The introduction of Wolfram Alpha defined a fundamentally new paradigm for getting knowledge and answers—not by searching the web, but by doing dynamic computations based on a vast collection of built-in data, algorithms and methods. Bringing broad, deep, expert-level knowledge to everyone Our mission is to collect and curate all objective data; implement every known model, method and algorithm; and make it possible to compute whatever can be computed about anything.
Our work builds on the achievements of science and other systematizations of knowledge to provide a single source that can be relied on by everyone for definitive answers to factual queries. Wolfram Alpha brings expert-level knowledge and capabilities to the broadest possible range of people—spanning all professions and education levels. We work to accept completely free-form input, and to serve as a knowledge engine that generates powerful results and presents them with maximum clarity.
Energetically developed for more than a decade, Wolfram Alpha is an ambitious, long-term intellectual endeavor that we intend will deliver ever-increasing capabilities over the years to come. With a world-class team and participation from top outside experts in countless fields, we are constantly working to create what we hope will stand as a major milestone of twenty-first century intellectual achievement. 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 Well, in nature we see lots of complexity. Well, I thought this was pretty exciting.
Well, I did lots of work in this direction myself. And I pulled in quite a few other people as well. And I wanted to start some kind of institute for the science too. So it was hard really to fully staff up. But so I decided I need to build a new, more general, computational system. And that I needed to start and run a company to do that. Right here in Champaign, Illinois. At first, of course, it was a tiny operation.
And very quickly we started to grow our company in Champaign. And gradually we realized how to do this. In a sense my idea with it was to use it automate as much as possible. All algorithms.
All forms of computation. So it gets easier and easier to build. But by now, math is a small part of what Mathematica does. Well, so things with Mathematica were going really well. Well, then the web came along.
And we started doing things with that. With the numbers going up and down in different calculus seasons and so on. One day I expect that methodology will be the dominant one in engineering. And that will be the real mega-killer industry of NKS. But back in , I was thinking about the first killer app for NKS. Of course, it helped that we had Mathematica. But still, there were many things that could wrong.
And it might not be possible to curate it in any reasonable way. Or there might be too many different models and methods to implement. With everything having to be separately built for every tiny subspecialty. So we have to solve the problem of getting the system to understand that. Or whether I was off by a decade, or five decades.
But still, around I decided it was worth a try. We started on things like countries and chemicals and things like polyhedra. Who have been incredibly helpful to us. Meanwhile, we were busily constructing what would become Wolfram Alpha. But in a sense we were cheating. Implementing all those methods and models and so on. Well, then what about the natural language understanding? That might have just been plain impossible. Well, OK, so we can understand the question. And we can compute. But often we can compute lots and lots of different things.
So now the problem is to figure what to actually show as output. Of automating what to present and how to present it. Of course, then there were practical issues. And that takes quite a bit of CPU power. So we have to assemble a giant supercomputer-class cluster. Two years later, he was the youngest to receive a MacArthur grant — sometimes known as the "genius grant.
But academic life did not suit him. He liked solving problems and building things, and wanted to be able to build tools that would help him, and others, do better scientific research more efficiently. Computers were another great passion. Wolfram had used them since the age of 12 — when they were the size of a desk.
Still, he said, he "wasn't that excited about, or good at, doing mathematical calculations," and software helped automate much of that work. But by the time he received his doctorate, he was already reaching the edge of what was possible with available systems — he had even built one of his own with colleagues while still at Caltech. So when he left academia, he began working on a computer program that would automate complex mathematical tasks.
While intended for a technical audience, it would be accessible even to people who were not deeply experienced with computers. He founded Wolfram Research in with his own money, some of it from his MacArthur grant.
The company would be the home for Wolfram's new program. His friend Steve Jobs suggested Wolfram call it Mathematica. Wolfram had considered the name, but wanted something pithier. The software was considered revolutionary — Wolfram made his first deal with Jobs, who bundled the program with the high-end technical computers Jobs was making at NeXT Corporation, the company he founded after he was fired from Apple.
Deals with Sun Microsystems and others followed. Easy inputs with our form-based interfaces get you straight to answers. Our Web App collections cover a variety of topics from course assistants, finance to fitness, stock trading to password generation and more. Learn more about Web Apps ».
Use an extended keyboard to type math symbols right into the input field. Upload and process files, tabular data and images. With Wolfram Problem Generator, each question is generated instantly, just for you. Pro subscribers get integrated step-by-step solutions and can create printable worksheets for study sessions and quizzes. Learn more about Wolfram Problem Generator ». Explore features exclusively available on desktop ».
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