What Industries Are Getting Closer to Data Labeling Service?
At present, the demand for the highest quality AI training data in various industries is urgent. AI is implemented in various fields, such as education, law, intelligent driving, banking, and finance, etc. Each field has requirements for subdivision and specialization.
Among them, in particular, traditional enterprises with intelligent transformation and technology enterprises need the assistance of AI training data service providers with rich project experience to help them sort out the AI training data instruction and to obtain more suitable data.
The use of high-quality data in special scenarios reduces the research and development cycle, accelerates the implementation process, and helps enterprises to make faster and better intelligent transformations.
Except for the self-driving industry, there are other 6 industries getting closer to data labeling service as the relevant AI products are about to commercialization rapidly and maturely.
Travel Industry
In the travel industry, data annotation can not only be used for the research and development of autonomous driving but also further help to plan travel routes and optimize the driving environment by combining the data of the Internet of things, big data of traffic network and onboard application technology.
More info: How Data Labeling Services Empower Intelligent Transportation in 2021?
Common applications include: point annotation, line annotation, boxing annotation, 3D Point cloud annotation, scene semantic segmentation, and POI (Point of Interest) annotation.
Finance Industry
The financial companies are the main AI firm targeting companies, because the automation system of authentication, smart investment consultant, risk management, fraud detection can be easily launched, and people can quickly see the results. Using high-quality labeled data to improve the financial mechanic execution with efficiency and accuracy has become a tendency.
More info: How Data Labeling Service Helps Build a Smarter Finance Industry?
Common applications include text translation, semantic analysis, semantic transcription, and image annotation.
Household Industry
Every family can easily find certain smart home AI products, and the combination of increasingly mature Internet of things technology will create more opportunities.
Common applications include facial recognition, scene semantic segmentation, voice collection, etc.
Security Industry
The intelligent security is in the full power of development, although at the current stage more applications are utilized at the government level, the civilian landing still needs time. In order to further AI applications, we need to improve the speed and efficiency of data processing, and to deliver security risk from passive defense to early active warning. The demand for data labeling is increasing day by day.
More info: How Data Labeling Service Empowers Security Industry?
Common applications include face recognition, video segmentation, voice collection, pedestrian labeling, and so on.
Public Service Industry
Artificial intelligence processing of various service data is the key application to improve the level and efficiency of public service. Content audit (determine whether the content conforms to the description), semantic analysis, intention recognition, voice transcription, etc. are common data annotation types.
As for content audit, driven by artificial intelligence, the audit subject is gradually transferred to machines to help save labor costs. At present, many domestic content operating platforms have entrusted most of the audit work to machines. For these machines, it is necessary to first learn the annotated data and clarify the audit annotation, to improve the efficiency and accuracy.
E-commerce Industry
In the e-commerce industry, data annotation can help to further excavate the data set, establish the full life cycle customer data, predict the demand trend, optimize the price and inventory, and finally achieve the purpose of marketing precision.
The content relevance, the judgment of emotion, error correction through a sentence, as well as language translation are common labeling types.
At present, the core is to accurately label users, further establish user portraits, and recommend highly transformed user scenarios through the intelligent system.
Common applications include personalized shopping&discovery, product search relevance, object detection, product classification & categorization, product recognization, product review analysis & recommendation
More info: How Data Labeling Services Empower E-commerce in 2021?
End
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