Multimodal RAG, RAG that can also surface a variety of file types from text, images or videos, relies on embedding models that transform data into numerical representations that AI models can read.

In addition, and perhaps more importantly, the ability of AI to engage with us in a multimodal way is the future. Talking to an LLM is easier than writing and then reading through responses.

The team shared its experimentation journey of fine-tuning a multimodal RAG pipeline to best answer user queries that require both textual and image context. The detailed post delves deep into the ...

Multimodal: AI’s new frontier AI models that process multiple types of information at once bring even bigger opportunities, along with more complex challenges, than traditional unimodal AI.

Multimodal AI represents the next big race in AI development, and OpenAI seems to be winning. A key difference maker for GPT-4o is that the single AI model can natively process audio, video, and text.

Multimodal AI simultaneously combines text, audio, photos and video. (And to be clear, it can get the “text” information directly from the audio, photos or video.

Vertex AI, which already integrates with two of Google’s large language models — Gemini 1.5 Flash and MedLM — will now also be backed by Gemini 2.0, which was unveiled in December.