- and the Cyc project initiated by Douglas Lenat in 1984 (Lenat and Brown 1984), it was not until the fiftieth anniversary of the Dartmouth workshop and the rise of ML in the early 2000s that the original goal of AI, reincarnated as AGI, has again become acceptable and seen as realistic (Goertzel 2007: 1162).
Despite AGI sharing the original goal of AI, what the former refers to differs significantly from what AI has come to mean as it has been applied commercially in the 2010s. Whereas narrow AI focuses on solving narrowly constrained problems, the goal of AGI research and design is to create systems ‘with sufficiently broad (e.g. human-level) scope and strong generalization capability’ so that it is able to transfer knowledge ‘from one problem or context to others’ (Goertzel 2014: 2–3).
A term that appears to be near-synonymous with AGI is ‘human-level machine intelligence’ (HLMI).<sup>3</sup> What is interesting about the definition of this term is that it is grounded in labour.<sup>4</sup> One definition refers to HLMI as AI that ‘can carry out most human professions at least as well as a typical human’ (Müller and Bostrom 2016: 558), and another posits that it ‘is achieved when unaided machines can accomplish every task better and more cheaply than human workers’ (Grace et al. 2017: 1). A machine that is capable of doing many different types of jobs would almost by definition have to be generally intelligent if it were to master jobs that it had not been taught to do. While HLMI would appear to be a perfect fit to discuss the possibility of increasingly intelligent machines that eventually become capable of labour, we reject this term because of its anthropocentric notion that a machine can be defined as intelligent only if it is human-like. As Benjamin Bratton (2015) argues, ‘human intelligence simply can’t exhaust the possibilities’ of all forms of intelligence because we ‘would do better to presume that in our universe, “thinking” is much more diverse, even alien, than our own particular case’. AGI is therefore a preferable term to HLMI because it ‘stress[es] the “general” nature of the desired capabilities of the system’ (Wang and Goertzel 2007: 1). An AGI could possess intelligence equally general to that of a human being without mimicking human cognition. This difference of cognition is already readily clear with how narrow AI does things compared to humans. For example, ‘AI chess programs will use brute force searches in some instances in which humans use intuition, yet the AI can still perform at or beyond the human level’ (Baum 2017: 8). In addition, AGI is a preferable term because, as Ben Goertzel argues: (1) humans are not necessarily that smart, hence, ‘human-level’ may actually be limiting the scope of what AGI is; and (2) if it is possible to conceive of and thus develop ‘an AGI system with different strengths and weaknesses than humans, but still with the power to solve complex problems across a variety of domains and transfer knowledge flexibly between these domains’, then it becomes tough to define whether the system is human-level (2007: 1163). The arguments made by Bratton and Goertzel are redoubled by those who suggest that what is really at stake in AI is the dissociation of intelligence and consciousness (Shanahan 2015; Harari 2016), that is to say, the possibility that a machine could behave intelligently without possessing a self-awareness similar to that experienced by humans.
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