Testing the Quantitative Spacetime Hypothesis using Artificial Narrative Comprehension (I) : Bootstrapping Meaning from Episodic Narrative viewed as a Feature Landscape
This work addresses the challenge of concept extraction from spacetime processes for hybrid symbolic-learning AI, offering a novel semantic preprocessor that is incremental in its approach.
The study tackled the problem of extracting meaningful parts from sensory data streams without prior training, using textual narrative as a test case, and found that simple spacetime process cues can bootstrap cognitive processing, with lightweight procedures running in seconds on a single CPU.
The problem of extracting important and meaningful parts of a sensory data stream, without prior training, is studied for symbolic sequences, by using textual narrative as a test case. This is part of a larger study concerning the extraction of concepts from spacetime processes, and their knowledge representations within hybrid symbolic-learning `Artificial Intelligence'. Most approaches to text analysis make extensive use of the evolved human sense of language and semantics. In this work, streams are parsed without knowledge of semantics, using only measurable patterns (size and time) within the changing stream of symbols -- as an event `landscape'. This is a form of interferometry. Using lightweight procedures that can be run in just a few seconds on a single CPU, this work studies the validity of the Semantic Spacetime Hypothesis, for the extraction of concepts as process invariants. This `semantic preprocessor' may then act as a front-end for more sophisticated long-term graph-based learning techniques. The results suggest that what we consider important and interesting about sensory experience is not solely based on higher reasoning, but on simple spacetime process cues, and this may be how cognitive processing is bootstrapped in the beginning.