Thinking is not computation
Nor a conditioned reflex
Computers are still programmed with fixed algorithms or trained on static data. Our architecture bridges both approaches and is capable of evolving much like a human.
Ontological Shift
We represent space, time, and entities in a novel way, reflecting reality just as humans perceive it.
Architectural Shift
An LLM is a single machine learning model with numerous algorithmic tweaks. Living beings, however, possess a multitude of reflexes or ML models within an algorithmic nervous system.
Paradigm Shift
Our architecture adapts dynamically and reliably to external changes, making it suitable for complex, real-world automation tasks.
The Cognito Engine
A Post Von Neumann Runtime
We are architecting an implementation of Monad Algebra based on yet-unpublished Differential Phenomenology.
The Next Layer of Abstraction
Monadic formalization bridges the gap between data, structure, and logic. Causal monadic state shifts are compilable into - but not limited to - functions or instructions.
Paradigm Symphony
We have transmuted the essence of procedural, object-oriented, logical, concurrent, and reactive programming, alongside ML. Ontological and behavioral hypotheses are generated via reflexes and verified logically. Reflex training relies on real-world data and a validated ontology.
Transcending Determinism
Dynamic ontology and behavior allow the system to temporarily increase its internal entropy when interacting with an indeterminate reality, subsequently transforming it into deterministic complexity through hypothesis validation.