More and more AI products have been landing in the commercial scenarios, the industrial robot is one outbreak.
At present, the application of AI in the manufacturing industry is mainly focused on industrial Internet, defect detection, random sorting, intelligent handling, network security, robotics. The application is evidently related to automobile energy, electric power, pharmaceutical, heavy metal, etc.
The AI automation technology, not only can help empower industrial robot applications but also can reduce costs and improve efficiency, reliability, and safety.
The industrial robot itself is not “Intelligent”, the “smart eyes” are giving by image recognition technology. In the process, we mainly use machines to simulate the visual function of human beings, that is, to extract, process, and understand the moving objectives, and the well-trained AI product is used for actual detection, measurement, and control.
Data, Algorithms, and Processing are Three indispensable Elements of AI.
Data is the starting point. It makes sense for AI to start with data and take advantage of learning itself. In scalable data and the high-speed era, it is very convenient to use data to train artificial intelligence.
Companies that have a long history of business intelligence conduct lots of works around data. It’s the same for artificial intelligence.
The original data is generally acquired through data collection, and the subsequent data cleaning and data annotation are equivalent to processing the data, and then transferred to the artificial intelligence algorithm and model for invocation.
If the data used in artificial intelligence training is not sufficiently diverse and unbiased, problems such as artificial “AI bias” may arise.
There are three common types of data annotation in the field of industrial robots:
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Data quality guarantee
1 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.
2 All results are thoroughly assessed and verified by a human workforce and machine
In this way, ByteBridge can affirm the data acceptance and accuracy rate is over 98%.
In addition, we can provide personalized annotation tools and services according to customer requirements.
A collaboration of the human-work force and AI algorithms ensure a 50% lower price compared to the conventional market.
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: email@example.com
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