Animal behaviour is more general than Marx believed and so is their cognition. Indeed, researchers of cognitive evolution no longer posit that animal behaviour can be fitted into a hierarchy of complexity, but instead focus on identifying and describing animals’ ‘mental organs’ or modules that each have a domain-specific function and evolved to only accept a ‘certain kind of input’ (e.g. optical, aural or olfactory) in relation to a given ecological problem (Vitti 2013: 394). While initially specialized, ‘modular cognition’, due to increasing encephalization, evolved into ‘flexible and domain-general’ cognition (Vitti 2013: 395). What has driven this evolution may be ‘omnivorous diets, variable habitat, long developmental periods, extended longevity, and pervasive social interactivity’ (Vitti 2013: 396). While most animals, the honeybee included, ‘specialize in the skills that their ecological niche requires’, some species, with high encephalization levels (mammals, birds and cephalopods), are considered ‘generalists’ (Vitti 2013: 395).
Timothy Morton (2017: 56) takes a more fundamental issue with Marx’s argument that the labour of human architects is different from that of bees. He contends that it is impossible to demonstrate that either humans or bees have the capacity of imagination, or that humans are not executing algorithms when they plan. The assumptions Marx makes to argue that human productive activity is uniquely distinct from that of animals is, Morton argues, ‘based on a metaphysical assertion about humans’ (2017: 56).
What these critiques make clear is that the distinction between humans and animals is more blurred than Marx imagined. We argue that the distinctions between humans and intelligent machines are also becoming increasingly blurred. Indeed, many of the examples of the complex behaviour and inner lives of mammals and insects that are presented against Marx’s insistence that labour is inherently human have analogues in the functioning of ML systems. If Marx’s conceptualization of labour as human can be challenged with examples of animals doing things previously thought uniquely human, the same holds for machines, especially those that exhibit behaviours that appear to be productive, creative, imaginative and even adaptive. Some ML systems even appear to envision plans or concepts in their neural architectures that are then respectively carried out or recognized in new corresponding examples the system has never before encountered.
perfect machines, inhuman labour
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