Unlocking Autonomy in AI
Enabling Adaptive & Autonomous AI systems
The Core Challenge in Today’s AI ...
... they are Still Dependent on Humans to tell them WHAT to do, and HOW to do it!
AI seeks to build systems capable of performing diverse tasks and adapting to new environments and tasks without human oversight. Yet, current AI faces a fundamental limitation: it lacks an intrinsic mechanism for self-evaluation. This necessitates a reliance on human designers to define objectives, assess outcomes, and adjust behavior. Without an internal benchmark for performance, true autonomy remains elusive, regardless of the system's scale, modality, or training.​​​​
Entanglement Learning - The Breakthrough
Entanglement Learning (EL) addresses this challenge by providing AI with an intrinsic measure: information throughput—the continuous, bidirectional flow of information between an agent and its environment. EL quantifies the predictability of the environment for the agent (and vice versa), driving the system to maximize this alignment. Consequently, adaptation emerges as a natural outcome of optimizing this flow, not as a programmed feature, but as a fundamental imperative for maintaining informational coherence. Discover the underlying mechanisms and processes of EL in our How it Works section.
The Information Digital Twin (IDT) is the technical implementation of Entanglement Learning that enables practical deployment across different systems. Operating as an independent architectural layer, the IDT continuously monitors, models, and maximizes the information throughput between an agent and its environment.
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The IDT functions alongside the primary system without disrupting operations, tracking how information throughput (bidirectional information flow) changes during operation. It measures entanglement (sustained mutual predictability across interaction cycles) through calculation of mutual information (statistical dependencies quantified in bits), and generates information gradients (directional signals indicating parameter adjustments) to restore optimal alignment when performance declines.
Curious whether Entanglement Learning stands on solid ground—or whether it could solve challenges in your AI systems?
Download the full EL technical reference and put it to the test—use it with Claude, ChatGPT, or similar tools to explore the math, architecture, and feasibility.
Start with: Is the math behind Entanglement Learning sound?
EL Reference
For a broader perspective, try sharing our website link with your favorite AI assistant and asking it to evaluate Entanglement Learning – you'll receive insightful analysis of how our approach compares to other technologies and its potential applications across industries.
The Human Digital Twin extends our Entanglement Learning framework beyond technical systems to human-centered applications. While the Information Digital Twin (IDT) monitors and optimizes information flow between components of technical systems, the HDT applies these same principles to the complex interactions between humans and their technological environment.
The HDT creates a multi-modal architecture that measures information throughput across the various systems that interact with a person—from medical devices and wearables to vehicles and smart environments. By calculating entanglement metrics across these interactions, the HDT detects when information flow becomes suboptimal and generates recommendations to restore alignment.
This approach is particularly valuable in contexts where direct communication is limited or where subtle patterns across multiple systems provide early indicators of important changes. From healthcare applications that monitor patient-treatment information flow to professional environments that optimize human-machine teaming, the HDT represents a frontier application of Entanglement Learning that bridges technical systems and human experience.
Intelligence Reimagined
Human intelligence excels through the establishment of rich, predictable connections—socially, technologically, symbolically—with the world. This "entanglement" enables us to anticipate, shape, and adapt to change with unparalleled agility. EL aims to imbue machines with this principle, creating AI that learns and aligns with its environment not through explicit instructions, but through its inherent informational dynamics. Explore the diverse applications and industries where Entanglement Learning can be applied on our Use Cases page.
Information Throughput
The continuous bidirectional exchange of information between agent and environment, which the system actively seeks to enhance.
Mutual Information
A quantitative measure (in bits) of the reduction in uncertainty about one part of the system given knowledge of another, guiding alignment
Entanglement
The sustained mutual predictability across a system observations, internal models, and outcomes—ensuring continuous throughput.
Information Gradients
Indicators of the direction and magnitude of change required to restore or enhance entanglement, driving autonomous updates to the agent’s internal model or decision logic.
Advancing Autonomous Intelligence Through Research and Collaboration
As a research-focused company, we believe Entanglement Learning offers a novel architectural foundation for achieving true autonomy in artificial intelligence. We are actively exploring diverse use cases and seek partnerships to further mature this concept and translate its potential into real-world applications. We invite interested researchers and organizations to join us in this endeavor to redefine the future of intelligent systems.
The following conceptual implementations illustrate how Entanglement Learning is being explored across diverse AI domains. Each use case outlines the core challenge, proposed EL-based approach, and the expected impact on system autonomy and adaptability.
EL for Adaptive Convolutional Neural Networks (CNN)
Challenge: Image classification networks remain vulnerable to distribution shifts and adversarial attacks, with no reliable way to detect when internal representations no longer align with reality without external validation.
EL Implementation: Our Information Digital Twin monitors the mutual predictability between activation layers and classification outputs, detecting subtle changes in information flow that signal misalignment before classification accuracy visibly degrades.
Impact: EL-enabled CNNs identify adversarial inputs and distribution shifts in real time, maintaining reliable performance through targeted adaptations rather than requiring complete retraining when environments change.
EL for Adaptive Model Predictive Controller (MPC)
Challenge: Traditional MPC systems for autonomous vehicles struggle to maintain performance when facing unexpected conditions like wind gusts or component degradation, requiring frequent manual recalibration.
EL Implementation: By measuring information throughput between state predictions, control actions, and resulting vehicle dynamics, our framework detects misalignments before they impact flight stability and generates precise parameter adjustment signals.
Impact: UAVs equipped with EL-enhanced MPC maintain optimal flight performance across changing environmental conditions without requiring pre-programmed adaptation rules or human intervention.
EL for Adaptive Reinforcement Learning (RL)
Challenge: RL-trained robotic manipulators lack a universal mechanism to detect when their learned policies no longer match current operational conditions, leading to performance degradation and potential failures.
EL Implementation: Information throughput measurement across state-action-result sequences allows the system to identify specific aspects of its policy that require adjustment, guiding targeted updates without disrupting well-functioning behaviors.
Impact: Robotic systems maintain manipulation precision across changing payloads, surface conditions, and wear patterns, extending operational life while reducing supervision requirements.
EL for Adaptive DC Motor Controller
Challenge: Electric vehicle controllers struggle to adapt to changing road conditions, battery characteristics, and component wear, requiring periodic recalibration to maintain optimal performance and efficiency.
EL Implementation: By monitoring entanglement between controller inputs, outputs, and motor responses, the system detects when control parameters no longer align with actual motor behavior and generates adaptation signals to restore optimal relationships.
Impact: EL-enhanced motor controllers provide consistent performance throughout the vehicle lifecycle while maximizing energy efficiency, extending range and reducing maintenance requirements.
EL for Double Pendulum State Prediction
Challenge: Complex physical systems exhibit behavior that traditional models struggle to predict and control, particularly during transitions between regular and chaotic motion regimes.
EL Implementation: Our framework would measure information relationships between energy states and transitions, revealing predictable information gradients patterns in seemingly chaotic behavior and generating control signals that maintain system coherence across operating regimes.
Impact: This fundamental research demonstrates how information throughput optimization can reveal hidden order in complex systems, establishing a foundation for controlling previously unpredictable physical processes in manufacturing, fluid dynamics, and other fields.
