Breaking AI is hard to land, and data annotation industry needs to take the lead in transforming Mobil technology


In 2019, the enthusiasm for investment and financing in the field of artificial intelligence in China has greatly decreased, and a considerable number of AI enterprises have completely disappeared in the long river of history, “the cold wave of artificial intelligence has arrived” has even become the industry‘s annual hot word.

Compared with the boom of entrepreneurship and investment in the past few years, the AI industry has obviously been in a lot of depression in recent years.

To find out the reason, “Ai landing difficult” should take the main responsibility.

From the age of automation to the age of intelligence, the value created by artificial intelligence is growing. At the same time, the precision and complexity of business scenarios are also constantly improving, which brings a series of challenges to the landing of artificial intelligence technology.

Take domestic artificial intelligence enterprises as an example. At present, several large domestic enterprises of artificial intelligence Unicorn mainly focus on three fields: finance, security monitoring and mobile Internet, while other fields are flat.

Detailed to specific business scenarios, auto driving is the most important commercial landing field of AI. Relevant AI enterprises have invested heavily in driverless / auto driving, but it is still far away from large-scale commercial applications.

At present, the main application scenarios of automatic driving are just road test, exhibition and demonstration, and test drive in driverless Park, but these obviously can not bring any substantial income for a profit-making enterprise.
Breaking AI is hard to land, and data annotation industry needs to take the lead in transforming Mobil technology
Auto driving is still a long way from large-scale commercial use

The long-term healthy survival of enterprises needs to be profitable, and AI enterprises are no exception. The most urgent real demand for AI enterprises is how to solve the dilemma of “Ai landing is difficult”.

There is an old saying that “you have to tie the bell to get rid of the bell”. The key to the difficulty of AI landing is to find out what factors lead to this result.

In the field of artificial intelligence, algorithm, computing power and data are three important basic elements of the industry. For a long time, AI enterprises have focused on the field of algorithm and computing power, while the focus on the field of data is generally low.

In fact, as the basis of AI industry, the role of data in the process of AI landing is obviously ignored. In order to apply artificial intelligence to specific business scenarios, first of all, we need to solve data acquisition and data governance and other related problems. Specifically, in the industry, the data annotation industry needs to take the lead in transformation.
Breaking AI is hard to land, and data annotation industry needs to take the lead in transforming Mobil technology
A picture after data annotation (source: MF technology data annotation platform)

There is a simple but important consensus in the AI industry:

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

In the early days of the rise of artificial intelligence industry, the focus of the industry is mainly on theory and technology itself. At this time, a cutting-edge technology concept may bring large-scale external investment to enterprises.

However, when the technology is relatively mature, the focus of attention of investors and AI enterprises turns to the commercialization of technology. After all, the most important thing for enterprises and investors is profit.

However, the combination of theory and practice is not so smooth. In the process of commercial landing of AI enterprises, a very difficult problem has been found: the quality of labeled data sets can meet the basic needs of the laboratory, but it can not support the development of AI landing.

We take examples as evidence:

In face recognition and other single point scenes, the data types involved are generally simple. But in a more complete business scenario, the data will become more complex;

In the industrial scene, it will involve more refined data annotation, such as industrial field image data, technological process text data and time sequence data of equipment operation;

In the medical scene, the annotation of medical images and texts needs to be carried out by personnel with medical expertise

In the past, only a small amount of data sets with acceptable quality were needed in the laboratory to meet the needs of basic experiments. However, in the specific commercial landing scenario, many new requirements were put forward for annotation data sets

Massive, high-quality, scene oriented, customized, intelligent
Breaking AI is hard to land, and data annotation industry needs to take the lead in transforming Mobil technology
High quality annotation data set can support the future of artificial intelligence industry (picture source: MF technology data annotation platform)

In such a new situation, the key to the difficulty of breaking AI lies in the first change of data annotation industry.

As the basis of artificial intelligence industry, data annotation industry has been in the extensive state of slash and burn for a long time, which is covered with artificial intelligence, but it is still a labor-intensive industry in essence.

In the tide of AI commercialization, the data tagging industry should not drag the development of the industry behind, but should take the initiative to protect the development of artificial intelligence industry.

Taking the data annotation service of MF technology as an example, on the one hand, through training the professional annotation team and providing customized services, to solve the quality problems of data acquisition and data annotation; on the other hand, through self-developed SaaS data annotation service platform and automatic auxiliary tools, to solve the efficiency problems of data annotation, the specific efforts are as follows:

**1. The professional team builds a high-quality data service platform, reducing the service cost by more than 30%;

2. Independent research SaaS data annotation platform, pre annotation technology can enhance the efficiency of subscript injection by more than 4 times;

3. Real time accurate estimation and AI assisted screening, the data accuracy is more than 99%;

4. Support private cloud deployment and real-time monitoring to strengthen security protection;

5. Customized scene building, 7×24 hour rapid technical response. * *

Through the above efforts, MF technology hopes to rebuild the cornerstone of the development of artificial intelligence industry, break the dilemma of “Ai landing difficulty” with high-quality labeled data sets, and clear the obstacles for the commercial landing of relevant artificial intelligence enterprises.

At present, Manfu technology’s annotation data set is widely used in the fields of automatic driving, security, VR / AR, UAV, new retail, AI education, industrial robot, etc. Manfu technology expects to support the new future of artificial intelligence industry with high-quality data!