So what exactly would a fruit fly and search engines ever have in common? Of course, this does sound like a weird joke or something, but this is actually something very serious. Just ask scientists from the prestigious Salk Institute and at UC San Diego. The thing is that our search engines daily go through many permutations to provide the content that you are seeking when you click in the search terms. So we need search engines that are faster and more efficient – and demands keep growing for the computer scientists are trying to keep up. They are forever tackling what they refer to as “a fundamental machine learning problem: approximate similarity (or nearest-neighbors) search.”
Fruit Fly Brains Make a Contribution
As it just so happens, a common fruit fly brain actually goes through a very similar process of matching like a search engine, and it does it very quickly and efficiently. This happens within the fly’s olfactory circuit which is referred to as a neural algorithm. It processes as a variation of what is called locality-sensitive hashing (LSH). Hashes are sort of a shorthand that is used to make searches go quicker as they limit the quantity of information that is known about each piece of data.
So rather than collecting a group of different breeds of dogs that are grouped together, where it would be challenging to sort out a certain breed, you would place all of them in the dog hash or container. This gives you a bin that is holding all the dogs. So when these algorithms are requested to search for a Doberman, rather than sorting through all the data available, which would be very time consuming, it will go straight to the dog hash and pull out the feisty canine.
New Ways to Process Information
In nature, searches are conducted differently. The olfactory circuit of the fruit fly operates by assigning neural operating patterns to objects that have a similar smells. Although we have been aware of the way these circuits operate for quite some time, this one of the very first studies that has revealed a direct link between neural circuits and the way that algorithms will process information. It is also the first to document how a process like this can be employed to actually speed up search engines for computers in the future.
Every time a fruit fly detects a new smell, it quickly characterizes its behavior, in accordance with experiences with this type odors in its past. The innovation with the brain of fruit fly is that it employs a very non-traditional method which is far more efficient than what computers use today.
These researchers posted their finding in the awesome publication journal Science. In that report, they said, “This perception helps illuminate the logic supporting an important sensory function and provides a conceptually new algorithm for solving a fundamental computational problem.” Saket Navlakha was the primary author of this research. He is currently an assistant professor at the Salk Institute. His efforts have focused primarily on various algorithms that are used in nature. These will outline exactly how different organisms process data.
Navlakha recently claimed, “In the natural world, you’re not going to encounter exactly the same odor every time; there’s going to be some noise and fluctuation. But if you smell something that you’ve previously associated with a behavior, you need to be able to identify that similarity and recall that behavior.”