What is “multimodal AI”?
Multimodal AI describes AI systems that process and generate across multiple input/output modalities — text, image, audio, video, structured data — within a single model architecture. GPT-4o, Claude 3.5 Sonnet, Gemini 2.0 and Llama 3.2 Vision are flagship multimodal systems that accept image and text inputs, generate text, and increasingly speak audio. Multimodal capability enables applications previously requiring multiple specialised models stitched together.
Common multimodal capabilities
- Vision + text: describe images, read documents, analyse screenshots, OCR.
- Audio + text: transcription, real-time voice assistants, audio analysis.
- Video + text: summarise video content, extract events, identify objects across frames.
- Generation: text-to-image (DALL-E, Stable Diffusion), text-to-video (Sora, Runway), text-to-speech.
Multimodal vs. single-modality
- Single-modality: dedicated model per task; multiple integrations required for cross-modal workflows.
- Multimodal: single API, unified context understanding; lower latency for cross-modal tasks.
- Trade-off: multimodal models may underperform specialised models on specific narrow tasks but excel at integrated workflows.
Legal and compliance considerations
- Biometric data: face, voice and gait in multimodal inputs may constitute special-category data under KVKK and GDPR Article 9.
- EU AI Act: emotion-recognition systems and certain biometric categorisations face additional restrictions.
- Copyright: training on copyrighted images, audio and video creates more litigation exposure than text-only.
- Cross-modal injection: images and audio can carry adversarial instructions invisible to text-only filters.
Türk startup’larında
Türk multimodal AI uygulamaları (sağlık görüntüleme, belge işleme, perakende katalog) için KVKK’nın biyometrik veri kategorisi (Madde 6) özel rıza yapısı gerektirir. Multimodal vendor seçiminde verinin Türkiye’de işlenmesi veya AB adequacy korumalı bir bölgede tutulması tipik gereksinimdir.
Do: classify multimodal inputs by sensitivity (especially biometric); document data flows for KVKK / GDPR.
Don’t: assume vision/audio models are exempt from the same governance as text — the inputs they accept are often more sensitive.