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.

Artificial intelligence and machine learning processes are being utilized in more and procedures which impact our day-to-day life. From giving you more applicable advertisements to helping you pick the correct movie to watch, such tools extend from simple information matching, to progressively complex forecasts. What's more, those varying uses can have critical implications on their utility and advantage pushing ahead. The key interesting point when taking a look at such matches is the dataset being utilized to foresee the ultimate result. Machines are not ready to'think' like an individual, they don't utilize individual judgment.