EpiK Protocol x ChatGPT: The Road to Ultra-Scale AI Preprocessing Models
The emergence of ChatGPT, a mega-scale AI pre-training model, has led to a dramatic change in the field of machine learning. It enables using pre-trained models for tasks such as natural language processing, image recognition, and speech recognition, significantly improving AI techniques. However, despite its robust understanding and learning capabilities, the ChatGPT model still needs to be learned and optimized for specific problems under specific scenarios.
Since each domain and each scenario has unique characteristics, developing AI services requires a scenario-based approach. This requires a large amount of high-quality method AI training data to train the models, enabling them to better adapt to different scenarios and tasks. Therefore, it is essential to collect scenario-based AI training data. As an example, a mega-scale AI pre-training model like ChatGPT is like a college graduate with mature understanding and learning ability, but also like a fresh college graduate who still needs to learn more in-depth and detailed knowledge of his industry to do his job well, which requires the collection of scenario-based AI training data to strengthen ChatGPT’s problem-solving in specific scenarios This involves the collection of scenario-based AI training data to strengthen ChatGPT’s problem-solving capabilities in particular situations.
EpiK Protocol focuses on a decentralized collaborative network of AI training data by domain and scenario, integrating collection, annotation, storage, and sharing to form a precision machine for producing high-quality AI training data. The function of this precision machine is to integrate scenario AI training data into models such as ChatGPT so that the models can better understand and respond to different scenarios. As an example of AI painting, AI can automatically classify and label essential elements such as numbers, shapes, and colors through image recognition technology while performing advanced operations such as image segmentation and semantic analysis. However, AI needs to capture scene-based AI training data to be better at drawing.
Suppose EpiK Protocol allows users to decentralize the acquisition of AI training data for different scenes. In that case, various types of drawing data (sketches, colored pencils, watercolors, etc.) of these collected drawing data can be used to train ChatGPT models to understand better and respond to drawing problems in different scenes, thus giving a better description of AI drawings or keywords.
In addition, EpiK Protocol will also provide AI Bots Store, a comprehensive service marketplace for AI applications, where AI developers can publish scenario-based AI services to the platform, and users can pay for these services using EPK. The data generated during these services will also be fed back to Mindsight Continuum for accuracy labeling, thus further improving data quality and optimizing the understanding of models.
In short, EpiK Protocol provides a decentralized AI training data collaboration network with sub-domain and sub-scenario, which is a way to provide high-quality AI training data for ultra-large-scale AI pre-processing models such as ChatGPT to improve the adaptability and comprehension of models further and achieve better general AI services.
