In reality, sensors and effectors exist in space as well as time. Serializing the spatial dimension of observations to formalize their Kolmogorov Complexity, so they conform to the serialized input to a Universal Turing machine, over-constrains the observations, introducing order not relevant to their natural information content, hence artificially inflating the, so-defined, KC.
Since virtually all models in machine learning are based on tabular data, even if they can be cast as time series, row-indexed by a timestamp, each row is an observation with multiple dimensions. So it seems rather interesting, if not frustrating, that the default assumption in Algorithmic Information Theory is of a serial UTM. ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/Tc33b8ed7189d2a18-M52c8573613f4210aba7709dd Delivery options: https://agi.topicbox.com/groups/agi/subscription
