People are used to producing prose—and sometimes pictures—to express themselves. But in the modern age of computation, something new has become possible that I’d like to call the computational essay.
I’ve been working on building the technology to support computational essays for several decades, but it’s only very recently that I’ve realized just how central computational essays can be to both the way people learn, and the way they communicate facts and ideas. Professionals of the future will routinely deliver results and reports as computational essays. Educators will routinely explain concepts using computational essays. Students will routinely produce computational essays as homework for their classes.
Here’s a very simple example of a computational essay:
I’m pleased to announce that as of today, the Wolfram Data Repository is officially launched! It’s been a long road. I actually initiated the project a decade ago—but it’s only now, with all sorts of innovations in the Wolfram Language and its symbolic ways of representing data, as well as with the arrival of the Wolfram Cloud, that all the pieces are finally in place to make a true computable data repository that works the way I think it should.
I’m pleased to announce the release today of Version 11.1 of the Wolfram Language (and Mathematica). As of now, Version 11.1 is what’s running in the Wolfram Cloud—and desktop versions are available for immediate download for Mac, Windows and Linux.
What’s new in Version 11.1? Well, actually a remarkable amount. Here’s a summary:
Computational thinking needs to be an integral part of modern education—and today I’m excited to be able to launch another contribution to this goal: Wolfram|Alpha Open Code.
Every day, millions of students around the world use Wolfram|Alpha to compute answers. With Wolfram|Alpha Open Code they’ll now not just be able to get answers, but also be able to get code that lets them explore further and immediately apply computational thinking. Continue reading
I’m thrilled today to announce the release of a major new version of Mathematica and the Wolfram Language: Version 11, available immediately for both desktop and cloud. Hundreds of us have been energetically working on building this for the past two years—and in fact I’ve personally put several thousand hours into it. I’m very excited about what’s in it; it’s a major step forward, with a lot of both breadth and depth—and with remarkably central relevance to many of today’s most prominent technology areas.
It’s been more than 28 years since Version 1 came out—and nearly 30 years since I started its development. And all that time I’ve been continuing to pursue a bold vision—and to build a taller and taller stack of technology. With most software, after a few years and a few versions, not a lot of important new stuff ever gets added. But with Mathematica and the Wolfram Language it’s been a completely different story: for three decades we’ve been taking major steps forward at every version, progressively conquering vast numbers of new areas. Continue reading
Six and a half years ago we put Wolfram|Alpha and the sophisticated computational knowledge it delivers out free on the web for anyone in the world to use. Now we’re launching the Wolfram Open Cloud to let anyone in the world use the Wolfram Language—and do sophisticated knowledge-based programming—free on the web.
It’s been very satisfying to see how successfully Wolfram|Alpha has democratized computational knowledge and how its effects have grown over the years. Now I want to do the same thing with knowledge-based programming—through the Wolfram Open Cloud.
Last week we released Wolfram Programming Lab as an environment for people to learn knowledge-based programming with the Wolfram Language. Today I’m pleased to announce that we’re making Wolfram Programming Lab available for free use on the web in the Wolfram Open Cloud. Continue reading
I’m excited today to be able to announce the launch of Wolfram Programming Lab—an environment for anyone to learn programming and computational thinking through the Wolfram Language. You can run Wolfram Programming Lab through a web browser, as well as natively on desktop systems (Mac, Windows, Linux).
“What is this a picture of?” Humans can usually answer such questions instantly, but in the past it’s always seemed out of reach for computers to do this. For nearly 40 years I’ve been sure computers would eventually get there—but I’ve wondered when.
I’ve built systems that give computers all sorts of intelligence, much of it far beyond the human level. And for a long time we’ve been integrating all that intelligence into the Wolfram Language.
Now I’m excited to be able to say that we’ve reached a milestone: there’s finally a function called ImageIdentify built into the Wolfram Language that lets you ask, “What is this a picture of?”—and get an answer.
My goal with the Wolfram Language is to take programming to a new level. And over the past year we’ve been rolling out ways to use and deploy the language in many places—desktop, cloud, mobile, embedded, etc. So what about wearables? And in particular, what about the Apple Watch? A few days ago I decided to explore what could be done. So I cleared my schedule for the day, and started writing code.
My idea was to write code with our standard Wolfram Programming Cloud, but instead of producing a web app or web API, to produce an app for the Apple Watch. And conveniently enough, a preliminary version of our Wolfram Cloud app just became available in the App Store—letting me deploy from the Wolfram Cloud to both mobile devices and the watch.
Where should data from the Internet of Things go? We’ve got great technology in the Wolfram Language for interpreting, visualizing, analyzing, querying and otherwise doing interesting things with it. But the question is, how should the data from all those connected devices and everything else actually get to where good things can be done with it? Today we’re launching what I think is a great solution: the Wolfram Data Drop.
When I first started thinking about the Data Drop, I viewed it mainly as a convenience—a means to get data from here to there. But now that we’ve built the Data Drop, I’ve realized it’s much more than that. And in fact, it’s a major step in our continuing efforts to integrate computation and the real world.
So what is the Wolfram Data Drop? At a functional level, it’s a universal accumulator of data, set up to get—and organize—data coming from sensors, devices, programs, or for that matter, humans or anything else. And to store this data in the cloud in a way that makes it completely seamless to compute with. Continue reading
It’s been many years in the making, and today I’m excited to announce the launch of Mathematica Online: a version of Mathematica that operates completely in the cloud—and is accessible just through any modern web browser.
In the past, using Mathematica has always involved first installing software on your computer. But as of today that’s no longer true. Instead, all you have to do is point a web browser at Mathematica Online, then log in, and immediately you can start to use Mathematica—with zero configuration.
We released Mathematica 1 just over 26 years ago—on June 23, 1988. And ever since we’ve been systematically making Mathematica ever bigger, stronger, broader and deeper. But Mathematica 10—released today—represents the single biggest jump in new functionality in the entire history of Mathematica. Continue reading
My goal with the Wolfram Language in general—and Wolfram Programming Cloud in particular—is to redefine the process of programming, and to automate as much as possible, so that once a human can express what they want to do with sufficient clarity, all the details of how it is done should be handled automatically.
I’ve been working toward this for nearly 30 years, gradually building up the technology stack that is needed—at first in Mathematica, later also in Wolfram|Alpha, and now in definitive form in the Wolfram Language. The Wolfram Language, as I have explained elsewhere, is a new type of programming language: a knowledge-based language, whose philosophy is to build in as much knowledge about computation and about the world as possible—so that, among other things, as much as possible can be automated. Continue reading
Two weeks ago I spoke at SXSW Interactive in Austin, TX. Here’s a slightly edited transcript (it’s the “speaker’s cut”, including some demos I had to abandon during the talk):
Well, I’ve got a lot planned for this hour.
Basically, I want to tell you a story that’s been unfolding for me for about the last 40 years, and that’s just coming to fruition in a really exciting way. And by just coming to fruition, I mean pretty much today. Because I’m planning to show you today a whole lot of technology that’s the result of that 40-year story—that I’ve never shown before, and that I think is going to be pretty important.
I always like to do live demos. But today I’m going to be pretty extreme. Showing you a lot of stuff that’s very very fresh. And I hope at least a decent fraction of it is going to work.
OK, here’s the big theme: taking computation seriously. Really understanding the idea of computation. And then building technology that lets one inject it everywhere—and then seeing what that means. Continue reading
We’re getting closer to the first official release of the Wolfram Language—so I am starting to demo it more publicly.
Here’s a short video demo I just made. It’s amazing to me how much of this is based on things I hadn’t even thought of just a few months ago. Knowledge-based programming is going to be much bigger than I imagined…
Connected devices are central to our long-term strategy of injecting sophisticated computation and knowledge into everything. With the Wolfram Language we now have a way to describe and compute about things in the world. Connected devices are what we need to measure and interface with those things.
In the end, we want every type of connected device to be seamlessly integrated with the Wolfram Language. And this will have all sorts of important consequences. But as we work toward this, there’s an obvious first step: we have to know what types of connected devices there actually are.
So to have a way to answer that question, today we’re launching the Wolfram Connected Devices Project—whose goal is to work with device manufacturers and the technical community to provide a definitive, curated, source of systematic knowledge about connected devices.
Last week I wrote about our large-scale plan to use new technology we’re building to inject sophisticated computation and knowledge into everything. Today I’m pleased to announce a step in that direction: working with the Raspberry Pi Foundation, effective immediately there’s a pilot release of the Wolfram Language—as well as Mathematica—that will soon be bundled as part of the standard system software for every Raspberry Pi computer.
Computational knowledge. Symbolic programming. Algorithm automation. Dynamic interactivity. Natural language. Computable documents. The cloud. Connected devices. Symbolic ontology. Algorithm discovery. These are all things we’ve been energetically working on—mostly for years—in the context of Wolfram|Alpha, Mathematica, CDF and so on.
But recently something amazing has happened. We’ve figured out how to take all these threads, and all the technology we’ve built, to create something at a whole different level. The power of what is emerging continues to surprise me. But already I think it’s clear that it’s going to be profoundly important in the technological world, and beyond.
At some level it’s a vast unified web of technology that builds on what we’ve created over the past quarter century. At some level it’s an intellectual structure that actualizes a new computational view of the world. And at some level it’s a practical system and framework that’s going to be a fount of incredibly useful new services and products.
I have to admit I didn’t entirely see it coming. For years I have gradually understood more and more about what the paradigms we’ve created make possible. But what snuck up on me is a breathtaking new level of unification—that lets one begin to see that all the things we’ve achieved in the past 25+ years are just steps on a path to something much bigger and more important.
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.
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. Continue reading
I have four children, all with very different interests. My second-youngest, Christopher, age 13, has always liked technology. And last weekend he and I went to see the wild, wacky and creative technology (and other things) on display at the Maker Faire in New York.
I had told the organizers I could give a talk. But a week or so before the event, Christopher told me he thought what I planned to talk about wasn’t as interesting as it could be. And that actually he could give some demos that would be a lot more interesting and relevant.
Christopher has been an avid Mathematica user for years now. And he likes hooking Mathematica up to interesting devices—with two recent favorites being Arduino boards and quadricopter drones.
And so it was that last Sunday I walked onto a stage with him in front of a standing-room-only crowd of a little over 300 people, carrying a quadricopter. (I wasn’t trusted with the Arduino board.)
Christopher had told me that I shouldn’t talk too long—and that then I should hand over to him. He’d been working on his demo the night before, and earlier that morning. I suggested he should practice what he was going to say, but he’d have none of that. Instead, up to the last minute, he spent his time cleaning up code for the demo.
I must have given thousands of talks in my life, but the whole situation made me quite nervous. Would the Arduino board work? Would the quadricopter fly? What would Christopher do if it didn’t?
I don’t think my talk was particularly good. But then Christopher bounced onto the stage, and soon was typing raw Mathematica code in front of everyone—with me now safely off on the side (where I snapped this picture):
Note added: Since this blog was written, Facebook has modified their API to make much less information available about Facebook friends. While I think adding privacy controls is a good idea, what Facebook has done reduces the richness of the results that Wolfram|Alpha Personal Analytics can give for Facebook users.
After I wrote about doing personal analytics with data I’ve collected about myself, many people asked how they could do similar things themselves.
Now of course most people haven’t been doing the kind of data collecting that I’ve been doing for the past couple of decades. But these days a lot of people do have a rich source of data about themselves: their Facebook histories.
And today I’m excited to announce that we’ve developed a first round of capabilities in Wolfram|Alpha to let anyone do personal analytics with Facebook data. Wolfram|Alpha knows about all kinds of knowledge domains; now it can know about you, and apply its powers of analysis to give you all sorts of personal analytics. And this is just the beginning; over the months to come, particularly as we see about how people use this, we’ll be adding more and more capabilities.
If you’re doing this for the first time, you’ll be prompted to authenticate the Wolfram Connection app in Facebook, and then sign in to Wolfram|Alpha (yes, it’s free). And as soon as you’ve done that, Wolfram|Alpha will immediately get to work generating a personal analytics report from the data it can get about you through Facebook.
Here’s the beginning of the report I get today when I do this:
Yes, it was my birthday yesterday. And yes, as my children are fond of pointing out, I’m getting quite ancient… Continue reading
Today I’m excited to be able to announce that our company is moving into yet another new area: large-scale system modeling. Last year, I wrote about our plans to initiate a new generation of large-scale system modeling. Now we are taking a major step in that direction with the release of Wolfram SystemModeler.
SystemModeler is a very general environment that handles modeling of systems with mechanical, electrical, thermal, chemical, biological, and other components, as well as combinations of different types of components. It’s based—like Mathematica—on the very general idea of representing everything in symbolic form.
In SystemModeler, a system is built from a hierarchy of connected components—often assembled interactively using SystemModeler‘s drag-and-drop interface. Internally, what SystemModeler does is to derive from its symbolic system description a large collection of differential-algebraic and other equations and event specifications—which it then solves using powerful built-in hybrid symbolic-numeric methods. The result of this is a fully computable representation of the system—that mirrors what an actual physical version of the system would do, but allows instant visualization, simulation, analysis, or whatever.
Here’s an example of SystemModeler in action—with a 2,685-equation dynamic model of an airplane being used to analyze the control loop for continuous descent landings:
It’s a sad but true fact that most data that’s generated or collected—even with considerable effort—never gets any kind of serious analysis. But in a sense that’s not surprising. Because doing data science has always been hard. And even expert data scientists usually have to spend lots of time wrangling code and data to do any particular analysis.
I myself have been using computers to work with data for more than a third of a century. And over that time my tools and methods have gradually evolved. But this week—with the release of Wolfram|Alpha Pro—something dramatic has happened, that will forever change the way I approach data.
The key idea is automation. The concept in Wolfram|Alpha Pro is that I should just be able to take my data in whatever raw form it arrives, and throw it into Wolfram|Alpha Pro. And then Wolfram|Alpha Pro should automatically do a whole bunch of analysis, and then give me a well-organized report about my data. And if my data isn’t too large, this should all happen in a few seconds.
And what’s amazing to me is that it actually works. I’ve got all kinds of data lying around: measurements, business reports, personal analytics, whatever. And I’ve been feeding it into Wolfram|Alpha Pro. And Wolfram|Alpha Pro has been showing me visualizations and coming up with analyses that tell me all kinds of useful things about the data.
Today I’m excited to be able to announce the launch of Wolfram|Alpha Pro—the biggest single step in the development of Wolfram|Alpha since its original introduction.
Over the two and a half years since we first launched, Wolfram|Alpha has been growing rapidly in content and capabilities. But today’s introduction of Wolfram|Alpha Pro in effect adds a whole new model for interacting with Wolfram|Alpha—and brings all sorts of fundamentally new and remarkable capabilities.
Starting today, everyone has access to Wolfram|Alpha Pro at wolframalpha.com. Unlike the “tourist” version of Wolfram|Alpha, though, you have to log in, and, yes, to get full capabilities there’s a subscription ($4.99/month, or $2.99/month for students). (Right now, you can try it for free with a trial subscription.)
Two weeks ago we made a major announcement: building on technology that we’ve been developing for more than 20 years, we released Computable Document Format (CDF). I think CDF is going to have a big effect on the way all sorts of things can be communicated. Because for the first time it makes it practical to include live computation as a routine part of a document.
There are many important applications of CDF that we’ll no doubt be seeing over the months and years to come. But today I’m pleased to announce an experimental one from us: Wolfram|Alpha with CDF.
Starting today, as soon as you have the free CDF plugin installed (or if you have Mathematica 8 on your system) you can go to the top right-hand corner of the Wolfram|Alpha website, and set CDF on, with the result that Wolfram|Alpha will generate not just a static web page, but instead full CDF output—that you can directly interact and compute with. Continue reading
Over the past 25 years, we’ve been fortunate enough to make a mark in all sorts of areas of science and technology. Today I’m excited to announce that we’re in a position to tackle another major area: large-scale systems modeling.
It’s a huge and important area, long central to engineering, and increasingly central to fields like biomedicine. To do it right is also incredibly algorithmically demanding. But the exciting thing is that now we’ve finally assembled the technology stack that we need to do it—and we’re able to begin the process of making large-scale systems modeling an integrated core feature of Mathematica, accessible to a very broad range of users.
Lots of remarkable things will become possible. Using the methodology we’ve developed for Wolfram|Alpha, we’ll be curating not only data about systems and their components, but also complete dynamic models. Then we’ll have the tools to easily assemble models of almost arbitrary complexity—and to put them into algorithmic form so that they can be simulated, optimized, validated, visualized or manipulated by anything across the Mathematica system.
And then we’ll also be able to inject large-scale models into the Wolfram|Alpha system, and all its deployment channels.
So what does this mean? Here’s an example. Imagine that there’s a model for a new kind of car engine—probably involving thousands of individual components. The model is running in Mathematica, inside a Wolfram|Alpha server. Now imagine someone out in the field with a smartphone, wondering what will happen if they do a particular thing with an engine.
Well, with the technology we’re building, they should be able to just type (or say) into an app: “Compare the frequency spectrum for the crankshaft in gears 1 and 5″. Back on the server, Wolfram|Alpha technology will convert the natural language into a definite symbolic query. Then in Mathematica the model will be simulated and analyzed, and the results—quantitative, visual or otherwise—will be sent back to the user. Like a much more elaborate and customized version of what Wolfram|Alpha would do today with a question about a satellite position or a tide.
OK. So what needs to happen to make all this stuff possible? To begin with, how can Mathematica even represent something like a car—with all its various physical components, moving and running and acting on each other? Continue reading
Wolfram|Alpha is making possible a whole new very interesting and very powerful kind of computing. And with the release today of version 2.0 of the Wolfram|Alpha API, it’s going to be considerably easier for a broad range of software developers to take advantage of it.
I’m happy to say that it seems as if Wolfram|Alpha is pretty useful to humans—for example through the wolframalpha.com website. But it also turns out that Wolfram|Alpha is extremely useful to programs. And in fact, even today, the number of requests coming to Wolfram|Alpha each second from programs often exceeds by some margin all the requests coming directly from humans.
The reason for this popularity is really pretty simple: Wolfram|Alpha completely changes the economics of a lot of programming. You see, these days a remarkable number of programs rely on having some kind of knowledge. And traditionally, the only way to get knowledge into a program was for the programmer to painstakingly put it there.
But with Wolfram|Alpha in the picture, it’s a different story. Because built into Wolfram|Alpha is already a huge amount of computable knowledge. And if a program is connected to Wolfram|Alpha, then it can immediately make use of all that knowledge.
Whether one’s building a website or a mobile app or desktop software or an enterprise application, the point is that one can use Wolfram|Alpha as a “knowledge-based computing” platform—so that having all sorts of computable knowledge becomes effectively free from an engineering point of view.
How does a program communicate with Wolfram|Alpha? It uses the Wolfram|Alpha API. (These days, API is pretty much a term on its own, but it comes from “Application Program Interface”.) Continue reading
The long-term goal is to have an assistant app for every major course, from elementary school to graduate school. And the good news is that Wolfram|Alpha has the breadth and depth of capabilities to make this possible—and not only in traditionally “computational” kinds of courses.
The concept of these apps is to make it as quick and easy as possible to access the particular capabilities of Wolfram|Alpha relevant for specific courses. Each app is organized according to the major curriculum units of a course. Then within each section of the app, there are parts that cover each of the particular types of problems relevant to that unit. Continue reading
“Someone has to make the first great ebook publishing company; it might as well be us.” So I said a few weeks before the iPad was released this April. And a little while later Touch Press was formed. The iPad was released, and simultaneously, Touch Press’s first book The Elementswas released. The book has been on the iPad bestseller list ever since—in addition to being featured in all sorts of iPad television commercials and the like.
Well, it’s good for a publishing company to have a successful first book. But for me it’s been getting a little old telling people that I’m a partner in a new publishing company, but so far we’ve only published one book. So it’s exciting to be able to say that as of this week, Touch Press has a second book: Solar System.
With the release of Mathematica 8 today, the single most dramatic change is that you don’t have to communicate with Mathematica in the Mathematica language any more: you can just use free-form English instead.
Wolfram|Alpha has pioneered the concept of specifying computations with free-form linguistic input. And with Mathematica 8, the powerful methods of Wolfram|Alpha become available within the Mathematica environment.
All one has to do is to type an = at the beginning of a line. Then what follows is taken as free-form linguistic input.
You don’t have to use precise Mathematica syntax. You can type things in just the way you think about them, in free-form English. But what happens is that Mathematica calls on Wolfram|Alpha to try to interpret your input, and turn it into precise Mathematica code.
Mathematica 8 is released today! It’s a huge and important release. With dramatic breakthroughs—and major broadening of the whole scope of Mathematica.
After 8 versions and 22 years most software systems have decayed to slow and lumbering development. But not Mathematica. In fact, with Mathematica it’s quite the opposite. As the years go by, Mathematica development is actually speeding up.
What has made that happen? Partly it’s our tenacious and broadening pursuit of ambitious long-term goals. But partly, it’s a remarkable reflection—and validation—of the core principles on which Mathematica has always been built.
In the middle of last year, we finished our decade-long project to reinvent Mathematica, and we released Mathematica 6.
We introduced a great many highly visible innovations in Mathematica 6—like dynamic interactivity and computable data. But we were also building a quite unprecedented platform for developing software.
And even long before Mathematica 6 was released, we were already working on versions of Mathematica well beyond 6.
And something remarkable was happening. There’d been all sorts of areas we’d talked about someday being in Mathematica. But they’d always seemed far off.
Well, now, suddenly, lots of them seemed like they were within reach. It seemed as if everything we’d built into Mathematica was coming together to make a huge number of new things possible.
All over our company, efforts were starting up to build remarkable things.
It was crucial that over the years, we’d invested a huge amount in creating long-term systems for organizing our software development efforts. So we were able to take those remarkable things that were being built, and flow them into Mathematica.
And at some point, we realized we just couldn’t wait any longer. Even though Mathematica 6 had come out only last year, we had assembled so much new functionality that we just had to release Mathematica 7.
So 18 months after the release of Mathematica 6, I’m happy to be able to announce that today Mathematica 7 is released!
Mathematica 1.0 was released on June 23, 1988—now nearly 19 years ago. And normally, after 19 years, pretty much all one expects from software products is slow growth and incremental updates.
But as in so many things, Mathematica today just became a big exception.
Some people have said that Mathematica 6.0 shouldn’t even be called “Mathematica” at all. That it’s something so qualitatively new and different that it should be given a completely different name.
Well, perhaps I’m just too sentimental. Or too steeped in history. Or too naive about branding. But to me there’s no choice. We owe it to all the foundations we’ve laid these past twenty years to still call what we’ve built today “Mathematica.”
Realistically, I think it took us ten years after Mathematica 1.0 just to realize what a powerful thing we had in Mathematica.
We’d always talked about “symbolic programming,” and how it let us unify a lot of different ideas and areas. But sometime around the mid-1990s it began to dawn on us just what an amazing thing symbolic programming actually is.
And we began to think that there might be a whole new level one could reach in computing if one really did everything one could with symbolic programming.
Well, that was an intellectual challenge we couldn’t resist. So about ten years ago, we embarked on seeing just what might be possible. Continue reading