Twenty Years Later: The Surprising Greater Implications of A New Kind of Science

Twenty Years Later: The Surprising Greater Implications of A New Kind of Science

From the Foundations Laid by A New Kind of Science

When A New Kind of Science was published twenty years ago I thought what it had to say was important. But what’s become increasingly clear—particularly in the last few years—is that it’s actually even much more important than I ever imagined. My original goal in A New Kind of Science was to take a step beyond the mathematical paradigm that had defined the state of the art in science for three centuries—and to introduce a new paradigm based on computation and on the exploration of the computational universe of possible programs. And already in A New Kind of Science one can see that there’s immense richness to what can be done with this new paradigm. Continue reading

The Making of A New Kind of Science

The Making of A New Kind of Science

I Think I Should Write a Quick Book…

In the end it’s about five and a half pounds of paper, 1280 pages, 973 illustrations and 583,313 words. And its creation took more than a decade of my life. Almost every day of my thirties, and a little beyond, I tenaciously worked on it. Figuring out more and more science. Developing new kinds of computational diagrams. Crafting an exposition that I wrote and rewrote to make as clear as possible. And painstakingly laying out page after page of what on May 14, 2002, would be published as A New Kind of Science.

I’ve written before (even in the book itself) about the intellectual journey involved in the creation of A New Kind of Science. But here I want to share some of the more practical “behind the scenes” journey of the making of what I and others usually now call simply “the NKS book”. Some of what I’ll talk about happened twenty years ago, some more like thirty years ago. And it’s been interesting to go back into my archives (and, yes, those backup tapes from 30 years ago were hard to read!) and relive some of what finally led to the delivery of the ideas and results of A New Kind of Science as truckloads of elegantly printed books with striking covers. Continue reading

We’ve Got a Science Opportunity Overload: It’s Time to Launch the Wolfram Institute!

Suddenly There’s Just So Much New Science to Do

Something remarkable has happened these past two years. For 45 years I’ve devoted myself to building a taller and taller tower of science and technology—which along the way has delivered many outputs of which I’m quite proud. But starting in 2020 with the unexpected breakthroughs of our Wolfram Physics Project we’ve jumped to a whole new level. And suddenly—yes, building on our multi-decade tower—it seems as if we’ve found a new paradigm that’s incredibly powerful, and that’s going to let us tackle an almost absurd range of longstanding questions in all sorts of areas of science.

Developing a fundamental theory of physics is certainly an ambitious place to start, and I’m happy to say that things seem to be going quite excellently there, not least in providing new foundations for many existing results and initiatives in physics. But the amazing (and to me very unexpected) thing is that we can take our new paradigm and also apply it to a huge range of other areas. Just a couple of weeks ago I published a 250-page treatise about its application to the “physicalization of metamathematics”—and to providing a very new view of the foundations of mathematics (with implications both for the question of what mathematics really is, and for the practical long-term future of mathematics). Continue reading

On the Concept of Motion

How Is It That Things Can Move?

It seems like the kind of question that might have been hotly debated by ancient philosophers, but would have been settled long ago: how is it that things can move? And indeed with the view of physical space that’s been almost universally adopted for the past two thousand years it’s basically a non-question. As crystallized by the likes of Euclid it’s been assumed that space is ultimately just a kind of “geometrical background” into which any physical thing can be put—and then moved around.

But in our Physics Project we’ve developed a fundamentally different view of space—in which space is not just a background, but has its own elaborate composition and structure. And in fact, we posit that space is in a sense everything that exists, and that all “things” are ultimately just features of the structure of space. We imagine that at the lowest level, space consists of large numbers of abstract “atoms of space” connected in a hypergraph that’s continually getting updated according to definite rules and that’s a huge version of something like this:


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The Physicalization of Metamathematics and Its Implications for the Foundations of Mathematics

1 | Mathematics and Physics Have the Same Foundations

One of the many surprising (and to me, unexpected) implications of our Physics Project is its suggestion of a very deep correspondence between the foundations of physics and mathematics. We might have imagined that physics would have certain laws, and mathematics would have certain theories, and that while they might be historically related, there wouldn’t be any fundamental formal correspondence between them.

But what our Physics Project suggests is that underneath everything we physically experience there is a single very general abstract structure—that we call the ruliad—and that our physical laws arise in an inexorable way from the particular samples we take of this structure. We can think of the ruliad as the entangled limit of all possible computations—or in effect a representation of all possible formal processes. And this then leads us to the idea that perhaps the ruliad might underlie not only physics but also mathematics—and that everything in mathematics, like everything in physics, might just be the result of sampling the ruliad. Continue reading

Launching Version 13.0 of Wolfram Language + Mathematica

The March of Innovation Continues

Just a few weeks ago it was 1/3 of a century since Mathematica 1.0 was released. Today I’m excited to announce the latest results of our long-running R&D pipeline: Version 13 of Wolfram Language and Mathematica. (Yes, the 1, 3 theme—complete with the fact that it’s the 13th of the month today—is amusing, if coincidental.)

It’s 207 days—or a little over 6 months—since we released Version 12.3. And I’m pleased to say that in that short time an impressive amount of R&D has come to fruition: not only a total of 117 completely new functions, but also many hundreds of updated and upgraded functions, several thousand bug fixes and small enhancements, and a host of new ideas to make the system ever easier and smoother to use.

Every day, every week, every month for the past third of a century we’ve been pushing hard to add more to the vast integrated framework that is Mathematica and the Wolfram Language. And now we can see the results of all those individual ideas and projects and pieces of work: a steady drumbeat of innovation sustained now over the course of more than a third of a century:


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The Concept of the Ruliad

The Concept of the Ruliad

The Entangled Limit of Everything

I call it the ruliad. Think of it as the entangled limit of everything that is computationally possible: the result of following all possible computational rules in all possible ways. It’s yet another surprising construct that’s arisen from our Physics Project. And it’s one that I think has extremely deep implications—both in science and beyond.

In many ways, the ruliad is a strange and profoundly abstract thing. But it’s something very universal—a kind of ultimate limit of all abstraction and generalization. And it encapsulates not only all formal possibilities but also everything about our physical universe—and everything we experience can be thought of as sampling that part of the ruliad that corresponds to our particular way of perceiving and interpreting the universe.

We’re going to be able to say many things about the ruliad without engaging in all its technical details. (And—it should be said at the outset—we’re still only at the very beginning of nailing down those technical details and setting up the difficult mathematics and formalism they involve.) But to ground things here, let’s start with a slightly technical discussion of what the ruliad is. Continue reading

Celebrating a Third of a Century of Mathematica, and Looking Forward

From the 30th anniversary of Mathematica, see also: “We’ve Come a Long Way in 30 Years (But You Haven’t Seen Anything Yet!)”.

Celebrating a Third of a Century of Mathematica, and Looking Forward

Mathematica 1.0 was launched on June 23, 1988. So (depending a little on how you do the computation) today is its one-third-century anniversary. And it’s wonderful to see how the tower of ideas and technology that we’ve worked so hard on for so long has grown in that third of a century—and how tall it’s become and how rapidly it still goes on growing.

In the past few years, I’ve come to have an ever-greater appreciation for just how unique what we’ve ended up building is, and just how fortunate our original choices of foundations and principles were. And even after a third of a century, what we have still seems like an artifact from the future—indeed ever more so with each passing year as it continues to grow and develop.

In the long view of intellectual history, this past one-third century will be seen as the time when the computational paradigm first took serious root, and when all its implications for “computational X” began to grow. And personally I feel very fortunate to have lived at the right time in history to have been able to be deeply involved with this and for what we have built to have made such a contribution to it. Continue reading

Multicomputation with Numbers: The Case of Simple Multiway Systems

Multicomputation with Numbers: The Case of Simple Multiway Systems

A Minimal Example of Multicomputation

Multicomputation is an important new paradigm, but one that can be quite difficult to understand. Here my goal is to discuss a minimal example: multiway systems based on numbers. Many general multicomputational phenomena will show up here in simple forms (though others will not). And the involvement of numbers will often allow us to make immediate use of traditional mathematical methods.

A multiway system can be described as taking each of its states and repeatedly replacing it according to some rule or rules with a collection of states, merging any states produced that are identical. In our Physics Project, the states are combinations of relations between elements, represented by hypergraphs. We’ve also often considered string substitution systems, in which the states are strings of characters. But here I’ll consider the case in which the states are numbers, and for now just single integers.

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Charting a Course for “Complexity”: Metamodeling, Ruliology and More

This is the first of a series of pieces I’m planning in connection with the upcoming 20th anniversary of the publication of A New Kind of Science.

“There’s a Whole New Field to Build…”

For me the story began nearly 50 years ago—with what I saw as a great and fundamental mystery of science. We see all sorts of complexity in nature and elsewhere. But where does it come from? How is it made? There are so many examples. Snowflakes. Galaxies. Lifeforms. Turbulence. Do they all work differently? Or is there some common underlying cause? Some essential “phenomenon of complexity”?

It was 1980 when I began to seriously work on these questions. And at first I did so in the main scientific paradigm I knew: models based on mathematics and mathematical equations. I studied the approaches people had tried to use. Nonequilibrium thermodynamics. Synergetics. Nonlinear dynamics. Cybernetics. General systems theory. I imagined that the key question was: “Starting from disorder and randomness, how could spontaneous self-organization occur, to produce the complexity we see?” For somehow I assumed that complexity must be created as a kind of filtering of ubiquitous thermodynamic-like randomness in the world.

At first I didn’t get very far. I could write down equations and do math. But there wasn’t any real complexity in sight. But in a quirk of history that I now realize had tremendous significance, I had just spent a couple of years creating a big computer system that was ultimately a direct forerunner of our modern Wolfram Language. So for me it was obvious: if I couldn’t figure out things myself with math, I should use a computer. Continue reading