Real-time filters work like magic. Usually out of sight, they clean data to make it useful for the larger system they are part of, and sometimes even for human consumption. A fascinating thing about these filters is that they don’t have a big-picture perspective. They work wonders with only a small window into the data that is streaming in. On the other hand, if I had a stream of numbers flying across my screen, I would at the very least need to plot it to make sense of the data. These types of filters are very simple as well.
Date Archive: 2019 September
Our interactive Multiparadigm Data Science (MPDS) course has been up at Wolfram U for over a month now, and we’re pretty satisfied with the results so far. Hundreds of people have started the course—including students from our first Data Science Boot Camp, who joined us at Wolfram headquarters for a three-week training camp. Thanks to the success of the boot camp, we have also had several projects submitted for advanced MPDS certification, which will soon be available within the interactive course.
But what exactly does it mean to be a practitioner of MPDS? And how might the multiparadigm approach improve my computational projects? To find out, I decided to try this free course for myself.
The Next Big Step for Wolfram|Alpha
Wolfram|Alpha has been a huge hit with students. Whether in college or high school, Wolfram|Alpha has become a ubiquitous way for students to get answers. But it’s a one-shot process: a student enters the question they want to ask (say in math) and Wolfram|Alpha gives them the (usually richly contextualized) answer. It’s incredibly useful---especially when coupled with its step-by-step solution capabilities.
But what if one doesn't want just a one-shot answer? What if one wants to build up (or work through) a whole computation? Well, that's what we created Mathematica and its whole notebook interface to do. And for more than 30 years that's how countless inventions and discoveries have been made around the world. It's also how generations of higher-level students have been taught.
But what about students who aren't ready to use Mathematica yet? What if we could take the power of Mathematica (and what's now the Wolfram Language), but combine it with the ease of Wolfram|Alpha?
Well, that's what we've done in Wolfram|Alpha Notebook Edition.
A Year Ago Today
On September 5 of last year, The New York Times took the unusual step of publishing an op-ed anonymously. It began “I Am Part of the Resistance inside the Trump Administration,” and quickly became known as the “Resistance” op-ed. From the start, there was wide‐ranging speculation as to who might have been the author(s); to this day, that has not been settled. (Spoiler alert: it will not be settled in this blog post, either. But that’s getting ahead of things.) When I learned of this op-ed, the first thing that came to mind, of course, was, “I wonder if authorship attribution software could....” This was followed by, “Well, of course it could. If given the right training data.” When time permitted, I had a look on the internet into where one might find training data, and for that matter who were the people to consider for the pool of candidate authors. I found at least a couple of blog posts that mentioned the possibility of using tweets from administration officials. One gave a preliminary analysis (with President Trump himself receiving the highest score, though by a narrow margin—go figure). It even provided a means of downloading a dataset that the poster had gone to some work to cull from the Twitter site.
The code from that blog was in a language/script in which I am not fluent. My coauthor on two authorship attribution papers (and other work), Catalin Stoean, was able to download the data successfully. I first did some quick validation (to be seen) and got solid results. Upon setting the software loose on the op-ed in question, a clear winner emerged. So for a short time I “knew” who wrote that piece. Except. I decided more serious testing was required.