Making Information Making Information Systems IntelligentSystems Intelligent

Dr. Geoffrey P MalafskyDr. Geoffrey P MalafskyTECHi2TECHi2

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The NeedThe Need

Information overloadInformation overload Time compressionTime compression UncertaintyUncertainty Proactive decision making and Proactive decision making and

actionsactions

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What is IntelligenceWhat is Intelligence Turing testTuring test ReasoningReasoning AccuracyAccuracy Fusion and Fusion and

transformation of transformation of inputsinputs SensorSensor DataData LearningLearning

44

Time and CertaintyTime and Certainty

55

Intertwined Complex Intertwined Complex InformationInformation

Example from DARPA Evidence Example from DARPA Evidence Extraction & Link DiscoveryExtraction & Link Discovery

Today’s Situation: ~10k Today’s Situation: ~10k messages/day from multiple messages/day from multiple sources read by multiple sources read by multiple analysts and analyzed in analysts and analyzed in multiple manual non-multiple manual non-integrated toolsintegrated tools

Similar to Social Network Similar to Social Network AnalysisAnalysis

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Knowledge is PersonalKnowledge is Personal

““Set the soldering iron to 350 degrees”Set the soldering iron to 350 degrees” informationinformation from manual for general use from manual for general use knowledgeknowledge from expert for specific from expert for specific

manufacturing processmanufacturing process ““If the soldering iron is even 20 If the soldering iron is even 20

degrees hotter or colder, the degrees hotter or colder, the connection will fail and the part will be connection will fail and the part will be returned and eliminate all profit. Watch returned and eliminate all profit. Watch carefully for the color of the solder”carefully for the color of the solder”

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Taxonomy ComplexityTaxonomy Complexity 80. INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE ANDTECHNOLOGY 81. Materials science 81.05. t Specific materials: fabrication, treatment, testing and analysis Superconducting materials, see 74.70 and 74.72 Magnetic materials, see 75.50 Optical materials, see 42.70 Dielectric, piezoelectric, and ferroelectric materials, see 77.80 Colloids, gels, and emulsions, see 82.70.D, G, K respectively Biological materials, see 87.14 81.05.Bx Metals, semimetals, and alloys 81.05.Cy Elemental semiconductors 81.05.Dz II–VI semiconductors 81.05.Ea III–V semiconductors 81.05.Gc Amorphous semiconductors 81.05.Hd Other semiconductors 81.05.Je Ceramics and refractories (including borides, carbides, hydrides, nitrides,

oxides, and silicides) 81.05.Kf Glasses (including metallic glasses) 81.05.Lg Polymers and plastics; rubber; synthetic and natural fibers; organometallic

and organic materials 81.05.Mh Cermets, ceramic and refractory composites 81.05.Ni Dispersion-, fiber-, and platelet-reinforced metal-based composites 81.05.Pj Glass-based composites, vitroceramics 81.05.Qk Reinforced polymers and polymer-based composites 81.05.Rm Porous materials; granular materials

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What We Need: IT What We Need: IT ConversationsConversations

From James Hendler, Agents and the Semantic Web, IEEE Intel Sys, Mar/Apr 2001

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Current Technology Current Technology PerformancePerformanceAspects of Knowledge DiscoveryAspects of Knowledge Discovery

State-of-Art

BeyondState-of-Art

Far Beyond

State-of-Art

StatusStatusKnowledgeRepresentation

DataVolume

Human-ComputerInteraction

NaïveDiscovery

AdvancedDiscovery

GuidedDiscovery

ComplexityComplexity

Complex Relational Information

Relations across time and space for people, places & things

Vast

>106 attributes, links, nodes

Iterative

Incremental

Active Learning

Unspecified, evolving problem

Simple Relational Information

Relations among people, places & things

Substantial

103 - 104 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

1010

Pe

rfo

rma

nc

e

Maturity

Augmented Cognition: LargeKB+Models+Human engineering

Intelligent Systems

NaturalLanguage+Ontology

Search/classification

Human

Densi

ty

Current PerformanceCurrent Performance

1111

Systems Engineering: Systems Engineering: Matching Functional Matching Functional

ComponentsComponents

1212

Coupling to the HumanCoupling to the Human

1313

DARPA Augmented CognitionDARPA Augmented Cognition

1414

Multisensor FusionMultisensor Fusion

1515

DARPA EELD: Knowledge Creation Technologies

Knowledge AcquisitionKnowledge Acquisition

Facts(Database)

Facts(Database)

UpperOntology

Core Theories

Domain-Specific Theories/Models

Evidence ExtractionEvidence Extraction

Knowledge EngineeringKnowledge Engineering

Link Discovery

Link Discovery

AI/KRExpert

TextDocuments

Patterns(models)(e.g. HPKB)Domain

Expert

(e.g. RKF)

LabeledExamples

P P P PP P P

PPP

PositiveExamples

NegativeExamples

N N N NN N N

NNN

Pattern LearningPattern

Learning

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Semantic WebSemantic Web

Create a Web where Create a Web where information can be information can be “understood” by “understood” by machines as well as machines as well as humanshumans

Must convey machine-Must convey machine-accessible semanticsaccessible semantics

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Ontology Contains Context and Ontology Contains Context and RelationshipsRelationships

- Madache, Schnurr, Staab & Studer, Representation Language-Neutral Modeling of Ontologies

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Integrated PresentationIntegrated Presentation

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DRAFT OV-1