Multimodal AI
Multimodal AI refers to models that process and generate more than one type of data—text, images, audio, video—within a single system. GPT-4o can read a photo, transcribe speech, and respond in text in one call. Gemini can reason across video frames. These are multimodal models. The significance is integration: instead of chaining a speech-to-text model into a language model into an image generator, you get one model that handles the translation between modalities internally. For practical applications, multimodal capability means an AI can analyze a screenshot of a dashboard and explain the trend, or watch a product demo and write the marketing copy. The gap between multimodal demos and production reliability is still wide—image understanding is good, video understanding is inconsistent, and audio reasoning is early—but the trajectory is clear.
Related terms:
Agentic AI
Agentic AI refers to systems that autonomously pursue goals—planning actions, employing tools, and adapting based on feedback—without waiting for human...
Generative Engine Optimization
Generative engine optimization (GEO) is the practice of structuring content so AI systems—such as ChatGPT, Perplexity, Google AI Overviews, and Bing...
Prompt Injection
Prompt injection is an attack where a user or data source inserts instructions that override a language model’s intended behavior.