Topic: predictive world model

How could machines learn as efficiently as humans and animals? How could machines learn to reason and plan? How could machines learn representations of percepts and action plans at multiple levels of abstraction, enabling them to reason, predict, and plan at multiple time horizons? I will propose a possible path towards autonomous intelligent agents, based on a new modular cognitive architecture and a somewhat new self supervised training paradigm. The centerpiece of the proposed architecture is a configurable predictive world model that allows the agent to plan. Behavior and learning are dri

How could machines learn as efficiently as humans and animals? How could machines learn to reason and plan? How could machines learn representations of percepts and action plans at multiple levels of abstraction, enabling them to reason, predict, and plan at multiple time horizons? I will propose a possible path towards autonomous intelligent agents, based on a new modular cognitive architecture and a somewhat new self-supervised training paradigm. The centerpiece of the proposed architecture is a configurable predictive world model that allows the agent to plan. Behavior and learning are driv

Predicting sensory input responses to update an internal ``world model'' is a core principle in cognitive learning theories that try to explain mechanisms behind representation learning in the brain [1,2]. Recent machine learning methods have also adopted predictive world modeling for self-supervised representation learning [3,4,5,6]. In this work, we investigate the perceptual and visuospatial capabilities of a self-supervised predictive world model trained in a manner approximating the active nature of human visual perception. We sample a sequence of fixations from image saliency maps [7], e