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Development Trend of China’s Artificial Intelligence Application Market in 2021

China’s AI technology transformation and application will drive enterprise revenue growth

1. Companies focus on AI self-research capabilities to drive revenue growth

From the perspective of enterprises that have applied AI projects, 61% of the companies have chosen the independent R&D path. 40% of enterprises choose to use the AIPD/SDK to adapt the third-party AI platform and conduct the industry-university-research cooperation.

31.7% of enterprises use open source technology. 26.8% of enterprises entrust third-party companies to provide solutions. While 17.1% of enterprises entrust the research and development of external professional AI companies is 17.1%.

Based on the implementation of AI projects, 97.6% of enterprises’ AI transformations have driven corporate revenue growth. 34.1% of companies believe that AI technology applications can increase revenue by 5%. 4.9% of enterprises’ revenue growth can reach 20% to 50 %. The implementation of AI projects can play an essential role in income growth.

2. Computer vision is currently the leading application technology

At present, the most commonly used AI project technology in enterprises is computer vision, accounting for 63.4%, followed by machine learning, accounting for 58.5%, and then knowledge graphs, accounting for 56.1%.

Typical applicable scenarios for enterprise applications of different AI technologies have gradually emerged. The application scenarios of computer vision are mainly security monitoring and large-screen interactive speech recognition. Among these, the purchase rate of AI customer service systems and intelligent voice assistants exceeds 70%. Natural language processing is mainly used for intelligent review and report generation.

The knowledge graph is used primarily in decision-making assistance and smart diagnosis scenarios. The application scenarios of machine learning are relatively scattered. And the selection rate of prediction models and intelligent risk control is relatively high.

Computer vision and machine learning will still be among the key application technologies in the next three years, but they will fall back to the second and third places with a ratio of 48.8% and 46.3%, respectively. Deep learning is ranked first with a percentage of 53.7%.

3. Plans to go overseas

In recent years, due to the great potential of the regional economy and the vigorous development of the consumer Internet industry, Southeast Asia has become the first station for many Chinese companies to go overseas. The advantage of minor languages will provide a data foundation for the minor languages scenarios for overseas AI applications.

4. Customized dataset

With the acceleration of the commercialization of AI and the application of AI technologies such as assisted driving and customer service chatbot in all walks of life, the expectation of data quality in the special scenarios is getting higher and higher. High-quality labeled data would be one of the core competitiveness of AI companies.

If the general datasets used by the previous algorithm model are coarse grains, what the algorithm model needs at present is a customized nutritious meal. If companies want to further improve certain models’ commercialization, they must gradually move forward from the general dataset to create the unique one.

ByteBridge, a human-powered and ML-powered data labeling tooling platform

ByteBridge is a data labeling SaaS platform with robust tools and real-time workflow management. It provides high-quality training data for the machine learning industry.

Accuracy

  • ML-assisted capacity can help reduce human errors by automatically pre-labeling
  • The real-time QA and QC are integrated into the labeling workflow as the consensus mechanism is introduced to ensure accuracy.
  • Consensus — Assign the same task to several workers, and the correct answer is the one that comes back from the majority output.
  • All results are thoroughly assessed and verified by a human workforce and machine
ByteBridge: a Human-powered and ML-powered Data Labeling SaaS Platform

In this way, ByteBridge can affirm the data acceptance and accuracy rate is over 98%.

Configure Your Own 2D Images Annotation Project

  • Developers can control the labeling project from setting labeling instructions to output review on a pay-per-task model with a clear estimated time and price
  • Real-time management and monitoring of project
  • Real-time Outputs: clients can get real-time output results through API. (We support JSON, XML, CSV, etc. And we can provide customizable datatype to meet your needs)
ByteBridge: a Human-powered and ML-powered Data Labeling SaaS Platform

These labeling tools are available: Image Classification, 2D Boxing, Polygon, Cuboid.

We can provide personalized annotation tools and services according to customer requirements.

Cost-effective

A collaboration of the human-work force and AI algorithms ensure a 50% lower price compared to the conventional market.

NLP Service

We provide different types of NLP in E-commerce, Retail, Search engines, Social Media, etc. Our service includes Voice Classification, Sentiment Analysis, Text Recognition and Text Classification(Chatbot Relevance).

Partnered with over 30 different language-speaking communities across the globe, ByteBridge now provides data collection and text annotation services covering languages such as English, Chinese, Spanish, Korean, Bengali, Vietnamese, Indonesian, Turkish, Arabic, Russian and more.

End

If you need data labeling and collection services, please have a look at bytebridge.io, the clear pricing is available.

Please feel free to contact us: support@bytebridge.io

Source:http://www.myzaker.com/article/613f1fcb8e9f0932c351e601

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ByteBridge

ByteBridge

A data labeling platform with robust tools for real-time workflow management, providing high-quality training data with efficiency. — https://bytebridge.io/#/

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