Multimodal Models
Current LLMs like Google's Gemini, are mainly limited to analyzing prompt / text inputs but this is rapidly changing to include many forms of data (images, sound etc)
See here an example of Multimodal prompting for Google Gemini courtesy of Google for Developers [uploading an image and then typing in a question all in the same prompt] and Guide to multimodal prompts + further tips on Prompt Engineering (Gemini)
Multimodal LLMs go beyond text, enabling them to understand and process information from different modalities like:
Images: Analyzing and interpreting visual information.
Audio: Comprehending sounds, speech, and music.
Video: Combining visual and audio understanding.
Code: Analyzing and generating code snippets.
Other data types: Depending on the model's training, it could grasp various formats like graphs, scientific data, or sensor readings.
How it works: Imagine each modality like a different language. Multimodal LLMs involve:
Individual encoders: Specialized AI models tuned to understand each modality, translating them into a common format (like numerical representations).
Fusion layer: This "translator" integrates information from different encoders, allowing the LLM to understand the relationships and context between them.
Multimodal language model: This core LLM, similar to myself but trained on multimodal data, leverages the combined information for tasks like:
Answering questions based on images and text.
Generating captions for videos.
Summarizing scientific papers with graphs and data.
Translating between languages and modalities (e.g., text to image).
Benefits: Multimodal LLMs have exciting potential:
Richer understanding: Analyzing the world through multiple senses, like humans do, leads to deeper and more accurate comprehension.
New applications: Imagine AI assistants that understand your voice and gestures, robots that navigate using vision and touch, or education platforms that combine text, videos, and interactive elements.
Bridging the gap: They can link previously separate domains, enabling communication and collaboration between AI systems focusing on different modalities.