How we're predicting AI–or failing to
In Jan Romportl, Eva Zackova, and Jozef Kelemen (eds.) Beyond Artificial Intelligence: The Disappearing Human-Machine Divide, Cham, 2015, pp. 11–29
Abstract
This paper will look at the various predictions that have been made about AI and pro-pose decomposition schemas for analyzing them. It will propose a variety of theoretical tools for analyzing, judging, and improving these predictions. Focusing specifically on timeline predictions (dates given by which we should expect the creation of AI), it will show that there are strong theoretical grounds to expect predictions to be quite poor in this area. Using a database of 95 AI timeline predictions, it will show that these expec-tations are borne out in practice: expert predictions contradict each other considerably, and are indistinguishable from non-expert predictions and past failed predictions. Pre-dictions that AI lie 15 to 25 years in the future are the most common, from experts and non-experts alike. Armstrong, Stuart, and Kaj Sotala. 2012. " How We’re Predicting AI—or Failing To. " In Beyond AI: Artificial Dreams, edited
