Where is wolfram alpha

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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. Some of what will happen with them we can foresee. But some—as we build out these new paradigms—will be as seemingly unexpected as Wolfram Alpha. It was quite tense. We were going to launch Wolfram Alpha. Leibniz was talking about a version of it years ago.

But I decided that we should give it a try. It had all started with some abstract intellectual ideas. Some news had come out about our project.

So there was a lot of anticipation. Well, at the appointed time we started the live webcast. There was a horrible networking and load balancing problem. Well, fortunately we did actually have good weather and news feeds. Because this was May in the Midwest. Here, we can actually look at live Wolfram Alpha to find out about it. See that giant spike in wind speed just before 8pm? That was a tornado. Approaching our location. But still, we had a tornado coming straight for us.

Well, fortunately, at the last minute, it turned away. And our giant project was launched—out of the starting gate. The story goes a long way back. I was a kid, growing up in England in the s. At first, I had really been into the space program. A thing called an Elliott C. About the size of a large desk. With 8K of bit words of core memory.

And programmed with paper tape. Well, I started programming that machine. My top goal was to reproduce this physics process. And in the process, I learned quite a bit about programming. And it might have been a disqualifying handicap. I wanted to make a very general system. Well, almost exactly 30 years ago today the system first came alive. And in the system was to the point where it could really be released.

I was by then a young faculty member in physics at Caltech. But the company did get started. Well, at first I thought about all sorts of complicated models for that. So I started looking at the very simplest possible programs. Well, here was the big experiment I did. In line-printer-output form from The search engine covers a whole host of functions and calculations, whether you need to work out the lengths of the sides of a triangle or locate the inflection points of a function.

Start with the basics of addition and subtraction, then go as deep as you need to. The plotting and graphics capabilities of Wolfram Alpha are particularly impressive. Algebra is included too, of course. There are plenty of statistics-related queries you can try, too. Regression analysis and statistical inference are covered, too.

Finally, Wolfram Alpha is smart enough to work out fun calculations, too. David Nield is a tech journalist from the UK who has been writing about gadgets and apps since way before the iPhone and Twitter were invented. When he's not busy doing that, he usually takes breaks from all things tech with long walks in the countryside.

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