News, Views & Insights
Nearly a year and a half ago—just a few months after ChatGPT burst on the scene—we introduced the first version of our Chat Notebook technology to integrate LLM-based chat into Wolfram Notebooks. For the past year and a half we’ve been building on those foundations. And today I’m excited to be able to announce that we’re releasing the fruits of those efforts: the first version of our Wolfram Notebook Assistant.
Today we celebrate the arrival of the 36th (x.x) version of the Wolfram Language and Mathematica: Version 14.1. We’ve been doing this since 1986: continually inventing new ideas and implementing them in our larger and larger tower of technology. And it’s always very satisfying to be able to deliver our latest achievements to the world.
In March, my work life changed dramatically, as it did for many around the world. After working in an office environment for almost 25 years, I was told that I needed to work from home for the first time. So I took my laptop, power cord and extra battery home with the expectation that I’d be there for one to two weeks. The first couple of days were a mad dash with our IT department to ensure my VPN, softphone and main tools were set up correctly for remote work. They were great, and for the most part, I was working at 90% right away. The experience opened my eyes, as many of my sales employees work remotely, so I learned a lot about the positives (and, of course, the negatives) they often experience that I may not have appreciated in the past.
Wolfram|Alpha Notebook Edition was released nearly a year ago, and we're proud to share what the team has been working on since. In addition to the improvements made to Wolfram|Alpha itself, new input and output suggestions were added. There were parsing fixes, additions to the Wolfram|Alpha-to-Wolfram Language translation and some of the normal improvements one would expect. There are also some bigger features and interesting new capabilities that we will explore in a bit more detail here.
If you haven't checked out Wolfram|Alpha Notebook Edition in a while, we'd like to invite you to revisit. With education looking a little different for many people right now, this could be a great time to explore this exciting new way to interface with Wolfram technologies.
This roundup of Wolfram Community contributions features several different functions and tools related to current times, from the global pandemic to sustainable spaces and homeschool puzzles. Read on to see just a few creative examples from some of our favorite Community members and Wolfram Language wizzes.
Schooling looks a little different in 2020. Whether it’s technical issues with the transition to online courses, decreased physical attendance or delayed and last-minute openings, we’re all feeling the pressure caused by the COVID-19 pandemic.
At Wolfram Research, we've been involved in education for decades and count millions of parents, teachers and students as both our customers and our friends. Because of this longstanding commitment to learning, we have content, experts and platforms at the ready to help parents and students who are looking for ways to augment their learning.
While programming in the Wolfram Language, I am able to quickly and easily get results—one of the best aspects of writing code in a high-level language. The Wolfram Language is so easy to use that I have the freedom to pursue ideas on a whim, even if I know those ideas may not accomplish anything great or work toward a larger goal. In most cases, within a few minutes I figure out if the idea is a dead end. I also figure out if I am on the path to creating something useful or, better yet, fun.
A noteworthy achievement of artificial intelligence, since it is driven by artificial neural networks under the label deep learning, is the ability to create artistic works to generate images, text and sounds. At the core of this breakthrough is a basic method to train neural networks that was introduced by Ian Goodfellow in 2014 and was called by Yann LeCun “the most interesting idea in the last 10 years in machine learning”: generative adversarial networks (GANs). A GAN is a way to train a generative network that produces realistic-looking fake samples out of a latent seed, which can be some arbitrary data or random numbers sampled from a simple distribution. Let’s look at how to do so with some of the new capabilities developed for Mathematica Version 12.1.
Linear algebra is probably the easiest and the most useful branch of modern mathematics. Indeed, topics such as matrices and linear equations are often taught in middle or high school. On the other hand, concepts and techniques from linear algebra underlie cutting-edge disciplines such as data science and quantum computation. And in the field of numerical analysis, everything is linear algebra!
Today, I am proud to announce a free interactive course, Introduction to Linear Algebra, that will help students all over the world to master this wonderful subject. The course uses the powerful functions for matrix operations in the Wolfram Language and addresses questions such as "How long would it take to solve a system of 500 linear equations?" or "How does data compression work?"
While catching up with my old friends Alice and Bob on Zoom a few days ago, I became intrigued by their recent card game hobby—and how they used the Wolfram Language to settle an argument. To figure out who gets to go first at the start of the game, they take one suit (spades) from a full deck, and each draws a card. Then, the person with the highest card value wins. Because they are using only one suit, there can be no ties. Simple, right?
For more on Wolfram’s summer education programs, read our post about the Wolfram Summer School.
This year marked the ninth annual Wolfram High School Summer Camp, and it was a truly amazing experience. Forty-four students joined us remotely from around the world—from almost on the doorstep of Wolfram’s headquarters in Illinois to as far-flung as Kazakhstan, Germany, Russia, South Korea and India. They dedicated two weeks to learning the Wolfram Language, creating remarkably high-level independent projects and developing strong computational thinking skills. Our students had the opportunity to learn from subject experts from Wolfram Research, their mentors and teaching assistants (TAs) and, of course, our CEO Stephen Wolfram, who met with each student to discuss their projects.