Decoding Geometric Properties in Non-Random Data from First Information-Theoretic Principles
<|reference_start|>Decoding Geometric Properties in Non-Random Data from First Information-Theoretic Principles: Based on the principles of information theory, measure theory, and theoretical computer science, we introduce a univariate signal deconvolution method with a wide range of applications to coding theory, particularly in zero-knowledge one-way communication channels, such as in deciphering messages from unknown generating sources about which no prior knowledge is available and to which no return message can be sent. Our multidimensional space reconstruction method from an arbitrary received signal is proven to be agnostic vis-a-vis the encoding-decoding scheme, computation model, programming language, formal theory, the computable (or semi-computable) method of approximation to algorithmic complexity, and any arbitrarily chosen (computable) probability measure of the events. The method derives from the principles of an approach to Artificial General Intelligence capable of building a general-purpose model of models independent of any arbitrarily assumed prior probability distribution. We argue that this optimal and universal method of decoding non-random data has applications to signal processing, causal deconvolution, topological and geometric properties encoding, cryptography, and bio- and technosignature detection.<|reference_end|>
@article{zenil2024decoding,
    title={Decoding Geometric Properties in Non-Random Data from First
  Information-Theoretic Principles},
    author={Hector Zenil and Felipe S. Abrah~ao},
    journal={arXiv preprint arXiv:2405.07803},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.07803},
    primaryClass={cs.IT cs.CL cs.CR cs.IR math.IT math.ST stat.TH}
}