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.
November 30, 2017 — Vitaliy Kaurov, Academic Director, Wolfram Science and Innovation Initiatives
When Does a Word Become a Word?
“A shot of expresso, please.” “You mean ‘espresso,’ don’t you?” A baffled customer, a smug barista—media is abuzz with one version or another of this story. But the real question is not whether “expresso” is a correct spelling, but rather how spellings evolve and enter dictionaries. Lexicographers do not directly decide that; the data does. Long and frequent usage may qualify a word for endorsement. Moreover, I believe the emergent proliferation of computational approaches can help to form an even deeper insight into the language. The tale of expresso is a thriller from a computational perspective.
November 20, 2017 — Jon McLoone, Director, Technical Communication & Strategy
The classic board game Risk involves conquering the world by winning battles that are played out using dice. There are lots of places on the web where you can find out the odds of winning a battle given the number of armies that each player has. However, all the ones that I have seen do this by Monte Carlo simulation, and so are innately approximate. The Wolfram Language makes it so easy to work out the exact values that I couldn’t resist calculating them once and for all.
November 14, 2017 — Stephen Wolfram
A Powerful Way to Express Ideas
People are used to producing prose—and sometimes pictures—to express themselves. But in the modern age of computation, something new has become possible that I’d like to call the computational essay.
I’ve been working on building the technology to support computational essays for several decades, but it’s only very recently that I’ve realized just how central computational essays can be to both the way people learn, and the way they communicate facts and ideas. Professionals of the future will routinely deliver results and reports as computational essays. Educators will routinely explain concepts using computational essays. Students will routinely produce computational essays as homework for their classes.
Here’s a very simple example of a computational essay:
June 29, 2017 — Swede White, Public Relations Manager
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.
June 22, 2017 — Andrew Steinacher, Lead Developer, Wolfram|Alpha Scientific Content
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.
April 20, 2017 — Stephen Wolfram
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 as with the arrival of the Wolfram Cloud, that all the pieces are finally in place to make a true computable data repository that works the way I think it should.
February 24, 2017
Jeffrey Bryant, Research Programmer, Wolfram|Alpha Scientific Content
Paco Jain, Research Programmer, Wolfram|Alpha Scientific Content
Michael Trott, Chief Scientist
The movie Hidden Figures was released in theaters recently and has been getting good reviews. It also deals with an important time in US history, touching on a number of topics, including civil rights and the Space Race. The movie details the hidden story of Katherine Johnson and her coworkers (Dorothy Vaughan and Mary Jackson) at NASA during the Mercury missions and the United States’ early explorations into manned space flight. The movie focuses heavily on the dramatic civil rights struggle of African American women in NASA at the time, and these struggles are set against the number-crunching ability of Johnson and her coworkers. Computers were in their early days at this time, so Johnson and her team’s ability to perform complicated navigational orbital mechanics problems without the use of a computer provided an important sanity check against the early computer results.
![Row[{Show[ Entity["Movie", "HiddenFigures::k39bj"][ EntityProperty["Movie", "Image"]], ImageSize -> 101], " ", Show[Entity["PopularCurve", "KatherineJohnsonCurve"][ EntityProperty["PopularCurve", "Image"]], Axes -> False, Background -> LightBlue, ImageSize -> 120]}]](https://blog.wolfram.com/data/uploads/2017/02/HF-Input-1.jpg "Row[{Show[ Entity[\"Movie\", \"HiddenFigures::k39bj\"][ EntityProperty[\"Movie\", \"Image\"]], ImageSize -> 101], \" \", Show[Entity[\"PopularCurve\", \"KatherineJohnsonCurve\"][ EntityProperty[\"PopularCurve\", \"Image\"]], Axes -> False, Background -> LightBlue, ImageSize -> 120]}]")
January 31, 2017 — Michael Gammon, Blog Administrator, Document and Media Systems
If aliens actually visited Earth, world leaders would bring in a scientist to develop a process for understanding their language. So when director Denis Villeneuve began working on the science fiction movie Arrival, he and his team turned to real-life computer scientists Stephen and Christopher Wolfram to bring authentic science to the big screen. Christopher specifically was tasked with analyzing and writing code for a fictional nonlinear visual language. On January 31, he demonstrated the development process he went through in a livecoding event you can watch on YouTube.
January 17, 2017 — Jofre Espigule-Pons, Consultant, Technical Communications and Strategy Group
Muhammad Ali (born Cassius Marcellus Clay Jr.; January 17, 1942–June 3, 2016) is considered one of the greatest heavyweight boxers in history, with a record of 56 wins and 5 losses. He remains the only three-time lineal heavyweight champion, so there’s no doubt why he is nicknamed “The Greatest.”
I used the Wolfram Language to create several visualizations to celebrate his work and gain some new insights into his life. Last June, I wrote a Wolfram Community post about Ali’s career. On what would have been The Greatest’s 75th birthday, I wanted to take a minute to explore the larger context of Ali’s career, from late-career boxing stats to poetry.
First, I created a PieChart showing Ali’s record:
![bouts = <|"TKO" -> 21, "KO" -> 11, "UD" -> 18, "RTD" -> 5, "SD" -> 1, "LUD" -> 2, "LSD" -> 2, "LRTD" -> 1|>; PieChart[bouts, ChartStyle -> 24, ChartLabels -> Placed[{Map[Style[#, Bold, FontSize -> 14] &, Values[bouts]], Map[Style[#, FontFamily -> "Helvetica Neue", Bold, FontSize -> 16] &, Keys[bouts]]}, {"RadialCenter", "RadialCallout"}], PlotRange -> All, SectorOrigin -> {Automatic, 1}, ChartLegends -> {"Technical Knockout", "Knockout", "Unanimous Decision", "Retired", "Split-Decision", "Lost - Unanimous Decision", "Lost - Split-Decision", "Lost - Retired"}, PlotLabel -> Style["Ali's Record", Bold, FontFamily -> "Helvetica Neue", FontSize -> 22], ImageSize -> 410]](https://blog.wolfram.com/data/uploads/2017/01/1-In-Ali.png "bouts = <|\"TKO\" -> 21, \"KO\" -> 11, \"UD\" -> 18, \"RTD\" -> 5, \"SD\" -> 1, \"LUD\" -> 2, \"LSD\" -> 2, \"LRTD\" -> 1|>; PieChart[bouts, ChartStyle -> 24, ChartLabels -> Placed[{Map[Style[#, Bold, FontSize -> 14] &, Values[bouts]], Map[Style[#, FontFamily -> \"Helvetica Neue\", Bold, FontSize -> 16] &, Keys[bouts]]}, {\"RadialCenter\", \"RadialCallout\"}], PlotRange -> All, SectorOrigin -> {Automatic, 1}, ChartLegends -> {\"Technical Knockout\", \"Knockout\", \"Unanimous Decision\", \"Retired\", \"Split-Decision\", \"Lost - Unanimous Decision\", \"Lost - Split-Decision\", \"Lost - Retired\"}, PlotLabel -> Style[\"Ali's Record\", Bold, FontFamily -> \"Helvetica Neue\", FontSize -> 22], ImageSize -> 410]")
