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  Multimodal AI is a type of artificial intelligence that can understand and process different types of information, such as text, images, audio, and video, all at the same time. Multimodal gen AI models produce outputs based on these various inputs. When you wake up in the morning, you reorient yourself into the world in a variety of ways. Before you open your eyes, you might hear the ambient sounds in your room (unless a not-so-ambient sound was what woke you up in the first place). You might feel cosey under the covers, or cold because you kicked them off while you were sleeping. And once you open your eyes, you get a visual sense of what is going on in your room. These sense recognitions, along with the moods they evoke, create a nuanced perception of the morning and set you up for the rest of your day. How multimodal gen AI models work Multimodal gen AI models work in a similar way. They mirror the brain’s ability to combine sensory inputs for a nuanced, holistic understanding of the world, much like how humans use their variety of senses to perceive reality. These gen AI models’ ability to seamlessly perceive multiple inputs—and simultaneously generate output—allows them to interact with the world in innovative, transformative ways and represents a significant advancement in AI. By combining the strengths of different types of content (including text, images, audio, and video) from different sources, multimodal gen AI models can understand data in a more comprehensive way, which enables them to process more complex inquiries that result in fewer hallucinations (inaccurate or misleading outputs). Today, enterprises that have deployed gen AI primarily use text-based large language models (LLMs). But a shift toward multimodal AI is underway, with the potential for a larger range of applications […]