{"id":15366,"date":"2017-11-09T11:25:12","date_gmt":"2017-11-09T19:25:12","guid":{"rendered":"https:\/\/vermont.salk.edu\/?post_type=disclosure&p=15366"},"modified":"2018-10-03T14:37:51","modified_gmt":"2018-10-03T21:37:51","slug":"fruit-fly-brains-inform-search-engines-future","status":"publish","type":"disclosure","link":"https:\/\/www.salk.edu\/es\/news-release\/fruit-fly-brains-inform-search-engines-future\/","title":{"rendered":"Fruit fly brains inform search engines of the future"},"content":{"rendered":"

LA JOLLA\u2014Every day, websites you visit and smartphone apps that you use are crunching huge sets of data to find things that resemble each other: products that are similar to your past purchases; songs that are similar to tunes you\u2019ve liked; faces that are similar to people you\u2019ve identified in photos. All these tasks are known as similarity searches, and the ability to perform these massive matching games well\u2014and fast\u2014has been an ongoing challenge for computer scientists. <\/p>\n

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From left: Saket Navlakha, Sanjoy Dasgupta, Charles Stevens<\/p>\n

Click here<\/a> for a high-resolution image<\/p>\n

Credit: Salk Institute<\/figcaption><\/figure>\n

Now, Salk and UC San Diego scientists have discovered that the fruit fly brain has an elegant and efficient method of performing similarity searches. For flies, it helps them identify odors that are most similar to those they\u2019ve encountered before, so they know how to behave in response to the odor, such as to approach or avoid it. New details on the fly\u2019s computational approach to smelly similarity searches, described in the journal Science<\/em> on November 9, 2017, could inform computer algorithms of the future. <\/p>\n

\u201cThis is a problem that pretty much every technology company with any kind of information retrieval system has to solve, so it\u2019s been something that computer scientists have studied for years,\u201d says Saket Navlakha<\/a>, assistant professor in Salk\u2019s Integrative Biology Laboratory and lead author of the new paper. \u201cNow, we have this new approach to similarity searches thanks to the fly.\u201d<\/p>\n

The way most computerized data systems categorize items\u2015from songs to images\u2015to optimize similarity searches is by reducing the amount of information associated with each item. These systems assign short \u201chashes\u201d to each item so that similar items are more likely to be assigned the same or a similar hash compared to two very different items. (Hashes are a kind of digital shorthand, the way a bitly is a shorter version of a URL.) Assigning hashes in this way is called \u201clocality-sensitive hashing\u201d to computer scientists. When searching for similar items, a program looks through the hashes, rather than the original items, to find similarities quickly.<\/p>\n

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