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The Emergence of Human-like Knowledge in Artificial Associative Systems

This paper explores how human-like knowledge can be represented in artificial associative systems. It discusses the active aggregation of data, facts, and rules, emphasizing the significance of context and associations in knowledge recall and generalization. The work highlights the limitations of traditional databases in representing knowledge and elaborates on neural associative systems that mimic biological processes. The goal is to understand how artificial systems can create, generalize, and recall knowledge akin to human cognitive behavior.

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The Emergence of Human-like Knowledge in Artificial Associative Systems

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  1. Can we representknowledge? HOW DOESHUMAN-LIKE KNOWLEDGECOME INTO BEING INARTIFICIAL ASSOCIATIVE SYSTEMS? Adrian Horzyk horzyk@agh.edu.pl AGH UNIVERSITY OF SCIENCE AND TECHNOLOGY Faculty of Electrical Engineering, Automatics,Computer Science and Biomedical Engineering Department of Automatics and Biomedical Engineering Unit of Biocybernetics POLAND, 30-059 CRACOW, MICKIEWICZA AV. 30

  2. Knowledgeallows to: • Rememberfacts, rules, objectsorclasses of them. • Consolidatevariousfacts and rulesaftertheirsimiliarities. • Associateobjects, facts, rules with contexts of theiroccurences. • Recallfacts and rulesusingcontext and associations. • Generalizeobjects, facts and rules. • Be creativeusinglearnedclasses of objects, facts and rules. HUMAN-LIKE KNOWLEDGE • Variousfacts and rulescan be associated and recalledthanks to: • Similaritiesof the data thatdefinethem. • Subsequencesof the data thatoccurinsidethem. Knowledge isactiveaggregation of data, facts and rulesthatcan be recalled and generalizedaccording to the context of theirrecalling. Human-likeknowledgecan be representedonly in reactivesystemsthatcanrepresentsuchnot redundantaggregations.

  3. Knowledge: • Isnot a set of facts, rules, objectsorclasses of them. • Isnokindof a computermemoryor a database. • Doesnotremembereverythingprecisely. • Cannot be collectedalike data, facts and rules but itcan be formed for givenorcollected data, facts and rules. • Cannot be easytransferedfrom one system to anotheralike data, databases, facts and rules etc. Onlypieces of information, facts and rulescan be transferedintoanother system. Can be partiallytransferedthroughrecalledfacts and rules. • Isnotlimitedto any set of facts, rulesorobjectsbecausenew, creativeinputcontextscanlead to newfacts, rules, notices, observations and remarks on the basis of the same knowledge. WHAT IS NOT KNOWLEDGE ? Knowledge can be automaticallyformedonly in specialsystemsthatallow to activellyassociateand aggregatedata, factsand rules, and theirvariouscombinations and sequences.

  4. Neuralassociativesystemsallows to: • Representvariousobjects, facts and rules in a unified form of data combinationsusingneurons. • Createclassesof representedobjectsafter most representativefeatures and theircombinations. • Triggerneuronsaccording to the context of otheractivatedneuronsorsensereceptors. • Use the contextof previouslyactivatedneuronsaccording to the timethathaselapsed from theiractivations. • Consolidate and combinevariousobjects, facts and rulesaftertheirsimiliarities and subsequences. • Associateobjects, facts, rules with contexts of theiroccurences. • Recallassociatedobjects, facts, rulesusingneworpreviouslyusedcontexts, questions etc. • Generalizeand evencreatenewobjects, facts and rules. NEURAL ASSOCIATIVE SYSTEMS

  5. Artificialassociativesystems: • Model biologicalneuralassociativesystems, nervoussystems etc. • Defineassociative model of neurons(as-neurons)thatareable to reproducecontext and timedependencies of biologicalneurons. • Can be simulated, trained and adapted on today’scomputers. • Canusevarioustraining data setand evensetsof trainingsequences. • Canreproducetrainingsequencesorcreatenewones- be creative! • Cangeneralizeatvariouslevels: ARTIFICIALASSOCIATIVE SYSTEMS ArtificialAssociative Systems and AssociativeArtificialIntelligence (Polish) Sequencelevel Object level

  6. Transformation of databasetablesintoassociativestructures ASSORTcreates the basisgraphstructure of associativesystems. TABLE Adrian Horzyk, horzyk@agh.edu.pl, AGH University of Science and Technology

  7. Transformation of databasetablesintoassociativestructures ASSORTcreates the basisgraphstructure of associativesystems. TABLE Adrian Horzyk, horzyk@agh.edu.pl, AGH University of Science and Technology

  8. Transformation of databasetablesintoassociativestructures ASSORTcreates the basisgraphstructure of associativesystems. TABLE Adrian Horzyk, horzyk@agh.edu.pl, AGH University of Science and Technology

  9. Transformation of databasetablesintoassociativestructures ASSORTcreates the basisgraphstructure of associativesystems. TABLE Adrian Horzyk, horzyk@agh.edu.pl, AGH University of Science and Technology

  10. Transformation of databasetablesintoassociativestructures ASSORTcreates the basisgraphstructure of associativesystems. TABLE Adrian Horzyk, horzyk@agh.edu.pl, AGH University of Science and Technology

  11. Transformation of databasetablesintoassociativestructures ASSORTcreates the basisgraphstructure of associativesystems. TABLE Adrian Horzyk, horzyk@agh.edu.pl, AGH University of Science and Technology

  12. Transformation of databasetablesintoassociativestructures ASSORTcreates the basisgraphstructure of associativesystems. TABLE Adrian Horzyk, horzyk@agh.edu.pl, AGH University of Science and Technology

  13. Transformation of databasetablesintoassociativestructures ASSORTcreates the basisgraphstructure of associativesystems. TABLE Adrian Horzyk, horzyk@agh.edu.pl, AGH University of Science and Technology

  14. Transformation of databasetablesintoassociativestructures ASSORTcreates the basisgraphstructure of associativesystems. TABLE Adrian Horzyk, horzyk@agh.edu.pl, AGH University of Science and Technology

  15. Transformation of databasetablesintoassociativestructures ASSORTcreates the basisgraphstructure of associativesystems. TABLE Adrian Horzyk, horzyk@agh.edu.pl, AGH University of Science and Technology

  16. Transformation of databasetablesintoassociativestructures ASSORTcreates the basisgraphstructure of associativesystems. TABLE Adrian Horzyk, horzyk@agh.edu.pl, AGH University of Science and Technology

  17. for trainingsequences: S1, S2, S3, S4, S5 ASSOCIATIVE NEURAL GRAPH CONSTRUCTION

  18. for trainingsequences: S1, S2, S3, S4, S5 ASSOCIATIVE NEURAL GRAPH CONSTRUCTION

  19. for trainingsequences: S1, S2, S3, S4, S5 ASSOCIATIVE NEURAL GRAPH CONSTRUCTION

  20. for trainingsequences: S1, S2, S3, S4, S5 ASSOCIATIVE NEURAL GRAPH CONSTRUCTION

  21. for trainingsequences: S1, S2, S3, S4, S5 ASSOCIATIVE NEURAL GRAPH CONSTRUCTION

  22. for trainingsequences: S1, S2, S3, S4, S5 ASSOCIATIVE NEURAL GRAPH CONSTRUCTION

  23. for trainingsequences: S1, S2, S3, S4, S5 ASSOCIATIVE NEURAL GRAPH CONSTRUCTION

  24. The externalexcitation of neuron E4 triggers the followingactivations of neurons: E4  E5  E2  E6 ASSOCIATIVE NEURAL GRAPH EVALUATION We gotsequence S2 as the answer for the externalexcitement of neuron E4:

  25. Neuralassociativestructure for the linguisticobjects THE SIMPLE NEURAL STRUCTURE OF THE CONSECUTIVE LINGUISTIC OBJECTSrepresenting 7 sentences

  26. Response to „Whatisknowledge?” • As-neuronsareconsecutivelyactivatedaftertrainingsequences and give the answers: • Knowledgeisfundamental for intelligence. • Knowledgeis not a set of facts and rules

  27. Associative model of neurons • AS-NEURON: • Works in timethatiscrucial for allassociativeprocessesin the network of connected as-neurons. • Modelsrelaxation and refractionprocesses of biologicalneurons • Relaxation – continuousgradualreturning to itsrestingstate • Refraction – gradualreturning to itsrestingstateafteractivation • Optimizesitsactivityresponcesfor input data combinationschosingonlythe the most intensiveand frequentsubset of them. • Conditionallyplasticallychangesitssize, synaptictransmission and connections to other as-neurons. • Canrepresentmanysimilar as well as quitedifferentcombinations of inputstimuli (data). ASSOCIATIVE MODEL OF NEURONS

  28. Knowledgecan be modelledusingartificialassociativesystems. • Training sequencescan be used to adaptartificialassociativesystems • Associativesystemssupplyus with ability to generalizeon variouslevels: • classescreated for objects • sequencesdescribingfacts and rules • Associativesystemscan be creativeaccording to the context, whichcanrecallnewassociations. CONCLUSION

  29. ? Theory of neural associativecomputationsand knowledge engineeringin the associativesystems Questions? Remarks? ArtificialAssociative Systems and AssociativeArtificialIntelligence (Polish) Google: Horzyk Adrian horzyk@agh.edu.pl

  30. We canmakeour World a better place to live!

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