Artificial Intelligence Trainer | Making Machines Smarter

5 min readNov 1, 2020

Artificial intelligence trainers are a new profession. They formulate data labeling rules, then “feed” the data to the robots, “teach” them, and continuously optimize them, so that the robots are “reasonable and understand human nature” and better serve humans. service.

What is an artificial intelligence trainer?

When artificial intelligence was first designed, the IQ may be equivalent to a five or six-year-old child, and then artificial intelligence trainers need to continuously train, tune, and feed them to make their IQ higher and higher.

The job responsibilities of an artificial intelligence trainer mainly include the following three points:

—Provide data labeling rules: extract industry feature scenes from data through algorithmic clustering, labeling analysis, etc., and combine industry ;knowledge to provide data labeling rules that are accurate and logically clear to ensure that the data training effect can meet the needs of the product;

—Data acceptance and management: Participate in model building and data acceptance, and be responsible for the daily tracking and maintenance of core indicators and data;

—Accumulate general data in the field: According to the data application requirements of the subdivision field, select general data (applicable to different customers/users in the same field) from the existing data to form the precipitation and accumulation of data.


Generally speaking, the raw data obtained by AI companies from customers (users) cannot be directly used for model training. Before the emergence of “artificial intelligence trainers”, AI product managers used relevant tools to simply process them and then hand them over to the data. Annotators perform annotation processing, but because annotators’ understanding of the data and the quality of annotation are very different, the efficiency and effect of the overall annotation work are not ideal. At the same time, AI companies have accumulated a large amount of data in their subdivisions. These data often no longer generate more value after being used once. This brings about a second problem: the data cannot be deposited and reused.




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