You’ve been there.  We all have.  We’ve all tried to make the round peg fit into the square hole.  Only this time I’m not referring to it figuratively.  I mean we’ve all literally tried to make the round peg fit.  It didn’t fit, so we tried to jam it.  Occasionally it actually works out:  “Who needs the square peg anyway?  Square pegs are a total ripoff.”  But sometimes, you give up and shell out for the square peg.  And it’s incredible how well it gets the job done.  You never seem to fully appreciate the power of having the right tool until you’ve been in one of these situations.  You also never seem to learn your lesson, “Hmm… I wonder if this square thing can do an octagonal hole…”

You’re probably wondering, what does this have to do with anything?

I’m actually writing about a new tool that was recently built by Nicolas Pinto and several other artificial vision researchers at MIT.  The goal: learn to replicate the human vision system.  One of the big challenges in artificial vision research is testing your hypotheses efficiently.  Since we’re trying to replicate the human vision system, we can look to biological neuroscience for qualitative hints on how our artificial systems should work.  But there are still so many unknowns that, even when we’ve nailed down an algorithm, we end up with a wide swath of magical parameters to fill in.  Artificial vision is so computationally complex, that running even a small-scale test with one parameter setting could be time-consuming on a normal CPU.  If the test “fails,” should we blame the parameter setting, the scale, or the whole approach?

Nicolas and the team recently assembled a computer with sixteen graphics processors.  The idea is that this new tool can run tests so much faster (kinda like Devver!), that they can afford to toggle more parameters and run at a larger scale.  I’ve personally been frustrated with the inability to run a lot of tests, so I envy this new square peg, and intend to follow what they manage to learn with it.

In case you’re wondering what this thing looks like:

(Thanks Nicolas for permission to republish the photo.)