In Artificial Intelligence, there have existed two paradigms: Symbolic Architectures (often known as GOFAI), and deep learning. Symbolic AI is great at doing deductive logic, but can't generalize (inductive logic), and can't learn from high dimensional input, ultamitely leading to them being unable to flexibly learn. In Contrast, Modern AI are really good at inductive logic, but struggle with deductive logic. However, since deductive logic is needed for learning, models must be designed to incorporate this deductive logic inside.

Taking a closer look, both appear to solve the problem the other struggles with. The industry has presented something called neurosymbolics for this, which is essentially an architecture made up of both symbolic and deep layers. However, these layers exist as separate units This begs the question: What if there was a way to combine both technologies, such that they are not separate units and integrate into each other.

The question described above is the question Cyquential AI tries to answer. Through a research process of 6 years, Cyquential AI is a completely new architecture that tries to do just that. It uses a new propositional learning hypothesis, called the Cyquential hypothesis, that has been brainstormed and engineered for over 6 years, redesigning symbolic AI in such a way that symbols are generated autonomously in the knowledge base from real time perceptual data, as opposed to being hardcoded in. The symbols generated represent task generalized object representations, and are created sequentially such that they build off each other.

Due to this, Cyquential AI becomes a completely unseen before self building architecture, where neurons and synapses form by themselves as opposed to being fixed hyperparameters. This functionality is what enables Cyquential AI to perform full continual learning, with very minimal catastrophic forgetting as no neurons are deleted or changed.