Generative AI refers to a type of artificial intelligence systems that can learn from existing data and generate new data, thus achieving functions similar to human creativity. Unlike traditional AI systems, generative AI can create new content on its own, rather than only processing and categorizing input data. Generative AI can be implemented using various technologies, including deep learning and GAN (Generative Adversarial Networks). Deep learning can train neural networks to learn the features and patterns of input data and generate new data based on these patterns. GAN uses two neural networks to play a game, with a generator network responsible for generating new data and a discriminator network responsible for judging whether the generated data is real, thus prompting the generator network to continuously improve the quality of generation.

The application of GenAI technology is very extensive, and it can be used to solve complex problems including optimization, machine learning, image processing, and automated design. Compared to traditional algorithms, GenAI technology can better handle complex problems and has strong robustness and global search capabilities.