  Recently, the multimodal and crossmodal AI techniques have attracted the
attention of communities. The former aims to collect disjointed and
heterogeneous data to compensate for complementary information to enhance
robust prediction. The latter targets to utilize one modality to predict
another modality by discovering the common attention sharing between them.
Although both approaches share the same target: generate smart data from
collected raw data, the former demands more modalities while the latter aims to
decrease the variety of modalities. This paper first discusses the role of
multimodal and crossmodal AI in smart data analysis in general. Then, we
introduce the multimodal and crossmodal AI framework (MMCRAI) to balance the
abovementioned approaches and make it easy to scale into different domains.
This framework is integrated into xDataPF (the cross-data platform
https://www.xdata.nict.jp/). We also introduce and discuss various applications
built on this framework and xDataPF.
