Origins of "Augmented Intelligence" concept (based on the Shyam Sankar's TED talk) List of top 3 Augmented Intelligence companies with deep dive into their products' details (and quick look into their business models, w/o numbers). Deep dive into the "Augmented Intelligence" technology (by using Palantir as an example). A look at the future of the Augmented Intelligence.

1. FRONTIER OF
AUGMENTED INTELLIGENCE
What’s next after Palantir, Quid, and
Recorded Future

2. AUGMENTED INTELLIGENCE
ORIGINS

6. UNEXPECTED RESULT
STRONG HUMAN + MACHINE + INFERIOR PROCESS
WEAK HUMAN + MACHINE + BETTER PROCESS
>

9. AUGMENTED INTELLIGENCE 1.0
WEAK HUMAN + MACHINE + BETTER PROCESS

10. • 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

11. • 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

12. • Allows the user to define a set of things
• Computes links between these things by
analyzing text
• The user then explores the graph directly

13. 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.

14. SOME NUMBERS
Private/Public sources
694’040 sources
250’000 sources

15. SPECIALIZATION
Corporate & Government
Knowledge Mgmt and
Analysis
Public & Private Data 
Threats Prediction
Public & Commercial
News  Market Analysis

16. 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

17. AUGMENTED INTELLIGENCE
TECHNOLOGY

18. WHAT IS GRAPH?

19. External Network
DMZ
Internal Network
Dispatch Server
Rev DB
JDBC 3.0
w/ SSL
Oracle
Database
Storage
Raptor Server
Lucene
Index
Storage
HTTPS
Shared
Storage
HTTPS
Job Server
Job Data
and Specs
Job Logs
and Results
HTTPS
Client
PALANTIR GOTHAM

20. INTEGRATES WITH EXISTING IT
INFRASTRUCTURE
• Your existing IT infrastructure
• Authentication
• Information Extractors
• Legacy data stores
• Rapidly changing data sources

21. 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

22. • 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

23. 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

24. 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

27. AUGMENTED INTELLIGENCE
FRONTIER

28. CONSUMERS WILL WORK WITH
AUGMENTED INTELLIGENT SYSTEMS
• Consumer-focused PIAs are inherently limiting
• Forces a user into one of two extremes
Siri,
Google Now
Palantir
Gotham
Too-
General
Too-Enterprise
Focused

29. 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)

30. AUGMENTED INTELLIGENT SYSTEMS
WILL WORK ON BEHALF OF USERS
• Gather data on user’s demand (e.g., prepare
reports)
• Check teammates’ work progress

31. 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

32. AUGMENTED INTELLIGENT SYSTEMS
WILL BE EMBEDDED INTO THE BRAIN
CORTICAL MODEM ENABLES CYBER PROJECTIONS

33. www.zetuniverse.com
contactus@zetuniverse.com