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Can Mental Imagery Improve the Thinking Capabilities of AI Systems?

Created by
  • Haebom

Author

Slimane Larabi

Outline

This paper points out the lack of autonomous behavior and independent reasoning ability of existing artificial intelligence models, and the limitations of data input methods that depend on explicit queries, and proposes a new machine thinking framework that mimics the way humans utilize mental images. This framework consists of three auxiliary units: an input data unit, a desire unit, and a mental image unit centered on a cognitive thinking unit, and uses natural language sentences or picture sketches as data to provide information and make decisions. In this paper, we present and discuss the results of verification experiments on the proposed framework.

Takeaways, Limitations

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Takeaways:
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Presenting a new machine thinking framework that can overcome the limitations of existing AI models
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Presenting the possibility of AI development through imitation of cognitive processes using mental images
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Presenting the possibility of building a flexible thinking system by utilizing various data types (natural language, picture sketches)
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Presenting the possibility of building a goal-oriented thinking system that takes needs into account
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Limitations:
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Lack of detailed description of actual implementation and performance of the proposed framework.
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Lack of detailed information on the size and design of validation trials.
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Lack of validation of applicability and generalizability to a variety of complex situations.
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Lack of review of whether there is a perfect imitation of the process of human mental image creation and utilization.
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