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In the mechanical mode of operation, which is built on linear causation, a contingent event may lead to the collapse of the system. For example, machinery may malfunction and cause an industrial catastrophe. But in the recursive mode of operation, contingency is necessary since it enriches the system and allows it to develop. A living organism can absorb contingency and render it valuable. So can today’s machine learning.
In the time of Descartes, and later Marx (who described human–machine relations in the factories of nineteenth-century Manchester), automated machines performed homogeneous, repetitive work, like a clock. As Marx wrote, a craftsman-turned-factory-worker failed to cooperate with this kind of machine on both a psychological and somatic level because a machine enclosed within itself is a separated reality. Marx attributed this failure to alienation. In our time, however, automated machines are no longer based on the same epistemology. Rather, they are recursive—capable of integrating contingency into their operations.
