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