Can Mental Imagery Improve the Thinking Capabilities of AI Systems?

TL;DR

This paper explores integrating mental imagery into AI systems to enhance autonomous reasoning and multi-domain knowledge integration, proposing a new framework supported by validation tests.

Contribution

It introduces a novel machine thinking framework that incorporates mental imagery and auxiliary units to improve AI reasoning capabilities.

Findings

Framework supports reasoning with natural language and sketches

Validation shows improved autonomous decision-making

Mental imagery integration enhances multi-domain knowledge handling

Abstract

Although existing models can interact with humans and provide satisfactory responses, they lack the ability to act autonomously or engage in independent reasoning. Furthermore, input data in these models is typically provided as explicit queries, even when some sensory data is already acquired. In addition, AI agents, which are computational entities designed to perform tasks and make decisions autonomously based on their programming, data inputs, and learned knowledge, have shown significant progress. However, they struggle with integrating knowledge across multiple domains, unlike humans. Mental imagery plays a fundamental role in the brain's thinking process, which involves performing tasks based on internal multisensory data, planned actions, needs, and reasoning capabilities. In this paper, we investigate how to integrate mental imagery into a machine thinking framework and how this could be beneficial in initiating the thinking process. Our proposed machine thinking framework integrates a Cognitive thinking unit supported by three auxiliary units: the Input Data Unit, the Needs Unit, and the Mental Imagery Unit. Within this framework, data is represented as natural language sentences or drawn sketches, serving both informative and decision-making purposes. We conducted validation tests for this framework, and the results are presented and discussed.