Large language models, like GPT-4, are capable of parsing, understanding, and generating text as well as most humans, but they still have limitations, such as not being able to understand different forms of inputs like spoken or handwritten instructions. Researchers are working on training large AI models to be multimodal, meaning they can handle multiple modalities like images, videos, and audio, which could revolutionize AI research. Large multimodal models are similar to language models in training design and operation but are trained on a vast amount of data from various modalities. These models learn to recognize concepts beyond just text and can perform tasks such as image recognition, text-to-image generation, and voice chat. They also offer features like automatic translation, chart analysis, and code generation, making them capable of handling everyday tasks with ease. With the advancement of multimodal AI models, we can expect to see a wide range of applications in various industries, from automating workflows to creating innovative tools for human-AI collaboration.