Financial Times Technology•about 14 hours ago·1 min read
New AI espionage powers trigger Putin camera scare
Russia paused surveillance system after killing of Iran’s Supreme Leader exposed how AI can be used on CCTV data to target enemies
Russia paused surveillance system after killing of Iran’s Supreme Leader exposed how AI can be used on CCTV data to target enemies
Value of private equity software acquisitions struck in the first five months of the year tumbles to $50bn
arXiv:2606.06509v1 Announce Type: cross Abstract: Numerous medical imaging problems must be solved under limited labels and constrained compute, yet it remains unclear whether performance gains are driven mainly by more expressive models or by better representation of clinically meaningful anatomy. We study this question through a low-data anatomy-aware benchmark for 5-class cardiac pathology prediction on the public ACDC MRI dataset. Using segmentation-derived patient descriptors from the right ventricle, myocardium, and left ventricle, we compare anatomy-specific and multi-structure representations across linear, kernel, and tree-based classifiers. We find that under limited label settings, representation dominates complexity. These results suggest that in resource-constrained healthcare settings, identifying and representing the most informative anatomy may matter more than the increasing complexity of the model alone.