On December 21, 2020, a visual astronomical spectacle will occur. The planets Jupiter and Saturn will pass so close to each other in the sky that, to the unaided eye, they will be difficult to separate. This is the closest the two planets have come in 397 years; the last time they were this close was July 16, 1623. When Jupiter and Saturn come close to each other in the sky as seen from Earth, this is known as a “great conjunction” and happens about every 20 years or so. But not all great conjunctions are as close as this one. The next great conjunction will be on November 5, 2040, and again on April 10, 2060, but the planets will be a bit over a degree apart, so not as close as the 2020 event. The next comparable event will be on March 15, 2080.
In early October, by what at this point can only be a time-honored tradition, the Livecoding Championship returned in its fifth annual iteration as a special event during the 2020 Wolfram Technology Conference. As in preceding years, the championship offered top Wolfram Language programmers a chance to show off their knowledge, agility, typing speed and documentation-reading skills to an unfailingly adoring audience.
Halloween this year had a surprise up its sleeve. In rare celestial serendipity, the night of costume metamorphosis also featured a full moon, which helped to conjure the spooky mood. Because it might have been the first and last full-moon Halloween that some people witnessed in their lifetime (cue ominous music), I think it was significantly underrated. Moreover, it was the day of a blue moon (the second full moon within a month), but that is not a surprise, as any Halloween’s full moon is always a blue moon. The Moon’s color did not change, though, at least for those away from the smoke of volcanos and forest fires that are capable of turning it visibly blue. To appreciate the science and uniqueness of a full moon this Halloween, I built this visualization that tells the whole story in one picture. This is how I did it.
Although this year’s Wolfram Technology Conference was virtual, that didn’t stop us from running the ninth annual One-Liner Competition, where attendees vie to produce the most amazing results they can with 128 or fewer characters of Wolfram Language code. Here are the winners, including an audio game, a hands-free 3D viewer and code that makes up countries.
Blockchain was integrated into the Wolfram Language in 2018 with the release of Version 11.3, featuring a set of functions that is constantly improved and expanded upon by our team. Currently supporting a seamless connection to the Bitcoin, Ethereum, ARK and bloxberg mainnets, testnets and devnets, Wolfram introduced to the distributed ledger technology (DLT) space its philosophy of injecting computational intelligence everywhere through Wolfram Blockchain Labs, with the mission of enabling blockchain-based commerce and business model innovation.
Earth has experienced five major extinctions since life first appeared almost four billion years ago. The sixth is happening right now; the current extinction rate is between one hundred and one thousand times greater than what it was before 1800.
Despite the alarming extinction rate, it’s easier than ever to document biodiversity with the help of the Wolfram Language. Using the monarch butterfly as an example, I will explore the new biodiversity data access functions in the Wolfram Function Repository and how they can help you join a community of thousands of citizen scientists from iNaturalist in preserving biodiversity.
This roundup of Wolfram Community contributions features several different functions and tools related to current times, from the global pandemic to sustainable spaces and homeschool puzzles. Read on to see just a few creative examples from some of our favorite Community members and Wolfram Language wizzes.
While programming in the Wolfram Language, I am able to quickly and easily get results—one of the best aspects of writing code in a high-level language. The Wolfram Language is so easy to use that I have the freedom to pursue ideas on a whim, even if I know those ideas may not accomplish anything great or work toward a larger goal. In most cases, within a few minutes I figure out if the idea is a dead end. I also figure out if I am on the path to creating something useful or, better yet, fun.
A noteworthy achievement of artificial intelligence, since it is driven by artificial neural networks under the label deep learning, is the ability to create artistic works to generate images, text and sounds. At the core of this breakthrough is a basic method to train neural networks that was introduced by Ian Goodfellow in 2014 and was called by Yann LeCun “the most interesting idea in the last 10 years in machine learning”: generative adversarial networks (GANs). A GAN is a way to train a generative network that produces realistic-looking fake samples out of a latent seed, which can be some arbitrary data or random numbers sampled from a simple distribution. Let’s look at how to do so with some of the new capabilities developed for Mathematica Version 12.1.