Artificial intelligence is ＂windy＂, how to make steady progress in the data labeling industry？

Artificial intelligence is "windy", how to make steady progress in the data labeling industry?

Recently, AI (artificial intelligence) has once again ushered in a wave with the fiery chat bots, and it is even called "it will trigger the next industrial revolution". During this period, the field of data annotation has also appeared in various fields, such as the high valuation of data annotation platforms, the surge in demand for text companies, and the beginning of the split of data annotation teams by AI manufacturers … To describe the data annotation industry in a network term, it is once again.

More demanding data labeling requirements

"Artificial intelligence will change the world, so who will change artificial intelligence?" This is the question of AI scientist Li Feifei. Fifteen years ago, the AI ? ? community generally believed that better algorithms could bring better decisions, but it was discovered in the real application process:

The breakthrough of the algorithm’s trajectory to the deep neural network has led to many application scenarios, and then more data can be used for training, and the computing power will be cheaper because of its mass production. The above process will also promote the algorithm’s further improvement. On the contrary, some industries have not produced data that can be used for training, which leads to the stagnation of algorithms and computing power in this field.

Algorithm, data and computing power complement each other and restrict each other.Fortunately, Li Feifei realized 15 years ago that even the best algorithm can’t be used without good training data that can reflect the real world. Therefore, in today’s explosion of Chat GPT, people not only pay attention to its algorithm, but also realize.In the wave of large model trainingThe demand for traditional data annotation is likely to decline, but at the same time, it isBring another higher demand for data annotation.

The "Obstacle" of Data Labeling Development

Data annotation is a very important part in the field of artificial intelligence, but it has not been established yet.Sound industry standardsTherefore, it is marked that the competition between companies is blindly fighting for low prices, and Party B who finally gets the project is often unable to undertake it. The chaos in the industry makes it difficult for labeling companies that survive in the cracks to develop in a positive direction, and the internal friction of the whole industry is tantamount to self-destruction.

At the same time, there are serious problems such as insufficient professional knowledge background, low understanding of the industry and low labeling efficiency, which are also obstacles to the development of data labeling industry. However, if we ignore the professional ability of data annotators, there will be a growing gap between the data annotator industry and the overall development trend of artificial intelligence. So, in the face of the difficulty of the development of data annotator industry, where should we start?

Starting with the cultivation of talents, it is a key step for enterprises that want to start a business in the field of AI data labeling to do a good job in the incubation of artificial intelligence trainers.

Industry development is difficult, and talent training is the foundation.

As we all know, the overall development of artificial intelligence industry is rapid, and the application fields and scenes are becoming more and more complex. Therefore, simple labeling work such as making a box and marking a point will soon be replaced by AI, and the labeling work in the future will only become more and more professional and complex. ChatGPT is a typical example, and people have realized AI education AI.

But in any field, talent training is always the first. For AI training, the quality of data labeling is of great significance. If there are inaccuracies or even errors in the labeling process, it is likely to lead to very serious consequences. Even if fully automatic AI tagging is realized in the later stage, the data annotators still need to participate in the auditing process, so the importance of AI artificial intelligence trainers as data annotators is self-evident.

When we want to enter the field of data annotation, we need to pay more attention to the relevant training of data annotation talents.

Personnel training of artificial intelligence trainers

Speaking of this, some people will think: I understand that talent training is actually a new model in the development process of the data labeling industry. If you want to start a business and prepare for team transformation, you need to find expansion projects for the company, and it is completely no problem to consider the project about the incubation of data labeling artificial intelligence trainers. Then, what if individuals want to become artificial intelligence trainers?

No problem, the purpose of the artificial intelligence trainer talent incubation project is to cultivate excellent data annotation talents, build excellent annotation teams, provide a systematic talent training system for the industry, and provide a channel for data annotators to continuously improve their professional skills. Therefore, this project has a cooperation entry point suitable for both individuals and teams.

Finally, I hope that more enterprises and individuals who are interested in the labeling business can pay attention to and join the artificial intelligence data labeling industry, and jointly participate in the development of talent training and recommendation industry in the data labeling industry.