• How does Physix encode complexity beyond surface-level intent?
    • Physix is a meta-language. Any complexity can be inferred through words and math, Physix brings what is invisible to the
    • You mention that Physix is simple enough for a two-year-old to grasp, but AGI needs to handle high-dimensional, ambiguous, and contextually rich data. Human language is nuanced because meaning is not just explicit but also derived from indirect cues, long-term memory, and cultural context. If Physix reduces complexity, how does it handle subtlety, contradiction, or multi-layered reasoning?

How does this system computationally model intelligence?
You’ve described what the system represents, but not how an AI would use it to process, learn, or infer knowledge. A universal character set doesn’t inherently create intelligence; it needs algorithms for reasoning, learning, and adaptation. What specific AI or machine learning methods are you envisioning? Are there models, training methods, or a defined computational structure?

What makes Physix different from or superior to existing AI models?
NLP systems today already detect intent, sentiment, and ambiguity using embeddings, transformers, and reinforcement learning. If AI struggles with sarcasm, satire, or out-of-context speech, it’s not due to a lack of symbols but because meaning often depends on world knowledge, speaker history, and social cues. How does Physix provide a fundamental improvement over techniques like deep contextual embeddings or adversarial training?

How does Physix scale beyond human interpretation?
Right now, it seems like a human categorization tool, a way for people to manually express intent. AGI isn’t just about categorization; it’s about self-directed learning, prediction, and adaptation. If an AI were trained on Physix, how would it generalize beyond predefined rules to make new inferences?

How does this system move beyond a categorization tool?
From what I can tell, this is a structured symbolic representation rather than an AI framework that processes data, learns, or generalizes. Are you envisioning this as an input representation for an AI model, or are you suggesting that this system itself performs AI tasks?