November 18, 2013 — Wolfram Blog Team
Wolfram Research hosted the annual Technology Conference at our headquarters in Champaign, Illinois, October 21–23. We welcomed over two hundred attendees from twenty different countries, making this our largest turnout yet!
The event was once again jam-packed with exciting talks, Q&As, workshops, and even hands-on time with some of our top-secret upcoming products. (Sorry, but you had to be there to find out what they are—all attendees signed a Nondisclosure Agreement in order to hear the latest about our unreleased technologies!) We enjoyed a stellar opening keynote from Stephen Wolfram, which left everyone buzzing with anticipation for the sessions and speakers to follow.
August 13, 2013 — Theodore Gray, Co-founder, Wolfram Research, Inc; Founder, Touch Press; Proprietor, periodictable.com
I just finished giving a short presentation to several thousand screaming fans at the D23 Disney fan convention in Anaheim, California. When I say “screaming fans,” what I mean is Disney fans who were literally screaming at what I had to say.
This already somewhat improbable situation was made all the more surprising by the fact that they were screaming about FindClusters.
Well, technically, most of them may not have actually realized that’s what they were screaming about, because they were seeing only the output of the command, not the actual Mathematica code. But the thing they were so excited about was direct output from Mathematica, and the key differentiating factor that made it so interesting to them was the ability of FindClusters to discern, differentiate, and illuminate the shifting moods and emotions of animated feature films.
August 5, 2013 — Crystal Fantry, Manager, Education Content
Thirty-three extremely intelligent high school students gathered at Bentley University July 7-19 to participate in our second annual Mathematica Summer Camp. The program lasted two weeks, and within this small window of time, students created their very own Mathematica projects. At the end of the camp, students presented these projects to their peers, camp instructors, and Stephen Wolfram. Projects ranged from games created in Mathematica to a Demonstration of the “Wavefunction and Probability Density of a Coupled Quantum Harmonic Oscillator.” These projects will be posted to the Wolfram Demonstrations Project here, adding to the great work of those from 2012!
June 20, 2013 — Wolfram Blog Team
Have an interest in mathematics or science? Wolfram technologies like Mathematica and the Computable Document Format (CDF) are bringing us to the next level of educational experience through top-notch publishing environments and interactive course content from premier math and science leaders.
May 22, 2013 — Jon McLoone, International Business & Strategic Development
The benefits of linking from Mathematica to other languages and tools differ from case to case. But unusually, in the case of the new RLink in Mathematica 9, I think the benefits have very little to do with R, the language. The real benefit, I believe, is in the connection it makes to the R community.
When we first added the MathLink libraries for C, there were real benefits in farming out intensive numerical work (though Mathematica performance improvements over the years and development of the compiler have greatly reduced the occasions where that would be worth the effort). Creating an Excel link added an alternative interface paradigm to Mathematica that wasn’t available in the Mathematica front end. But in the case of R, it isn’t immediately obvious that it does many things that you can’t already do in Mathematica or many that it does significantly better.
However, with RLink I now have immediate access to the work of the R community through the add-on libraries that they have created to extend R into their field. A great zoo of these free libraries fill out thousands of niches–sometimes popular, sometimes obscure–but lots of them. There are over 4,000 right here and more elsewhere. At a stroke, all of them are made immediately available to the Mathematica environment, interpreted through the R language runtime.
January 3, 2013 — Wolfram Blog Team
From the beginning, the founders of the biotechnology startup Emerald Therapeutics wanted to develop an ideal research platform that would allow for lab and process automation during experiments as well as easy communication of their findings. Brian Frezza, Emerald’s Co-founder and Co-CEO, says Mathematica‘s flexible programming language and interactive notebook environment made it the clear choice.
The company’s scientists and engineers have a shared codebase in Mathematica, which allows them to use one platform for all of the tasks in their antiviral research workflow—from developing functions to processing and storing data, designing and managing experiments, presenting findings, and directly controlling lab instruments.
In this video, Frezza takes us into the company’s lab to show us the advantages of having Mathematica as the company’s core platform, including how it’s used to automate experiments.
December 6, 2012 — Stephen Wolfram
There aren’t very many qualitatively different types of computer interfaces in use in the world today. But with the release of Mathematica 9 I think we have the first truly practical example of a new kind—the computed predictive interface.
If one’s dealing with a system that has a small fixed set of possible actions or inputs, one can typically build an interface out of elements like menus or forms. But if one has a more open-ended system, one typically has to define some kind of language. Usually this will be basically textual (as it is for the most part for Mathematica); sometimes it may be visual (as for Wolfram SystemModeler).
The challenge is then to make the language broad and powerful, while keeping it as easy as possible for humans to write and understand. And as a committed computer language designer for the past 30+ years, I have devoted an immense amount of effort to this.
But with Wolfram|Alpha I had a different idea. Don’t try to define the best possible artificial computer language, that humans then have to learn. Instead, use natural language, just like humans do among themselves, and then have the computer do its best to understand this. At first, it was not at all clear that such an approach was going to work. But one of the big things we’ve learned from Wolfram|Alpha is with enough effort (and enough built-in knowledge), it can. And indeed two years ago in Mathematica 8 we used what we’d done with Wolfram|Alpha to add to Mathematica the capability of taking free-form natural language input, and automatically generating from it precise Mathematica language code.
But let’s say one’s just got some output from Mathematica. What should one do next? One may know the appropriate Mathematica language input to give. Or at least one may be able to express what one wants to do in free-form natural language. But in both cases there’s a kind of creative act required: starting from nothing one has figure out what to say.
So can we make this easier? The answer, I think, is yes. And that’s what we’ve now done with the Predictive Interface in Mathematica 9.
The concept of the Predictive Interface is to take what you’ve done so far, and from it predict a few possibilities for what you’re likely to want to do next.
December 5, 2012 — Wolfram Blog Team
Curious about Mathematica 9? You can see it in action in three free online events. Our experts will introduce you to new features in usability, computation, data manipulation, and visualization. Live Q&A sessions during each event will give you a chance to ask questions.
- Predictive Interface and Units: December 10, 1–2pm EST
Get a look at the new interface paradigm and systemwide units support. Our experts will demonstrate the next-computation Suggestions Bar, context-sensitive Input Assistant, and units features, from unit conversion to dimensional analysis.
- Social Networks and Data Science: December 12, 1–2:30pm EST
Learn about Mathematica 9′s new social network analysis capabilities with built-in access to social media data, plus other graphs and networks enhancements and new computational features in data science, such as reliability, survival analysis, and random processes.
- Data Manipulation and Visualization: December 14, 1–2:30pm EST
Get the scoop on new features for image and signal processing, interactive gauges, legends for plots and charts, and integrating with R directly from our experts.
November 28, 2012 — Stephen Wolfram
I’m excited to be able to announce that today we’re releasing Mathematica 9—and it’s big! A whole array of new ideas and new application areas… and major advances along a great many algorithmic frontiers.
Next year Mathematica will be 25 years old (and all sorts of festivities are planned!). And in that quarter century we’ve just been building and building. The core principles that we began with have been validated over and over again. And with them we’ve created a larger and larger stack of technology, that allows us to do more and more, and reach further and further.
From the beginning, our goal has been an ambitious one: to cover and automate every area of computational and algorithmic work. Having built the foundations of the Mathematica language, we started a quarter century ago attacking core areas of mathematics. And over the years since then, we have been expanding outward at an ever-increasing pace, conquering one area after another.
As with Wolfram|Alpha, we’ll never be finished. But as the years go by, the scope of what we’ve done becomes more and more immense. And with Mathematica 9 today we are taking yet another huge step.
So what’s new in Mathematica 9? Lots and lots of important things. An amazing range—something for almost everyone. And actually just the very size of it already represents an important challenge. Because as Mathematica grows bigger and bigger, it becomes more and more difficult for one to grasp everything that’s in it.
November 14, 2012 — Jon McLoone, International Business & Strategic Development
I stumbled upon a nice project called Rosetta Code. Their stated aim is “to present solutions to the same task in as many different languages as possible, to demonstrate how languages are similar and different, and to aid a person with a grounding in one approach to a problem in learning another.”
After amusing myself by contributing a few solutions (Flood filling, Mean angle, and Sum digits of an integer being some of mine), I realized that the data hidden in the site provided an opportunity to quantify a claim that I have often made over the years—that Mathematica code tends to be shorter than equivalent code in other languages. This is due to both its high-level nature and built-in computational knowledge.
Here is what I found.
Mathematica code is typically less than a third of the length of the same tasks written in other languages, and often much better.