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:
Token
In large language models, a token is the basic unit of text—usually chunks of three to four characters—that the model reads and generates.
Foundation Model
A foundation model is a large AI model trained on broad data at massive scale, designed to be adapted to a wide range of downstream tasks rather than built...
AI Governance
AI governance comprises the policies, processes, and technical controls that organizations use to manage the risks of AI deployment, from deciding...