Conceptual Spaces, a confirmation of the old intuitions from programming languages?

Programming languages have been introducing features over 70+ years, and some of them became ubiquitous (suggesting we find them useful). Separately, the theory of conceptual spaces also exhibits features, that match quite well the most ubiquitous PL features. Let's have a brief look at that during this short talk.

1. Conceptual Spaces A confirmation of old intuitions from programming languages & software development practices @cyriux
2. This tool is useful. No idea why.
3. 70+ years of Programming language evolutions… Some features became ubiquitous.
4. Ubiquitous features of Programming Languages
5. naming with natural language
6. enum basic types, primitives + operations
7. Classes Records Tables Prototypes Product types grouping related things
8. navigable relations
9. classification, partitioning of cases
10. multiple models, by context (purpose)
11. multiple models, by context (purpose)
12. “At Amazon, what a book is for you?” •Catalog: Picture, title, authors, rating, format (ebook or paper), category •Recommandation: List of books often bought together with it •Shipping: Dimensions, weight, international restrictions due to content •Shopping cart: Price, discount eligible •Customer review: List of (rating, review, review rating) •Book Search: title, isbn, authors multiple models, by context (purpose)
13. Meanwhile in academia…
14. Metaphors All The Things! Conceptual Spaces FTW!
15. The key features of Conceptual Spaces
16. language-driven
17. Quality Dimensions e.g. Kinship Metric structure Cyclic group Boolean Dead Alive
18. Biological Shapes analysis of shapes by Marr and Nishihara (1978) cylinder = [length width]T relative angle = [ ]T relative position = [a b]T Meronomic relations (part-whole)
19. color = [brightness intensity hue]T taste = [sweet bitter saline sour]T Basic Domains (grouping dimensions ) Color domain Taste Domains
20. emotion = [valency arousal]T [] emotion = [valency arousal dominance]T Russell's circumplex Lövheim's emotion cube Emotions Basic Domains (grouping dimensions )
21. Concept = Product of Convex Sub-Regions https://golem.ph.utexas.edu/category/2018/03/cognition_convexity_and_catego.html Basic Domains (grouping dimensions ) A banana instance
22. Voronoï tesselation to classify things prototypical exemplar (black crossses) change of Voronoï tesselation
23. car weight engine power prototypical exemplar (black dots) Voronoï Tesselation
24. Convexity, Monotony, Continuity
25. Prominence (weighted dimensions by context) ”For a purpose, weights on each dimensions to focus the most useful and ignore the other.”
26. “At Amazon, what a book is for you?” •Catalog: Picture, title, authors, rating, format (ebook or paper), category •Recommandation: List of books often bought together with it •Shipping: Dimensions, weight, international restrictions due to content •Shopping cart: Price, discount eligible •Customer review: List of (rating, review, review rating) •Book Search: title, isbn, authors Prominence (weighted dimensions by context)
27. Programming languages Naming everything with identifiers Basic dimensions: primitives, collections, enums Grouping of dimensions: Struct, Tables, Classes, Prototypes, Product types Navigable Relations: Associations, pointers, references, foreign keys) Bounded Contexts: microservices, modular monolith, no more one unified model but one model by context Partitioning, categorizing: IF statements on values, inheritance, ML Conceptual Spaces 1-to-1
28. Programming languages Naming everything with identifiers Basic dimensions: primitives, collections, enums Grouping of dimensions: Struct, Tables, Classes, Prototypes, Product types Navigable Relations: Associations, pointers, references, foreign keys) Bounded Contexts: microservices, modular monolith, no more one unified model but one model by context Partitioning, categorizing: IF statements on values, inheritance, ML Language-driven Quality dimensions: line, bounded line, circle… Grouping of dimensions: Domain, Set of quality dimensions Navigable Relations: kinship relations, meronomic relations (part-whole) Bounded Contexts: Domains in an object category are weighted by their prominence in a [usage/finality] context Partitioning, categorizing: Voronoï tesselation Conceptual Spaces 1-to-1
29. Programming languages Naming everything with identifiers Basic dimensions: primitives, collections, enums Grouping of dimensions: Struct, Tables, Classes, Prototypes, Product types Navigable Relations: Associations, pointers, references, foreign keys) Bounded Contexts: microservices, modular monolith, no more one unified model but one model by context Partitioning, categorizing: IF statements on values, inheritance, ML Language-driven Quality dimensions: line, bounded line, circle… Grouping of dimensions: Domain, Set of quality dimensions Navigable Relations: kinship relations, meronomic relations (part-whole) Bounded Contexts: Domains in an object category are weighted by their prominence in a [usage/finality] context Partitioning, categorizing: Voronoï tesselation Conceptual Spaces 1-to-1 Sounds like our Programming Language inventors got it right after all!
30. Perhaps our Programming Language features were not that arbitrary.
31. Our capacity to think the world is limited to the same set of basic constructs that mathematicians have been inventorying for centuries but perhaps it’s unlikely we discover radically new way to program then.
32. Programming Languages Conceptual Spaces Still, the remaining gap suggests potential new features for Programming Languages…
33. ★ understand the human language much more directly (native interpretation of common metaphors & metonymies) ★ support more built-in basic topologies (bounded numbers, cyclic group, whole-part…), ★ support progressive/incremental refinements of concepts, ★ … all that through much cheaper computations than our current ML. For Programming Languages that would… Potential from Conceptual Spaces
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35. @cyriux BUY MY BOOK!
36. arolla.fr @arollafr
37. Transformations
38. Thank you! @cyriux