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Computational Thinking

Education & Academic

The Computational Classroom: Easy Ways to Introduce Computational Thinking into Your Lessons

A version of this post was originally published on the Tech-Based Teaching blog as “Computational Lesson-Planning: Easy Ways to Introduce Computational Thinking into Your Lessons.” Tech-Based Teaching explores the intersections between computational thinking, edtech and learning. Sometimes a syllabus is set in stone. You’ve got to cover X, Y and Z, and no amount of reworking or shifting assignments around can change that. Other factors can play a role too: limited time, limited resources or even a bit of nervousness at trying something new. But what if you’d like to introduce some new ideas into your lessons—ideas like digital citizenship or computational thinking? Introducing computational thinking to fields that are not traditionally part of STEM can sometimes be a challenge, so feel free to share this journey with your children's teachers, friends and colleagues.
Jesika Brooks, Blog Editor - EduTech, Outreach & Communications
Computation & Analysis

Deep Learning and Computer Vision: Converting Models for the Wolfram Neural Net Repository

Julian Francis, a longtime user of the Wolfram Language, contacted us with a potential submission for the Wolfram Neural Net Repository. The Wolfram Neural Net Repository consists of models that researchers at Wolfram have either trained in house or converted from the original code source, curated, thoroughly tested and finally have rendered the output in a very rich computable knowledge format. Julian was our very first user to go through the process of converting and testing the nets.

We thought it would be interesting to interview him on the entire process of converting the models for the repository so that he could share his experiences and future plans to inspire others.
Tuseeta Banerjee, Research Scientist, Machine Learning
Education & Academic

Computation + Literature in High School: Doctoral-Level Digital Humanities

Thanks to the Wolfram Language, English teacher Peter Nilsson is empowering his students with computational methods in literature, history, geography and a range of other non-STEM fields. Working with a group of other teachers at Deerfield Academy, he developed Distant Reading: an innovative course for introducing high-level digital humanities concepts to high-school students. Throughout the course, students learn in-demand coding skills and data science techniques while also finding creative ways to apply computational thinking to real-world topics that interest them.

In this video, Nilsson describes how the built-in knowledge, broad subject coverage and intuitive coding workflow of the Wolfram Language were crucial to the success of his course:

Brian Wood, Technical Communications & Outreach Manager
Current Events & History

Martian Commutes and Werewolf Teeth: Using Wolfram|Alpha for Writing Research

This post was initially published on Tech-Based Teaching, a blog about computational thinking, educational technology and the spaces in between. Rather than prioritizing a single discipline, Tech-Based Teaching aims to show how edtech can cultivate learning for all students. Past posts have explored the value of writing in math class, the whys and hows of distant reading and the role of tech in libraries. It’s November, also known as National Novel Writing Month (NaNoWriMo). This annual celebration of all things writerly is the perfect excuse for would-be authors to sit down and start writing. For educators and librarians, NaNoWriMo is a great time to weave creative writing into curricula, be it through short fiction activities, campus groups or library meet-ups. During NaNoWriMo, authors are typically categorized into two distinct types: pantsers, who “write by the seat of their pants,” and plotters, who are meticulous in their planning. While plotters are likely writing from preplanned outlines, pantsers may need some inspiration. That’s where Wolfram|Alpha comes in handy.
Jesika Brooks, Blog Editor - EduTech, Outreach & Communications
Announcements & Events

The Winners of the 2018 One-Liner Competition

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.
Christopher Carlson, Senior User Interface Developer, User Interfaces
Computation & Analysis

Cleaning and Structuring Large Datasets: Web Scraping with the Wolfram Language, Part 2

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:

Brian Wood, Technical Communications & Outreach Manager
Computation & Analysis

Data Science + Engineering: Building a Centralized Computation Hub

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.
Brian Wood, Technical Communications & Outreach Manager
Computation & Analysis

Former Astronaut Creates Virtual Copilot with Wolfram Neural Nets and a Raspberry Pi

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.
Erez Kaminski, Wolfram Technology Specialist, Wolfram Technology Group
Education & Academic

Mathematics Genealogy Project: Computational Exploration in the Wolfram Language

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.
Aaron Enright, Senior Data Scientist, Wolfram|Alpha Socioeconomic Content Eric Weisstein, Senior Researcher, Wolfram|Alpha Scientific Content
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