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 16, 2019
Dorian Birraux, Engine Connectivity Engineering
Arnoud Buzing, Director of Quality and Release Management
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.
April 2, 2019 — Jon McLoone, Director, Technical Communication & Strategy
Over the years, I have been asked many times about my opinions on free and open-source software. Sometimes the questions are driven by comparison to some promising or newly fashionable open-source project, sometimes by comparison to a stagnating open-source project and sometimes by the belief that Wolfram technology would be better if it were open source.
At the risk of provoking the fundamentalist end of the open-source community, I thought I would share some of my views in this blog. While there are counterexamples to most of what I have to say, not every point applies to every project, and I am somewhat glossing over the different kinds of “free” and “open,” I hope I have crystallized some key points.
November 29, 2018 — Parik Kapadia, Algorithms R&D
How does it feel to be an intern at Wolfram?
Most undergraduate college students chase opportunities for internships in New York, Miami, Seattle and particularly San Francisco at very young but large high-tech companies like Uber, Pinterest, Quora, Expedia and similar internet companies. These companies offer the best salaries, perks, bosses, coworkers, catered lunches and other luxurious amenities available in such large cities. You would seldom hear about any of these people pursuing opportunities in small, lesser-known towns like Ames, Iowa, or Laramie, Wyoming—and Champaign, Illinois, where Wolfram Research is based, is one of those smaller towns.
Many students want to go into computer science, as it’s such a rapidly developing field. They especially want to work in those companies on the West Coast. If you’re in a different field, like natural science, you might think there’s nothing beyond on-campus research for work experience. At Wolfram Research, though, there is.
August 2, 2018
Aaron Enright, Senior Data Scientist, Wolfram|Alpha Socioeconomic Content
Eric Weisstein, Senior Researcher, Wolfram|Alpha Scientific Content
The Mathematics Genealogy Project (MGP) is a project dedicated to the compilation of information about all mathematicians of the world, storing this information in a database and exposing it via a web-based search interface. The MGP database contains more than 230,000 mathematicians as of July 2018, and has continued to grow roughly linearly in size since its inception in 1997.
In order to make this data more accessible and easily computable, we created an internal version of the MGP data using the Wolfram Language’s entity framework. Using this dataset within the Wolfram Language allows one to easily make computations and visualizations that provide interesting and sometimes unexpected insights into mathematicians and their works. Note that for the time being, these entities are defined only in our private dataset and so are not (yet) available for general use.
July 26, 2018 — Itai Seggev, Senior Kernel Developer, Algorithms R&D
One of the many beautiful aspects of mathematics is that often, things that look radically different are in fact the same—or at least share a common core. On their faces, algorithm analysis, function approximation and number theory seem radically different. After all, the first is about computer programs, the second is about smooth functions and the third is about whole numbers. However, they share a common toolset: asymptotic relations and the important concept of asymptotic scale.
By comparing the “important parts” of two functions—a common trick in mathematics—asymptotic analysis classifies functions based on the relative size of their absolute values near a particular point. Depending on the application, this comparison provides quantitative answers to questions such as “Which of these algorithms is fastest?” or “Is function 
a good approximation to function g?”. Version 11.3 of the Wolfram Language introduces six of these relations, summarized in the following table.
July 19, 2018 — Devendra Kapadia, Kernel Developer, Algorithms R&D
Asymptotic expansions have played a key role in the development of fields such as aerodynamics, quantum physics and mathematical analysis, as they allow us to bridge the gap between intricate theories and practical calculations. Indeed, the leading term in such an expansion often gives more insight into the solution of a problem than a long and complicated exact solution. Version 11.3 of the Wolfram Language introduces two new functions, AsymptoticDSolveValue and AsymptoticIntegrate, which compute asymptotic expansions for differential equations and integrals, respectively. Here, I would like to give you an introduction to asymptotic expansions using these new functions.
November 9, 2017 — Devendra Kapadia, Kernel Developer, Algorithms R&D
Here are 10 terms in a sequence:
![Table[(2/(2 n + 1)) ((2 n)!!/(2 n - 1)!!)^2, {n, 10}]](https://blog.wolfram.com/data/uploads/2017/11/InOutImg2.png "Table[(2/(2 n + 1)) ((2 n)!!/(2 n - 1)!!)^2, {n, 10}]")
And here’s what their numerical values are:
![N[%]](https://blog.wolfram.com/data/uploads/2017/11/InOutImg3.png "N[%]"){kind=small}
But what is the limit of the sequence? What would one get if one continued the sequence forever?
In Mathematica and the Wolfram Language, there’s a function to compute that:
![DiscreteLimit[(2/(2 n + 1)) ((2 n)!!/(2 n - 1)!!)^2, n -> \[Infinity]]](https://blog.wolfram.com/data/uploads/2017/11/InOutImg4.png "DiscreteLimit[(2/(2 n + 1)) ((2 n)!!/(2 n - 1)!!)^2, n -> \[Infinity]]"){kind=small}
Limits are a central concept in many areas, including number theory, geometry and computational complexity. They’re also at the heart of calculus, not least since they’re used to define the very notions of derivatives and integrals.
Mathematica and the Wolfram Language have always had capabilities for computing limits; in Version 11.2, they’ve been dramatically expanded. We’ve leveraged many areas of the Wolfram Language to achieve this, and we’ve invented some completely new algorithms too. And to make sure we’ve covered what people want, we’ve sampled over a million limits from Wolfram|Alpha.
July 19, 2017 — Stephen Wolfram
The Philosophy of Chemicals
“We’ve just got to decide: is a chemical like a city or like a number?” I spent my day yesterday—as I have for much of the past 30 years—designing new features of the Wolfram Language. And yesterday afternoon one of my meetings was a fast-paced discussion about how to extend the chemistry capabilities of the language.
At some level the problem we were discussing was quintessentially practical. But as so often turns out to be the case for things we do, it ultimately involves some deep intellectual issues. And to actually get the right answer—and to successfully design language features that will stand the test of time—we needed to plumb those depths, and talk about things that usually wouldn’t be considered outside of some kind of philosophy seminar.
