Multimodal AI

Multimodal AI processes and generates more than one type of data — combining text, images, audio, video, and sometimes 3D or sensor data. Modern multimodal models include OpenAI's GPT-4o (text + image + audio), Google's Gemini (text + image + video + audio), Anthropic's Claude 3 (text + image), and open models like LLaVA and Qwen-VL. Capabilities span describing what's in an image, transcribing and translating spoken audio, generating images from text (DALL-E, Midjourney), generating video from text (Sora, Veo), and answering questions about videos. Enterprise applications include analyzing security camera footage, reviewing medical images alongside chart notes, summarizing meeting recordings, and content moderation across formats. Multimodal systems open new enterprise AI use cases but multiply AI governance, AI ethics, and AI compliance concerns — particularly around biometric data, copyrighted images, and deepfake potential. Responsible AI programs evaluate each modality and their interactions as part of AI risk management.

Centralpoint Governs Multimodal AI Across Every Channel: Oxcyon's Centralpoint AI Governance Platform handles text, image, and audio AI under one model-agnostic roof. Centralpoint supports ChatGPT, Gemini, Llama, and embedded models, meters consumption per modality, keeps prompts and skills on-prem, and deploys multimodal chatbots to your portals via a single JavaScript line.


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