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Leading Edge

Why You Should Care about the Obscure

Mathematica has always had the most complete collection of special functions available. You might think that by now there were no more to add, but the next release of Mathematica will add another five. You might also think that any that are left to add are too obscure for you to care about. They are getting fairly obscure, but you should still care. Let's look at one of them: Owen's T function.
Jon McLoone, Director, Technical Communication & Strategy
Computation & Analysis

Happy Vampire Day

I recently was asked about Fibonacci Day. I think I replied "What is Fibonacci Day?" Then the person explained it. November 23 is 11/23. Or 1, 1, 2, 3—the start of the Fibonacci sequence. Other yearly math-related days I found were Pi Day (3/14), Foursquare Day (4/16), Pi Approximation Day (22/7, in European format), Opposite Day (12/21), and Mole Day (6:02 10/23). A lot of these seem a bit arbitrary. I thought I might be able to do better, so here's what I came up with for the month of September.
September 2010
Ed Pegg Jr, Scientific Information Editor, Scientific Information Group
Education & Academic

New Algorithm to Make Short Work of Challenging Problems

Buried deep in the list of new technology in the Mathematica development pipeline was the item "integration of oscillatory functions (univariate, multivariate)---new algorithm". I expect most people will overlook it, as I did, in favor of the new functions, new directions, big infrastructure, and the eye candy. Even worse, most people who will use it won't even know---it will be selected automatically when needed, like many of Mathematica's algorithms. So I think it's my duty to share my discovery that this algorithm is actually really cool. Why is it so cool? The first clue I had was when I read in the notes that this was the first time anyone had fully automated the algorithm into a very wide class of problems. Second, that it was a hybrid numeric-symbolic method (putting it beyond the reach of most numerical systems). And finally, that it was developed by the talented Wolfram Research developer Andrew Moylan.
Jon McLoone, Director, Technical Communication & Strategy
Products

Let’s Do It Again

Iteration usually increases complexity. For example, ponder the following "Fractal Maze”. The green lines mark the boundaries of a frame that shows the black paths of a maze. Copies of that frame and the paths are copied inside. With 4 levels of nested frames, it is possible to get from 1 to 8 on the outer frame. When pictures are repeated inside themselves, it's usually called the Droste effect.
Ed Pegg Jr, Scientific Information Editor, Scientific Information Group
Best of Blog

Do Computers Dumb Down Math Education?

Since I just heard that the video for Conrad Wolfram's recent TED talk "Stop teaching calculating, start teaching math" will be coming out soon, I thought I would address the single biggest fear that I hear when I talk about using computers in math education. The objection that using computers will "dumb down" education comes with the related ideas "students have to learn to do it by hand or how will they know they have got the right answer", "they won't understand what is happening unless they do it themselves", and so on. Well, let's examine this by looking at a typical math question that I know I had to solve at some point in my education.
Jon McLoone, Director, Technical Communication & Strategy
Announcements & Events

Stephen Wolfram Discusses Making the World’s Data Computable

Wolfram Research and Wolfram|Alpha hosted the first Wolfram Data Summit in Washington, DC this September. Leaders of the world's primary data repositories attended the summit, exchanging experiences and brainstorming ideas for the future of data collection, management, and dispersion. In his keynote speech, Stephen Wolfram discussed the complex nature of gathering systematic knowledge and data together. He also talked about the creation of Wolfram|Alpha, how Mathematica helps with the challenges of making all data computable, and what we can expect moving forward. The transcript is available below.
Wolfram Blog Team
Announcements & Events

A New Kind of Science is on the iPad!

I spent a decade of my life writing A New Kind of Science. Most of that time was devoted to discovering the science in the book. But another part was spent figuring out how to present the science in the best possible way—using words and pictures. It took a lot of technology to do that […]

Stephen Wolfram
Computation & Analysis

Tapping Into the Power of GPU in Mathematica

Last week we posted an item about Wolfram Research's partnership with NVIDIA to integrate GPU programming into Mathematica. With NVIDIA's GPU Technology Conference 2010 starting today, we thought we would share a little more for those who won't be at the show to see us (booth #31, for those who are attending). Mathematica's GPU programming integration is not just about performance. Yes, of course, with GPU power you get some of your answers several times faster than before---but that is only half the story. The heart of the integration is the full automation of the GPU function developing process. With proper hardware, you can write, compile, test, and run your code in a single transparent step. There is no need to worry about details, such as memory allocation or library binding. Mathematica handles it elegantly and gracefully for you. As a developer, you will be able to focus on developing and optimizing your algorithms, and nothing else. Here are a couple of examples to give you a taste of the upcoming feature.
Ulises Cervantes-Pimentel, Senior Kernel Developer Abdul Dakkak, Senior Compiler Developer, Compiler Development
Computation & Analysis

Mathematica and NVIDIA in Action: See Your GPU in a Whole Different Light

Wolfram Research is partnering with NVIDIA to integrate GPU programming into Mathematica. CUDA is NVIDIA's performance computing architecture that harnesses modern GPU's potential. The new partnership means that if you have GPU-equipped hardware, you can transform Mathematica's computing, modeling, simulation, or visualization performance, boosting speed by factors easily exceeding 100. Now that's fast! Afraid of the programming involved? Don't be. Mathematica's new CUDA programming capabilities dramatically reduce the complexity of coding required to take advantage of GPU's parallel power. So you can focus on innovating your algorithms rather than spending time on repetitive tasks, such as GUI design.
Angela Sims, Government Account Executive, Government Sales
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