News, Views & Insights
Today we celebrate the arrival of the 36th (x.x) version of the Wolfram Language and Mathematica: Version 14.1. We’ve been doing this since 1986: continually inventing new ideas and implementing them in our larger and larger tower of technology. And it’s always very satisfying to be able to deliver our latest achievements to the world.
Today we celebrate a new waypoint on our journey of nearly four decades with the release of Version 14.0 of Wolfram Language and Mathematica. Over the two years since we released Version 13.0 we’ve been steadily delivering the fruits of our research and development in .1 releases every six months. Today we’re aggregating these—and more—into Version 14.0.
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 […]
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.
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.
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.
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 […]
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 […]
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.
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 […]
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.