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This briefing examines three developments in how AI systems find and use structure in large, interlinked data. We cover relational deep learning, which treats enterprise databases as graphs and applies transformer-style attention over foreign-key relationships to build foundation models for tabular data, as framed by Kumo.AI co-founder Jure Leskovec. We then turn to probabilistic methods for high-dimensional similarity search developed by Vahab Mirrokni and collaborators, and the operational implications for retrieval, recommendation, and vector-search layers. Finally, we look at Arm’s Neural Super Sampling and Denoising and Neural Dawn, which embed learned reconstruction into real-time mobile rendering under strict frame-budget constraints. The throughline for engineers and architects is the shift toward modality-specific architectures and the governing question of where inference runs and within what latency and resource budgets.