We face up to the challenge of explainability in Multimodal Artificial Intelligence (MMAI). At the nexus of neuroscience-inspired and quantum computing, interpretable and transparent spin-geometrical neural architectures for early fusion of large-scale, heterogeneous, graph-structured data are envisioned, harnessing recent evidence for relativistic quantum neural coding of (co-)behavioral states in the self-organizing brain, under competitive, multidimensional dynamics. The designs draw on a self-dual classical description - via special Clifford-Lipschitz operations - of spinorial quantum states within registers of at most 16 qubits for efficient encoding of exponentially large neural structures. Formally 'trained', Lorentz neural architectures with precisely one lateral layer of exclusively inhibitory interneurons accounting for anti-modalities, as well as their co-architectures with intra-layer connections are highlighted. The approach accommodates the fusion of up to 16 time-invariant interconnected (anti-)modalities and the crystallization of latent multidimensional patterns. Comprehensive insights are expected to be gained through applications to Multimodal Big Data, under diverse real-world scenarios.

翻译：我们直面多模态人工智能（MMAI）可解释性这一挑战。在神经科学启发与量子计算的交汇点上，本文提出了可解释且透明的自旋几何神经架构，用于早期融合大规模、异构、图结构数据。该架构利用了近期关于自组织大脑中（共）行为状态的相对论量子神经编码证据，在竞争性的多维动力学框架下运行。其设计基于自对偶经典描述（通过特殊的克利福德-利普希茨运算），在最多16量子比特的寄存器内对旋量量子态进行编码，以实现指数级神经结构的高效表示。本文重点介绍了形式上经过“训练”的洛伦兹神经架构——其单一侧层仅由抑制性中间神经元组成以处理反模态，以及包含层内连接的协同架构。该方法支持多达16个时间不变的互连（反）模态的融合，以及潜在多维模式的结晶。通过应用于多模态大数据及多样化的真实场景，预期将获得全面的见解。