The excitement surrounding large language models (LLMs) is rapidly increasing, with industries widely exploring diverse use cases. As a transformative technology, LLMs are being closely monitored for their potential to revolutionize and optimize everything from customer service to complex data analysis to advance health care. Bill Gates recently wrote a blog on how agents will be the next big thing in software. He further claimed that in the next 5 years, anyone who’s online will be able to have a personal assistant powered by artificial intelligence.
While the industries & user community are still embracing the euphoria of Large Language Models (LLMs), the Hi-Tech industry has already started to work on evolution of Large Multimodal Models (LMM) - a step towards extending the ‘emergent’ abilities of LLMs beyond text-only input/output models.
▾ Large Multimodal Models
We human beings are blessed with multiple sensory & cognitive capabilities and our intelligence is a collective intelligence derived from multiple sources. As we grow, we learn to use one or more of these ‘Modes of interactions’ to interact with the world around us. The future of AI will likely follow the same realm and will work on integrating multiple data modalities at input and/or output into AI models, leading to the development of LMMs. The input or output modes of interest could be text/language, images, video, audio, sensors data, actuator data, etc. Till recently, the focus was on unimodal models which could process only one data mode (such as text or speech or image) at a time.
By combining these different types of data, LMMs can achieve a more holistic understanding of the world, enabling them to perform complex tasks. For instance, an LMM could analyze a video, recognize objects, understand spoken language, and generate descriptive text all in one seamless iteration.
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