June 11, 2019 — Stephen Wolfram

What the Wolfram Language Makes Possible

We’re on an exciting path these days with the Wolfram Language. Just three weeks ago we launched the Free Wolfram Engine for Developers to help people integrate the Wolfram Language into large-scale software projects. Now, today, we’re launching the Wolfram Function Repository to provide an organized platform for functions that are built to extend the Wolfram Language—and we’re opening up the Function Repository for anyone to contribute.

The Wolfram Function Repository is something that’s made possible by the unique nature of the Wolfram Language as not just a programming language, but a full-scale computational language. In a traditional programming language, adding significant new functionality typically involves building whole libraries, which may or may not work together. But in the Wolfram Language, there’s so much already built into the language that it’s possible to add significant functionality just by introducing individual new functions—which can immediately integrate into the coherent design of the whole language.

To get it started, we’ve already got 532 functions in the Wolfram Function Repository, in 26 categories:

The Wolfram Function Repository

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June 6, 2019 — Alec Shedelbower, Kernel Developer, Algorithms R&D

How I Built a Virtual Piano with the Wolfram Language and the Unity Game Engine

You know what’s harder than learning the piano? Learning the piano without a piano, and without any knowledge of music theory. For me, acquiring a real piano was out of the question; I had neither the funds nor space in my small college apartment. So naturally, it looked like I would have to build one myself—digitally, of course. And luckily, I had Mathematica, Unity and a few hours to spare. Because working in Unity is incredibly quick and efficient with the Wolfram Language and UnityLink, I’ve created a playable section of piano, and even learned a bit of music theory in the process.

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May 21, 2019 — Stephen Wolfram

Why Aren’t You Using Our Technology?

It happens far too often. I’ll be talking to a software developer, and they’ll be saying how great they think our technology is, and how it helped them so much in school, or in doing R&D. But then I’ll ask them, “So, are you using Wolfram Language and its computational intelligence in your production software system?” Sometimes the answer is yes. But too often, there’s an awkward silence, and then they’ll say, “Well, no. Could I?”

Free Wolfram Engine for DevelopersI want to make sure the answer to this can always be: “Yes, it’s easy!” And to help achieve that, we’re releasing today the Free Wolfram Engine for Developers. It’s a full engine for the Wolfram Language, that can be deployed on any system—and called from programs, languages, web servers, or anything.

The Wolfram Engine is the heart of all our products. It’s what implements the Wolfram Language, with all its computational intelligence, algorithms, knowledgebase, and so on. It’s what powers our desktop products (including Mathematica), as well as our cloud platform. It’s what’s inside Wolfram|Alpha—as well as an increasing number of major production systems out in the world. And as of today, we’re making it available for anyone to download, for free, to use in their software development projects.

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May 9, 2019 — Stephen Wolfram

Wolfie

What Kind of a Thing Is the Wolfram Language?

I’ve sometimes found it a bit of a struggle to explain what the Wolfram Language really is. Yes, it’s a computer language—a programming language. And it does—in a uniquely productive way, I might add—what standard programming languages do. But that’s only a very small part of the story. And what I’ve finally come to realize is that one should actually think of the Wolfram Language as an entirely different—and new—kind of thing: what one can call a computational language.

So what is a computational language? It’s a language for expressing things in a computational way—and for capturing computational ways of thinking about things. It’s not just a language for telling computers what to do. It’s a language that both computers and humans can use to represent computational ways of thinking about things. It’s a language that puts into concrete form a computational view of everything. It’s a language that lets one use the computational paradigm as a framework for formulating and organizing one’s thoughts.

It’s only recently that I’ve begun to properly internalize just how broad the implications of having a computational language really are—even though, ironically, I’ve spent much of my life engaged precisely in the consuming task of building the world’s only large-scale computational language.

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March 7, 2019 — Ed Pegg Jr, Editor, Wolfram Demonstrations Project

The sqrt(χ) substitution tiling fractal

Similar Triangle Dissections

Version 12 of the Wolfram Language introduces solvers for geometry problems. The documentation for the new function GeometricScene has a neat example showing the following piece of code, with GeometricAssertion calling for seven similar triangles:

Sqrt(ρ) substitution tiling
&#10005

o=Sequence[Opacity[.9],EdgeForm[Black]];plasticDissection=RandomInstance[GeometricScene[{a,b,c,d,e,f,g},{
a=={1,0},e=={0,0},Line[{a,e,d,c}],
p0==Polygon[{a,b,c}],
p1==Style[Polygon[{b,d,c}],Orange,o],
p2==Style[Polygon[{d,f,e}],Yellow,o],
p3==Style[Polygon[{b,f,d}],Blue,o],
p4==Style[Polygon[{g,f,b}],Green,o],
p5==Style[Polygon[{e,g,f}],Magenta,o],
p6==Style[Polygon[{a,e,g}],Purple,o],
GeometricAssertion[{p0,p1,p2,p3,p4,p5,p6},"Similar"]}],RandomSeeding->28]

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February 1, 2019 — Andrew Steinacher, Lead Developer, Wolfram|Alpha Scientific Content

New Archive Conversion Utility in Version 12

Soon there will be 100,000 questions on MathOverflow.net, a question-and-answer site for professional mathematicians! To celebrate this event, I have been working on a Wolfram Language utility package to convert archives of Stack Exchange network websites into Wolfram Language entity stores.

The archives are hosted on the Internet Archive and are updated every few months. The package, although not yet publicly available, will be released in the coming weeks as part of Version 12 of the Wolfram Language—so keep watching this space for more news about the release!

MathOverflow

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September 20, 2018
Greg Hurst, Wolfram|Alpha Math Content
Matt Gelber, Postdoctoral Researcher, University of Illinois at Urbana-Champaign

In past blog posts, we’ve talked about the Wolfram Language’s built-in, high-level functionality for 3D printing. Today we’re excited to share an example of how some more general functionality in the language is being used to push the boundaries of this technology. Specifically, we’ll look at how computation enables 3D printing of very intricate sugar structures, which can be used to artificially create physiological channel networks like blood vessels.

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June 14, 2018
Sebastian Bodenstein, Machine Learning
Matteo Salvarezza, Machine Learning
Meghan Rieu-Werden, Data Manager, Machine Learning
Taliesin Beynon, Machine Learning

Hero

Today, we are excited to announce the official launch of the Wolfram Neural Net Repository! A huge amount of work has gone into training or converting around 70 neural net models that now live in the repository, and can be accessed programmatically in the Wolfram Language via NetModel:

net = NetModel

net = NetModel["ResNet-101 Trained on ImageNet Competition Data"]

Peacock Input

net[]

Peacock Output

Neural nets have generated a lot of interest recently, and rightly so: they form the basis for state-of-the-art solutions to a dizzying array of problems, from speech recognition to machine translation, from autonomous driving to playing Go. Fortunately, the Wolfram Language now has a state-of-the-art neural net framework (and a growing tutorial collection). This has made possible a whole new set of Wolfram Language functions, such as FindTextualAnswer, ImageIdentify, ImageRestyle and FacialFeatures. And deep learning will no doubt play an important role in our continuing mission to make human knowledge computable.

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May 24, 2018 — Carlo Giacometti, Kernel Developer, Algorithms R&D

Introduction

Recognizing words is one of the simplest tasks a human can do, yet it has proven extremely difficult for machines to achieve similar levels of performance. Things have changed dramatically with the ubiquity of machine learning and neural networks, though: the performance achieved by modern techniques is dramatically higher compared with the results from just a few years ago. In this post, I’m excited to show a reduced but practical and educational version of the speech recognition problem—the assumption is that we’ll consider only a limited set of words. This has two main advantages: first of all, we have easy access to a dataset through the Wolfram Data Repository (the Spoken Digit Commands dataset), and, maybe most importantly, all of the classifiers/networks I’ll present can be trained in a reasonable time on a laptop.

It’s been about two years since the initial introduction of the Audio object into the Wolfram Language, and we are thrilled to see so many interesting applications of it. One of the main additions to Version 11.3 of the Wolfram Language was tight integration of Audio objects into our machine learning and neural net framework, and this will be a cornerstone in all of the examples I’ll be showing today.

Without further ado, let’s squeeze out as much information as possible from the Spoken Digit Commands dataset!

Spoken Digit Commands dataset

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April 19, 2018 — Joanna Crown, Strategic Projects, Strategic Initiatives

“Tell me and I forget. Teach me and I remember. Involve me and I learn.” — Benjamin Franklin

I can count on one hand the best presentations I have ever experienced, the most recent being my university dynamics lecturer bringing out his electric guitar at the end of term to demonstrate sound waves; a pharmaceutical CEO giving an impassioned after-dinner oration about how his love of music influenced his business decisions; and last but not least, my award-winning attempt at explaining quantum entanglement using a marble run and a cardboard box (I won a bottle of wine).

It’s perhaps equally easy to recall all the worst presentations I’ve experienced as well—for example, too many PowerPoint presentations crammed full of more bullet points than a shooting target; infinitesimally small text that only Superman’s telescopic vision could handle; presenters intent on slowly reading every word that they’ve squeezed onto a screen and thoroughly missing the point of a presentation: that of succinctly communicating interesting ideas to an audience.

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