Palantir, Quid, RecordedFuture: Augmented Intelligence Frontier

FRONTIER OFAUGMENTED INTELLIGENCE

What’s next after Palantir, Quid, and Recorded Future

AUGMENTED INTELLIGENCE

ORIGINS

UNEXPECTED RESULT

STRONG HUMAN + MACHINE + INFERIOR PROCESS

WEAK HUMAN + MACHINE + BETTER PROCESS

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AUGMENTED INTELLIGENCE 1.0

WEAK HUMAN + MACHINE + BETTER PROCESS

• Allows enterprise to define a set of things

• Computes links between these things by analyzing text, metadata, relational data, etc.

• The user then interacts with the graph directly

• Tracks myriads of data points [series of events] from the public Web and private data sources

• Computes links and predicts the future [series of events]

• The user than interacts with the data directly and gets insights about what might happen

• Allows the user to define a set of things

• Computes links between these things by analyzing text

• The user then explores the graph directly

BETTER PROCESS

• Keyword/key phrase extraction

• Concept extraction

• Entity extraction: people | events | orgs | etc.

• Sentiment analysis

• Dynamic ontologies

• Spatio-temporal analysis

• Rich visualizations: graph | map | trends | etc.

SOME NUMBERS

Private/Public sources

694’040 sources

250’000 sources

SPECIALIZATION

Corporate & Government Knowledge Mgmt and Analysis

Public & Private Data Threats Prediction

Public & Commercial News Market Analysis

WHAT’S IN COMMON?

• Work at the Big Data Scale

• Data Scientists

• Customer-focused special teams (“forward engineers” – Palantir)

• Enterprise customers

• Graphs

• Data Visualization

• Live Data

AUGMENTED INTELLIGENCE

TECHNOLOGY

WHAT IS GRAPH?

External Network

DMZ

Internal Network

Dispatch Server

Rev DB

JDBC 3.0w/ SSL

OracleDatabase Storage

Raptor Server

Lucene Index

Storage

HTTPS

Shared Storage

HTTPS

Job Server

Job Data and Specs

Job Logsand Results

HTTPS

Client

PALANTIR GOTHAM

INTEGRATES WITH EXISTING IT INFRASTRUCTURE

• Your existing IT infrastructure

• Authentication

• Information Extractors

• Legacy data stores

• Rapidly changing data sources

INFORMATION EXTRACTORS

• Large repositories of unstructured text

• Multiple information extractors have been run across the text

• Provide different types of extraction• Entities• Relationships• Metadata• Geotagging

• Siloed view of each entity extractors output

• Want to combine these views alongside structured data into one interface

• Objects• Latin taxonomy of animals

• Objects and Properties• Periodic Table (has implicit relationships)

• Objects and Relationships• Properties can be modeled as relationships to ‘data’

objects

• Objects and Properties and Relationships• How information can be modeled in Palantir

DYNAMIC ONTOLOGY

WHY SOFT-CODE THE ONTOLOGY?

• A hard-coded Ontology is inherently limiting• Forces an organization into one of two extremes

General Ontology

Specific Ontology

No Semantics

Over-Defined Semantics

PALANTIR GOTHAM UI: SEARCH

• Data Scale• 100 million row Netflix dataset

• 10 million document usenet corpus

• 1.5 million entity extracted Wikipedia corpus

• Indexing Performance• 1m rows/hour structured indexing

• 500k docs/hour unstructured document indexing

• 100k docs/hour entity-extracted document indexing

• Searching Performance• Sub-second search processing

AUGMENTED INTELLIGENCE

FRONTIER

CONSUMERS WILL WORK WITH AUGMENTED INTELLIGENT SYSTEMS

• Consumer-focused PIAs are inherently limiting• Forces a user into one of two extremes

Siri,Google Now

PalantirGotham

Too-General

Too-EnterpriseFocused

AUGMENTED INTELLIGENT SYSTEMS WILL LEARN FROM THEIR USERS

• They will learn user’s own dynamic ontology (as opposed to the corporate ontology) by using Semantic Steering

• They will learn end user’s priorities (as opposed to the corporate priorities)

AUGMENTED INTELLIGENT SYSTEMS WILL WORK ON BEHALF OF USERS

• Gather data on user’s demand (e.g., prepare reports)

• Check teammates’ work progress

AUGMENTED INTELLIGENT SYSTEMS WILL PREDICT & ALERT

• They will use knowledge about their user context (interests, goals, priorities, etc.)

• They will combine it with data about the non-user context

• To predict what’s next and alert user if necessary

AUGMENTED INTELLIGENT SYSTEMS WILL BE EMBEDDED INTO THE BRAIN

CORTICAL MODEM ENABLES CYBER PROJECTIONS

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