Cortical Computing Engine with Growing Networks for Lifelong AGI
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Solution Overview
Problem
Existing artificial neural network (ANN)-based approaches for artificial general intelligence (AGI) face limitations such as inflexible model architecture, task interference, unidirectional information flow, bounded temporal credit assignment, bottom-up representation learning, episodic learning, data-driven reasoning, unrealistic objectives, and back-propagation of reward signals, which hinder their scalability and adaptability to diverse and dynamic environments.
Innovation Solution
The Cortical Computing Engine (CCE) employs a connectionist framework with a growing parametric model, Hebbian-type learning, bidirectional information flow, top-down representation refinement, continuous-time learning, and counterfactual reasoning to address these limitations, enabling a scalable and adaptive AGI system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If artificial neural networks (ANN) are used for AGI, then learning capability is achieved, but model architecture inflexibility and task interference occur
Solution Approach 1:
The patent implements dynamic network architecture where compute units can be added, removed, or reconfigured during operation. The network structure evolves based on learning needs rather than being fixed beforehand, allowing the system to adapt its complexity to match task requirements while maintaining learning capability.
Solution Approach 2:
The network is divided into independent compute units that can function autonomously or be combined. Each compute unit processes information independently, eliminating task interference between different functions while maintaining overall network coherence through standardized communication protocols.
2Ease of manufacture
If batched discrete sequences are used for learning, then data processing is simplified, but continuous-time world modeling capability is lost
Solution Approach 1:
The system processes information continuously rather than in discrete batches. Compute units receive and process continuous streams of sensory inputs and internal states, maintaining an ongoing model of the world that captures temporal dynamics and causal relationships without breaking time into artificial segments.
3Device complexity
If heterogeneous pre-defined modules are used, then system structure is organized, but integration and parallelization efficiency decrease
Solution Approach 1:
The patent employs homogeneous compute units that can perform multiple functions depending on their connections and activation states. Each compute unit can participate in different computational patterns and information flows, enabling efficient parallelization while maintaining organized system structure through universal building blocks.
4Device complexity
If fixed number of parameters is assumed, then model simplicity is maintained, but parametric model growth capability is limited
Solution Approach 1:
The network begins with a simple configuration of compute units and parameters, but can dynamically add new compute units and parameters as learning progresses. This allows the model to start simple for ease of initialization while having the capability to grow its parametric complexity to match the demands of complex tasks and environments.
Data Source
AI summary
Described are systems for determining domain observations of an environment. Systems may include: a domain engine module, an active sensing module, a fractal network module, and an execution agent module. Modules may be configured to perform methods for determining domain observations of the environment. Methods may include generating or receiving domain observations, generating or receiving sim actions, generating fractal networks associated with the domain observations or the sim actions, generating observation sequences from the fractal networks, and comparing the observation sequences to the domain observations.