Original article was published on Artificial Intelligence on Medium
Artificial intelligence is not “artificial + intelligent” but “data + intelligent”
The third season of the HBO fire science fiction drama “Western World” has come to an end. “Western World” tells the story of a robot receptionist who has consciousness and thinking in the AI robot theme park to awaken and resist humans. Similarly, “Western World” is also a vast artificial intelligence “cultivation”, robot Time and time again, we follow the human-designed story into a cycle of sorrow, and finally get rid of “artificial” and derive true “intelligence.”
Artificial intelligence needs to be trained by human teaching. This is true in “Western World”, and it is also true in the real world. From June 23 to June 24, the 4th World Intelligence Conference was held in Tianjin, with the theme of “A New Era of Intelligence Innovation, Empowerment, and Ecology.” The teachings and “textbooks” required by these artificial intelligences are trained behind a large amount of training data. At a time when artificial intelligence is heating up, more high-quality AI data is needed to realize the evolution of artificial intelligence. Therefore, we see that the development of AI has spawned new industries such as data labeling, and with the implementation of the application of artificial intelligence, it continues to develop towards high-end, high-quality.
Artificial intelligence is not “artificial + intelligent” but “data + intelligent”
As one of the most important technologies in the world today, artificial intelligence has gone through 60 years of “three ups and downs” development process and has been integrated into all aspects of people’s production and life. In the “smart+” era, building application scenarios and finding breakthroughs have become the top priority for the application of artificial intelligence.
This breakthrough is AI data. Today’s artificial intelligence is essentially machine learning, and data is the most fundamental competition in the AI world. AI forms “intelligence” based on a large number of effective data summary rules, which are then applied in different scenarios. In fact, AI data as a means of production is a necessary link to promote the development of the entire AI industry, and it is also one of the main driving forces for the commercialization of artificial intelligence Moreover the high-quality data determines AI The degree of landing is not excessive.”
From the unlocking of smart phone faces to the smart security of smart cities, from vehicle autonomous driving to AI chat robots, from medical imaging and diagnosis to crop monitoring, AI data is playing an increasingly important role. If artificial intelligence is to be implemented, it must use AI data for iterative optimization.
At present, scene-based AI data is the key point for the development and commercialization of artificial intelligence at this stage. But machines cannot understand the original data that humans can recognize. These raw data need to be artificially “labeled” before they can be used for model training. The more and more accurate these “labeled” data, the more accurate the results obtained. For example, the algorithm model of autonomous driving is trained with a large number of scene AI data, continuous learning and optimization, which gradually makes the system more intelligent.
AI speeds up, and the data labeling industry should develop in a scene-oriented and refined manner
AI data is the fuel of artificial intelligence. The accuracy of AI data directly determines the application of artificial intelligence in new retail, smart driving, smart security, smart home and other related fields.
As mentioned earlier, “labeled” data makes sense for artificial intelligence algorithms. How to “label” involves the link of “data labeling”, that is, for voice, image, text and other data, through labeling, marking, coloring, or highlighting, to mark the different points of the target data, Similarities or categories.
Data annotation is an important part of transforming data into AI business value. The higher the accuracy of data annotation, the more accurate the results of AI learning and output, and the smarter the AI. As a result, a new industry of data labeling has emerged.
From the point of view of cloud measurement data, AI is ultimately for landing and being used, so the data quality requirements for AI will be higher and more accurate, and there will even be more AI data requirements in customized scenarios. In addition to improving data security and privacy protection, ensuring the uniqueness and contextualization of data can really help companies build data core barriers and greatly promote the further landing of AI. This is also the role of cloud measurement data in positioning itself.
Contextualization means that the data labeling industry must meet the needs of diversified application scenarios labeling. Taking the field of computer vision as an example, the current cloud measurement data data annotation service capabilities cover scenarios such as autonomous driving, drones, intelligent education, smart finance, industrial robots, new retail, and security protection.
The requirements of different scenarios in different fields have their own data types and specific labeling requirements. Therefore, they are extremely testing the ability of AI data service providers to provide scene services and professional domain knowledge.
For example, in the financial industry, the early requirements for AI customer service robots only stayed after “users asked questions, extracted keywords from them, and answered them according to established utterances”. During this period, artificial customer service was the main force to answer user questions. The customer service robot is just a supporting role. However, with the fierce competition in Internet finance business today, more and more users are used to handling business online. AI customer service robots are replacing artificial customer service on a large scale. The accuracy of AI question and answer will directly determine the efficiency and cost of the business and affect User experience largely determines the competitiveness of financial institutions.
In addition, as AI is more closely integrated with various industries, the commercialization degree of AI has entered a new height, and companies have become more and more demanding on the performance of AI in commercialization. In order to ensure the recognition accuracy of AI algorithms, the quality of AI data becomes crucial.
Sceneization + high quality + security, cloud measurement data helps AI commercialization
The huge amount of data generated by the huge user scale is the advantage of my country’s development of artificial intelligence. With the further expansion of the artificial intelligence industry, there is an urgent need for more accurate and scenario-based high-standard data training to promote the landing of artificial intelligence. Since its establishment, Cloud Test Data has been committed to providing high-quality data support for AI scenarios, and has established good and lasting cooperation with many leading companies in the industry. Covered industries include smart cities, smart homes, smart driving, smart finance, new retail, and other fields, including Internet companies, technology companies, and many smart traditional enterprises.
In terms of ensuring production efficiency, cloud measurement data places great emphasis on collaboration of operations. In the production of AI data, cloud measurement data has designed a more complete management process from creating tasks, assigning tasks, marking transfer, to quality inspection/sampling inspection links and final acceptance, and each link has corresponding professionals to mark control data The quality and time of the node, the link between the upstream and downstream work links, and the quality can be truly improved under the premise of ensuring quality.
In terms of scenario-based data delivery, cloud measurement data provides high-quality data collection and data annotation services for many fields such as smart driving, smart cities, smart homes, smart finance, and new retail through self-built laboratories and data annotation bases. The orientation supports the processing of various types of data such as text, voice, image, and video.
At the same time, cloud test data has always placed AI data privacy and security as the top priority for business development. In terms of ensuring AI data privacy and security, cloud test data has a three-pronged approach. Before the data collection, the cloud measurement data will sign a data authorization agreement with all the data collection users to ensure that the data used by AI companies for training is legal and compliant; at the same time, never leave the bottom after the AI data is qualified and delivered, insisting that the data never The core principle of reuse. In addition, cloud measurement data has established an accurate data protection mechanism from firewall settings, internal information system management to standardized process operation system, trying to achieve step-by-step protection and layer-by-layer check.
With the acceleration of the “new infrastructure” construction, the AI industry will develop at a high speed, the application of AI will land, and the rise of emerging industries such as AI data will be accelerated. As a leader in the data labeling industry, cloud measurement data has also ushered in an unprecedented development opportunity, and will follow the trend to promote the AI industry to a higher quality development.