Artificial Intelligence Is Stupid and Causal Reasoning Will Not Fix It - Bishop, J. Mark (Frontiers in Psychology, 2021)
Artificial Neural Networks have reached ‘Grandmaster’ and even ‘super-human’ performance’ across a variety of games, from those involving perfect-information, such as Go ((Silver et al. (2016)); to those involving imperfect-information, such as ‘Starcraft’ (Vinyals et al. (2019)). Such technological developments from AI-labs have ushered concomitant applications across the world of business, where an ‘AI’ brand-tag is fast becoming ubiquitous. A corollary of such widespread commercial deployment is that when AI gets things wrong - an autonomous vehicle crashes; a chatbot exhibits ‘racist’ behaviour; automated credit-scoring processes ‘discriminate’ on gender etc. - there are often significant financial, legal and brand consequences, and the incident becomes major news. As Judea Pearl sees it, the underlying reason for such mistakes is that “... all the impressive achievements of deep learning amount to just curve fitting”. The key, Pearl suggests (Pearl and Mackenzie (2018)), is to replace ‘reasoning by association’ with ‘causal reasoning’ - the ability to infer causes from observed phenomena. It is a point that was echoed by Gary Marcus and Ernest Davis in a recent piece for the New York Times (Marcus and Davis (2019)): “we need to stop building computer systems that merely get better and better at detecting statistical patterns in data sets – often using an approach known as “Deep Learning” – and start building computer systems that from the moment of their assembly innately grasp three basic concepts: time, space and causality”. In this paper, foregrounding what in 1949 Gilbert Ryle termed ‘a category mistake’ (Ryle (1949), pp. 16), I will offer an alternative explanation for AI errors; it is not so much that AI machinery cannot ‘grasp’ causality, but that AI machinery (qua computation) cannot understand anything at all.