To recognise an object of interest from the features in a data environment, to call it to attention, and to decide, is a critical problem for neural net algorithms in production line robots, surgical robotics, and military autonomous weapons systems. The \u2018Dex-net\u2019 algorithm I address in this paper is trained on an archive of point cloud data on objects. The neural net must learn to recognise the object\u2019s form and to optimize the robot\u2019s reach and grasp. As the computer scientists working on Dex-net explain, \u201cthe goal of cloud robotics\u201d is to \u201cpre-compute\u201d so that \u201cat least one grasp is achievable in the presence of clutter and occlusions\u201d. This is an algorithmic problem centred on how machines become attentive to the world, how machine learning algorithms learn. It is a problem that extends from attentiveness to the shape of a human organ amid \u2018noisy data\u2019 in robotic surgery, to the recognition of a civilian body in the screened occluded data of a UAV. The condition of possibility of the action is an unrecoverable archive of cloud data, filtered via the infinitely malleable value system of posthuman probabilities, thresholds and bias.<\/p>\n