January 12, 2018 — Jesse Dohmann, Strategic Development Specialist, Strategic Initiatives
With the images from the Juno mission being made available to the public, I thought it might be fun to try my hand at some image processing with them. Though my background is not in image processing, the Wolfram Language has some really nice tools that lessen the learning curve, so you can focus on what you want to do vs. how to do it.
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.
December 28, 2017 — Kevin Daily, Wolfram Technology Group, Team Lead
An earlier version of this post appeared on Wolfram Community, where the creation of a game interface earned the author a staff pick from the forum moderators. Be sure to head over to Wolfram Community and check out other innovative uses of the Wolfram Language!
If you like video games and you’re interested in designing them, you should know that the Wolfram Language is great at making dynamic interfaces. I’ve taken a simple game, reproduced it and modded it with ease. Yes, it’s true—interactive games are yet another avenue for creative people to use the versatile Wolfram Language to fulfill their electronic visions.
The game I’m using for this demonstration is Flappy Bird, a well-known mobile game with a simple yet captivating interactive element that has helped many people kill a lot of time. The goal of the game is to navigate a series of pipes, where each successful pass adds a point to your score. The challenge is that the character, the bird, is not so easy to control. Gravity is constantly pulling it down. You “flap” to boost yourself upward by repeatedly tapping the screen, but you must accurately time your flaps to navigate the narrow gaps between pipes.
So follow along and see what kind of graphical gaming mayhem is possible in just a few short lines of code!
December 22, 2017 — Micah Lindley, Junior Research Programmer, Wolfram|Alpha Scientific Content
In recent years there’s been a growing interest in the intersection of food and technology. However, many of the new technologies used in the kitchen are cooking tools and devices such as immersion circulators, silicone steam baskets and pressure ovens. Here at Wolfram, our approach has been a bit different, with a focus on providing tools that can query for, organize, visualize and compute data about food, cooking and nutrition.
Last Christmas I went home to Tucson, Arizona, to spend time with my family over the holidays. Because I studied the culinary arts and food science, I was quickly enlisted to cook Christmas dinner. There were going to be a lot of us at my parents’ house, so I was aware this would be no small task. But I curate food and nutrition data for Wolfram|Alpha, so I knew the Wolfram technology stack had some excellent resources for pulling off this big meal without a hitch.
December 14, 2017 — Michael Gammon, Blog Administrator, Document and Media Systems
The Wolfram Community group dedicated to visual arts is abound with technically and aesthetically stunning contributions. Many of these posts come from prolific contributor Clayton Shonkwiler, who has racked up over 75 “staff pick” accolades. Recently I got the chance to interview him and learn more about the role of the Wolfram Language in his art and creative process. But first, I asked Wolfram Community’s staff lead, Vitaliy Kaurov, what makes Shonkwiler a standout among mathematical artists.
December 7, 2017 — Jon McLoone, Director, Technical Communication & Strategy
Computation is no longer the preserve of science and engineering, so I thought I would share a simple computational literary analysis that I did with my daughter.
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:
October 10, 2017 — Etienne Bernard, Lead Architect, Advanced Research Group
Automated Data Science
Imagine a baker connecting a data science application to his database and asking it, “How many croissants are we going to sell next Sunday?” The application would simply answer, “According to your recorded data and other factors such as the predicted weather, there is a 90% chance that between 62 and 67 croissants will be sold.” The baker could then plan accordingly. This is an example of an automated data scientist, a system to which you could throw arbitrary data and get insights or predictions in return.
One key component in making this a reality is the ability to learn a predictive model without specifications from humans besides the data. In the Wolfram Language, this is the role of the functions Classify and Predict. For example, let’s train a classifier to recognize morels from hedgehog mushrooms: