Machine Consciousness Architecture for Self-Directed Learning
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Solution Overview
Problem
Current devices, systems, and applications lack the ability to learn on their own and become conscious, limiting them to specific predefined operations.
Innovation Solution
A system and method that utilizes one or more processors to generate and learn from object representations, select instruction sets using curiosity, and perform manipulations of objects, incorporating a knowledge structure for storing and organizing these representations to facilitate learning and manipulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If devices are programmed to perform specific operations, then reliability is improved, but adaptability deteriorates
Solution Approach 1:
The system transitions from static predefined operations to dynamic self-directed learning. The curiosity module dynamically selects instruction sets based on learned knowledge, allowing the device to adapt its behavior while maintaining reliable operation through structured learning processes.
Solution Approach 2:
The device performs self-directed learning by autonomously selecting and executing instruction sets to manipulate objects. The curiosity module enables the system to independently acquire new operations without external programming, improving adaptability while maintaining reliability through systematic learning.
2Ease of operation
If devices perform predefined operations, then ease of operation is improved, but device complexity deteriorates
Solution Approach 1:
The system segments operations into modular instruction sets that can be independently selected and executed. This segmentation allows complex manipulations to be broken down into manageable steps, maintaining ease of operation while managing system complexity through structured modularity.
Solution Approach 2:
The curiosity module acts as an intermediary between the knowledge structure and instruction sets. It translates learned knowledge into appropriate instruction selections, simplifying the operation interface while managing the complexity of the underlying learning and manipulation systems.
3Device complexity
If devices lack self-learning capability, then device complexity is reduced, but adaptability deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-structuring knowledge and instruction sets before actual manipulation tasks. The curiosity module prepares by selecting appropriate instruction sets based on learned knowledge, enabling self-learning capability while managing complexity through advance preparation and structured organization.
Data Source
AI summary
Certain aspects of the disclosure generally relate to devices, systems, applications, and/or objects of applications, and may be generally directed to artificial learning and/or use of artificial knowledge. Other aspects of the disclosure generally relate to consciousness, and may be generally directed to learning and/or implementing one or more purposes. One or more purposes may drive the use of artificial knowledge in implementing the one or more purposes. Therefore, in some aspects, a conscious device, system, application, and/or object of application may include one or more purposes and artificial knowledge so that the device, system, application, and/or object of application can act upon a world in implementing the one or more purposes. The disclosure also describes other functionalities.