LMMS are the new LLMs

LMM Transition

The transition from large language models (LLMs) to large multimodal models (LMMs) marks a significant shift in machine learning. Jerod Santo highlights Chip Huyin's insights on this evolution, noting that traditional ML models were limited to single data modes like text, image, or audio 1. However, natural intelligence operates across multiple modalities, and AI must do the same to function effectively in the real world.

Incorporating additional modalities into llms produces large multimodal models, lmms, and everyone's doing it.

--- Jerod Santo

This shift is evidenced by major players like DeepMind, Salesforce, and OpenAI, who are already integrating multimodal capabilities into their systems.

   

Multimodality Benefits

The integration of multiple data modalities into AI models offers numerous advantages. Jerod Santo explains that by mimicking human intelligence, which naturally processes text, images, and sounds, AI can better navigate and interpret the complexities of the real world 1. This capability is crucial for developing AI that can perform tasks across diverse environments and applications.

Being able to work with multimodal data is essential for us, or any AI, to operate in the real world.

--- Jerod Santo

The move towards LMMs not only enhances AI's functionality but also broadens its potential use cases, making it a pivotal development in the field.