November 5, 2015 — Christopher Carlson, Senior User Interface Developer, User Interfaces
The One-Liner Competition has become a tradition at our annual Wolfram Technology Conference. It’s an opportunity for some of the most talented Wolfram Language developers to show the world what amazing things can be done with a mere 128 characters of Wolfram Language code.
More than any other programming language, the Wolfram Language gives you a wealth of sophisticated built-in algorithms that you can combine and recombine to do things you wouldn’t think possible without reams of computer code. This year’s One-Liner submissions showed the diversity of the language. There were news monitors, sonifications, file system indexers, web mappers, geographic mappers, anatomical visualizations, retro graphics, animations, hypnotic dynamic graphics, and web data miners… all implemented with 128 or fewer characters.
The first of three honorable mentions went to Richard Gass for his New York Times Word Cloud. With 127 characters of Wolfram Language code, he builds a word cloud of topics on the current New York Times front page by pulling nouns out of the headlines:
September 21, 2015 — Arnoud Buzing, Director of Quality and Release Management
I drink too much coffee—it’s one of my few vices. Recently, my favorite espresso machine at the Wolfram Research headquarters in Champaign, Illinois, was replaced with a fancy new combination coffee and espresso maker. The coffee now comes in little pouches of various flavors, ranging from “light and smooth” to “dark and intense”. There even is a “hot chocolate” pouch and a way to make cappuccinos using both a “froth” pouch and an “espresso” pouch. Here is a picture of the new coffee selection:
May 13, 2015 — Stephen Wolfram
“What is this a picture of?” Humans can usually answer such questions instantly, but in the past it’s always seemed out of reach for computers to do this. For nearly 40 years I’ve been sure computers would eventually get there—but I’ve wondered when.
I’ve built systems that give computers all sorts of intelligence, much of it far beyond the human level. And for a long time we’ve been integrating all that intelligence into the Wolfram Language.
Now I’m excited to be able to say that we’ve reached a milestone: there’s finally a function called ImageIdentify built into the Wolfram Language that lets you ask, “What is this a picture of?”—and get an answer.
And today we’re launching the Wolfram Language Image Identification Project on the web to let anyone easily take any picture (drag it from a web page, snap it on your phone, or load it from a file) and see what ImageIdentify thinks it is:
May 7, 2015 — Robert Nachbar, Consultant
Spring is here, finally, and everyone around here is tired of snow this year! Some of the hardier flowers are up already, such as daffodils and hyacinths. So, naturally, I started thinking about when I could put in the more delicate annuals, or even my tomatoes. I don’t want them to be bitten by a late frost (we had one the other day!). And in the autumn, we want to know how late we can harvest before a frost might damage the produce.
Well, I could consult The Old Farmer’s Almanac for the last frost date, but how accurate is it for my specific locale? What about the variability? Might there be a trend to earlier dates due to global warming? To answer these questions, I need historical temperature data. The Wolfram Language has weather data available, so maybe I could do a little data mining and come up with our own planting chart, and you could for your town, too.
April 28, 2015 — Stephen Wolfram
My goal with the Wolfram Language is to take programming to a new level. And over the past year we’ve been rolling out ways to use and deploy the language in many places—desktop, cloud, mobile, embedded, etc. So what about wearables? And in particular, what about the Apple Watch? A few days ago I decided to explore what could be done. So I cleared my schedule for the day, and started writing code.
My idea was to write code with our standard Wolfram Programming Cloud, but instead of producing a web app or web API, to produce an app for the Apple Watch. And conveniently enough, a preliminary version of our Wolfram Cloud app just became available in the App Store—letting me deploy from the Wolfram Cloud to both mobile devices and the watch.
March 24, 2015 — Mariusz Jankowski
Recently, during a particularly severe patch of winter weather and much too much shoveling of snow off my driveway, I decided, with help from the Wolfram Language, to bring back memories of fairer weather by looking at commuting to work on a bicycle.
This past year, I finally succumbed to the increasingly common practice of recording personal activity data. Over the last few years, I’d noted that my rides had become shorter and easier as the season progressed, so I was mildly interested in verifying this improvement in personal fitness. Using nothing more than a smart phone and a suitable application, I recorded 27 rides between home and work, and then used the Wolfram Language to read, analyze, and visualize the results.
Here is a Google Earth image showing my morning bike route covering a distance of a little under 11 miles, running from east to west.
March 17, 2015 — Arnoud Buzing, Director of Quality and Release Management
Recently Stephen Wolfram announced the Wolfram Data Drop, which is a great new tool to upload any type of data from any type of device. I’ll show how you can use the Wolfram Data Drop with a weather station you build using some basic hardware and a few lines of code. Once completed, your device will take temperature measurements every second for 60 seconds, and upload their average value to the Wolfram Data Drop every minute. This will give you 60 data points per hour and 1,440 data points per day. With this data you can use Wolfram Programming Cloud to understand how the temperature changes over time. You can find the exact times in a given day when the temperature was the highest or lowest, when the temperature changed the fastest, and maybe even use the data to make predictions! Can you beat your local weather station and make a prediction that is better?
March 12, 2015 — Stephen Wolfram
Pictures from Pi Day now added »
Between Mathematica and Wolfram|Alpha, I’m pretty sure our company has delivered more π to the world than any other organization in history. So of course we have to do something special for Pi Day of the Century.
March 4, 2015 — Stephen Wolfram
Where should data from the Internet of Things go? We’ve got great technology in the Wolfram Language for interpreting, visualizing, analyzing, querying and otherwise doing interesting things with it. But the question is, how should the data from all those connected devices and everything else actually get to where good things can be done with it? Today we’re launching what I think is a great solution: the Wolfram Data Drop.
When I first started thinking about the Data Drop, I viewed it mainly as a convenience—a means to get data from here to there. But now that we’ve built the Data Drop, I’ve realized it’s much more than that. And in fact, it’s a major step in our continuing efforts to integrate computation and the real world.
So what is the Wolfram Data Drop? At a functional level, it’s a universal accumulator of data, set up to get—and organize—data coming from sensors, devices, programs, or for that matter, humans or anything else. And to store this data in the cloud in a way that makes it completely seamless to compute with.
February 27, 2015 — Vitaliy Kaurov, Technical Communication & Strategy
Martin Handford can spend weeks creating a single Where’s Waldo puzzle hiding a tiny red and white striped character wearing Lennon glasses and a bobble hat among an ocean of cartoon figures that are immersed in amusing activities. Finding Waldo is the puzzle’s objective, so hiding him well, perhaps, is even more challenging. Martin once said, “As I work my way through a picture, I add Wally when I come to what I feel is a good place to hide him.” Aware of this, Ben Blatt from Slate magazine wondered if it’s possible “to master Where’s Waldo by mapping Handford’s patterns?” Ben devised a simple trick to speed up a Waldo search. In a sense, it’s the same observation that allowed Jon McLoone to write an algorithm that can beat a human in a Rock-Paper-Scissors game. As Jon puts it, “we can rely on the fact that humans are not very good at being random.”