What are pre-trained multi-task Generative AI models called?

Pre-trained multi-task generative AI models are called foundation models. These are large, deep-learning models trained on vast datasets to perform multiple tasks without needing task-specific retraining. They form the backbone of many AI applications, enabling text generation, image synthesis, translation, summarization, and reasoning within a single model.

Key Characteristics of Foundation Models

✔ Pre-Trained on Massive Datasets – Trained on diverse data sources (text, images, code, audio) to develop general knowledge.
✔ Multi-Task Capabilities – Can perform multiple generative tasks without needing a separate model for each.
✔ Scalable & Adaptable – Developers can fine-tune these models for industry-specific applications, reducing training costs.
✔ Self-Supervised Learning – Unlike traditional AI models, foundation models learn from unlabeled data, making them highly versatile.
✔ Multimodal Support – Some foundation models, like Google's Gemini 1.5, process both text and images within the same model.

Examples of Foundation Models

✔ GPT-4 (OpenAI) – Powers ChatGPT and is optimized for natural language generation, coding, and reasoning.
✔ Gemini (Google DeepMind) – Supports text, image, and video understanding, making it ideal for multimodal AI applications.
✔ Claude (Anthropic) – Focuses on AI safety, interpretability, and responsible AI alignment.
✔ LLaMA (Meta AI) – Open-source AI model designed for research and enterprise applications.
✔ Mistral 7B – Compact, high-performance model optimized for low-latency AI applications.

Why Do Foundation Models Matter?
Foundation models reduce AI development time, allow for rapid customization, and enable scalable AI-powered solutions across industries like healthcare, finance, and software development.