Wednesday, March 30, 2011

Professor Whoopie Rides Again

Mechanisms are exciting for their own sake.  Folks like Arthur Ganson have shown us that machinations have their own mesmerizing magic built in.

But there's a special power in understanding what's going on in there.  This video is a perfect example of a clear, well paced mechanical explanation that starts at the beginning and moves in traceable, gradual steps towards an increasingly complex concept.  Its a perfect example of the kind of explanation Professor Whoopie, the man with all the answers drew on his 3D blackboard all those years ago.

Can iCarly or Hanah Montana explain how a rocket works?  Or why a sailboat goes forward?  Or how a lightbulb lights?  I didn't think so

Tuesday, March 29, 2011

Flying like a Bird is Hard

Our friends at Festo have done a lot of cool stuff.  Inflatable architecture.  Pneumatic muscle.  But this is really incredible.  Its hard to fly like a bird for lots of reasons, but mostly its power to weight.  Watching Boston Dynamics' terrifyingly determined, gasoline powered dog-bot you get some idea of how much power it takes just to walk.  It takes a chainsaw's worth!

But festo's bird has somehow gotten enough juice in a light weight enough package to fly.  Amazing!  A chain saw is a pretty lightweight power source.  What's in the bird?

Monday, March 28, 2011

Bigger First Aid Kits

I am beside myself in anticipation of the America's Cup racing that's coming to the bay.  And seeing the first fleet of AC45s racing in New Zealand is a nice taste of the racing to come.  Yeah, I bet they are "very physical."  And yes, you are going to need a serious first aid kit.  Not to go all NASCAR, but it will be amazing to see what happens when these things make contact at 30+ knots.

Tuesday, March 22, 2011

Can Data Take the Place of Hypothesis?

For years, folks have been talking about the analysis of huge data sets (and by huge, I mean really, really huge) superseding our ability to hypothesize.  A hypothesis to prove or disprove, that essential building block of the scientific method, is becoming less important than just knowing everything you can know about the problem.

This is the best example I have seen so far that this might be the case.  Deb Roy's analysis of his son's language acquisition yielded several simultaneous findings because the data was so rich and there was just so damned much of it.  Sure, you have to decide which axes to measure.  But what makes massive data manipulation so interesting is you don't necessarily need to know why you're interested in those axes.  Science becomes a more improvisational, reacting to the data-shapes changing right in front of you.

You still have to see the results, you just don't have to predict them quite as much.