AirborneHRS Docs

The 'Living AI' Manifesto

Current AI is 'Dead on Arrival'. Development stops the moment training.py finishes.

AirborneHRS introduces the Neuro-Dynamic Wrapper concept: a parasitic efficiency layer that grants biological properties (Memory, Sleep, Pain) to static mathematical models.

Static AI

"I learned X in 2023. I cannot learn Y without forgetting X."

Airborne AI

"I learned X. Now I am learning Y. I remember X. I am growing."

Architecture

A high-level overview of the components that power Living Intelligence.

System Diagram

User input flows into a base model, which generates predictions. These predictions are then processed by the AirborneHRS Limbic System, a suite of components that enable the model to learn and adapt.

Component Deep Dive

The Hippocampus (FeedbackBuffer)

Short-term potentiation. Stores raw tensors in a Reservoir Sampling buffer to be "dreamed" about later, enabling replay and learning from past experiences.

The Cortex (UnifiedMemoryHandler)

Long-term potentiation. Protects critical weights using Fisher Information, preventing catastrophic forgetting by adding a quadratic penalty to the loss function.

Introspection Engine

The "consciousness" layer. It analyzes model predictions for metrics like "Surprise" to dynamically control model plasticity and learning.

Autonomic Nervous System (PerformanceMonitor)

Maintains homeostasis. It monitors for exploding gradients ("Seizures") or vanishing weights ("Comas") and automatically applies countermeasures like gradient clipping.

API Reference

Comprehensive reference for the AirborneHRS library.

Usage Examples

Practical, copy-paste ready examples to get you started.

1. Minimal "Hello World"

See how to make a standard PyTorch model "alive" with a single line of code.

import torch
from airbornehrs import AdaptiveFramework

model = torch.nn.Linear(10, 2)
agent = AdaptiveFramework(model)

# The model is now Alive.
# Just train normally:
x, y = torch.randn(5, 10), torch.randn(5, 2)
agent.train_step(x, target_data=y)

2. "Warm Start" with a Pre-Trained Model

Integrate a pre-trained model and protect its ancestral knowledge before fine-tuning on a new task.

import torchvision

# 1. Load Pre-trained
my_resnet = torchvision.models.resnet18(pretrained=True)

# 2. Wrap
agent = AdaptiveFramework(my_resnet)

# 3. Lock Ancestral Knowledge (Task 0)
# Feed 50 images to let it "feel" what's important
agent.memory.register_importance(image_loader) 

# 4. Now learn "Medical X-Rays" (Task 1)
# The ImageNet weights are protected.

Industry Use Cases

Real-world applications where AirborneHRS provides a competitive advantage.

Financial Forecasting

Markets change (Regimes). A model trained on 2020 (Volatile) fails in 2024 (Stable).

The EWC tether keeps the "Universal Rules" (Supply/Demand) while the Neuroplasticity adapts to the "Current Regime".

Suggested Config

dream_interval=0, ewc_lambda=2000

Robotics / Drones

Drone learns to fly in Wind. Then learns to fly Indoors. It enters Wind again and crashes (Forgets Wind).

By replaying "Windy Memories" during "Indoor Training", it maintains a universal flight controller.

Suggested Config

dream_interval=10, memory_type='hybrid'

Medical Imaging

Privacy. You cannot store the patient dataset forever to retrain.

The "Memory" is stored in the weights, not the JPGs. The buffer can be cleared for privacy, but the Synaptic Constraints remain.

Suggested Config

buffer_size=100, ewc_lambda=10000

Troubleshooting Guide

Diagnose and fix common issues you might encounter.