It should somehow be possible to do a finite project that can capture it. That can make all that systematic knowledge computable. Which is an incredibly productive language to be able to write things in.
And a great practical platform for deploying things. There could just be too much knowledge—too much data—in the world. With Mathematica , we could define a complete formal language for interacting with the system. People would never be able to learn a formal language. Really the only way people can interact is using the language they all know: human natural language, English or whatever. So that might have been another place our project would have been impossible.
We had Mathematica to actually build the thing. We had NKS as a paradigm for seeing what to do. And we had a great company, with the resources to take on an impossible project like this. And, perhaps even more importantly, with people who knew about all these different areas that would be needed in the project. From building large-scale server systems, to creating linguistic algorithms, to the details of a zillion content specialties.
Who were used to working together, combining very different talents, on very diverse projects. Well, the first thing that we did in thinking about our computable knowledge system was to work on data. Could we take data on all sorts of things in the world, and curate it to the point where it was clean enough to compute with?
You see, in Mathematica you can compute the values of all sorts of mathematical functions and so on. Well, that was great experience. And in doing it, we were really ramping up our data curation system. These days we have a giant network of data source providers that we interact with. What comes next is organizing it.
Figuring out all its conventions and units and definitions. Figuring out how it connects to other data. Figuring out what algorithms and methods can be based on it. Fortunately at our company we have experts in a remarkably wide range of areas. Well, anyway, in we had released our first computable data—in Mathematica. In the past one might have thought that what we were doing would need having a whole artificial intelligence or something.
We were going to have it set up the equations, and then just blast through to the answer using the best modern mathematical physics. In a sense the usual AI approach would have been to think like people imagined one had to in the Middle Ages: to figure everything out by logic. But we were going to leverage the or so years of development in science and so on, and just use the very best possible modern methods.
Of course, it helped that we had Mathematica , so that if we needed to solve a differential equation, or figure out some combinatorial optimization problem, that was, sort of, just free.
Well, so we set about just implementing all the systematic methods and models and so on from all areas of science, and beyond. Then it was 2 million lines. Perhaps what saved us is that the problem we have to solve is sort of the inverse of the problem people have spent so long on. Go understand them.
Now take a look at each human utterance that comes in. Then try to understand it, and see if we can compute from it. Well, as it happens, I think we made some serious breakthroughs—pretty much on the basis of NKS ideas—that let us really do things there. Taking the sort of undigested stream of thoughts that people, for example, type into an input field on our website. And having lots of little programs pick away at them, gradually understanding pieces.
Well, putting all those things together, we gradually began to build Wolfram Alpha. And hook it all up computing pieces of answers in parallel, and having web Mathematica send back the results. But one that came together that night 11 months ago today, 2 miles from here. A little bit of evolution on the part of the people, about what to expect.
And a lot of evolution on the part of the system, adapting to the foibles of all those humans out there using it. Certainly in terms of the extent to which we can immediately democratize access to everything. Now I suppose even though we were off by 12 years, we should have made a big effort to launch Wolfram Alpha from Urbana rather than Champaign—just to give a bit of truth to the fictional birth of HAL.
Well, first of all, we continue to grind through more and more areas of knowledge. But beyond adding existing knowledge, we want to start doing more things that are like what HAL might do: actually watching our environment. Letting people, say, put an image feed into Wolfram Alpha.
Or upload data from their sensors. And have Wolfram Alpha use the knowledge it has, and the algorithms and methods it has, to do things with that. You see, in a sense, right now Wolfram Alpha is a bit old-fashioned with respect to that. But one of the points of NKS is that you can go onto into the computational universe, and in effect make new, creative discoveries automatically.
So that instead of just using existing methods and models and so on, Wolfram Alpha can potentially discover new ones on the fly. It was that way with Mathematica , and with NKS. Wolfram Alpha in a sense makes possible a new kind of computing: knowledge-based computing. And then building up computations. It was in a sense very liberating with Wolfram Alpha to take such a different approach to functionality and interface than in Mathematica.
They seemed in a sense quite incompatible. But now as we bring them together, we see that there is amazing strength in that diversity. Well, one consequence of that is that one builds lots of technology along the way. That has its own significance. You know, I was thinking about Wolfram Alpha, and about the long chain of circumstances that have led us to be able to build it.
But I had my 50 th birthday last year. And for that I was looking at a bunch of archived material I have. There, with a typewriter and mapping pen, were collections of knowledge set up as best I could then. Of course, I started typing in things from old stuff, and, yes, modern Wolfram Alpha gets them right. But I realized that in some sense I was probably fated for nearly 40 years to build a Wolfram Alpha.
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Uh oh! Wolfram Alpha doesn't run without JavaScript. Please enable JavaScript. If you don't know how, you can find instructions here. Once you've done that, refresh this page to start using Wolfram Alpha. Frequently Asked Questions. General Is Wolfram Alpha a search engine? Is Wolfram Alpha free to use?
Who is Wolfram Alpha for? Its goal is to bring expert-level knowledge to everybody. Where can I see a demo of Wolfram Alpha? How can I find the latest information about Wolfram Alpha? How often is Wolfram Alpha updated? When will Wolfram Alpha be finished? Can I make a suggestion about Wolfram Alpha?
We welcome your suggestions. Just use the feedback form on every result page. Can I trust results from Wolfram Alpha? What are Wolfram Alpha's terms of use?
They are described on the Terms of Use page. Can I assume that my inputs to Wolfram Alpha are private? Can I contribute to Wolfram Alpha's knowledgebase? How can I get involved with Wolfram Alpha? How should the name Wolfram Alpha be written? What are the parts of Wolfram Alpha's output called? What can I do with Wolfram Alpha?
Why doesn't Wolfram Alpha understand what I asked? How can I get help with Wolfram Alpha? How do I report a bug or give feedback about Wolfram Alpha? Is there a manual for Wolfram Alpha? How can I optimize my queries to Wolfram Alpha? How does Wolfram Alpha deal with linguistic ambiguity? When Wolfram Alpha prompts for input, what can I enter? Will I always get the same answer to a particular question?
How does Wolfram Alpha know where I am located? Why does Wolfram Alpha use the wrong location for me? How can I tell Wolfram Alpha my location? Does Wolfram Alpha handle languages other than English? Can I interact with Wolfram Alpha graphics? Can I get data from Wolfram Alpha in spreadsheet format? Wolfram Language Revolutionary knowledge-based programming language.
Wolfram Science Technology-enabling science of the computational universe. Wolfram Notebooks The preeminent environment for any technical workflows. See timeline ». What makes Wolfram Alpha possible today is a somewhat unique set of circumstances—and the singular vision of Stephen Wolfram. For the first time in history, computers are powerful enough to support the capabilities of Wolfram Alpha, and the web provides a broad-based means of delivery.
But this technology alone was not enough for Wolfram Alpha to be possible. What was also needed were two developments that have been driven by Stephen Wolfram over the course of more than 30 years. The first was the Wolfram Language—the language that grew out of Mathematica and in which Wolfram Alpha is implemented.
The Wolfram Language has three crucial roles in Wolfram Alpha. First, its very general symbolic structure provides the framework in which all the diverse knowledge in Wolfram Alpha is represented and all the operations on it are implemented.
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