Mission Statement
Our mission is to progress the sectionalization of artificial neural networks in artificial intelligence systems into lobes modeled after the human brain with a localized multi-AI model system built around humans rather than over.
Vision Statement
We aspire for the manifestation of a derivative form of agentic AI called Suggentic AI in our society, with the intent to intercede agentic AI processes that are replacing the jobs of humanity and diminishing the integrity of our society's artistic content.
A framework for Suggentic AI
AIBAware is being developed as a localized AI environment where specialized models, project knowledge, and approved tools can work together through one human-directed system.
Suggentic AI
A semi-autonomous form of agentic AI workflows combining procedural generation with the complementary process of procedural collaboration as to provide users with iterative contextual control over the models’ ongoing processes by previewing request outputs, controlling the amount of those deviations, and suggesting clarifications throughout the process.
Localized Multi-AI/ML System
Multiple specialized AI models, selected at the users discretion, operate within a localized compute environment under supervisory AI layers that route tasks, optimize outputs, guide decisions, and flag disruptions to improve user control.
Procedural Collaboration
The procedure of a system coordinating user interactions through a model acting as both a behavioral analyzer over other models utilized in the request and a liaison for the user’s ongoing compute, tasked with keeping those processes aligned by offering or requesting clarification from the user while preserving their control over deviations in the output.
Procedural Deviation
The iterative process of previewing outputs for solutions to requests before they are generated, as to align finalized outputs with their intended direction, or even unintended directions when novelty and the unseen meet, revealing a more preferable PATH.
Proportional Role Tagging
Distributes responsibility for a request across specialized AI roles in proportion to the type and relevance of each of the requests components.
Confidence Tagging
Evaluates each component of the request and output processes by its categorical level of acceptance and relative significance to the core goal of the request.