But how seriously should we consider the possible emergence of AGI given the nascence of the field of AI? It is possible that, as Kurzweil (2005a: 33–4) and Bostrom (2014: 29) have suggested, AGI could emerge through a process of recursive self-improvement whereby an AI with access to its own design, and an ability to upgrade it, makes an improved or entirely new version of itself (e.g. with a completely different neural architecture or moving from parallel to quantum computing), which in turn improves itself again ad infinitum. This process would also accelerate, meaning that improvements in machine intelligence would come at closer and closer intervals, and since recursive self-improvement would also be done by AGIs, it could lead to the emergence of artificial superintelligence (ASI).
This argument is not entirely foreign to Marx’s thought. As we discussed in Chapter 1, Marx argued that large-scale industry did not have ‘an adequate technical foundation’ and could not ‘stand on its own feet’ until the general conditions of production had changed so that almost everything, including machines, was produced by machines (1990: 506). Marx understood this phenomenon to be the central characteristic of the industrial revolution. It is possible that, for AGI to emerge, capitalism must go through another revolution akin to the original industrial revolution in order to create a technical foundation that is adequate to ‘fully developed AI-capitalism’. AI must become a general condition of production such that AI is not just produced by means of AI, but that AI also recursively improves itself.
Google’s AutoML (automated machine learning) project is an example of such a process because it is a ‘machine-learning algorithm that learns to build other machine-learning algorithms’ (Metz 2017b).<sup>15</sup> When researchers build a neural network they have to run hundreds of experiments on powerful computers to test which of countless possible permutations works best; this process requires adjusting the model and its parameters many times over, and researchers cannot always precisely explain why they make this adjustment over that; an undefined sense of intuition is often the only guide. It is this difficult process that Google aims at automating by creating ‘algorithms that analyse the development of other algorithms, learning which methods are successful and which are not’ (Metz 2017b). While a far cry from recursive self-improvement, it is part of a significant current trend in AI research that is also important to AGI, namely the focus on how algorithms can learn to learn.
perfect machines, inhuman labour
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