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"But in internet time 2009 was aeons ago. Now, intensive surveillance is available to anyone. And you don't have to be a tech wizard to do it. In mid-January this year, Kashmir Hill, a talented American tech reporter, used three bits of everyday consumer electronics - Apple AirTags, Tiles and a GPS tracker - to track her husband's every move. He agreed to this in principle, but didn't realise just how many devices she had planted on him. He found only two of the trackers: a Tile he felt in the breast pocket of his coat and an AirTag in his backpack when he was looking for something else. "It is impossible to find a device that makes no noise and gives no warning," he said when she showed him the ones he missed."

"Cancer diagnosis and management depend upon the extraction of complex information from microscopy images by pathologists, which requires time-consuming expert interpretation prone to human bias. Supervised deep learning approaches have proven powerful, but are inherently limited by the cost and quality of annotations used for training. Therefore, we present Histomorphological Phenotype Learning, a self-supervised methodology requiring no labels and operating via the automatic discovery of discriminatory features in image tiles. Tiles are grouped into morphologically similar clusters which constitute an atlas of histomorphological phenotypes (HP-Atlas), revealing trajectories from benign to malignant tissue via inflammatory and reactive phenotypes. These clusters have distinct features which can be identified using orthogonal methods, linking histologic, molecular and clinical phenotypes. Applied to lung cancer, we show that they align closely with patient survival, with histopathologically recognised tumor types and growth patterns, and with transcriptomic measures of immunophenotype. These properties are maintained in a multi-cancer study."