In plain words: A set of clear goals for general AI plus a simple testing platform that checks how close a machine comes to them, keeping everything else basic. Unlike narrow tests such as image labeling, it gives an objective way to track progress toward broad intelligence.
Abstract
With machine learning successfully applied to new daunting problems almost every day, general AI starts looking like an attainable goal. However, most current research focuses instead on important but narrow applications, such as image classification or machine translation. We believe this to be largely due to the lack of objective ways to measure progress towards broad machine intelligence. In order to fill this gap, we propose here a set of concrete desiderata for general AI, together with a platform to test machines on how well they satisfy such desiderata, while keeping all further complexities to a minimum.
Marco Baroni, Armand Joulin, Allan Jabri, Germàn Kruszewski, Angeliki Lazaridou, Klemen Simonic, Tomas Mikolov
arXiv:1701.08954 · cs.LG, cs.AI, cs.CL · submitted Jan 31, 2017 · updated Mar 27, 2017
abstract · pdf · html · Published in ICLR 2017 Workshop Track