Houses of Mirrors: Deeply Adaptive Designs for Machine Cognition

1. Houses of Mirrors: Deeply Adaptive Designs for Machine Cognition Deborah Duong, Michael Ross

2. Next for MICCE: Ontological Level • Emergence of Data Driven Ontologies from Text • Looking for High Independence of grouping and low variance within groupings • In other words, the highest mutual information, lowest entropy grouping

3. Social Hierarchies • At INSCOM, subsumption hierarchical trees of roles and role relations • Entities grouped into roles • Paths grouped into role relations • isa relations: • Black-market-merchant isa merchant

4. MICCE finds Systems • Finds systemic relations in common to similar processes • Common paths between roles become role relations • Higher levels of hierarchy have more abstract processes. • Happen to be social systems at INSCOM • Regular Structural Equivalence in Social Networks • Can help to find terrorist organizations

5. Ontologies • Users may browse data in terms they are used to, at any level of generalization • Ex. The query: “terrorists bombing civilians” can find “Joe suicide-vest-bombing subway-riders” • Hierarchy gives AI programs a gradient, a measure of semantic distance from every concept to every other concept, making the space navigable.

6. Concepts of Concepts • We will implement ontologies by sending concepts through the feedback loop • Concepts will form based on similarity, split based on variance • Concepts become more independent as dependent concepts are merged • With iteration concepts will become more like orthogonal bases

7. Greater Independence • More accurate semantic distance computed • Helps to minimize variance • Example: Taking cosine coefficient with 50 synonyms for a word rather than a single concept that combines them • Calculations more accurate because we don’t make false distinctions due to noise

8. Another Level of Feedback • Types of Feedback • Side-to-Side: Between entity and link assignments • Upper-Lower: Between parse, word sense, ontological levels • We already have feedback between parse selection and word sense • Parses are chosen to reinforce existing patterns of concepts • Now higher level ontological categories can feedback into the grouping of concepts • Ex. Concept of mammal needed to split “dolphin” from “tuna” • Feedback between parse, word sense and ontological levels for global consensus on meaning

9. Ontologies Problematic • MICCE will approximate most likely (highest mutual information) ontology • BUT, analysts want their own ontologies • Different experts look at same data • At INSCOM • Data stored in primitive entities and paths • MICCE to make semantic model on the fly tailored to ontology of who is looking at it.

10. Hypothesis Driven AND Data Driven • MICCE can flexibly take in analyst input • MICCE can align its ontology to another with very few points of correspondence • Feedback gives MICCE advantage over other systems that generate ontologies: • Global consensus • Ability to adapt to any amount of user input

11. House Of Mirrors Design Pattern • In this design pattern, every thing is defined by everything else • In MICCE, every concept is defined by its relation with every other concept • Houses of mirrors use self fulfilling prophecy: they are highly seedable • If an analyst groups concepts: • Collocated paths found • These help develop analyst’s concept • More consonant concepts and paths found • RELATIVELY FEW points of correspondence needed

12. Nonlinguistic world: Abstractions of processes • In text, MICCE separates different roles in the same person and different abstract processes that apply to these roles • Applied to the non-linguistic world, it will find different function in the same items, abstracting on the different processes performed • These processes can be abstracted and specify simulations

13. MICCES align ontologies with each other • 2 MICCES, one in the linguistic and the other in the nonlinguistic realm, may be aligned through very few points of correspondence “pointing to ball and saying ball” • They perform collocations for each other, ie, images of cats serving to collocate the words “kitty” and “cat” • Where there are no points of correspondence, both would fill in the gaps in consonance with the other

14. Simulacra • A proposed coevolving simulation system, which is also a house of mirrors, can be used to perform the more complicated collocations • By adapting to the seeds of both MICCES, it helps to fuse the data • Simulacra will accept the systems that both MICCES extract, and translate them into a a “language of process” , like a language of thought • Our approach to natural language understanding: To Understand is to Simulate