- Generative A is an artificial intelligence capable of using complex algorithms, models, and rules, learning from large-scale data sets, and then generating new data with similar features, comprehensively surpassing the data processing and analysis capabilities of traditional software.
- There are many types of generative AI models, each with its unique approach to content generation. The most used models are Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs).
Products like chatbots such as ChatGPT, Copilot, Gemini, and LLaMA, text-to-image AI image generation systems such as Stable Diffusion, Midjourney, and DALL-E, as well as text-to-video AI generators such as Sora all contributed to our daily life. Behind these products are different models that support their operation and maintenance.
Generative AI
Generative AI（GenAI or GAI) is an artificial intelligence capable of using generative models to generate text, images, videos, or other data, by utilising complex algorithms, models, and rules, learning from large-scale data sets, and then generating new data with similar features, comprehensively surpassing the data processing and analysis capabilities of traditional software.
Also read: What is the difference between generative AI and LLM?
2023 is known as the breakthrough year of generative artificial intelligence, the technology from a single language generation gradually to multi-modal, embodied rapid development.
Companies such as Anthropic, Microsoft, Google, and Baidu, as well as many smaller companies, have developed generative AI models that are widely used in various industries, including software development, healthcare, finance, entertainment, and more.
Types of generative models
There are many types of generative AI models, each with its unique approach to content generation. Some of the most well-known types of generative AI models include Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion, Transformer, and Neural Radiation Field (NeRF) techniques.
Also read: Kai-Fu Lee’s 01.AI hits $1 billion valuation, leads open-source AI models
The most used models are VAEs and GANs. Each of these models has advantages and disadvantages, depending on the complexity and quality of the data.
1. VAEs
VAEs were developed in 2014 to use neural networks to encode data more efficiently. They are ideal for generating new instances from smaller pieces of information, fixing noisy images or data, detecting abnormal content in the data and filling in missing information.
VAEs are used in anomaly detection and security. For example, in response to abnormal network activity or fraudulent transactions, they can understand the normal pattern of data and identify anomalies or potential security vulnerabilities.
The next iteration of VAEs is likely to focus on improving the quality of generated data, speeding up training, and exploring its applicability with sequence data.
2.GANs
GANs were developed in 2014 and are used to generate realistic faces and print numbers. Gans can be used to generate real synthetic data for training robust models and testing safe systems.
Examples include creating real network traffic data to test the resilience of an intrusion detection system or generating real malware samples to evaluate antivirus software.
On the other hand, Gans can also be used maliciously to generate synthetic data similar to sensitive information, posing privacy risks. Gans can also suffer from mode crashes, causing generators to produce limited and repetitive outputs, making them difficult to train, and with no clear control over the generated samples.
The next generation of Gans will focus on improving the stability and integration of the training process, extending its applicability to other areas, and developing more effective evaluation metrics.
