One reaction to the problems of GOFAI is the ‘situated, embodied, dynamical (SED) framework’ (Beer 2014: 128); Rodney Brooks, a pioneer in the field, called his approach ‘nouvelle AI’ to emphasize its qualitative break with GOFAI (Copeland 2000). SED researchers are often motivated by ‘Moravec’s paradox’ – the observation by roboticist Hans Moravec that ‘it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility’ (1988: 15). SED approaches to AI emphasize the irreducible importance of the body – with its perceptual apparatuses and morphology – to cognition: for this school, it is through solving material problems that machines can evolve intelligent behaviours. Such approaches are therefore often concerned with robotics and artificial life in addition to AI. SED practitioners initially focused on very simple, insect-like robots, but in the 2010s more complex, partially humanoid robots became possible and have been introduced into industrial settings. It is possible that some variety of the SED framework could be the next dominant AI paradigm.
Another reaction to GOFAI was machine learning (ML). The ML school, formerly called connectionism, existed as early as the Dartmouth workshop, gained some traction in the 1980s with advances in learning algorithms, but did not explode until the 2010s when big data and cheap computing power proliferated. As of 2019, ML is the dominant approach to AI.<sup>3</sup> It is a statistical pattern-recognition approach. One NVidia researcher has described ML as a process comprised of three steps: ‘(1) take some data, (2) train a model on that data, and (3) use the trained model to make predictions on new data’ (Dettmers 2015). In other words, ML systems can be understood as creating their own models of inference.
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inhuman power