Referenced CTT Structure: Part II–4, II–5
Scholarly Keywords: Human-Robot Interaction, Perception Delay, Reaction Time Modeling
This section outlines how Constructive Tensor Theory (CTT) can address key unresolved limitations in modern AI's semantic understanding, as identified through DeepResearch analysis.
AI Limitation | DeepResearch Summary | CTT Contribution |
---|---|---|
Statistical Pattern Imitation (Stochastic Parrot) | Lacks true semantic comprehension | CTT enables transition from form to meaning via constructive observer frames |
Symbol Grounding Problem | No link between symbols and external world | CTT allows selection and transformation of semantic bases using pullback structures |
Lack of World Models | Time, space, and causality are missing | CTT embeds causality into hierarchical manifold pullbacks |
Deficiency in Causal Inference | Only correlations are learned, not causes | CTT constructs causal laws via tensorial transformation rates |
Embodiment Deficit | No linkage with sensorimotor experience | CTT represents embodied observers explicitly in tensor hierarchies |
Lack of Intentional Agency | No context-aware, autonomous behavior | CTT defines observers as agent-based systems unified by observation and action |
symbol grounding problem + manifold representation
causal structure in agent-based AI
tensor-based semantics for embodied AI
observer frame and meaning construction in neural-symbolic models
compositionality and pullback structures in AI