It became a defining discovery in the history of chaos theory. But when it was first discovered, it was a surprising, almost bizarre result, that didn’t really connect with anything that had been studied before. Somehow, though, it’s fitting that it should have been Mitchell Feigenbaum—who I knew for nearly 40 years—who would discover it.
Trained in theoretical physics, and a connoisseur of its mathematical traditions, Mitchell always seemed to see himself as an outsider. He looked a bit like Beethoven—and projected a certain stylish sense of intellectual mystery. He would often make strong assertions, usually with a conspiratorial air, a twinkle in his eye, and a glass of wine or a cigarette in his hand. Continue reading
But then the email went on: “The hearing is intended to examine, among other things, whether algorithmic transparency or algorithmic explanation are policy options Congress should be considering.” That piqued my interest, because, yes, I have thought about “algorithmic transparency” and “algorithmic explanation”, and their implications for the deployment of artificial intelligence.
Generally I stay far away from anything to do with politics. But figuring out how the world should interact with AI is really important. So I decided that—even though it was logistically a bit difficult—I should do my civic duty and go to Washington and testify. Continue reading
It was a slightly dark room, decorated with Native American artifacts. Around it were tables arranged in a large rectangle, at which sat a couple dozen men (yes, all men), mostly in their sixties. The afternoon was wearing on, with many different people giving their various views about how to organize what amounted to a putative great new interdisciplinary university.
Here’s the original seating chart, together with a current view of the meeting room. (I’m only “Steve” to Americans currently over the age of 60…):
The idea of modifying images is as old as photography. At first, it had to be done by hand (sometimes with airbrushing). By the 1990s, it was routinely being done with image manipulation software such as Photoshop. But it’s something of an art to get a convincing result, say for a person inserted into a scene. And if, for example, the lighting or shadows don’t agree, it’s easy to tell that what one has isn’t real.
What about videos? If one does motion capture, and spends enough effort, it’s perfectly possible to get quite convincing results—say for animating aliens, or for putting dead actors into movies. The way this works, at least in a first approximation, is for example to painstakingly pick out the keypoints on one face, and map them onto another.
What’s new in the past couple of years is that this process can basically be automated using machine learning. And, for example, there are now neural nets that are simply trained to do “face swapping”:
We’re on an exciting path these days with the Wolfram Language. Just three weeks ago we launched the Free Wolfram Engine for Developers to help people integrate the Wolfram Language into large-scale software projects. Now, today, we’re launching the Wolfram Function Repository to provide an organized platform for functions that are built to extend the Wolfram Language—and we’re opening up the Function Repository for anyone to contribute.
The Wolfram Function Repository is something that’s made possible by the unique nature of the Wolfram Language as not just a programming language, but a full-scale computational language. In a traditional programming language, adding significant new functionality typically involves building whole libraries, which may or may not work together. But in the Wolfram Language, there’s so much already built into the language that it’s possible to add significant functionality just by introducing individual new functions—which can immediately integrate into the coherent design of the whole language.
To get it started, we’ve already got 532 functions in the Wolfram Function Repository, in 26 categories:
In the mid-1970s, particle physics was hot. Quarks were in. Group theory was in. Field theory was in. And so much progress was being made that it seemed like the fundamental theory of physics might be close at hand.
Right in the middle of all this was Murray Gell-Mann—responsible for not one, but most of the leaps of intuition that had brought particle physics to where it was. There’d been other theories, but Murray’s—with their somewhat elaborate and abstract mathematics—were always the ones that seemed to carry the day.
It was the spring of 1978 and I was 18 years old. I’d been publishing papers on particle physics for a few years, and had gotten quite known around the international particle physics community (and, yes, it took decades to live down my teenage-particle-physicist persona). I was in England, but planned to soon go to graduate school in the US, and was choosing between Caltech and Princeton. And one weekend afternoon when I was about to go out, the phone rang. In those days, it was obvious if it was an international call. “This is Murray Gell-Mann”, the caller said, then launched into a monologue about why Caltech was the center of the universe for particle physics at the time. Continue reading
It happens far too often. I’ll be talking to a software developer, and they’ll be saying how great they think our technology is, and how it helped them so much in school, or in doing R&D. But then I’ll ask them, “So, are you using Wolfram Language and its computational intelligence in your production software system?” Sometimes the answer is yes. But too often, there’s an awkward silence, and then they’ll say, “Well, no. Could I?”
I want to make sure the answer to this can always be: “Yes, it’s easy!” And to help achieve that, we’re releasing today the Free Wolfram Engine for Developers. It’s a full engine for the Wolfram Language, that can be deployed on any system—and called from programs, languages, web servers, or anything.
Today it’s 10 years since we launched Wolfram|Alpha. At some level, Wolfram|Alpha is a never-ending project. But it’s had a great first 10 years. It was a unique and surprising achievement when it first arrived, and over its first decade it’s become ever stronger and more unique. It’s found its way into more and more of the fabric of the computational world, both realizing some of the long-term aspirations of artificial intelligence, and defining new directions for what one can expect to be possible. Oh, and by now, a significant fraction of a billion people have used it. And we’ve been able to keep it private and independent, and its main website has stayed free and without external advertising. Continue reading
I’ve sometimes found it a bit of a struggle to explain what the Wolfram Language really is. Yes, it’s a computer language—a programming language. And it does—in a uniquely productive way, I might add—what standard programming languages do. But that’s only a very small part of the story. And what I’ve finally come to realize is that one should actually think of the Wolfram Language as an entirely different—and new—kind of thing: what one can call a computational language.
So what is a computational language? It’s a language for expressing things in a computational way—and for capturing computational ways of thinking about things. It’s not just a language for telling computers what to do. It’s a language that both computers and humans can use to represent computational ways of thinking about things. It’s a language that puts into concrete form a computational view of everything. It’s a language that lets one use the computational paradigm as a framework for formulating and organizing one’s thoughts.
It’s only recently that I’ve begun to properly internalize just how broad the implications of having a computational language really are—even though, ironically, I’ve spent much of my life engaged precisely in the consuming task of building the world’s only large-scale computational language. Continue reading
This is an edited transcript of a recent talk I gave at a blockchain conference, where I said I’d talk about “What will the world be like when computational intelligence and computational contracts are ubiquitous?”
We live in an interesting time today—a time when we’re just beginning to see the implications of what we might call “the force of computation”. In the end, it’s something that’s going to affect almost everything. And what’s going to happen is really a deep story about the interplay between the human condition, the achievements of human civilization—and the fundamental nature of this thing we call computation.