Data Annotation

Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving. In AI  Data labeling basically tells the AI model to classify and assign a result to a dataset and it is considered as the core of data preparation that gives life to your AI but the major problem people are facing with AI is it lacks common sense.

Due to a lack of common sense, many problems are unsolved and to get there, we need to provide AI with a very deep understanding of the language, along with the expressions. 

Power to Understand Customers 

Beginning with sentiment analysis (the ability to tell whether or not a statement is negative or positive) while sentiment analysis didn’t cover one significant step it didn’t include the positive or negative, how to address it, or which aspects are more positive than others.

Through various Data annotations including image annotation & deep learning techniques, AI became more smart and intelligent, but a different problem emerged: lack of data and through a large number of Data labeling provided by data labeling companies it taught  AI how to understand what customers are saying, you can’t exactly have it talk to itself to generate new training data. As an outcome, it was clear that in order to build an AI that could learn how to understand customer feedback and add value to the conversation and provide more sources of knowledge to AI

In artificial intelligence research, commonsense knowledge consists of facts about the everyday world, that all humans are expected to know. Common sense knowledge also helps to solve problems in the face of incomplete information. AI still doesn’t have the common sense to understand human language. AI does not have common sense knowledge and to gain it is one of the important tasks from the very beginning. It’s become very obvious that building common sense reasoning systems is a work-intensive and costly task. 

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