This work introduces a novel quantum artificial intelligence (AI) that is an assimilative intelligence (AI). It is a closed-loop intelligence where learning, prediction, observation, and correction occur continuously in feedback. Hereafter, this novel concept is referred to as qAI$^2$. This closed-loop intelligence is enabled by combining data assimilation (DA) and machine learning (ML) to unify physics-based modeling, data-driven learning, and uncertainty quantification into a single adaptive framework. This integration, as illustrated in Fig.1, yields robust, interpretable, and self-correcting predictions that outperform standalone approaches, particularly in high-dimensional, sparse-data, and nonstationary systems. Nevertheless, Bayesian DA commonly involves an ensemble of state realizations whose evolution and updates to reflect observations require the propagation of probability distributions through nonlinear models - a task that is computationally intensive for classical computers. ML is subject to issues in scaling learning, data movement, and adaptivity efficiently on extreme, heterogeneous architectures. Therefore, we aim to build a quantum AI2 framework using continuous-variable quantum computing (CVQC) to achieve twofold objectives, making efficient quantum AI-algorithms and accelerating quantum algorithms. CVQC leverages qumodes as computational primitives. These qumodes inherently encode continuous variables, enabling a more direct representation of continuous physical quantities such as fields, and thus empowering probabilistic computing and precise field sensing. Furthermore, CVQC is particularly well-suited for implementation in photonic systems, where light modes naturally carry quantum information. Together, photonic CVQC offers a promising route toward encoding and processing continuous distributions using qumodes to efficiently represent and update ensembles in high-dimensional spaces --- hence, precise prediction. In contexts like uncertainty quantification, stochastic modeling, or Liouville-based ensemble dynamics, qAI2 efficiently exploring high-dimensional configuration spaces without exponential resource scaling, presents a significant advantage. For applications that are data-dependent prediction and response, qAI$^2$ will aid in precision for navigation, maneuverability, firing, and quick-reaction active-protection. qAI$^2$ provides