Two-story architecture of intelligence

Abstract

This paper proposes an integrated architecture for intelligence based on recent advances in deep learning. Two systems, called BeastOS and Language App, represent the sensori-motor and symbolic processing systems. The world model is acquired through physical interaction in the environment. By disentangling factors in the world model, a counter-factual imagination becomes possible. A query to Language App can trigger the generation of data using the world model and generate an answer based on that. Such integration of deep learning models with external modules has been shown to be possible in a number of existing studies. Furthermore, we argue that primitive features such as knowledge processing, reasoning, long-term planning, and decision making can be obtained by learning on the corresponding datasets or tasks, called linguistic tasks. The main claim of this proposal is that symbolic processing is a set of functions acquired through deep learning and discrete inputs and outputs. The proposed model is novel in that it integrates a large amount of prior research discussion in the field of AI and cognitive science with the latest findings in deep learning.