Symbiotic Neural Network - The way to create Evolutionary Ai AGI

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I made this video with Fliki Ai to attract attention to the project. Video is not so important you can just read the text at github to get the idea

attention: here in description is only part of the text published...

Symbiotic Neural Network

Step 1: Original Concept – "Adaptive Modular Neural Network" (AMNN) Inspiration: Mimicking biological neural plasticity and human problem-solving. Core Features:

Modular Architecture: o The network is divided into independent, self-contained modules (e.g., vision, language, motor control). o Modules can dynamically reconfigure connections based on task demands.
Self-Optimizing Pathways: o Uses reinforcement learning to prioritize pathways that yield successful outcomes. o Weakly activated pathways are pruned automatically ("neural Darwinism").
Probabilistic Reasoning: o Decisions are made via Bayesian inference, weighing uncertainties in input data.
Continuous Learning: o Learns incrementally without catastrophic forgetting (e.g., synaptic consolidation).
Energy Efficiency: o Mimics sparse coding in biological brains; only critical modules activate for specific tasks. Example Use Case: A robot that learns to navigate a new environment by reallocating resources from its "object recognition" module to its "spatial mapping" module in real time.

Deep Definition: Adaptive Modular Neural Network (AMNN) The Adaptive Modular Neural Network (AMNN) is a dynamic, biologically inspired AI architecture designed for environments requiring flexibility, creativity, and continuous learning. Unlike rigid systems, the AMNN mimics the human brain’s ability to rewire itself, prioritize tasks, and adapt to novel challenges. Below is a comprehensive breakdown of its design, mechanics, and implications.

Foundational Philosophy • Inspiration: Neuroplasticity, evolutionary biology, and decentralized systems. • Core Tenet: “Adapt or perish”—the network evolves its structure and function in response to stimuli. • Purpose: To excel in unpredictable, open-ended domains where rules are unknown or fluid (e.g., robotics, creative AI).

Core Features a) Modular Architecture • Structure: A decentralized network of specialized, self-contained modules (e.g., vision, language, motor control). o Dynamic Reconfiguration: Modules form temporary alliances for specific tasks (e.g., combining "object recognition" and "spatial reasoning" for navigation). o Redundancy: Critical functions are duplicated across modules for fault tolerance. • Example: A robot’s "grasping" module collaborates with its "material analysis" module to handle fragile objects. b) Self-Optimizing Pathways • Mechanism: Uses reinforcement learning and Hebbian plasticity (“neurons that fire together wire together”). o Neural Darwinism: Weakly used pathways are pruned; high-reward pathways are reinforced. o Meta-Learning: The network learns how to learn, optimizing its own architecture over time. • Example: A drone learns to prioritize visual data over LiDAR in foggy conditions after repeated successful landings. c) Probabilistic Reasoning • Decision-Making: Employs Bayesian inference and uncertainty modeling. o Confidence Scores: Outputs include probabilistic estimates (e.g., “75% chance this image is a cat”). o Hypothesis Generation: Explores multiple solutions simultaneously, ranking them by likelihood. • Example: A medical AMNN suggests three possible diagnoses with confidence scores, aiding doctors in triage. d) Continuous Learning • Training: Lifelong learning without catastrophic forgetting. o Synaptic Consolidation: Critical knowledge is “anchored” to prevent overwriting. o Experience Replay: Revisits past data to refine models incrementally. • Example: A personal assistant AMNN learns a user’s preferences over years, adapting to career changes or new hobbies. e) Energy Efficiency • Execution: Sparse, context-aware activation. o Task-Specific Modules: Only relevant modules activate for a given input (e.g., “language” module sleeps during image processing). o Predictive Coding: Minimizes redundant computations by anticipating likely inputs. • Example: A surveillance AMNN idles its “facial recognition” module until motion is detected.

Technical Specifications Aspect AMNN Implementation Architecture Decentralized graph of spiking neural networks (SNNs). Activation Adaptive sigmoid/ReLU with dynamic thresholds. Training Hybrid unsupervised (self-supervised) + reinforcement learning. Scalability Horizontally scalable via module replication. Hardware Optimized for neuromorphic chips (e.g., Loihi) or GPUs.