Zach Littrell
John Moore

Edit Your NaNoWriMo Novel with the Wolfram Language

December 9, 2016
Zach Littrell, Technical Content Writer, Technical Communications and Strategy Group
John Moore, Wolfram Blog Team

If you’re like many of us at Wolfram, you probably know that November was National Novel Writing Month (NaNoWriMo). Maybe you even spent the past few weeks feverishly writing, pounding out that coming-of-age story about a lonely space dragon that you’ve been talking about for years.

Congratulations! Now what? Revisions, of course! And we, the kindly Wolfram Blog Team, are here to get you through your revisions with a little help from the Wolfram Language.

Woolf, Verne, You

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Alyson Gamble

Computational Thinking beyond Computer Science Education Week

December 5, 2016 — Alyson Gamble, Wolfram Blog Team

Whatever their future fields, students need to learn computational thinking, a method of problem solving in which questions are framed in a way that can be communicated to a computer.

compthinkingimage

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Etienne Bernard

New in the Wolfram Language: FeatureExtraction

December 2, 2016 — Etienne Bernard, Lead Architect, Advanced Research Group

Two years ago, we introduced the first high-level machine learning functions of the Wolfram Language, Classify and Predict. Since then, we have been creating a set of automatic machine learning functionalities (ClusterClassify, DimensionReduction, etc.). Today, I am happy to present a new function called FeatureExtraction that deals with another important machine learning task: extracting features from data. Unlike Classify and Predict, which follow the supervised learning paradigm, FeatureExtraction belongs to the unsupervised learning paradigm, meaning that the data to learn from is given as a set of unlabeled examples (i.e. without an input -> output relation). The main goal of FeatureExtraction is to transform these examples into numeric vectors (often called feature vectors). For example, let’s apply FeatureExtraction to a simple dataset:

fe = FeatureExtraction[{{1.4, "A"}, {1.5, "A"}, {2.3, "B"}, {5.4,      "B"}}]

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John Moore

Making Wikipedia Knowledge Visible

November 23, 2016 — John Moore, Wolfram Blog Team

Dylan's Infinite Universe

Over the past few months, Wolfram Community members have been exploring ways of visualizing the known universe of Wikipedia knowledge. From Bob Dylan’s networks to the persistence of “philosophy” as a category, Wolfram Community has been asking: “What does knowledge actually look like in the digital age?”

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John Fultz

Launching Wolfram Player for iOS

November 16, 2016 — John Fultz, Director of User Interface Technology

It’s been a long road.

Wolfram Player for iOS logo

To some degree, we’ve been working on a Wolfram notebook front end for iOS for about six years now. And in the process, we’ve learned a lot about notebook front ends, a thing we already knew a lot about. Let’s rewind the tape a bit and review.

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Kathryn Cramer

Celebrating Gottfried Leibniz on the 300th Anniversary of His Death

November 14, 2016 — Kathryn Cramer, Technical Communications and Strategy Group

Today is the 300th anniversary of the death of Gottfried Leibniz, a man whose work has had a deep influence on what we do here at Wolfram Research. He was born July 1, 1646, in Leipzig, and died November 14, 1716, in Hanover, which was, at the time, part of the Holy Roman Empire. I associate his name most strongly with my time learning calculus, which he invented in parallel with Isaac Newton. But Leibniz was a polymath, and his ideas and influence were much broader than that. He invented binary numbers, the integral sign and an early form of mechanical calculator.

Leibniz portrait and notebook

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Christopher Carlson

The 2016 Wolfram One-Liner Competition Winners

November 9, 2016 — Christopher Carlson, Senior User Interface Developer, User Interfaces

Could you fit the code for a fully functional game of Pong into a single tweet? One that gives you more points the more you take your chances in letting the “ball” escape? Philip Maymin did, and took first prize with that submission in the One-Liner Competition held at this year’s Wolfram Technology Conference.

Participants in the competition submit 128 or fewer tweetable characters of Wolfram Language code to perform the most impressive computation they can dream up. We had a bumper crop of entries this year that showed the surprising power of the Wolfram Language. You might think that after decades of experience creating and developing with the Wolfram Language, we at Wolfram Research would have seen and thought of it all. But every year our conference attendees surprise us. Read on to see the amazing effects you can achieve with a tweet of Wolfram Language code.

Honorable Mention
Amy Friedman: “The Song Titles” (110 characters)

Friedman's Submission

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Zach Littrell

My Wolfram Tech Conference 2016 Highlights

November 4, 2016 — Zach Littrell, Technical Content Writer, Technical Communications and Strategy Group

Here are just a handful of things I heard while attending my first Wolfram Technology Conference:

  • “We had a nearly 4-billion-time speedup on this code example.”
  • “We’ve worked together for over 9 years, and now we’re finally meeting!”
  • “Coding in the Wolfram Language is like collaborating with 200 or 300 experts.”
  • “You can turn financial data into rap music. Instead, how about we turn rap music into financial data?”

As a first-timer from the Wolfram Blog Team attending the Technology Conference, I wanted to share with you some of the highlights for me—making new friends, watching Wolfram Language experts code and seeing what the Wolfram family has been up to around the world this past year.

Images from the 2016 Wolfram Tech Conference

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John Moore

Less Than Five Lines of Code

October 27, 2016 — John Moore, Wolfram Blog Team

Software engineer and longtime Mathematica user Chad Slaughter uses the Wolfram Language to facilitate interdepartmental communication during software development. While most programming languages are designed to do one thing particularly well, developers like Slaughter often find that the Wolfram Language is more versatile: “With traditional C++, in order to develop a program, it’s going to take several hundred lines of code to do anything interesting. With Mathematica, I can do something interesting in less than five lines of code.”

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Patrik Ekenberg

Announcing Wolfram SystemModeler 4.3

October 25, 2016 — Patrik Ekenberg, Applications Engineer, Wolfram MathCore

Today I am excited to announce SystemModeler 4.3. This release focuses on three key areas: model analytics, collaboration and performance, which I will illustrate in this blog. You can see more on the What’s New page, or download a trial to try it yourself.

I’ll start by talking about our improvements in collaboration. I develop lots of models in SystemModeler, and when I do, I seldom develop them in a vacuum. Either I send a model to my colleagues for them to use, I receive one from them or models get sent back and forth while we work on them together. This is, of course, also true for novice users. A great way to learn how to use SystemModeler—or any product, for that matter—is to look at things other people have done, whether it be a coworker or other users online, and build upon that.

Whether you send your models to other people, receive models or send models between your own platforms, we want to make sure that you have everything you need to start using the model, straight out of the box.

As an example, I have built a model of an inverted pendulum using the PlanarMechanics library. It has a linear-quadratic regulator built using the Modelica Standard Library, and it also includes components from the ModelPlug library that connect to real-life hardware, such as actuators and sensors on an Arduino board (or any other board following the Firmata protocol).

Model of an inverted pendulum

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