The ML-based AI industry is much more diverse than the first era of expert systems; this is one reason why advanced capitalism has recently contracted a serious bout of AI fever. The Economist (2017a) has been enthusiastic about the prospects of targeting online advertisements and product recommendations; the creation of virtual personal assistants and of augmented reality systems; and autonomous vehicles. As of early 2019, some of these were already highly advanced, while others only incipient. Algorithmic targeting of advertisements and recommendations has been a foundation of digital Web 2.0 enterprises for over a decade. Digital personal assistants, such as Apple’s Siri, Amazon’s Alexa or Microsoft’s Cortana, are gradually becoming commonplace. Augmented reality (AR) products, overlaying physical reality with a mesh of virtual images and information, are only beginning to be sold as commodities or distributed as free vehicles for in-app purchases and data mining. Games such as Pokémon Go and other mobile apps are testing the AR waters, while further frontiers, such as medical applications, are being actively researched by companies such as Google, Apple and Microsoft. Perhaps the biggest prize for the commercial use of ML, but also its most daunting challenge, is the creation of self-driving cars and trucks, a ‘moonshot’ that has attracted leading information companies such as Google and Baidu, established auto-industry giants such as Ford, General Motors and Daimler, and upstart entrants such as Uber and Tesla, all racing to transform capitalism’s entire transportation sector.<sup>1</sup>
AI industry enterprises build ML technologies, often initially for use in their own business operations, but also as commodities for sale or rent, or as a ‘free’ service. They produce commodities for both of the major ‘departments’ into which Marx divided society’s total product and its total production process: (Department 1) means of production, i.e. commodities intended for productive consumption; (Department 2) means of subsistence, i.e. commodities destined for individual consumption (1992: 471). Some commentators on ML have suggested that, neatly corresponding with these two departments, there will be ‘two AIs’: one for business applications, the other for consumer devices (Economist 2017a). In Department 1 we find examples like SAP’s
means of cognition
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