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.

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