Making Information Systems Intelligent Making Information Systems Intelligent Dr. Geoffrey P Malafsky TECHi2

2 The Need Information overload Information overload Time compression Time compression Uncertainty Uncertainty Proactive decision making and actions Proactive decision making and actions

3 What is Intelligence Turing test Turing test Reasoning Reasoning Accuracy Accuracy Fusion and transformation of inputs Fusion and transformation of inputs Sensor Sensor Data Data Learning Learning

4 Time and Certainty

5 Intertwined Complex Information Example from DARPA Evidence Extraction & Link Discovery Example from DARPA Evidence Extraction & Link Discovery Today’s Situation: ~10k messages/day from multiple sources read by multiple analysts and analyzed in multiple manual non- integrated tools Today’s Situation: ~10k messages/day from multiple sources read by multiple analysts and analyzed in multiple manual non- integrated tools Similar to Social Network Analysis Similar to Social Network Analysis

6 Knowledge is Personal “Set the soldering iron to 350 degrees” information from manual for general use information from manual for general use knowledge from expert for specific manufacturing process knowledge from expert for specific manufacturing process “If the soldering iron is even 20 degrees hotter or colder, the connection will fail and the part will be returned and eliminate all profit. Watch carefully for the color of the solder” “If the soldering iron is even 20 degrees hotter or colder, the connection will fail and the part will be returned and eliminate all profit. Watch carefully for the color of the solder”

7 Taxonomy Complexity

8 What We Need: IT Conversations From James Hendler, Agents and the Semantic Web, IEEE Intel Sys, Mar/Apr 2001

9 Current Technology Performance Aspects of Knowledge Discovery State-of-Art Beyond State-of-Art Far Beyond State-of-Art Status Knowledge Representation Data Volume Human-Computer Interaction Naïve Discovery Advanced Discovery Guided Discovery Complexity Complex Relational Information Relations across time and space for people, places & things Vast >10 6 attributes, links, nodes Iterative Incremental Active Learning Unspecified, evolving problem Simple Relational Information Relations among people, places & things Substantial attributes, links, nodes Interactive User-specified problem, with suggested retargeting Some prior knowledge Propositional Information Simple attributes for people, places & things Minimal 100s of attributes, links, nodes Negligible User-specified problem No prior knowledge

10 Current Performance

11 Systems Engineering: Matching Functional Components

12 Coupling to the Human

13 DARPA Augmented Cognition

14 Multisensor Fusion

15 DARPA EELD: Knowledge Creation Technologies Knowledge Acquisition Facts (Database) Upper Ontology Core Theories Domain-Specific Theories/Models Evidence Extraction Knowledge Engineering Link Discovery AI/KR Expert Text Documents Patterns (models) (e.g. HPKB) Domain Expert (e.g. RKF) Labeled Examples PPPP PPP P PP Positive Examples Negative Examples NNNN NNN N NN Pattern Learning

16 Semantic Web Create a Web where information can be “understood” by machines as well as humans Create a Web where information can be “understood” by machines as well as humans Must convey machine- accessible semantics Must convey machine- accessible semantics

17 Ontology Contains Context and Relationships - Madache, Schnurr, Staab & Studer, Representation Language- Neutral Modeling of Ontologies

18 Integrated Presentation

19 DRAFT OV-1