Posthuman learning: AI from novice to expert?

Abstract

Will robots ever be able to learn like humans? To answer that question, one first needs to ask: what is learning? Hubert and Stuart Dreyfus had a point when they claimed that computers and robots would never be able to learn like humans because human learning, after an initial phase of rule-based learning, is uncertain, context sensitive and intuitive (Dreyfus and Dreyfus in A five stage model of the mental activities involved in directed skill acquisition. (Supported by the U.S. Air Force, Office of Scientific Research (AFSC) under contract F49620-C-0063 with the University of California) Berkeley, February 1980. (Unpublished study). Washington, DC: Storming Media. https://www.stormingmedia.us/15/1554/A155480.html. Accessed 10 Oct 2017, 1980). I would add that learning also builds on prior learning, and that from the outset (birth), human learning is a socio-cultural materially grounded collective epistemology. This posthuman acknowledgement shifts the focus from the individual learner to learning within collective phenomena. Dreyfus and Dreyfus (1980) do not seem to emphasise the essentially social and cultural nature of the human condition. Learning theory (especially the Vygotskyan perspective), new materialism (especially as presented by the physicist Karen Barad) and postphenomenology (especially as presented by Don Ihde) have emphasised in different ways the materially based socio-cultural nature of human learning. They thereby point towards a ‘posthuman’ learning that is far from the machine-like or enhanced creature envisioned by singularists. Until robots are essentially social and ground their epistemologies in socio-cultural materiality, I suggest that human-like AI is not possible.