This is Scientific American 60 Second Science.

I'm Christopher Intagiyata.

The goal for a lot of tech companies today,

figure out what you, their customer want next

before you even ask. It's driven by something called

similarity search. If you go to YouTube and you watch a video they're going to

suggest similar videos to that one that you're watching right that's

similarity search. If you go to Amazon and look for similar products

to the one that you're going to buy that similarity search.

Socket Navlika, a computer scientist at the Salk Institute. He says we do similarity searches too, for example, when we scan

faces in a crowd for the one we know, and even fruit flies do a version related to

smell. So the fly is having to solve a similar problem of kind of searching through its database of previous experiences and previous odors that it has smelled to determine what should be the most appropriate behavioral response to that odor.

But flies tag incoming odors differently from the way modern search algorithms parse similarity.

A small group of neurons makes an initial evaluation of the smell, then a much larger

set of neurons is activated to make a final decision about the smell. Rather than the way a computer

similarity search does it, which is taking something with many dimensions and simplifying it down to a few.

So Novica and his colleagues tweaked computer similarity search functions to do it fly style,

and then pitted the fly-inspired algorithms against

conventional ones and the biologically inspired code one out better at

telling like from unlike on an image similarity test. You know evolution figured it out,

I figured out a really elegant solution

to this very important problem.

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