Kris Carlson

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Wolfram on Wolfram Alpha

Here are some excerpts from Wolfram’s description of the history of Alpha that I find interesting. The talk is here:

http://www.stephenwolfram.com/publications/recent/50yearspc/

“For years and years we’d been pouring all those algorithms, and all that formal knowledge, into Mathematica. And extending its language to be able to represent the concepts that were involved. Well, while I’d been working on the NKS book, I’d kept on thinking: what will be the first killer app of this new kind of science?

When one goes out into the computational universe, one finds all these little programs that do these amazing things. And it’s a little like doing technology with materials: where one goes out into the physical world and finds materials, and then realizes they’re useful for different things. Well, it’s the same with those programs out in the computational universe. There’s a program there that’s great for random sequence generation. Another one for compression. Another one for representing Boolean algebra. Another one for evaluating some kind of mathematical function.

And actually, over the years, more and more of the algorithms we add to Mathematica were actually not engineering step by step… but were instead found by searching the computational universe.

One day I expect that methodology will be the dominant one in engineering.”

“We’d obviously achieved a lot in making formal knowledge computable with Mathematica.

But I wondered about all the other knowledge. Systematic knowledge. But knowledge about all these messy details of the world. Well, I got to thinking: if we believe the paradigm and the discoveries of NKS, then all this complicated knowledge should somehow have simple rules associated with it. It should somehow be possible to do a finite project that can capture it. That can make all that systematic knowledge computable.”

“And we actually at first built what we call “data paclets” for Mathematica. You see, in Mathematica you can compute the values of all sorts of mathematical functions and so on. But we wanted to make it so there’d be a function that, say, computes the GDP of a country—by using our curated collection of data. Well, we did lots of development of this, and in 2007, when we released our “reinvention” ofMathematica, it included lots of data paclets covering a variety of areas.

Well, that was great experience. And in doing it, we were really ramping up our data curation system. Where we take in data from all sorts of sources, sometimes in real time, and clean it to the point where it’s reliably computable. I know there are Library School people here today, so I’ll say: yes, good source identification really is absolutely crucial.

These days we have a giant network of data source providers that we interact with. And actually almost none of our data now for example “comes from the web”. It’s from primary sources. But once we have the raw data, then what we’ve found is that we’ve only done about 5% of the work.

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.

And another thing we’ve found is that to get the right answer, there always has to be a domain expert involved. Fortunately at our company we have experts in a remarkably wide range of areas. And through Mathematica—and particularly its incredibly widespread use in front-line R&D—we have access to world experts in almost anything.”

June 21, 2010 - Posted by | Artificial Intelligence, Complexity, Culture, Mathematics

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