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. On January 31, he demonstrated the development process he went through in a livecoding event broadcast on LiveEdu.tv.
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.
January 3, 2017 — John Moore, Marketing and Technical Content Team Lead
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.
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:
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?”
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.
If you’re like many of us at Wolfram, you probably know that November was National Novel Writing Month (NaNoWriMo). Maybe you even spent the past few weeks feverishly writing, pounding out that coming-of-age story about a lonely space dragon that you’ve been talking about for years.
Congratulations! Now what? Revisions, of course! And we, the kindly Wolfram Blog Team, are here to get you through your revisions with a little help from the Wolfram Language.
December 2, 2016 — Etienne Bernard, Lead Architect, Advanced Research Group
Two years ago, we introduced the first high-level machine learning functions of the Wolfram Language, Classify and Predict. Since then, we have been creating a set of automatic machine learning functionalities (ClusterClassify, DimensionReduction, etc.). Today, I am happy to present a new function called FeatureExtraction that deals with another important machine learning task: extracting features from data. Unlike Classify and Predict, which follow the supervised learning paradigm, FeatureExtraction belongs to the unsupervised learning paradigm, meaning that the data to learn from is given as a set of unlabeled examples (i.e. without an input -> output relation). The main goal of FeatureExtraction is to transform these examples into numeric vectors (often called feature vectors). For example, let’s apply FeatureExtraction to a simple dataset:
November 14, 2016 — Kathryn Cramer, Technical Communications and Strategy Group
Today is the 300th anniversary of the death of Gottfried Leibniz, a man whose work has had a deep influence on what we do here at Wolfram Research. He was born July 1, 1646, in Leipzig, and died November 14, 1716, in Hanover, which was, at the time, part of the Holy Roman Empire. I associate his name most strongly with my time learning calculus, which he invented in parallel with Isaac Newton. But Leibniz was a polymath, and his ideas and influence were much broader than that. He invented binary numbers, the integral sign and an early form of mechanical calculator.
November 9, 2016 — Christopher Carlson, Senior User Interface Developer, User Interfaces
Could you fit the code for a fully functional game of Pong into a single tweet? One that gives you more points the more you take your chances in letting the “ball” escape? Philip Maymin did, and took first prize with that submission in the One-Liner Competition held at this year’s Wolfram Technology Conference.
Participants in the competition submit 128 or fewer tweetable characters of Wolfram Language code to perform the most impressive computation they can dream up. We had a bumper crop of entries this year that showed the surprising power of the Wolfram Language. You might think that after decades of experience creating and developing with the Wolfram Language, we at Wolfram Research would have seen and thought of it all. But every year our conference attendees surprise us. Read on to see the amazing effects you can achieve with a tweet of Wolfram Language code.
Amy Friedman: “The Song Titles” (110 characters)