{"id":183431,"date":"2024-02-23T11:22:25","date_gmt":"2024-02-23T17:22:25","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2024\/02\/ai-in-the-developing-world-how-tiny-machine-learning-can-have-a-big-impact"},"modified":"2024-02-23T11:22:25","modified_gmt":"2024-02-23T17:22:25","slug":"ai-in-the-developing-world-how-tiny-machine-learning-can-have-a-big-impact","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2024\/02\/ai-in-the-developing-world-how-tiny-machine-learning-can-have-a-big-impact","title":{"rendered":"AI in the Developing World: How \u2018Tiny Machine Learning\u2019 can have a Big Impact"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/ai-in-the-developing-world-how-tiny-machine-learning-can-have-a-big-impact.jpg\"><\/a><\/p>\n<p>The landscape of artificial intelligence (AI) applications has traditionally been dominated by the use of resource-intensive servers centralized in industrialized nations. However, recent years have witnessed the emergence of small, energy-efficient devices for AI applications, a concept known as tiny machine learning (TinyML).<\/p>\n<p>We\u2019re most familiar with consumer-facing applications such as Siri, Alexa, and Google Assistant, but the limited cost and small size of such devices allow them to be deployed in the field. For example, the technology has been used to detect mosquito wingbeats and so help prevent the spread of malaria. It\u2019s also been part of the development of low-power animal collars to support conservation efforts.<\/p>\n<p>Small size, big impact Distinguished by their small size and low cost, TinyML devices operate within constraints reminiscent of the dawn of the personal-computer era\u2014memory is measured in kilobytes and hardware can be had for as little as US$1. This is possible because TinyML doesn\u2019t require a laptop computer or even a mobile phone. Instead, it can instead run on simple microcontrollers that power standard electronic components worldwide. In fact, given that there are already 250 billion microcontrollers deployed globally, devices that support TinyML are already available at scale.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The landscape of artificial intelligence (AI) applications has traditionally been dominated by the use of resource-intensive servers centralized in industrialized nations. However, recent years have witnessed the emergence of small, energy-efficient devices for AI applications, a concept known as tiny machine learning (TinyML). We\u2019re most familiar with consumer-facing applications such as Siri, Alexa, and Google [\u2026]<\/p>\n","protected":false},"author":707,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1512,6],"tags":[],"class_list":["post-183431","post","type-post","status-publish","format-standard","hentry","category-mobile-phones","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/183431","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\/707"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=183431"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/183431\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=183431"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=183431"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=183431"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}