What is the current scale of the domestic data labeling market?

According to the data, the market size of China's data labeling industry in 2019 will reach 100 million yuan, of which the demand for image, voice, and NLP data accounts for ., respectively1% and; According to the investment of the demand side and the revenue growth of the first side, it is expected that the market size of data labeling will exceed 10 billion yuan in 2025, with an annualized growth rate of .

The scale is quite large, with more than a dozen more mature companies dedicated to undertaking the data labeling of artificial intelligence companies, and the current growth trend should be good.

However, the new entrant company does not know to go to ** to get the order.

At present, the market size of domestic data labeling is still relatively good, and it can achieve a more ideal development trend through effective market development.

In terms of the scale of the domestic data labeling market, it is still relatively good, because its scale has come up now, and its market rationality is getting better and better.

At present, the market is relatively small, and on the whole, it can even be said that there are a lot of blanks, and it is not particularly professional to do this thing, and it still needs professional personnel to do professional things.

The scale of the data labeling market is actually very good, and on the whole, the scale of the domestic data labeling market and the prospects for development are particularly good.

At present, the scale of the domestic data labeling market is relatively large, and this should still be very good, and it should be liked by a lot.

At present, the scale of the domestic data labeling market needs to be further improved.

So you can consider getting involved in such an industry.

I just want to look at the scale of the country, or Shangshan Cocoa, and because its development is getting better and better, I think it's still pretty good.

The scale of the domestic data labeling market should be divided into megacities, large cities, second-tier cities, and third-tier cities.

At present, this field is not very popular in China, and basically few companies are back in this area.

At present, the scale of the domestic data labeling market is very good.

What is the current scale of the domestic data labeling market? How is the domestic data annotation plugged in quite well?

Data annotation is the Internet of Industries. Data annotator is an industry with the rise of the Internet, which is a bit similar to operation, simply put, it is to instill some specific symbols and marks into the artificial intelligence AI brain, such as making marks in text, speech, images, **, etc., so that the algorithm can understand these marks, and continue to learn, and finally achieve the effect of intelligence.

Introduction to Data Annotators:

1. The data annotator is to instill some specific symbols and marks into the AI brain of artificial intelligence, which is equivalent to the entry-level position of AI intelligence;

Data Annotator Responsibilities:

The responsibility of data annotation is to find out the target things in the large number of ** that will be provided to artificial intelligence in different scenarios as training data for artificial intelligence to practice cognition.

Hello, artificial intelligence is all the rage right now.

Data annotation is used for AI databases to train AI recognition capabilities.

1. Fully understand the background and standards of data labeling and evaluation, complete the task more accurately2, be responsible for data labeling of voice, image**, text and other samples3, be responsible for scientific and fair evaluation of product results, and count data indicators for simple analysis.

What exactly is data annotation? To understand data labeling, let's first take a simple example: when adults teach us to recognize flowers, when adults teach us to recognize flowers, they will point to flowers and plants and tell us that flowers will have many colors, and we will slowly remember what flowers bloom and what colors.

And artificial intelligence deep learning is also the same as our human cognition. The premise of artificial intelligence deep machine learning is also that data annotators identify various functional tags according to different **, voice, text and other data, and then machine learning recognizes different things according to different tags.

Data annotation is an act of processing artificial intelligence learning data through data annotation personnel with the help of computer annotation tool software. Generally, the types of data annotation include: image annotation, voice annotation, text annotation, ** annotation, etc.

The basic forms of markup include annotation frame, 3D frame, text transcription, image dotting, target object outline, etc. At present, most of the data annotation tool software supports various types such as images, text, polygons, and **. According to incomplete statistics nationwide, there are currently as many as 1 million employees in the data labeling industry across the country.

Behind the hot and shining artificial intelligence, the data labeling industry as the foundation of the support is indeed at the low end of the industry.

Data annotation belongs to the artificial intelligence industryThe data annotation industry mainly annotates images, sounds, texts and other objects in different ways according to the needs of users or enterprises, so as to provide a large amount of training data for artificial intelligence algorithms for machine learning.

Data annotation is a key part of the effective operation of most AI algorithms, and AI algorithms are data-driven algorithms, that is, if you want to realize artificial intelligence, you first need to teach the computer the ability of humans to understand and judge things, so that the computer can learn this recognition ability.

The process of data annotation is to manually label the sample for the machine system to learn. Data annotation is to label the data that needs to be recognized and distinguished by the machine, and then let the computer continuously learn the characteristics of these data, and finally realize that the computer can identify it independently.

There is space, there is much development. After all, human cognition has always been a long way ahead of machine intelligence, and the current AI is not up to the job of data annotators, machine learning relies on humans to "feed", and the "delicious food" that fills the machine needs to be cooked by annotators.

The market demand for the development prospects of the data labeling industry is still very huge, and the entry-level positions of AI can be transferred to other AI jobs in the future. Summarize more work skills, and accumulate more experience in the work.

The annotator is to annotate the data. Because for AI companies, high-quality data is indispensable. In other words, the real value of data lies not in the data itself, but in the authenticity and scientificity reflected behind the data.

The value of data can be realized by analyzing, developing and utilizing data, creating new value from it, and achieving practical application results, and data labeling is the process of reflecting the value of data.

The data annotation industry is constantly evolving and changing, and new trends will emerge in the future.

First, the development of the artificial intelligence industry has led to the vigorous rise of data labeling. The data that can be modeled and quantified is only a small fraction of the real world. At present, the business requirements of data annotation are mainly focused on the fields of safety and intelligent driving.

In the future, new needs will continue to emerge as new demands for AI will continue to emerge.

Second, high-precision and high-quality data annotation will become the core advantage of future competition. With the continuous optimization of algorithm models and the continuous improvement of application scenario requirements, the data quality and accuracy required by machines are also getting higher and higher. The real core strength of the market is the ability to provide high-quality and high-precision data annotation companies in the future.

Finally, the future layout of data labeling enterprises will greatly affect the development of the industry. As semi-supervised learning and unsupervised learning gradually become the mainstream of algorithms, the core business of enterprises will also shift from data annotation at the data supply end to resource docking at the algorithm R&D end. The company's own R&D capabilities also determine the final transformation and survival of the enterprise.

There is a future for development.

Data annotation is the foundation of the AI industry and the starting point for machines to perceive the real world. To put it simply, data annotation is an act of learning data processing from artificial intelligence through data annotators with the help of annotation tools. There are many types of data annotation, such as classification, frame, marker, and so on.

In a way, unlabeled data is useless data. The machine can only know what the object is by annotating some characteristics of the object through the data.

Because data annotation is the foundation of artificial intelligence, it is also a solid guarantee for the implementation of artificial intelligence technology. At present, the artificial intelligence industry has higher and higher requirements for the quality of data, and the data labeling industry is moving towards the era of refinement.

In the AI industry, annotating a large amount of data used to train machine learning models makes machines more and more human-like. Therefore, the labeling accuracy is very high.

At present, as long as artificial intelligence is mainly based on deep learning, its dependence on data is still relatively strong, so the business demand of the data labeling industry is huge and is in an increasing state.

The application scenarios of data annotation are very extensive, and the figure of data annotation can be seen in the fields of face payment, smart security, intelligent driving, industrial robots, etc., and artificial intelligence will be used in more fields in the future, which is naturally inseparable from the basic support of data annotation, and also adds more possibilities for the career development of data annotators.

01.Different industries and different business scenarios have certain differences in the requirements for data labeling, and the existing labeling tasks are not detailed enough, and there is a lack of customized labeling capabilities.

There are a wide range of application scenarios for data annotation, including autonomous driving, smart security, new retail, AI education, industrial robots, and smart agriculture.

Different application scenarios correspond to different labeling needs, such as pedestrian recognition, vehicle recognition, traffic light recognition, road recognition and other content in the field of autonomous driving, while the field of smart security mainly involves facial recognition, face detection, visual search, face key information point extraction and license plate recognition, etc., which poses new challenges to the customized labeling capabilities of data service providers.

02.The annotation efficiency and data quality are low, and there is a lack of human-machine collaboration capabilities.

The particularity of the data annotation industry determines its high dependence on manpower, and the current mainstream annotation method is that annotators complete tasks such as classification, framing, annotation and labeling on data with the help of relevant tools according to annotation needs.

Due to the uneven ability and quality of annotators and the imperfection of annotation tools, data service providers are lacking in annotation efficiency and data quality.

In addition, at present, many data service providers ignore or completely lack the ability of human-machine collaboration, and do not realize the feedback effect of AI on the data labeling industry.

Taking the annotation business of Mindfu Technology as an example, by introducing AI pre-labeling in the annotation process and AI quality inspection in the quality inspection process, it can not only effectively improve the annotation efficiency, but also greatly improve the accuracy of the annotation dataset.

03.Brand data annotation service providers rely on crowdsourcing and subcontracting models, resulting in uneven levels of quality of annotation results.

At this stage, data labeling mainly relies on manpower to complete, and labor costs account for the vast majority of the total cost of data labeling service enterprises. Therefore, many brand data service providers have given up building their own annotation teams and turned to complete the annotation business through subcontracting and subcontracting.

Compared with the self-built annotation team, the cost of crowdsourcing and subcontracting is lower and more flexible, but compared with the self-built annotation team, the information chain of these two modes is too long, and the quality is difficult to control, in the long run, the self-built annotation team is more in line with the needs of industry development.

04.Data annotation tasks based on crowdsourcing and subcontracting will cause a lack of security of user data and face the risk of privacy leakage.

The demand side of some special industries, such as financial institutions and departments, pays special attention to the security of labeled data, but some data labeling companies will distribute and subcontract these sensitive data to other service providers or individuals due to cost considerations, which brings a huge potential risk of data leakage. How to establish a complete set of data security protection mechanisms has become a factor that many data service providers need to consider.

Industry reshuffle, competition intensifies Industry reshuffle, competition intensifies.

With the subsidence of the last round of AI entrepreneurship boom, the industry has undergone a round of reshuffle, and the brand data service providers and small and medium-sized data providers that have stood out have formed the main first-party forces, but with the transition from rough to refined demand-side market, problems such as increased project requirements, profit compression, and rising management costs have forced a number of small and medium-sized manufacturers to leave the market in advance, and the industry will usher in a reshuffle again in the next one to two years.

This has brought a huge test to the productivity of the brand company, the ability to control the profits, the ability to control the profits, the marketing ability and the brand influence, in order to cope with the competition, the brand company should be laid out early in the dividend period, with technology application and research and development as the driving force, generating more industry barriers in exchange for more initiative.