fault tolerant

A system is fault tolerant if errors or faults are in some way managed to avoid catastrophic outcomes. This applies within AI algorithms; for example, systems with distrubuted knowledge represention, such as the weights in a neural network, have a natural level of fault tolerancein that slight variations of input can still be recognised. It also applies at the level of the archutectures supporting AI. Notably {[deep learning}} and other bg data techniques may require long-running computation in many machines. Frameworks such as MapReduce need to deal with the failure of one or more processors without needing to completely restart the process.