{"id":1984,"date":"2019-10-01T02:43:36","date_gmt":"2019-10-01T02:43:36","guid":{"rendered":"http:\/\/kusuaks7\/?p=1589"},"modified":"2024-03-19T09:48:21","modified_gmt":"2024-03-19T09:48:21","slug":"why-data-annotation-is-the-secret-to-hacking-ai","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/why-data-annotation-is-the-secret-to-hacking-ai\/","title":{"rendered":"Why Data Annotation is the Secret to Hacking AI"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\tIn case you\u2019ve been living under a rock, artificial intelligence (AI) is everywhere. It\u2019s infiltrated almost every aspect of our private and professional lives. From healthcare to transportation, AI aims to redefine how information is collected, integrated, and analyzed; ultimately leading to more informed insights and delivering better outcomes. But for all its hype, the full promise of AI rarely comes to fruition because of one four-letter word: \u201cdata.\u201d\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tWhile the AI story is all the rage, the data narrative is not as prominently discussed. Sure, data may not be as sexy as the automated systems that can learn and process information quicker than a human, but it is equally as important. And don\u2019t get me wrong, we all know that AI requires vast amounts of data to continually learn and identify patterns that humans can\u2019t. After all, it\u2019s the ability to process this information and make instant decisions that has led to AI being such a game changer for industries that rely on massive volumes of data.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tBut the real story is not about the algorithms powering the AI revolution, instead it\u2019s about the quality of data powering these systems. What enterprises really need as they develop their AI strategy is to integrate, clean, link, and supplement their data so they have an accurate foundation on which to build\u00a0and train their machine learning algorithms.\u00a0 For many organizations, this makes AI difficult if not impossible.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\u201cData-related challenges are a top reason (our) clients have halted or canceled artificial-intelligence projects,\u201d said IBM\u2019s senior vice president of cloud and cognitive software, Arvind Krishna, speaking at\u00a0The Wall Street Journal\u2019s<\/a><\/em>\u00a0Future of Everything Festival. He\u2019s certainly not alone in his assessment.\u00a0 According to a report by\u00a0MIT Technology Review<\/em>, insufficient data quality was one of\u00a0the biggest challenges<\/a>\u00a0to employing AI. What\u2019s more, 85% of AI projects will \u201cnot deliver\u201d for organizations, according to research and advisory company\u00a0Gartner<\/em>.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tCompanies need to think of AI and machine learning as the engines that will drive the amazing things they want to accomplish. But like every engine, it needs the right fuel to run well.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Enter Data Annotation<\/strong><\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tData annotation (also referred to as data labeling) is quite critical to ensuring your AI and machine learning projects can scale. It provides that initial setup for training a machine learning model with what it needs to understand and how to discriminate against various inputs to come up with accurate outputs.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThere are many different types of data annotation modalities, depending on what kind of form the data is in. It can range from image and video annotation, text categorization, semantic annotation, and content categorization. Humans are needed to identify and annotate specific data so machines can learn to identify and classy information. Without these labels, the machine learning algorithm will have a difficult time computing the necessary attributes.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThe unfortunate reality about all of this is that it\u2019s still a very manual process requiring manual labor. While tools for annotation are getting better, the difference between an ill-designed tool and an intuitive one makes significant difference in annotation productivity. According to\u00a0some estimates<\/a>, 80% of AI project time is currently spent on data preparation. But even small errors in the data could prove to be disastrous. In this area, humans actually have a leg up on machines. We\u2019re are simply better than computers at managing subjectivity, understanding intent, and coping with ambiguity \u2013 all of which are important factors of data annotation.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tRegardless of modality, the vast majority of problems in which AI models are being built to address them can fit into\u00a0one (or many) of the below annotation tasks:\n