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Analyzing Social Networks of Colonial Boston Revolutionaries with the Wolfram Language

As the Fourth of July approaches, many in America will celebrate 241 years since the founders of the United States of America signed the Declaration of Independence, their very own disruptive, revolutionary startup. Prior to independence, colonists would celebrate the birth of the king. However, after the Revolutionary War broke out in April of 1775, some colonists began holding mock funerals of King George III. Additionally, bonfires, celebratory cannon and musket fire and parades were common, along with public readings of the Declaration of Independence. There was also rum. Today, we often celebrate with BBQ, fireworks and a host of other festivities. As an aspiring data nerd and a sociologist, I thought I would use the Wolfram Language to explore the Declaration of Independence using some basic natural language processing. Using metadata, I'll also explore a political network of colonists with particular attention paid to Paul Revere, using built-in Wolfram Language functions and network science to uncover some hidden truths about colonial Boston and its key players leading up to the signing of the Declaration of Independence.
Swede White, Public Relations Manager
Best of Blog

Create a Tracker to Analyze Gas Mileage Using Wolfram Tech

When I first started driving in high school, I had to pay for my own gas. Since I was also saving for college, I had to be careful about my spending, so I started manually tracking how much I was paying for gas in a spreadsheet and calculating how much gas I was using. Whenever I filled my tank, I kept the receipts and wrote down how many miles I'd traveled and how many gallons I'd used. Every few weeks, I would manually enter all of this information into the spreadsheet and plot out the costs and the amount of fuel I had used. This process helped me both visualize how much money I was spending on fuel and manage my budget. Once I got to college, however, I got a more fuel-efficient car and my schedule got a lot busier, so I didn't have the time to track my fuel consumption like this anymore. Now I work at Wolfram Research and I'm still really busy, but the cool thing is that I can use our company technology to more easily accomplish my automotive assessments.
Andrew Steinacher, Lead Developer, Wolfram|Alpha Scientific Content
Computation & Analysis

Brain, Neurons, Cognition: Computational Neuroscience

As the next phase of Wolfram Research's endeavor to make biology computable, we are happy to announce the recent release of neuroscience-related content. The most central part of the human nervous system is the brain. It contains roughly 100 billion neurons that act together to process information, subdivided functionally and structurally into areas specialized for certain tasks. The brain's anatomy, the characteristics of neurons and cognitive maps are used to represent some key aspects of the functional organization and processing abilities of our nervous system. Our new neuroscience content will give you a sneak peek into the amazing world of neuroscience with some facts about brains, neurons and cognition.
Keiko Hirayama, Wolfram|Alpha Developer, Wolfram|Alpha Scientific Content
Education & Academic

New Books on Applications of the Wolfram Language

We're always excited to see new books that illustrate applications of Wolfram technology in a wide range of fields. Below is another set of recently published books using the Wolfram Language to explore computational thinking. From André Dauphiné's outstanding geographical studies of our planet to Romano and Caveliere's work on the geometric optics that help us study the stars, we find a variety of fields served by Wolfram technology.
Michael Gammon, Blog Administrator, Document & Media Systems
Education & Academic

New in the Wolfram Language: Enhanced Derivatives

Derivatives of functions play a fundamental role in calculus and its applications. In particular, they can be used to study the geometry of curves, solve optimization problems and formulate differential equations that provide mathematical models in areas such as physics, chemistry, biology and finance. The function D computes derivatives of various types in the Wolfram Language and is one of the most-used functions in the system. My aim in writing this post is to introduce you to the exciting new features for D in Version 11.1, starting with a brief history of derivatives.
Devendra Kapadia, Manager of Calculus and Algebra, Algorithms R&D
Education & Academic

Exploring Exoplanet Systems with the Wolfram Language

Exoplanets are currently an active area of research in astronomy. In the past few years, the number of exoplanet discoveries has exploded, mainly as the result of the Kepler mission to survey eclipsing exoplanet systems. But Kepler isn't the only exoplanet study mission going on. For example, the TRAnsiting Planets and PlanetesImals Small Telescope (TRAPPIST) studies its own set of targets. In fact, the media recently focused on an exoplanet system orbiting an obscure star known as TRAPPIST-1. As an introduction to exoplanet systems, we'll explore TRAPPIST-1 and its system of exoplanets using the Wolfram Language.
Jeff Bryant, Research Programmer, Wolfram|Alpha Scientific Content
Best of Blog

Launching the Wolfram Data Repository: Data Publishing that Really Works

After a Decade, It’s Finally Here! I’m pleased to announce that as of today, the Wolfram Data Repository is officially launched! It’s been a long road. I actually initiated the project a decade ago—but it’s only now, with all sorts of innovations in the Wolfram Language and its symbolic ways of representing data, as well […]

Stephen Wolfram
Products

Walking the Dog: Neural Nets, Image Identification and Geolocation

It's National Pet Day on April 11, the day we celebrate furry, feathered or otherwise nonhuman companions. To commemorate the date, we thought we'd use some new features in the Wolfram Language to map a dog walk using pictures taken with a smartphone along the way. After that, we'll use some neural net functions to identify the content in the photos. One of the great things about Wolfram Language 11.1 is pre-trained neural nets, including Inception V3 trained on ImageNet Competition data and Inception V1 trained on Places365 data, among others, making it super easy for a novice programmer to implement them. These two pre-trained neural nets make it easy to: 1) identify objects in images; and 2) tell a user what sort of landscape an image represents.
Swede White, Public Relations Manager
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