Theory of Machine Networks: A Case Study
This work addresses a foundational problem in AI for modeling consciousness, but appears incremental as it simplifies an existing architecture.
The paper tackles the problem of modeling complex, deterministic machines as a proxy for nondeterministic, conscious entities by proposing a simplification of the Theory-of-Mind Network architecture, and validates it in the context of understanding engines, though no concrete results or numbers are provided.
We propose a simplification of the Theory-of-Mind Network architecture, which focuses on modeling complex, deterministic machines as a proxy for modeling nondeterministic, conscious entities. We then validate this architecture in the context of understanding engines, which, we argue, meet the required internal and external complexity to yield meaningful abstractions.