Making the world’s knowledge computable
Today’s Wolfram|Alpha is the first step in an ambitious, long-term project to make all systematic knowledge immediately computable by anyone. You enter your question or calculation, and Wolfram|Alpha uses its built-in algorithms and growing collection of data to compute the answer. Based on a new kind of knowledge-based
Fifty years ago, when computers were young, people assumed that they’d quickly be able to handle all kinds of things and that one could be able to ask a computer any rationally factual question, and have it come up with the solution.
But that was not the case. Computers have been able to do many remarkable and unexpected things. But so far not that.
I’d always thought, though, that eventually it should be possible. And a few years ago, I realized that I was finally in a position to try to do it.
I had two crucial ingredients: Mathematica and NKS. With Mathematica, I had a symbolic language to represent anything—as well as the algorithmic power to do any kind of computation. And with NKS, I had a paradigm for understanding how all sorts of complexity could arise from simple rules.
But what about all the actual knowledge that we as humans have accumulated?
Lots of it is now online—in billions of text pages. And search engines can very efficiently search for specific terms and phrases in that text.
But we can’t compute from that. And in effect, we can only answer questions that have been literally asked before. We can search for information, but we can’t figure anything new out.
So how can we get around this? Well, some people have thought the way forward must be to somehow automatically resolve the natural language that exists online. Perhaps getting the web semantically tagged to make that easier.
But armed with Mathematica and NKS I realized there’s another way: explicitly implement methods and models, as algorithms, and explicitly curate all data so that it is immediately computable.
Tis is not easy to do. Every different kind of method and model—and data—has its own special features and character. But with a mixture of Mathematica, NKS automation and a lot of human experts, I’m happy to say that we’ve gotten a very long way.
But, OK. Let’s say we succeed in creating a system that knows a lot, and can figure a lot out. How can we interact with it?
The way humans naturally communicate is through language. And when one’s dealing with the whole spectrum of knowledge, I think that’s the only realistic option for communicating with computers too.
Of course, getting computers to deal with natural language has turned out to be very difficult. And for example we’re still very far away from having computers systematically understand large volumes of natural language text online.
But if one’s already made knowledge computable, one doesn’t need to do that kind of natural language understanding.
All one needs to be able to do is take questions people ask in natural language, and represent them in a precise form that fits into the computations.
Of course, even that has never been done in any generality, this is further complicated by the fact that one doesn’t just want to handle a language like English: one also wants to be able to handle all the shorthand notations that people use in every field.
I wasn’t at all sure it was going to work. But I’m happy to say that with a mixture of many clever algorithms and heuristics, lots of linguistic discovery and curation, and what probably amount to some serious theoretical breakthroughs, we’re actually managing to make it work.
Pulling all of this together to create a true computational knowledge engine is a very difficult task.
But I’m happy to say that we’ve almost reached the point where we feel we can expose the first part of it.
I think it’s going to be pretty exciting. A new paradigm for using computers and the internet.
That almost gets us to what people thought computers would be able to do 50 years ago!
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