Home Links

Home Links

classification emerges from usage

  • Objects are classified solely through ordinary use of the system. A separate classification process, involving topic authorities is not required.
  • Systematic distinction between classfier and classified object does not pertain. e.g. mammal / Andrew Henderson. Tags are  classified only according to their proximity to other tags.

objective data, subjective information

heterogeneity

  • Any pre-existing schema such as natural languages and newly emerging schemas may be assimilated into Akinity either indirectly through linking or directly through synthesis.
  • As a currency does for goods exchange, so Akinity brings fungibility to information exchange between heterogeneous schemas.

data distribution

  • Data is distributed unevenly and this pattern allows semantics to emerge.
  • Distribution patterns change over time. Therefore the system evolves.
  • There is no central locus of complete information.

probabilistic

  • The system does not deliver absolute certainties, but for typical use-cases confidence can be extremely high.

a system of tags

not a graph

  • Akinity's model is more like physical 3-D space than a logical network of nodes and edges. Any object in space has an intrinsic location that bestows it with a quantifiable distance from every other known object, without necessary recourse to some extrinsic maping table. This is also true for Akinity .

non-arbitrary data values

  • Values may only be assigned by the algorithms.
  • Values are binary. Nevertheless, they may be translated to text through an encoding such as Base64 or Base16

steganograpy

  • Interpretation in Akinity is an application of exformation.
  • The system is tolerant of asymmetric information in an exchange. Information may be passed through an unknowing subject to a third-party