Introduction
Although manual data labeling looks moderately simple, like everything manual, it is also extremely time-consuming. It takes great skill to correctly annotate every aspect of an image. While annotating manually, annotators are given a series of raw and unlabeled data like images, texts, and videos. They are asked to annotate the specific techniques directed by data labeling rules.
As is evident, manual data labeling is hardly a time-friendly process and it consumes a lot of resources and effort. This makes it an extremely tedious procedure and it is here that Automatic Data Labeling comes in. In this process, experts create artificial intelligence to label raw data which is then identified and verified by a human.
Now, the data identified is processed through two different courses of action. If the AI identifies the data correctly, it gets added to the labeled training data pool. However, if the identification is incorrect, this information is used to re-train the AI. This article analyzes the importance of AI in integrating automatic data labeling.
Smarter Marketing
The marketing world will be altered through the use of AI. The intended audience, which is the most important aspect of any business, can be easily targeted through the use of AI in labeling large amounts of data. Facebook and Google can become the source to search for consumers especially inclined towards your product.
Their information can then be collected and analyzed, only to be stored in large databases, reused, and classified against breaches. A properly trained AI will be able to decide the kind of promotion required to sell to this target audience and the amount that should be paid for the display of its advertisements. All this and more can be achieved by the AI itself without any effort from marketers.
Efficiency and Quality
AI can pre-label the data and this can bring a dramatic reduction in the rate of errors, a common instance in human-labeled data. Pre-labeling saves a lot of time and increases efficiency. The labeling workflow also incorporates the real-time QC and QA to increase accuracy rates. By integrating an AI in automated data labeling, a company can be reassured that most of its data is thoroughly screened and investigated by the AI itself. After the task flow has been established, the data labeling project can begin within the next day. A project which has 10,000 images can be labeled within less than one business day if AI is utilized.
Cost Effective
AI performs the work that would be accomplished by quite a number of data labelers. Additionally, automated data labeling methods also require much less time. With the use of AI, companies do not need to pay the salaries of human data labelers and this makes it extremely cost-effective. If, however, a company utilizes a collaborative team of both AI and human data labelers, it can ensure a 50% reduction in the cost. This is in comparison to using annotation services from only human labelers.
Flexible
AI can be made highly flexible to work with. This can be done by teaching it labeling rules that it should abide by. Data features and attributes can also be taught to the AI, including setting the task flows that it should follow during labeling crucial data. It can be taught to scale the data upwards or downwards and it can even learn to make changes. The labeling progress of the AI in automated data labeling can be monitored and reviewed to understand its progress.
Hiring the Best Candidates
The use of AI in data labeling also finds application when hiring for a job opening. A properly trained AI will be able to predict why some of the advertisements promoted to attract candidates do not work.
It can also show how the text can be rephrased to encourage applications from a diverse range of applicants. Automated data labeling also automatically screens resumes for the keywords related to the required skills and qualifications required for the job. As opposed to manually reviewing CVs, AI in automated data labeling can flag CVs which are ideal enough to be reviewed by the manager of the company.
Get Purified Data
The data collected anonymously from various websites contain a lot of unnecessary information. Data labeling tools can help in purifying this acquired data and increasing its applications in business. AI in automated data labeling spots the data most relevant to the company. It is also able to intelligently sort and classify it and develop advanced marketing strategies.
Conclusion
Businesses today utilize a lot of data gathered to promote their services to the right audience. However, such a quantity of information from so many different sources requires a large capacity for storing this data.
Additionally, to effectively drive a company’s business growth, analyzing and correctly using this information is also of the utmost importance. The use of AI annotation services in this sphere has great potential to develop a successful infrastructure for a business.
Founder Dinis Guarda
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