Multimodal learning emerged as a promising frontier in artificial intelligence, offering innovative approaches to harnessing the inherently diverse data available in the real world. By developing advanced multimodal learning techniques, we can unify disparate data streams, uncover cross-modal correlations, and detect subtle patterns overlooked by traditional single-modality approaches. This has applications across various domains, from robotics to complex decision-making systems, and has the potential to significantly advance the field of multimodal AI, paving the way for more sophisticated and accurate models that can better capture the richness of real-world information.
The challenge lies in effectively leveraging the potential of high-dimensional, multimodal data. Current research is limited in the number of modalities it can handle and struggles to address scenarios with missing or noisy data. Such limitations underscore the need for more robust, comprehensive approaches that can reflect and adapt to the complexities of real-world scenarios, bridging the gap between theoretical models and practical applications.
To address these issues, we aim to develop a large-scale, high-modality dataset and construct the first high-modality foundation model capable of handling various downstream tasks. Our comprehensive dataset, encompassing genomics, MRI, and retinal imaging, has the potential to elucidate the pathological trajectory of such impairments, advancing our scientific understanding of diseases. In addition, the process of building powerful multimodal models allows us to form a deeper scientific understanding of data heterogeneity, long-range relationships, and the way to leverage unpaired data, allowing researchers to work with diverse forms of data with ease in the future. Bridging the gap between diverse modalities and AI capabilities, this research can potentially transform the approach to medical research, artificial intelligence, and robotics, paving the way for more holistic and human-like AI systems.