Semantic Spacetimes
Formalizing the semantics of space and time, for cognition and measurement (a route to knowledge representation)
This project started in June 2014, to document puzzles that have bugged me since I started thinking about promises around 2002. The chosen language here is Promise Theory (2004-2014), and it touches on cross disciplinary problems around how we measure and explain behaviour in distributed systems.
A semantic spacetime is a discrete graph, which evolves, and whose properties vary from point to point. They can be described both in terms of their topological, dynamical, and semantic functional properties.
- What is semantic spacetime and why do we need it?
- Introduction and motivation essay
- Spacetimes with Semantics (I)
Notes on Theory and Formalism (2014) - From Einstein to Milner. - Spacetimes with Semantics (II)
Scaling of agency, semantics, and tenancy (2015) - from ensembles to name dropping - Spacetimes with Semantics (III)
The Structure of Functional Knowledge Representation and Artificial Reasoning (2016) - Supplementary work
On the scaling of functional spaces, from smart cities to cloud computing (2016) - Supplementary work
Smart cities - field considerations and IoT (2016) - Supplementary work
A Spacetime Approach to Generalized Cognitive Reasoning in Multi-scale Learning (2017) - Spacetime-Entangled Networks (I) Relativity and Observability of Stepwise Consensus (2018)
- Testing the Quantitative Spacetime Hypothesis using Artificial Narrative Comprehension (I) : Bootstrapping Meaning from Episodic Narrative viewed as a Feature Landscape (2020)
- Testing the Quantitative Spacetime Hypothesis using Artificial Narrative Comprehension (II) : Establishing the Geometry of Invariant Concepts, Themes, and Namespaces (2020)
- Virtual Motion I: Motion of the 3rd Kind - Draft paper on virtual motion (2021)
- Virtual motion II: Notes on kinematics, dynamics, and relativity in Semantic Spacetime
- Github repository for project software
- Universal Data Analytics as Semantic Spacetime (series in 11 parts on medium)
- Artificial reasoning in non-intelligent systems
- Virtualizing virtualization to scale infrastructure to an Internet of Things
- Laugh IT up --- the Internet is just a gas!
- Interview podcast with Jim Rutt on Semantics Spacetime and AI
Video of my talks about semantic spaces:
- Animated introduction to promise theory basics
- Functional spaces - 25 yr anniversary keynote (see list selectionon page)
- Brains, Societies, and Semantic Spaces (related to blog post).
- Thinking in Promises for the Cyborg Age - Percolate Transition conference NYC 2015
- Video of Papers We Love talks by Paul Borrill and Adrian Cockcroft about spacetime in IT.
The need for a semantic understanding of spacetime comes from the intersection between spacetime and information science. Does matter fill space, or replace space? Physics sees spacetime through the lens of symmetry i.e. invariance (as a tool for description). But information science sees spacetime through the lenses of change and boundaries. One points us to ensemble interpretations of continuity, the other points us to event-based (cognitive) evolution.
Semantic spacetime can be applied to both physical spaces like supermarkets, as well as virtual spaces like computer clusters or cloud computing. A unified description of subjects like shared tenancy, intentional and functional layout, automated learning, and artificial reasoning. Applications are many and the unification of pervasive fixed and mobile computing into smart spaces (Internet of Things meets cloud computing) as well as insight into fundamental questions about space and time in nature.
Cognition and observation as spacetime
We measure the world according to either timelike and spacelike models:
- Timelike is what we call cognition: there is a continual stream of input events, which alter our understanding and semantics framework in real time. This is a non-equilibrium view of observation.
- Spacelike is what we call ensemble statistics: we try to divorce experiments from time by averaging over repeated experiments under invariant conditions, where time plays no role. Reversibility or indistinguishability
These are spacetime concepts related to information. Only by understanding these ideas in a discrete intentional framework can we make sense of what we interpret by space and time.