Fractal Neural Cortex Architecture for Autonomous Agent Evolution
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Solution Overview
Problem
Current artificial intelligence systems face challenges in creating intelligent machines due to difficulties in building physical neural network hardware, which requires millions of neurons and weighted connections, leading to inefficiencies in computation time and power consumption, and limitations in reconfigurability for different architectures.
Innovation Solution
A compact physical computing architecture is developed, comprising sensors, motors, RIOS, and a cortex with fractal modules that self-organize and evolve to predict space-time structures, reward signals, and free energy, allowing for the creation of autonomous agents in real or virtual worlds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If physical neural network hardware is built with millions of neurons and weighted connections, then computational power and intelligence are improved, but computation time and power consumption increase exponentially
Solution Approach 1:
The patent divides the neural network into modular fractal units that can be independently configured and executed. Each fractal module represents a self-contained computational unit that can be activated selectively based on the task at hand, rather than requiring the entire network to operate simultaneously. This segmentation allows the system to achieve high computational power for specific tasks while consuming less energy by activating only necessary modules.
Solution Approach 2:
The system dynamically reconfigures its neural network architecture based on the specific computational task. Instead of a static hardware configuration that requires all components to be active, the patent implements dynamic activation and deactivation of neural pathways and modules. This dynamic approach allows the system to adapt its computational power to match task requirements, significantly reducing energy consumption when full computational capacity is not needed.
2Adaptability or versatility
If physical neural network hardware is built with millions of neurons and weighted connections, then computational power and intelligence are improved, but computation time increases
Solution Approach 1:
By segmenting the neural network into fractal modules, the patent enables parallel execution of independent computational tasks within different modules. This modular structure allows the system to process multiple aspects of a problem simultaneously, reducing overall computation time while maintaining high computational power. Each module can be optimized for specific computational patterns, further improving processing speed.
Solution Approach 2:
The system performs preliminary configuration and pre-processing of data within the fractal module structure before full computational execution. By organizing neural pathways and pre-configuring modules in advance based on expected task types, the system reduces the time required for actual computation. This preliminary action includes pre-establishing connection patterns and preparing module activation states.
3Productivity
If custom-designed neural network hardware is built for a specific architecture, then computational efficiency is improved, but reconfigurability for different architectures is limited
Solution Approach 1:
The patent implements a universal fractal module design that can function across multiple neural network architectures. Each module is designed with standardized interfaces and configurable parameters that allow it to operate effectively in different architectural contexts. This universality enables the hardware to maintain high computational efficiency while being reconfigurable for various neural network types and applications, eliminating the need for custom-designed hardware for each architecture.
Solution Approach 2:
The system dynamically reconfigures the fractal modules to match different neural network architectures and computational tasks. The modules can adjust their internal connection patterns, activation functions, and data flow configurations in real-time based on the required architecture. This dynamic reconfigurability allows the hardware to optimize computational efficiency for each specific task while maintaining adaptability across diverse applications.
Data Source
AI summary
Methods and systems for the evolution of electronic neural assemblies toward directed goals. A compact computing architecture includes electronics that allows users of such an architecture to create autonomous agents, in real or virtual world and add intelligence to machines. An intelligent machine is composed of four basic modules: one or more sensors, one or more motors, a (Reward Input Output System) RIOS and a cortex. A number of genetically evolved detectors can project both to cortex and RIOS. At first the neurons within the cortex evolve to predict the structure of the sensory data followed by the structure of proprioceptive activations of its own motor system. Finally, once the cortex has learned its sensory and motor programs, it evolves to predict the reward signals, which comes in multiple channels but is dominated by the detection of the acquisition of free-energy.