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 23, 2018 — Brian Wood, Lead Technical Marketing Writer, Document and Media Systems
As the technology manager for Assured Flow Solutions, Andrew Yule has long relied on the Wolfram Language as his go-to tool for petroleum production analytics, from quick computations to large-scale modeling and analysis. “I haven’t come across something yet that the Wolfram Language hasn’t been able to help me do,” he says. So when Yule set out to consolidate all of his team’s algorithms and data into one system, the Wolfram Language seemed like the obvious choice.
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.
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.
June 26, 2018 — Brian Wood, Lead Technical Marketing Writer, Document and Media Systems
In the past few decades, the process of redistricting has moved squarely into the computational realm, and with it the political practice of gerrymandering. But how can one solve the problem of equal representation mathematically? And what can be done to test the fairness of districts? In this post I’ll take a deeper dive with the Wolfram Language—using data exploration with Import and Association, built-in knowledge through the Entity framework and various GeoGraphics visualizations to better understand how redistricting works, where issues can arise and how to identify the effects of gerrymandering.
March 14, 2018 — Swede White, Lead Communications Strategist, Public Relations
Daniel George is a graduate student at the University of Illinois at Urbana-Champaign, Wolfram Summer School alum and Wolfram intern whose award-winning research on deep learning for gravitational wave detection recently landed in the prestigious pages of Physics Letters B in a special issue commemorating the Nobel Prize in 2017.
We sat down with Daniel to learn more about his research and how the Wolfram Language plays a part in it.
March 2, 2018 — Brian Wood, Lead Technical Marketing Writer, Document and Media Systems
Do you want to do more with data available on the web? Meaningful data exploration requires computation—and the Wolfram Language is well suited to the tasks of acquiring and organizing data. I’ll walk through the process of importing information from a webpage into a Wolfram Notebook and extracting specific parts for basic computation. Throughout this post, I’ll be referring to this website hosted by the National Weather Service, which gives 7-day forecasts for locations in the western US:
January 4, 2018 — Michael Gammon, Blog Administrator, Document and Media Systems
Whew! So much has happened in a year. Consider this number: we added 230 new functions to the Wolfram Language in 2017! The Wolfram Blog traces the path of our company’s technological advancement, so let’s take a look back at 2017 for the blog’s year in review.
December 22, 2017 — Micah Lindley, Junior Research Programmer, Wolfram|Alpha Scientific Content
In recent years there’s been a growing interest in the intersection of food and technology. However, many of the new technologies used in the kitchen are cooking tools and devices such as immersion circulators, silicone steam baskets and pressure ovens. Here at Wolfram, our approach has been a bit different, with a focus on providing tools that can query for, organize, visualize and compute data about food, cooking and nutrition.
Last Christmas I went home to Tucson, Arizona, to spend time with my family over the holidays. Because I studied the culinary arts and food science, I was quickly enlisted to cook Christmas dinner. There were going to be a lot of us at my parents’ house, so I was aware this would be no small task. But I curate food and nutrition data for Wolfram|Alpha, so I knew the Wolfram technology stack had some excellent resources for pulling off this big meal without a hitch.
December 7, 2017 — Jon McLoone, Director, Technical Communication & Strategy
Computation is no longer the preserve of science and engineering, so I thought I would share a simple computational literary analysis that I did with my daughter.