Analyzing and Translating an Alien Language: Arrival, Logograms and the Wolfram Language
January 31, 2017 — Michael Gammon, Communications Project Coordinator, Public Relations
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. Later today, he will be demonstrating the development process he went through in a livecoding event broadcast on LiveEdu.tv.
Meet the Authors of Hands-on Start to Wolfram Mathematica, Second Edition
January 24, 2017 — Jeremy Sykes, Publishing Assistant, Wolfram Media
Jeremy Sykes: To celebrate the release of Hands-on Start to Wolfram Mathematica and Programming with the Wolfram Language (HOS2), now in its second edition, I sat down with the authors. Working with Cliff, Kelvin and Michael as the book’s production manager has been an easy and engaging process. I’m thrilled to see the second edition in print, particularly now in its smaller, more conveniently sized format.
Exploring a Boxing Legend’s Career with the Wolfram Language: Ali at 75
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://webarchive.library.unt.edu/web/20170201021101im_/http://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]")
Automotive Reliability in the Wolfram Language
January 13, 2017 — Nick Lariviere, Kernel Developer, Core Mathematica Engineering
This post originally appeared on Wolfram Community, where the conversation about reliable cars continues. Be sure to check out that conversation and more—we can’t wait to see what you come up with!
For the past couple of years, I’ve been playing with, collecting and analyzing data from used car auctions in my free time with an automotive journalist named Steve Lang to try and get an idea of what the used car market looks like in terms of long-term vehicle reliability. I figured it was about time that I showed off some of the ways that the Wolfram Language has allowed us to parse through information on over one million vehicles (and counting).
Recent Wolfram Technology Books
January 9, 2017 — John Moore, Wolfram Blog Team
We’re always excited to see new books that explore new ways to use Wolfram technologies. Authors continue to find inventive ways to think with the Wolfram Language. A variety of new Wolfram technology books have been published over the past few months. We hope that you’ll find something on this list to support your new year’s resolution to upgrade your skills. (Update: also look for the newly released Chinese translation of Stephen Wolfram’s An Elementary Introduction to the Wolfram Language.)
Our Readers’ Favorite Stories from 2016
January 3, 2017 — John Moore, Wolfram Blog Team
It’s been a busy year here at the Wolfram Blog. We’ve written about ways to avoid the UK’s most unhygienic foods, exciting new developments in mathematics and even how you can become a better Pokémon GO player. Here are some of our most popular stories from the year.
Gardening à la Gardner
December 28, 2016 — Kathryn Cramer, Technical Communications and Strategy Group
When looking through the posts on Wolfram Community, the last thing I expected was to find exciting gardening ideas.
The general idea of Ed Pegg’s tribute post honoring Martin Gardner, “Extreme Orchards for Gardner,” is to find patterns for planting trees in configurations with constraints like “25 trees to get 18 lines, each having 5 trees.” Most of the configurations look like ridiculous ideas of how to plant actual trees. For example:
The Semantic Representation of Pure Mathematics
December 22, 2016
Eric Weisstein, Senior Researcher, Wolfram|Alpha Scientific Content
Ian Ford, Pure Math Developer, Wolfram|Alpha Scientific Content
Introduction
Building on thirty years of research, development and use throughout the world, Mathematica and the Wolfram Language continue to be both designed for the long term and extremely successful in doing computational mathematics. The nearly 6,000 symbols built into the Wolfram Language as of 2016 allow a huge variety of computational objects to be represented and manipulated—from special functions to graphics to geometric regions. In addition, the Wolfram Knowledgebase and its associated entity framework allow hundreds of concrete “things” (e.g. people, cities, foods and planets) to be expressed, manipulated and computed with.
Despite a rapidly and ever-increasing number of domains known to the Wolfram Language, many knowledge domains still await computational representation. In his blog “Computational Knowledge and the Future of Pure Mathematics,” Stephen Wolfram presented a grand vision for the representation of abstract mathematics, known variously as the Computable Archive of Mathematics or Mathematics Heritage Project (MHP). The eventual goal of this project is no less than to render all of the approximately 100 million pages of peer-reviewed research mathematics published over the last several centuries into a computer-readable form.
In today’s blog, we give a glimpse into the future of that vision based on two projects involving the semantic representation of abstract mathematics. By way of further background and motivation for this work, we first briefly discuss an international workshop dedicated to the semantic representation of mathematical knowledge, which took place earlier this year. Next, we present our work on representing the abstract mathematical concepts of function spaces and topological spaces. Finally, we showcase some experimental work on representing the concepts and theorems of general topology in the Wolfram Language.
Protecting NHS Patients with the Wolfram Language
December 16, 2016 — Robert Cook, Senior Consultant, Wolfram Technical Services
The UK’s National Health Service (NHS) is in crisis. With a current budget of just over £100 billion, the NHS predicts a £30 billion funding gap by 2020 or 2021 unless there is radical action. A key part of this is addressing how the NHS can predict and prevent harm well in advance and deliver a “digital healthcare transformation” to their frontline services, utilizing vast quantities of data to make informed and insightful decisions.
This is where Wolfram comes in. Our UK-based Technical Services Team worked with the British NHS to help solve a specific problem facing the NHS—one many organizations will recognize: data sitting in siloed databases, with limited analysis algorithms on offer. They wanted to see if it was possible to pull together multiple data sources, combining off-the-shelf clinical databases with the hospital trusts’ bespoke offerings and mine them for signals. We set out to help them answer questions like “Can the number of slips, trips and falls in hospitals be reduced?”
Launching Wolfram|Alpha Open Code
December 12, 2016 — Stephen Wolfram
Code for Everyone
Computational thinking needs to be an integral part of modern education—and today I’m excited to be able to launch another contribution to this goal: Wolfram|Alpha Open Code.
Every day, millions of students around the world use Wolfram|Alpha to compute answers. With Wolfram|Alpha Open Code they’ll now not just be able to get answers, but also be able to get code that lets them explore further and immediately apply computational thinking.
