of all acute liver failure cases are due to drug-induced liver injury, and the risk of injuring the liver is of major concern in testing new drug candidates. So in order to safely monitor the impact of a new drug candidate on the liver, researchers at the pharmaceutical company AstraZeneca have recently published a method for evaluating liver function that combines magnetic resonance imaging (MRI) and mathematical modeling—potentially allowing for early identification of any reduced liver function in humans.

Last year, Wolfram MathCore and AstraZeneca worked together on a project where we investigated some modifications of AstraZeneca’s modeling framework. We presented the promising results at the ISMRM-ESMRMB Joint Annual Meeting, which is the major international magnetic resonance conference. In this blog post, I’ll show how the Wolfram Language was used to calculate liver function and how more complex models of liver function can be implemented in Wolfram SystemModeler.

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December 29, 2014 — Tom Sherlock, User Interface Group

As an amateur astronomer, I’m always interested in ways to use Mathematica in my hobby. In earlier blog posts, I’ve written about how Mathematica can be used to process and improve images taken of planets and nebulae. However, I’d like to be able to control my astronomical hardware directly with the Wolfram Language.

In particular, I’ve been curious about using the Wolfram Language as a way to drive my telescope mount, for the purpose of automating an observing session. There is precedent for this because some amateurs use their computerized telescopes to hunt down transient phenomena like supernovas. Software already exists for performing many of the tasks that astronomers engage in—locating objects, managing data, and performing image processing. However, it would be quite cool to automate all the different tasks associated with an observing session from one notebook.

Mathematica is highly useful because it can perform many of these operations in a unified manner. For example, Mathematica incorporates a vast amount of useful astronomical data, including the celestial coordinates of hundreds of thousands of stars, nebula, galaxies, asteroids, and planets. In addition to this, Mathematica‘s image processing and data handling functionality are extremely useful when processing astronomical data.

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December 15, 2014 — Wolfram Blog Team

It’s the holiday season, and Wolfram is gearing up for bright lights and winter weather by holding a new Tweet-a-Program challenge. To help us celebrate the holidays, tweet your best holiday ornament-themed lines of Wolfram Language code. As with our other challenges, we’ll use the Wolfram Language to randomly select winning tweets (along with a few of our favorites) to pin, retweet, and share with our followers. If you’re a lucky winner, we’ll send you a free Wolfram T-shirt!

If you need some help getting into the holiday spirit, check out these examples:


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