{"id":56485,"date":"2020-11-26T07:00:59","date_gmt":"2020-11-26T06:00:59","guid":{"rendered":"https:\/\/www.clickworker.com\/?p=56485"},"modified":"2022-07-25T16:57:59","modified_gmt":"2022-07-25T15:57:59","slug":"ai-and-big-data","status":"publish","type":"post","link":"https:\/\/www.clickworker.com\/customer-blog\/ai-and-big-data\/","title":{"rendered":"Leveraging Big Data: Important Data AI Researcher Should Track"},"content":{"rendered":"
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Big Data appeared before everyone even realized its existence, and so far, it is the core element of modern technologies. This article will explain the intersection where big data meets AI as well as the key elements of their inseparable connection and the prospects for the further development of AI by using big data. <\/p>\r\n\r\n\r\n
There is a well-known phrase that big data<\/a> is the fuel that powers AI<\/a>, but what is the basis of this saying? To explain it briefly, AI builds a store of knowledge that will be used for further predictions about consumers\u2019 personal preferences, for instance, what you might be interested in or what things you are going to buy. The same process is used in AI Logo Maker<\/a>, as the smart system generates a unique label based on the target audience\u2019s fancies. Accumulating this information, AI can easily increase the possibility of offering the exact thing you were keeping in mind, thus, make the everyday routine less complicated. <\/p>\r\n\r\n For this purpose, the data that is gradually collected must be stored somewhere, and that\u2019s the exact thing big data deals with. Forrester Research<\/a> analyst Brandon Purcell explains that \u201cData is the lifeblood of AI. An AI system needs to learn from data in order to be able to fulfill its function.\u201d<\/p>\r\n\r\nThe Intersection of AI and Big Data<\/h2>\r\nAI enables us to make sense of massive data sets, representing a massive structure of different application sectors to meet the following goals<\/a>: machine learning<\/a>, computer vision<\/a>, robotics<\/a>, general intelligence, natural language processing<\/a>, automated learning, and scheduling, etc. For these and more modern aspects to mature, the AI algorithms<\/a> require huge amounts of data. \r\n\r\n\r\n