December 12, 2016 — Stephen Wolfram

Wolfram|Alpha and Wolfram Language logos

Code for Everyone

Computational thinking needs to be an integral part of modern education—and today I’m excited to be able to launch another contribution to this goal: Wolfram|Alpha Open Code.

Every day, millions of students around the world use Wolfram|Alpha to compute answers. With Wolfram|Alpha Open Code they’ll now not just be able to get answers, but also be able to get code that lets them explore further and immediately apply computational thinking.

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

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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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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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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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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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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October 27, 2016 — John Moore, Marketing and Technical Content Team Lead

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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October 14, 2016 — Carlo Barbieri, Applied Research Group

Making web forms should be dead simple. That has been one of our goals at Wolfram Research since the release of the Wolfram Cloud. We’ve made smart input fields, powered by Wolfram|Alpha technology, that understand almost anything users type. We’ve designed FormFunction and APIFunction so that you can build forms and APIs with the same readable syntax. And now with the newest version of the Wolfram Language, you can build interactive web forms with dynamic branching and control flow using the Ask family of functions.

Would you like to learn how to build web forms with Ask?

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September 7, 2016 — Stephen Wolfram

The Computational Future

Computational thinking is going to be a defining feature of the future—and it’s an incredibly important thing to be teaching to kids today. There’s always lots of discussion (and concern) about how to teach traditional mathematical thinking to kids. But looking to the future, this pales in comparison to the importance of teaching computational thinking. Yes, there’s a certain amount of traditional mathematical thinking that’s needed in everyday life, and in many careers. But computational thinking is going to be needed everywhere. And doing it well is going to be a key to success in almost all future careers.

Doctors, lawyers, teachers, farmers, whatever. The future of all these professions will be full of computational thinking. Whether it’s sensor-based medicine, computational contracts, education analytics or computational agriculture—success is going to rely on being able to do computational thinking well.

I’ve noticed an interesting trend. Pick any field X, from archeology to zoology. There either is now a “computational X” or there soon will be. And it’s widely viewed as the future of the field.

Computational Thinking WordCloud

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August 26, 2016 — Zach Littrell, Technical Content Writer, Technical Communications and Strategy Group

We are constantly surprised by what fascinating applications and topics Wolfram Language experts are writing about, and we’re happy to again share with you some of these amazing authors’ works. With topics ranging from learning to use the Wolfram Language on a Raspberry Pi to a groundbreaking book with a novel approach to calculations, you are bound to find a publication perfect for your interests.

Getting Started with Wolfram Language and Mathematica for Raspberry Pi, Essentials of Programming in Mathematica, Geospatial Algebraic Computations, Theory and Applications

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