competition-areas
Many types of generative AI models are in operation today, and the number continues to grow as AI experts experiment with existing models. Among the many types of generative AI models are text-to-text generators, text-to-image generators, image-to-image generators, and image-to-text generators. It’s possible for a model to fit into multiple categories—for example, the latest updates to ChatGPT, GPT-4, and other recent variations make it a transformer-based, large language, and multimodal model. Here are examples of the applications of AI models which are encouraged for companies to submit applications.
1. Generative Adversarial Networks (GANs): Best for image duplication and synthetic data generation.
2. Transformer-Based Models: Best for text generation and content/code completion—subsets include generative pre-trained transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) models.
3. Diffusion Models: Best for image generation and video/image synthesis.
4. Variational Autoencoders (VAEs): Best for image, audio, and video content creation, especially when synthetic data needs to be photorealistic.
5. Unimodal Models: Models that are set up to accept only one data input format; most generative AI models today are unimodal models.
6. Multimodal Models: Designed to accept multiple types of inputs and prompts when generating outputs—for example, GPT-4 can accept both text and images as inputs.
7. Large Language Models: Designed to generate and complete written content at scale, these are the most popular and well-known type of generative AI model right now.
8. Neural Radiance Fields (NeRFs): Emerging neural network technology that can be used to generate 3D imagery based on 2D image inputs.