Last weekend we all had a good chuckle when we saw that WolframAlpha knows — or anyway claims to know — the airspeed of an unladen swallow. But the more telling example, for me, was one that Stephen Wolfram showed in a post-demo discussion:
Suppose you want to know the distance to Pluto. We don’t just look it up. We answer the question: “What is the distance to Pluto right now?” And we compute the answer.
I reckon that this notion of computable knowledge is going to take a while to sink in. Here’s another example:
Q: length of grand canyon / height of mt. everest
These examples run the risk of seeming geeky and pointless. But twice in the last few days, I’ve found myself reaching for bits of computable knowledge that weren’t readily available, and that’s got me thinking about what things might be like when they are.
Both examples are from my elmcity+azure project. In one case, I needed to work out distances — based on latitude/longitude coordinates — for locations that might be written as Providence RI or Ann Arbor, MI. There’s no shortage of online services that can do this. But they all report results in different ways, and digging the answers out of XML responses — which may or may not require special handling for embedded namespaces — can be very tricky.
In the other case I wanted population data for cities whose names are written the same way. Here I wound up digging it out of a CSV file published at http://www.census.gov. It’s perfectly doable, but you’ve got to really want to do it. If you have, say, a count of calendar events in Providence, and you want to divide that by population in order to produce an experimental metric for creative class activity, you can’t just write “population of Providence RI” in the denominator and proceed with your experiment. You have to overcome some fairly serious data friction.
In a few months we’ll all get to tirekick WolframAlpha. Then we’ll draw our own conclusions about what it can or can’t do, and is or isn’t good for. I’m not expecting a Delphic oracle. But I would like to be able to compute with facts in a more frictionless way.