The development of large models is surging, making AI applications a hot spot in the market again. However, the background of this innovation movement is different from that of the previous round of AI boom. Today, the industry is not lacking in technology and players with healthy business models. The scarcest resource has become high-quality data. Where do the models for large models come from? In essence, it relies on "feeding" from massive data.
However, this process is by no means as simple as input and output. It starts with collecting data, goes through systematic engineering, and forms the final result through continuous training and fine-tuning. It also needs to be adapted to the application scenario and integrated into real applications. . The difficulty of achieving "intelligence" through continuous "artificial" efforts can be imagined.
Because of this, in the field of artificial intelligence, there is an increasingly obvious characteristic of “those who get the data win the world”. Considering the investment and difficulty of this work, it is obviously difficult for ordinary enterprises to complete the construction independently. In the era of big models, companies will most likely have to ask professional artificial intelligence data service providers for answers to new productivity needs.
In September this year, Cloud Test Data appeared at the 2023 China International Fair for Trade in Services (hereinafter referred to as the "Service Trade Fair") with its "AI Data Solution for Vertical Industry Large Models", bringing professional solutions to the development of industry large models. .
High-quality AI models are “raised” by good data
OpenAI surprises the world with GPT, and its conversational AI is refreshing to many users. But the productivity of large models is not just that. From the general large models of domestic manufacturers to the professional large models in marketing, finance and other fields that have emerged in overseas markets, this wave of upsurge is obviously similar to the industrial revolution. To use a once popular word Technique: Thousands of industries may be worth "redoing" with large models.
However, the "reworking" of large industry models means that general large models are not suitable for direct application by enterprises. Only by converting them into industry large models can productivity be more easily achieved. Large models are still built on the basis of "computing power + data". Computing power can be outsourced, but data is not a threshold.
On the one hand, the development of various industries collides with the concept of AI large models. The core of implementation is still the algorithm, and the quality of the algorithm depends on the quality of the data. For ordinary enterprises, it is difficult and cost-effective to configure a dedicated team to carry out AI data work, and the professionalism may not necessarily meet the needs of large model construction. Without high-quality AI data, scenario-based AI applications will be impossible.
At the same time, the scale of data to be processed is also a major difficulty. According to data from the Academy of Information and Communications Technology, since OpenAI launched GPT-3 in 2020, the number of ultra-large pre-training model parameters and the scale of training data have increased by 300 times every year. It is obviously difficult for ordinary enterprises to have such capabilities.
But on the other hand, purchasing AI data services like outsourced computing power is not that easy.
For example, the AI data of large industry models comes from application scenarios, and the collection ability affects the final accuracy. This requires service providers to have rich scenario awareness and understand industry needs; large models pay more attention to human-machine collaboration and need to be pre-trained. Afterwards, continuous fine-tuning is carried out and then integrated into the scene. Therefore, the pre-training link for large models contains a large amount of vertical industry data, which tests the service provider's processing capabilities in different links; and throughout the process, in order to improve the transformation of general large models into industry To achieve large-scale model capabilities, data service providers must have a complete set of tools, systems and platforms.
As a result, both the efficiency of collection and labeling and the quality requirements of massive data sets in vertical industries have put forward new requirements for AI data services under the development trend of large models. The intensive efforts in the industry have opened up deeper competition.
According to media disclosures, OpenAI has spent up to 1 billion US dollars on model training in the eight years since its establishment. It can be seen that it will be even more difficult to land in vertical industries. Pre-training, reinforcement learning, and manual feedback are all time-consuming, labor-intensive, and resource-consuming tasks. Only truly specialized AI data service providers can rely on in-depth understanding of the business and long-term construction of tools and capabilities to form advantages in scale, efficiency and other aspects. Only such third-party platforms can meet the vertical needs of enterprises and achieve high efficiency and strong applicability.
Today, professional AI data service providers have become a key player in solving the problem of large model data nourishment.
Full chain, multi-industry, cloud measurement data deeply escorts large industry models
The requirements for the quality, efficiency, scenario-based and other aspects of AI data services are essentially due to the need for AI technology to penetrate deeply into the industry. In the process of deepening the industry, large models require more industry data, and in the face of industry data, there are many practical requirements that must be met. In the data processing link, how to combine machine data processing and manual processing to ensure quality and efficiency in parallel; in terms of technical support, whether the advancement, ease of use, and richness of data processing tools can meet the requirements of AI projects; at the enterprise management level , whether capacity building such as scientific process management and perfect delivery system are advanced.
Therefore, the market requires AI data service providers to not only have specialized tools, capabilities and solutions, but also to be able to provide data solutions with industry depth to meet the needs of different levels of people.
Last year, Cloud Test Data released a "data solution for AI engineering". This year, on the basis of continuing its existing advantages and facing common problems in the construction of industry large models, it upgraded and released large model AI data for vertical industries. solution. Provide high-quality and efficient data for the development of industry large models in the end-to-end process from continuous pre-training, task fine-tuning, joint evaluation and testing to application release, laying a solid foundation for industry large models from the infrastructure level.
This full-chain capability comes from the long-term accumulated experience and technology of cloud measurement data. On the one hand, cloud measurement data has been deeply involved in intelligent driving, smart home, e-commerce, smart finance and other fields for a long time, and has a very in-depth understanding of scenarios, which has greatly improved the ability to build large industry models and apply them to scenarios.
For example, in the field of intelligent driving, Cloud Measurement Data, as the only representative manufacturer of training data services, participated in the preparation of "Intelligent Connected Vehicle Scene Data Image Annotation Requirements and Methods" and "Intelligent Connected Vehicle Lidar Point Cloud Data Annotation Requirements and Methods" , the co-preparation units are mostly the Institute of Automation of the Chinese Academy of Sciences, China Automotive Technology and Research Center, Beijing Automotive Research Institute and other units, which shows the professional level of cloud measurement data.
In addition to an in-depth understanding of professional scenarios, the advantage of cloud measurement data is that it has data collection capabilities and rich data set accumulation for industry scenarios. Through its collection scenario laboratory, it can provide biometric authentication, smart cockpits, home scenes, voice interaction, etc. Scenario data samples cover multi-modal types such as images, speech, and text, which can empower the pre-training of large models in the industry on a broad and continuous basis.
On the other hand, in the long-term service practice of cloud measurement data, the needs have gradually been deconstructed, and customized data services can be provided based on customer needs through different dimensions and different forms of data touch points. Moreover, due to its multi-dimensional data collection tools and rich data delivery experience, cloud measurement data has subscription-based collection capabilities that match the update frequency of data content, which lays the foundation for iterations to adapt to changes in scenarios and user needs. foundation.
In summary, as a professional AI data service provider, Cloud Measurement Data has formed a set of standardized and engineered data service models for multi-modal and multi-task requirements. As large models penetrate into thousands of industries, high-quality AI data services are used to promote the birth of high-quality industry large models.
Use higher-quality data services to help the industry accelerate
No matter what kind of large model, if it is to be transformed into enterprise productivity, it must first have the conditions to be integrated into the production process and integrate with the enterprise's capability base. From the perspective of the development process of "large model AI data solutions for vertical industries", that is, all aspects must be consistent with the needs of the enterprise, and the high quality of data must be implemented as a standard.
In the continuous pre-training process, cloud testing data uses the customized scenario-based data collection capabilities mentioned above and the continuous subscription service capabilities to complete data collection, cleaning, and classification according to enterprise requirements in fields such as finance, e-commerce, and intelligent driving. , to select the best from the best data. Among them, the cloud measurement data annotation platform and tools support integrated API interface capabilities and scientific work collaboration capabilities, which greatly improves data flow efficiency while ensuring data processing accuracy.
In the fine-tuning phase of downstream tasks, that is, the optimization of human-machine collaboration, cloud measurement data always insists on using more complete and flexible annotation tools to fine-tune multi-modal data to adapt to the needs of human-machine coupling, allowing everyone to The model is more accurate. Public data shows that the cloud test data annotation platform has a maximum delivery accuracy of 99.99%, and provides relevant capability support including text-based task projects such as QA-instruct, prompt and multi-modal large models, striving to ensure the effectiveness of data processing.
In the grayscale release of joint debugging, the cloud test data fully demonstrated the focus on specialization, scenario-based and business system integration.
The expert pool in specific fields of cloud measurement data has in-depth understanding of various vertical scenarios such as home and cockpit, and can propose unique and effective interactive content based on actual scenarios. In the RLHF (Reinforcement Learning from Human Feedback) process, artificial expertise is relied upon to bring higher-quality feedback, improve the final data quality, and amplify the value of the model. At the same time, by interpreting enterprise needs, cloud test data can build a real-scenario laboratory and a sample resource pool based on specific scenarios, and conduct in-depth testing of large industry models in vertical fields.
Finally, cloud measurement data provides a standard API interface. Through the data annotation platform with the integrated data base as the core, it can output data that has undergone multiple rounds of quality inspection while collecting difficult case data for reflow to complete cleaning and annotation, making model optimization a continuous process. process and connect various business systems to become a product that can finally be officially released.
Based on this, we can also believe that cloud measurement data has essentially created a set of "nanny-style" services for users in need - with scenario-based data collection capabilities and high-precision professional data annotation capabilities, and for multi-modal tasks. The advanced data processing platform has API tools and project management systems embedded in user business systems. The application of large-scale industrial models is no longer out of reach.
As Jia Yuhang, general manager of Cloud Measurement Data, said: "The quality of AI data determines the accuracy of the algorithm, and the accuracy of the AI algorithm determines the quality of the product." Andrew Ng, a top scholar in the field of artificial intelligence, has also expressed this view, that is, the value of artificial intelligence needs to be absorbed Annotated high-quality data will be released, and the increase of high-quality data will catalyze the faster development of artificial intelligence. The scale of China's market data is immeasurable and the market prospects are broad. Therefore, competition pays more attention to quality. Cloud measurement data insists on scenarioization, standardization, and engineering, just like helping AI data services move into the industrial era, allowing the release of data value to flow like a spring.
Previously, the “Twenty Data Measures” and other policy-level policies that stimulated the vitality of data elements and enriched the advantages of data application scenarios made the data market like a raging fire. The emergence of large-scale models has made enterprises regard data as the "oil" of the new era. The window period for the development of large models has naturally become a window period for the rapid advancement of AI data services.
But in the end, who can run further on this track ultimately depends on whether they can create value for customers and get through the positive cycle. It is still too early to talk about final victory, but one thing is certain. For cloud measurement data that has formed a mature solution, the dividend period has already begun.
Source: Pinecone Finance