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:
June 6, 2019 — Alec Shedelbower, Kernel Developer, Algorithms R&D
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.
May 28, 2019 — Daniel Lichtblau, Symbolic Algorithms Developer, Algorithms R&D
Several Months Ago…
I wrote a blog post about the disputed Federalist Papers. These were the 12 essays (out of a total of 85) with authorship claimed by both Alexander Hamilton and James Madison. Ever since the landmark statistical study by Mosteller and Wallace published in 1963, the consensus opinion has been that all 12 were written by Madison (the Adair article of 1944, which also takes this position, discusses the long history of competing authorship claims for these essays). The field of work that gave rise to the methods used often goes by the name of “stylometry,” and it lies behind most methods for determining authorship from text alone (that is to say, in the absence of other information such as a physical typewritten or handwritten note). In the case of the disputed essays, the pool size, at just two, is as small as can be. Even so, these essays have been regarded as difficult for authorship attribution due to many statistical similarities in style shared by Hamilton and Madison.
May 23, 2019 — Brian Wood, Lead Technical Marketing Writer, Document and Media Systems
Just as Wolfram was doing AI before it was cool, so have we been doing data science since before it was mainstream. A prime example is the creation of Wolfram|Alpha—a massive project that involved engineering, modeling, analyzing, visualizing and interfacing with terabytes of data, developing a natural language interface, and deploying results in a sensible way. Wolfram|Alpha itself is a tool for doing data science, and its continued success is largely because of the underlying strategy we used to build it: a multiparadigm approach driven by natural curiosity, exploring all kinds of data, using advanced methods from a range of areas and automating as much as possible.
Any approach to data science can only be as effective as the computational tools driving it; luckily for us, we had the Wolfram Language at our disposal. Leveraging its universal symbolic representation, high-level automation and human readability—as well as its broad range of built-in computation, knowledge and interfaces—streamlined our process to help bring Wolfram|Alpha to fruition. In this post, I’ll discuss some key tenets of the multiparadigm approach, then demonstrate how they combine with the computational intelligence of the Wolfram Language to make the ideal workflow for not only discovering and presenting insights from your data, but also for creating scalable, reusable applications that optimize your data science processes.
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?”
I 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.
May 18, 2019 — Stephen Wolfram
The Wolfram|Alpha Story
Today it’s 10 years since we launched Wolfram|Alpha. At some level, Wolfram|Alpha is a never-ending project. But it’s had a great first 10 years. It was a unique and surprising achievement when it first arrived, and over its first decade it’s become ever stronger and more unique. It’s found its way into more and more of the fabric of the computational world, both realizing some of the long-term aspirations of artificial intelligence, and defining new directions for what one can expect to be possible. Oh, and by now, a significant fraction of a billion people have used it. And we’ve been able to keep it private and independent, and its main website has stayed free and without external advertising.
Get Full Access to the Wolfram Language from Python
The Wolfram Language gives programmers a unique computational language with an enormous array of sophisticated algorithms and built-in real-world knowledge. For many years, people have asked us how to access all the power of our technology from other software environments and programming languages. And over the years, we have built many such connections, like Wolfram CloudConnector for Excel, WSTP (Wolfram Symbolic Transfer Protocol) for C/C++ programs and, of course, J/Link, which provides access to the Wolfram Language directly from Java.
So today we’re happy to formally announce a new and often-requested connection that allows you to call the Wolfram Language directly and efficiently from Python: the Wolfram Client Library for Python. And, even better, this client library is fully open source as the WolframClientForPython git repository under the MIT License, so you can clone it and use it any way you see fit.
May 9, 2019 — Stephen Wolfram
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.
May 2, 2019 — Tuseeta Banerjee, Research Scientist, Machine Learning
If you haven’t used machine learning, deep learning and neural networks yourself, you’ve almost certainly heard of them. You may be familiar with their commercial use in self-driving cars, image recognition, automatic text completion, text translation and other complex data analysis, but you can also train your own neural nets to accomplish tasks like identifying objects in images, generating sequences of text or segmenting pixels of an image. With the Wolfram Language, you can get started with machine learning and neural nets faster than you think. Since deep learning and neural networks are everywhere, let’s go ahead and explore what exactly they are and how you can start using them.
April 26, 2019 — Tim Shedelbower, Visualization Developer, Algorithms R&D
Connect the dots. It was exciting to draw from number to number until the sudden discovery of a hidden cartoon. That was my inadvertent introduction to graph theory very early in school. Little did I know adults used the same concept to discover hidden patterns to solve problems, such as proving that a single crossing of seven Königsberg bridges to four land masses is not possible, but coloring a map distinctly with four colors is. These problems inspired the methods we know today as graph theory. And in honor of the work of late mathematician and connect-the-dot author Elwyn Berlekamp, we see how sophisticated this “child’s play” can be by examining the different styles and themes we can apply to graphs.