Data Annotation for AI: from behind the scenes to the front stage

“Do you know anything about the AI industry?”

9 out of 10 people will probably say yes.

“Do you know data-annotation?”

Nine out of 10 people will probably shake their heads.

Unlike AI companies at the center of the spotlight, the data annotation industry has long been in the gray area outside the spotlight, and for a long period has been marginalized and in a low-profile existence.

However, with the change of demand brought by the development of times, the data annotation industry is also undergoing rapid changes, and it starts to move from the background to the foreground.

Behind the scenes: extensive and chaotic interweaving

There is a saying in the data-labeling industry: “As much intelligence as there is labor.”

In a way, it says something about the nature of artificial intelligence.

Machine learning is still the most effective way for AI to improve their cognitive abilities, and almost all the data that AI algorithms can learn are manually annotated one by one.

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Demand means the market, and according to relevant agencies, the domestic data services market will reach tens of billions in the next few years.

Such a huge market size, so many people want to take a piece of the action, so large and small labeling teams mushroomed like a spring.

However, problems arise.

Different from the high-tech, data annotation is still a labor-intensive industry, and the mode is usually outsourced.

Annotators are engaged in repetitive and boring work such as marking bounding box and drawing points every day. The uneven level of labor force leads to the low quality of output marking data, which cannot meet the needs of AI enterprises and affects the commercialization process of AI products.

At the same time, the low technical content of low-end production capacity also makes the data labeling industry almost free of any barriers and restrictions, and many labeling teams can bring in a few people at will to take over the business after simple training.

As a result, the industry is in constant chaos and competition. Most labeling teams can only survive at the bottom of the industrial chain and facing the situation of low prices.

Ii. Foreground: AI’s reliance on high-quality data

There is an important consensus in the AI industry:

The quality of the data set directly determines the effect of the final model.

In other words, data contributes the most to model performance. If we got more quantity, diversity, and representative data, then the better the model effect, the more robust algorithm will be.

With the accelerated commercialization process of AI enterprises, more and more enterprises begin to realize the importance of annotating data.

Take autonomous driving, for example, many companies have produced prototypes of their driverless cars, which frequently appear in public view.

However, although these prototypes perform well in the laboratory, they are still far from being commercially available. One important reason is that the gap between the real road conditions and the laboratory conditions is too large.

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In the laboratory, only a small amount of road data is needed to meet the needs of the experiment. However, on the real road, driverless cars will encounter many unpredictable situations. Without sufficient data support, build-in computers in cars cannot make their judgments, leading to a dramatic increase in potential risks.

Therefore, many AI enterprises represented by autonomous driving enterprises have put forward higher requirements for the data annotation industry, and the data annotation industry has begun to be in the spotlight, moving from the back to the foreground.

Iii. Future: Intelligence, refinement, and scenization

AI data is a top priority for ARTIFICIAL intelligence. As is known to all, the troika of ARTIFICIAL intelligence is the algorithm, computing power, and data, among which data is the development cornerstone of the artificial intelligence industry.

With the acceleration of the commercialization process of the artificial intelligence industry, the AI data service field is sinking, and the industry reform has begun to emerge. In the future, intelligence, refinement, and scenization will be the main development direction of the data annotation industry.

Intelligence means that the annotation tool is AI. AI pre-marking technology can automatically recognize the transliterated speech data, and the annotator only needs to make some modifications to the results of the tool’s pre-marking, which not only improves the marking efficiency but also reduces the dependence on human resources.

Refinement means a new requirement for the quality and detail of the annotated data set. The previous data set could reach more than 90% can meet the requirements of accuracy, the speeding up of the commercial floor, but with the AI enterprise-quality requirements for annotation data reached 95%, even more than 99%, at the same time pay more attention to details.

Scenization means that the data annotation industry needs to meet the requirements of diversified application scene annotation. Take the field of computer vision as an example. Currently, data annotation can be applied in automatic driving, unmanned aerial vehicles, AI education, industrial robots, new retail, safety protection, and other scenarios. Each application scenario has its data type and specific labeling requirements, so the ability of scenario-based labeling of data labeling enterprises is extremely tested.

It is foreseeable that the data annotation industry will usher in a big change in the next few years. AI data service enterprises with more advanced concepts, more hardcore technologies, and more professional services will bring the data annotation industry into a new era of refined operation.

In the future, it is believed that with the gradual application of 5G technology, the combination of data and 5G will collide more sparks of innovation and jointly hold up the cornerstone of AI development.

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