In 2020, Melbourne, Australia, had a 112-day lockdown of the entire city to help stop the spread of COVID-19. The wearing of masks was mandatory and we were limited to one hour a day of outside activity. Otherwise, we were stuck in our homes. This gave me lots of time to look into interesting problems I’d been putting off for years.
I was inspired by a YouTube video by David Oranchak, which looked at the Zodiac Killer’s 340-character cipher (Z340), which is pictured below. This cipher is considered one of the holy grails of cryptography, as at the time the cipher had resisted attacks for 50 years, so any attempts to find a solution were truly a moonshot.
I enjoy turning mathematical concepts into wearable pieces of art. That’s the idea behind my business, Hanusa Design. I make unique products that feature striking designs inspired by the beauty and precision of mathematics. These pieces are created using the range of functionality in the Wolfram Language. Just in time for Valentine’s Day, we recently launched Spikey earrings in the Wolfram Store, which are available in rose gold–plated brass and red nylon. In this blog, I’ll give a look under the hood and discuss how an idea becomes a product through the Wolfram Language.
Editor’s note: The following post is based on the 2020 Advent of Code challenge, “an Advent calendar of small programming puzzles for a variety of skill sets and skill levels that can be solved in any programming language you like.” Guest author Philip Maymin delves into why he chose to use the Wolfram Language to solve these queries with a how-to on learning the language.
Today’s handheld devices are powerful enough to run neural networks locally without the need for a cloud server connection, which can be a great convenience when you’re on the go. Deploying and running a custom neural network on your phone or tablet is not straightforward, though, and the process depends on the operating system of the machine. In this post, I will focus on iOS devices and walk you through all the necessary steps to train a custom image classifier neural network model using the Wolfram Language, export it through ONNX (new in Version 12.2), convert it to Core ML (Apple’s machine learning framework for iOS apps) and finally deploy it to your iPhone or iPad.
Cars are getting smarter and more connected, yet how much have you explored the technology that helps run our vehicles? I was curious to see how I could connect to my vehicle’s communication center and what kind of interface I could create in Wolfram Notebooks to report on the data gathered.