{"id":77224,"date":"2018-03-28T05:54:43","date_gmt":"2018-03-28T12:54:43","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2018\/03\/artificial-brains-shed-light-on-the-workings-of-our-own"},"modified":"2018-03-28T05:54:43","modified_gmt":"2018-03-28T12:54:43","slug":"artificial-brains-shed-light-on-the-workings-of-our-own","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2018\/03\/artificial-brains-shed-light-on-the-workings-of-our-own","title":{"rendered":"Artificial Brains Shed Light on the Workings of Our Own"},"content":{"rendered":"

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In the natural world, intelligence takes many forms. It could be a bat using echolocation to expertly navigate in the dark, or an octopus quickly adapting its behavior to survive in the deep ocean. Likewise, in the computer science world, multiple forms of artificial intelligence are emerging \u2014 different networks each trained to excel in a different task. And as will be presented today at the 25th annual meeting of the Cognitive Neuroscience Society (CNS), cognitive neuroscientists increasingly are using those emerging artificial networks to enhance their understanding of one of the most elusive intelligence systems, the human brain.<\/p>\n

\u201cThe fundamental questions cognitive neuroscientists and computer scientists seek to answer are similar,\u201d says Aude Oliva of MIT. \u201cThey have a complex system made of components \u2014 for one, it\u2019s called neurons and for the other, it\u2019s called units \u2014 and we are doing experiments to try to determine what those components calculate.\u201d<\/p>\n

In Oliva\u2019s work, which she is presenting at the CNS symposium, neuroscientists are learning much about the role of contextual clues in human image recognition. By using \u201cartificial neurons\u201d \u2014 essentially lines of code, software \u2014 with neural network models, they can parse out the various elements that go into recognizing a specific place or object.<\/p>\n

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Https:\/\/paper.li\/e-1437691924#\/ In the natural world, intelligence takes many forms. It could be a bat using echolocation to expertly navigate in the dark, or an octopus quickly adapting its behavior to survive in the deep ocean. Likewise, in the computer science world, multiple forms of artificial intelligence are emerging \u2014 different networks each trained to excel [\u2026]<\/p>\n","protected":false},"author":464,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[47,6],"tags":[],"class_list":["post-77224","post","type-post","status-publish","format-standard","hentry","category-neuroscience","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/77224","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/464"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=77224"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/77224\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=77224"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=77224"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=77224"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}