January 10, 2019 — Brian Wood, Lead Technical Marketing Writer, Document and Media Systems
So far in this series, I’ve covered the process of extracting, cleaning and structuring data from a website. So what does one do with a structured dataset? Continuing with the Election Atlas data from the previous post, this final entry will talk about how to store your scraped data permanently and deploy results to the web for universal access and sharing.
December 18, 2018 — Chapin Langenheim, Editorial Project Coordinator, Web and Product Release Management
Check out these fresh picks from authors utilizing the Wolfram Language! Covering a range of topics from algebraic curves to reaction kinetics to finance policy, these books are excellent additions to the extensive list of publications showing what’s possible with Wolfram technologies.
November 1, 2018 — Jesse Friedman, Intern, Document and Media Systems
For the third year in a row, the annual Wolfram Technology Conference played host to a new kind of esport—the Livecoding Championship. Expert programmers competed to solve challenges with the Wolfram Language, with the goal of winning the championship tournament belt and exclusive bragging rights.
This year I had the honor of composing the competition questions, in addition to serving as live commentator alongside trusty co-commentator (and Wolfram’s lead communications strategist) Swede White. You can view the entire recorded livestream of the event here—popcorn not included.
October 25, 2018 — Christopher Carlson, Senior User Interface Developer, User Interfaces
Images and machine learning were the dominant themes of submissions to the One-Liner Competition held at this year’s Wolfram Technology Conference. The competition challenges attendees to show us the most astounding things they can accomplish with 128 or fewer characters—less than one tweet—of Wolfram Language code. And astound us they did. Read on to see how.
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.
September 11, 2018 — Jon McLoone, Director, Technical Communication & Strategy
Having a really broad toolset and an open mind on how to approach data can lead to interesting insights that are missed when data is looked at only through the lens of statistics or machine learning. It’s something we at Wolfram Research call multiparadigm data science, which I use here for a small excursion through calculus, graph theory, signal processing, optimization and statistics to gain some interesting insights into the engineering of supersonic cars.
September 6, 2018 — Brian Wood, Lead Technical Marketing Writer, Document and Media Systems
In my previous post, I demonstrated the first step of a multiparadigm data science workflow: extracting data. Now it’s time to take a closer look at how the Wolfram Language can help make sense of that data by cleaning it, sorting it and structuring it for your workflow. I’ll discuss key Wolfram Language functions for making imported data easier to browse, query and compute with, as well as share some strategies for automating the process of importing and structuring data. Throughout this post, I’ll refer to the US Election Atlas website, which contains tables of US presidential election results for given years:
August 21, 2018 — Kyle Keane, Director of Summer Programs, Public Relations
The 16th annual Wolfram Summer School was another successful immersive education adventure made possible by the power of the Wolfram Language for rapid scientific exploration and software development. A select group of 62 participants from all around the world (ranging from advanced high-school students to postgraduate students and beyond) worked on a variety of computational projects related to science, technology and innovation and educational innovation. The three-week program was packed with cutting-edge technologies, intellectual discussions, innovation in action and community building.
August 16, 2018 — Erez Kaminski, Wolfram Technology Specialist, Wolfram Technology Group
For the past two years, FOALE AEROSPACE has been on an exhilarating journey to create an innovative machine learning–based system designed to help prevent airplane crashes, using what might be the most understated machine for the task—the Raspberry Pi. The system is marketed as a DIY kit for aircraft hobbyists, but the ideas it’s based upon can be applied to larger aircraft (and even spacecraft!).
FOALE AEROSPACE is the brainchild of astronaut Dr. Mike Foale and his daughter Jenna Foale. Mike is a man of many talents (pilot, astrophysicist, entrepreneur) and has spent an amazing 374 days in space! Together with Jenna (who is currently finishing her PhD in computational fluid dynamics), he was able to build a complex machine learning system at minimal cost. All their development work was done in-house, mainly using the Wolfram Language running on the desktop and a Raspberry Pi. FOALE AEROSPACE’s system, which it calls the Solar Pilot Guard (SPG), is a solar-charged probe that identifies and helps prevent loss-of-control (LOC) events during airplane flight. Using sensors to detect changes in the acceleration and air pressure, the system calculates the probability of each data point (an instance in time) to be in-family (normal flight) or out-of-family (non-normal flight/possible LOC event), and issues the pilot voice commands over a Bluetooth speaker. The system uses classical functions to interpolate the dynamic pressure changes around the airplane axes; then, through several layers of Wolfram’s automatic machine learning framework, it assesses when LOC is imminent and instructs the user on the proper countermeasures they should take.
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.