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HOW DOES HUMAN-LIKE KNOWLEDGE COME INTO BEING IN ARTIFICIAL ASSOCIATIVE SYSTEMS? AGH UNIVERSITY OF SCIENCE AND TECHNOLOGY Faculty of Electrical Engineering,

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Presentation on theme: "HOW DOES HUMAN-LIKE KNOWLEDGE COME INTO BEING IN ARTIFICIAL ASSOCIATIVE SYSTEMS? AGH UNIVERSITY OF SCIENCE AND TECHNOLOGY Faculty of Electrical Engineering,"— Presentation transcript:

1 HOW DOES HUMAN-LIKE KNOWLEDGE COME INTO BEING IN ARTIFICIAL ASSOCIATIVE SYSTEMS? 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, CRACOW, MICKIEWICZA AV. 30 Adrian Horzyk Adrian Can we represent knowledge?

2 HUMAN-LIKE KNOWLEDGE Knowledge allows to: Remember facts, rules, objects or classes of them. Remember facts, rules, objects or classes of them. Consolidate various facts and rules after their similiarities. Consolidate various facts and rules after their similiarities. Associate objects, facts, rules with contexts of their occurences. Associate objects, facts, rules with contexts of their occurences. Recall facts and rules using context and associations. Recall facts and rules using context and associations. Generalize objects, facts and rules. Generalize objects, facts and rules. Be creative using learned classes of objects, facts and rules. Be creative using learned classes of objects, facts and rules. Various facts and rules can be associated and recalled thanks to: Similarities of the data that define them. Similarities of the data that define them. Subsequences of the data that occur inside them. Subsequences of the data that occur inside them. Knowledge is active aggregation of data, facts and rules that can be recalled and generalized according to the context of their recalling. Human-like knowledge can be represented only in reactive systems that can represent such not redundant aggregations.

3 WHAT IS NOT KNOWLEDGE ? Knowledge: Is not a set of facts, rules, objects or classes of them. Is not a set of facts, rules, objects or classes of them. Is no kind of a computer memory or a database. Is no kind of a computer memory or a database. Does not remember everything precisely. Does not remember everything precisely. Cannot be collected alike data, facts and rules but it can be formed for given or collected data, facts and rules. Cannot be collected alike data, facts and rules but it can be formed for given or collected data, facts and rules. Cannot be easy transfered from one system to another alike data, databases, facts and rules etc. Only pieces of information, facts and rules can be transfered into another system. Can be partially transfered through recalled facts and rules. Cannot be easy transfered from one system to another alike data, databases, facts and rules etc. Only pieces of information, facts and rules can be transfered into another system. Can be partially transfered through recalled facts and rules. Is not limited to any set of facts, rules or objects because new, creative input contexts can lead to new facts, rules, notices, observations and remarks on the basis of the same knowledge. Is not limited to any set of facts, rules or objects because new, creative input contexts can lead to new facts, rules, notices, observations and remarks on the basis of the same knowledge. Knowledge can be automatically formed only in special systems that allow to activelly associate and aggregate data, facts and rules, and their various combinations and sequences.

4 NEURAL ASSOCIATIVE SYSTEMS Neural associative systems allows to: Represent various objects, facts and rules in a unified form of data combinations using neurons. Represent various objects, facts and rules in a unified form of data combinations using neurons. Create classes of represented objects after most representative features and their combinations. Create classes of represented objects after most representative features and their combinations. Trigger neurons according to the context of other activated neurons or sense receptors. Trigger neurons according to the context of other activated neurons or sense receptors. Use the context of previously activated neurons according to the time that has elapsed from their activations. Use the context of previously activated neurons according to the time that has elapsed from their activations. Consolidate and combine various objects, facts and rules after their similiarities and subsequences. Consolidate and combine various objects, facts and rules after their similiarities and subsequences. Associate objects, facts, rules with contexts of their occurences. Associate objects, facts, rules with contexts of their occurences. Recall associated objects, facts, rules using new or previously used contexts, questions etc. Recall associated objects, facts, rules using new or previously used contexts, questions etc. Generalize and even create new objects, facts and rules. Generalize and even create new objects, facts and rules.

5 ARTIFICIAL ASSOCIATIVE SYSTEMS Artificial associative systems: Model biological neural associative systems, nervous systems etc. Model biological neural associative systems, nervous systems etc. Define associative model of neurons (as-neurons) that are able to reproduce context and time dependencies of biological neurons. Define associative model of neurons (as-neurons) that are able to reproduce context and time dependencies of biological neurons. Can be simulated, trained and adapted on today’s computers. Can be simulated, trained and adapted on today’s computers. Can use various training data set and even sets of training sequences. Can use various training data set and even sets of training sequences. Can reproduce training sequences or create new ones - be creative! Can reproduce training sequences or create new ones - be creative! Can generalize at various levels: Can generalize at various levels: Object level Sequence level Artificial Associative Systems and Associative Artificial Intelligence (Polish)

6 Transformation of database tables into associative structures ASSORT creates the basis graph structure of associative systems. Adrian Horzyk, AGH University of Science and Technology Adrian Horzyk, AGH University of Science and Technology TABLE

7 Transformation of database tables into associative structures ASSORT creates the basis graph structure of associative systems. Adrian Horzyk, AGH University of Science and Technology Adrian Horzyk, AGH University of Science and Technology TABLE

8 Transformation of database tables into associative structures ASSORT creates the basis graph structure of associative systems. Adrian Horzyk, AGH University of Science and Technology Adrian Horzyk, AGH University of Science and Technology TABLE

9 Transformation of database tables into associative structures ASSORT creates the basis graph structure of associative systems. Adrian Horzyk, AGH University of Science and Technology Adrian Horzyk, AGH University of Science and Technology TABLE

10 Transformation of database tables into associative structures ASSORT creates the basis graph structure of associative systems. Adrian Horzyk, AGH University of Science and Technology Adrian Horzyk, AGH University of Science and Technology TABLE

11 Transformation of database tables into associative structures ASSORT creates the basis graph structure of associative systems. Adrian Horzyk, AGH University of Science and Technology Adrian Horzyk, AGH University of Science and Technology TABLE

12 Transformation of database tables into associative structures ASSORT creates the basis graph structure of associative systems. Adrian Horzyk, AGH University of Science and Technology Adrian Horzyk, AGH University of Science and Technology TABLE

13 Transformation of database tables into associative structures ASSORT creates the basis graph structure of associative systems. Adrian Horzyk, AGH University of Science and Technology Adrian Horzyk, AGH University of Science and Technology TABLE

14 Transformation of database tables into associative structures ASSORT creates the basis graph structure of associative systems. Adrian Horzyk, AGH University of Science and Technology Adrian Horzyk, AGH University of Science and Technology TABLE

15 Transformation of database tables into associative structures ASSORT creates the basis graph structure of associative systems. Adrian Horzyk, AGH University of Science and Technology Adrian Horzyk, AGH University of Science and Technology TABLE

16 Transformation of database tables into associative structures ASSORT creates the basis graph structure of associative systems. Adrian Horzyk, AGH University of Science and Technology Adrian Horzyk, AGH University of Science and Technology TABLE

17 ASSOCIATIVE NEURAL GRAPH CONSTRUCTION for training sequences: S1, S2, S3, S4, S5

18 ASSOCIATIVE NEURAL GRAPH CONSTRUCTION for training sequences: S1, S2, S3, S4, S5

19 ASSOCIATIVE NEURAL GRAPH CONSTRUCTION for training sequences: S1, S2, S3, S4, S5

20 ASSOCIATIVE NEURAL GRAPH CONSTRUCTION for training sequences: S1, S2, S3, S4, S5

21 ASSOCIATIVE NEURAL GRAPH CONSTRUCTION for training sequences: S1, S2, S3, S4, S5

22 ASSOCIATIVE NEURAL GRAPH CONSTRUCTION for training sequences: S1, S2, S3, S4, S5

23 ASSOCIATIVE NEURAL GRAPH CONSTRUCTION for training sequences: S1, S2, S3, S4, S5

24 ASSOCIATIVE NEURAL GRAPH EVALUATION The external excitation of neuron E4 triggers the following activations of neurons: E4  E5  E2  E6 We got sequence S2 as the answer for the external excitement of neuron E4:

25 THE SIMPLE NEURAL STRUCTURE OF THE CONSECUTIVE LINGUISTIC OBJECTS representing 7 sentences Neural associative structure for the linguistic objects

26 Response to „What is knowledge?” As-neurons are consecutively activated after training sequences and give the answers:  Knowledge is fundamental for intelligence.  Knowledge is not a set of facts and rules

27 ASSOCIATIVE MODEL OF NEURONS Associative model of neurons AS-NEURON:  Works in time that is crucial for all associative processes in the network of connected as-neurons.  Models relaxation and refraction processes of biological neurons  Relaxation – continuous gradual returning to its resting state  Refraction – gradual returning to its resting state after activation  Optimizes its activity responces for input data combinations chosing only the the most intensive and frequent subset of them.  Conditionally plastically changes its size, synaptic transmission and connections to other as-neurons.  Can represent many similar as well as quite different combinations of input stimuli (data).

28 CONCLUSION Knowledge can be modelled using artificial associative systems. Knowledge can be modelled using artificial associative systems. Training sequences can be used to adapt artificial associative systems Training sequences can be used to adapt artificial associative systems Associative systems supply us with ability to generalize on various levels: Associative systems supply us with ability to generalize on various levels:  classes created for objects  sequences describing facts and rules Associative systems can be creative according to the context, which can recall new associations. Associative systems can be creative according to the context, which can recall new associations.

29 ? Questions? Remarks? Google: Horzyk Adrian Theory of neural associative computations and knowledge engineering in the associative systems Artificial Associative Systems and Associative Artificial Intelligence (Polish)

30 We can make our World a better place to live!


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