May 31, 2018 — Sjoerd Smit, Technical Consultant
Neural networks are very well known for their uses in machine learning, but can be used as well in other, more specialized topics, like regression. Many people would probably first associate regression with statistics, but let me show you the ways in which neural networks can be helpful in this field. They are especially useful if the data you’re interested in doesn’t follow an obvious underlying trend you can exploit, like in polynomial regression.
In a sense, you can view neural network regression as a kind of intermediary solution between true regression (where you have a fixed probabilistic model with some underlying parameters you need to find) and interpolation (where your goal is mostly to draw an eye-pleasing line between your data points). Neural networks can get you something from both worlds: the flexibility of interpolation and the ability to produce predictions with error bars like when you do regression.
May 24, 2018 — Carlo Giacometti, Kernel Developer, Algorithms R&D
Recognizing words is one of the simplest tasks a human can do, yet it has proven extremely difficult for machines to achieve similar levels of performance. Things have changed dramatically with the ubiquity of machine learning and neural networks, though: the performance achieved by modern techniques is dramatically higher compared with the results from just a few years ago. In this post, I’m excited to show a reduced but practical and educational version of the speech recognition problem—the assumption is that we’ll consider only a limited set of words. This has two main advantages: first of all, we have easy access to a dataset through the Wolfram Data Repository (the Spoken Digit Commands dataset), and, maybe most importantly, all of the classifiers/networks I’ll present can be trained in a reasonable time on a laptop.
It’s been about two years since the initial introduction of the Audio object into the Wolfram Language, and we are thrilled to see so many interesting applications of it. One of the main additions to Version 11.3 of the Wolfram Language was tight integration of Audio objects into our machine learning and neural net framework, and this will be a cornerstone in all of the examples I’ll be showing today.
Without further ado, let’s squeeze out as much information as possible from the Spoken Digit Commands dataset!
April 12, 2018 — Stephen Wolfram
The more one does computational thinking, the better one gets at it. And today we’re launching the Wolfram Challenges site to give everyone a source of bite-sized computational thinking challenges based on the Wolfram Language. Use them to learn. Use them to stay sharp. Use them to prove how great you are.
The Challenges typically have the form: “Write a function to do X”. But because we’re using the Wolfram Language—with all its built-in computational intelligence—it’s easy to make the X be remarkably sophisticated.
The site has a range of levels of Challenges. Some are good for beginners, while others will require serious effort even for experienced programmers and computational thinkers. Typically each Challenge has at least some known solution that’s at most a few lines of Wolfram Language code. But what are those lines of code?
March 14, 2018 — Swede White, Lead Communications Strategist, Public Relations
Daniel George is a graduate student at the University of Illinois at Urbana-Champaign, Wolfram Summer School alum and Wolfram intern whose award-winning research on deep learning for gravitational wave detection recently landed in the prestigious pages of Physics Letters B in a special issue commemorating the Nobel Prize in 2017.
We sat down with Daniel to learn more about his research and how the Wolfram Language plays a part in it.
March 8, 2018 — Stephen Wolfram
The Release Pipeline
Last September we released Version 11.2 of the Wolfram Language and Mathematica—with all sorts of new functionality, including 100+ completely new functions. Version 11.2 was a big release. But today we’ve got a still bigger release: Version 11.3 that, among other things, includes nearly 120 completely new functions.
This June 23rd it’ll be 30 years since we released Version 1.0, and I’m very proud of the fact that we’ve now been able to maintain an accelerating rate of innovation and development for no less than three decades. Critical to this, of course, has been the fact that we use the Wolfram Language to develop the Wolfram Language—and indeed most of the things that we can now add in Version 11.3 are only possible because we’re making use of the huge stack of technology that we’ve been systematically building for more than 30 years.
We’ve always got a large pipeline of R&D underway, and our strategy for .1 versions is to use them to release everything that’s ready at a particular moment in time. Sometimes what’s in a .1 version may not completely fill out a new area, and some of the functions may be tagged as “experimental”. But our goal with .1 versions is to be able to deliver the latest fruits of our R&D efforts on as timely a basis as possible. Integer (.0) versions aim to be more systematic, and to provide full coverage of new areas, rounding out what has been delivered incrementally in .1 versions.
In addition to all the new functionality in 11.3, there’s a new element to our process. Starting a couple of months ago, we began livestreaming internal design review meetings that I held as we brought Version 11.3 to completion. So for those interested in “how the sausage is made”, there are now almost 122 hours of recorded meetings, from which you can find out exactly how some of the things you can now see released in Version 11.3 were originally invented. And in this post, I’m going to be linking to specific recorded livestreams relevant to features I’m discussing.
OK, so what’s new in Version 11.3? Well, a lot of things. And, by the way, Version 11.3 is available today on both desktop (Mac, Windows, Linux) and the Wolfram Cloud. (And yes, it takes extremely nontrivial software engineering, management and quality assurance to achieve simultaneous releases of this kind.)
March 2, 2018 — Brian Wood, Lead Technical Marketing Writer, Document and Media Systems
Do you want to do more with data available on the web? Meaningful data exploration requires computation—and the Wolfram Language is well suited to the tasks of acquiring and organizing data. I’ll walk through the process of importing information from a webpage into a Wolfram Notebook and extracting specific parts for basic computation. Throughout this post, I’ll be referring to this website hosted by the National Weather Service, which gives 7-day forecasts for locations in the western US:
February 2, 2018 — Ed Pegg Jr, Editor, Wolfram Demonstrations Project
Some trees are planted in an orchard. What is the maximum possible number of distinct lines of three trees? In his 1821 book Rational Amusement for Winter Evenings, J. Jackson put it this way:
Fain would I plant a grove in rows
But how must I its form compose
With three trees in each row;
To have as many rows as trees;
Now tell me, artists, if you please:
’Tis all I want to know.
Those familiar with tic-tac-toe, three-in-a-row might wonder how difficult this problem could be, but it’s actually been looked at by some of the most prominent mathematicians of the past and present. This essay presents many new solutions that haven’t been seen before, shows a general method for finding more solutions and points out where current best solutions are improvable.
January 26, 2018 — Christopher Carlson, Senior User Interface Developer, User Interfaces
Every summer, 200-some artists, mathematicians and technologists gather at the Bridges conference to celebrate connections between mathematics and the arts. It’s five exuberant days of sharing, exploring, puzzling, building, playing and discussing diverse artistic domains, from poetry to sculpture.
The Wolfram Language is essential to many Bridges attendees’ work. It’s used to explore ideas, puzzle out technical details, design prototypes and produce output that controls production machines. It’s applied to sculpture, graphics, origami, painting, weaving, quilting—even baking.
In the many years I’ve attended the Bridges conferences, I’ve enjoyed hearing about these diverse applications of the Wolfram Language in the arts. Here is a selection of Bridges artists’ work.
I love to run. A lot. And many of my coworkers do too. You can find us everywhere, and all the time: on roads, in parks, on hills and mountains, and even running up and down parking decks, a flat lander’s version of hills. And if there is a marathon to be run, we’ll be there as well. With all of the internal interest in running marathons, Wolfram Research created this Marathon Viewer as a sponsorship project for the Christie Clinic Illinois Marathon.
Here are four of us, shown as dots, participating in the 2017 Illinois Marathon:
How did the above animation and the in-depth look at our performance come about? Read on to find out.
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.