In today’s technology-driven world, artificial intelligence (AI) has become a vital catalyst driving growth for numerous industries, revolutionizing the way we live and work. One of AI’s latest and most exciting advancements is multimodality – a new cognitive AI frontier combining multiple sensory input forms to make more informed decisions. Multimodal AI came into the picture in 2022 and its possibilities are expanding ever since, with efforts to align text/NLP and vision in an embedding space to facilitate decision-making. AI models must possess a certain level of multimodal capability to handle certain tasks, such as recognizing emotions and identifying objects in images, making multimodal systems the future of AI.
Although in its early stages, multimodal AI has already surpassed the performance of humans in many tests. This is significant because AI is already a part of our daily lives, and such advancements have implications for various industries and sectors. Multimodal AI aims to replicate how the human brain functions, utilizing an encoder, input/output mixer, and decoder. This approach enables machine learning systems to handle tasks that involve images, text, or both. By connecting different sensory inputs with related concepts, these models can integrate multiple modalities, allowing for more comprehensive and nuanced problem-solving. Hence, the first crucial step in developing multimodal AI is aligning the internal representation of the model across all modalities.
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Matthew Haws 