Machine learning has made great progress during the last decades and is being deployed in a wide range of applications. However, current machine learning techniques are far from sufficient for achieving human-level intelligence. Here we identify the properties of learners required for human-level intelligence and suggest a new direction of machine learning research, i.e. the cognitive learning approach, that takes into account the recent findings in brain and cognitive sciences. In particular, we suggest two fundamental principles to achieve human-level machine learning: continuity (forming a lifelong memory continuously) and glocality (organizing a plastic structure of localized micromodules connected globally). We then propose a multimodal memory game as a research platform to study cognitive learning architectures and algorithms, where the machine learner and two human players question and answer about the scenes and dialogues after watching the movies. Concrete experimental results are presented to illustrate the usefulness of the game and the cognitive learning framework for studying human-level learning and intelligence.
['Byoung-Tak Zhang']
Cognitive learning and the multimodal memory game: Toward human-level machine learning