A talk, given as part of the FIU CIS invited lecture series, on various and select InferLink Corp R&D and commercialization works in AI and data driven solutions.

1. AI and Data, for Good
Naveen Ashish
InferLink Corporation
March 28th, 2019
Florida International University (FIU) , Computing & Information Sciences Lecture

2. InferLink
• Founded 2011
• Post Fetch Technologies
• Roots in USC/ISI
• R&D, Tools, Solutions, Spin-offs

3. Model
———— ————

4. Talk Organization
R&D
Tools
Solutions
Active Search
Complex Information Linkage
(for law enforcement)
RSX
Spin-offs
MachineReading
AutoScience
Evid Science
OpenAI
AI Resources

5. Information Retrieval: Active
Search

6. ActiveSearch: Background
• ISS Example: Find recent documents
mentioning Canada and Islamist
Extremist Groups (e.g., Report Desk
documents)
• AFIA Example: Find Russian aircraft
mentioned in Central Africa (e.g., on
Jetphotos.com)
• Cyber Intelligence example: Find reports
of web browsers with cross-site scripting
vulnerabilities
• ISS TopicBuilder example: Find articles
on “Al-Qaeda in the Arabian
Peninsula” (“Ansar al-Sharia”)
• information retrieval :
broad coverage, too
general, no notion of
entities, relations,
events, etc.
• information extraction :
notion of entities and
concepts, but too
specific, needs
customization, hard
failures

7. ActiveSearch
• A research engine
• Not one-size-fits-all (Google)
• Take advantage of current natural language technology
• Plug and play
• Works out-of-the-box
• Immediate value, rapid response
• Easy to customize to a domain

8. ActiveSearch Project
Multiple Extractors
(Plug and Play)
Stanford NER
Term extractor
Resolvers
Entity Res
(EntityBase)
Concept Res
(CP)
Indexer
Integrated Ontology
Documents
§ NLP + Massive Ontology

9. Step 1: Better Keyphrase Search

10. Knowledge Graph Customization

11. Knowledge Graph Customization

12. Knowledge Graph Customization

13. Concept completion Generalization / Specialization

14. Superfacets
Mixed queries Integration with ISS topic builder

15. ActiveSearch Use Case: Cytenna
• Analysts want to identify patterns in the
vulnerabilities and exploits
• What type of software is being exploited?
• Web servers, browsers, operating
systems, etc.
• What types of attacks are being committed?
• Denial of service, buffer overflow, XSS,
etc.
• More powerful search technologies are
needed to collect the data for pattern
analysis
“ransomware” as a concept

16. DHS Application: Dataset Search

17. Complex Information Linkage
Missing persons search
Human trafficking prevention
Preventing unlawful weapon sales

18. 1,973 “James Rodriguez” in California
People Search

19. Find sellers who don’t require
paperwork or a federal firearms
license

20. results, not answers

21. Profits per Year: $32 Billion
Average Age of Entry To Prostitution in the US: 14
PIMP’s Profit Per Victim Per Year: $150,000
Advertising Budget On the Web: $45 Million
Human Trafficking in the US
Find the locations where a potential victim
of human trafficking was advertised

22. San Diego, where else?

23. Understand Page Content
Find the locations where a potential victim
of human trafficking was advertised

26. Multiple Aspects to Solution

27. HTML
JSONSemi-Structured Data Extraction

28. “YOU don't wanna miss out
on ME :) Perfect lil booty
Green eyes Long curly black
hair Im a Irish,Armenian and
Filipino mixed princess :) ❤
Kim ❤ 7○7~7two7~7four77
❤ HH 80 roses ❤ Hour 120
roses ❤ 15 mins 60 roses”
Text Extraction
name: Kim
eye-color: green
hair-color: black
phone: 707-727-7477
rate: $60/15min $80/30min
$120/60min

29. Connect Graphs on Strong Attributes

30. Connecting Nodes Using All Attributes

31. Connecting Nodes Using All Attributes
same victims
same Trafficker

32. Connecting Nodes Using All Attributes
same victims
same Trafficker
Approach: K-Partite Graph Co-Clustering

33. Create Unified Database

34. One Index Per Main Class

35. Offers As Roots

36. Adult Service As Roots

37. Deployed to Law Enforcement:
Connect the Dots
Mary Lucy
222-0000 777-0000
Police Database
Bad Guy:
777-0000

38. RSX

39. RSX
https://rsx.inferlink.com/

40. Machine Reading:
Evid Science

41. PRODUCT
10,000 papers published per day
DATA
SOURCES:
JUST TO KEEP UP, ASSUMING 5 MINUTES/PAPER
= 24 SCIENTISTS READING 24 HOURS PER DAY
Example
Congress
Abstracts:
Volumes of Data … Per Day

42. PRODUCT
Guselkumab was also superior (P < .001) to adalimumab for Investigator Global Assessment
0/1 and PASI 90 at week 16 (85.1% vs 65.9% and 73.3% vs 49.7%), week 24 (84.2% vs
61.7% and 80.2% vs 53.0%), and week 48 (80.5% vs 55.4% and 76.3% vs 47.9%).
What do these refer to?
To our AI, it looks like this… but, instantly (25,000,000 articles per hour)
Intervention Outcome Measurment
guselkumab Investigator Global Assessment 0/1 at week 48 80.5%
adalimumab Investigator Global Assessment 0/1 at week 48 55.4%
AI: Automated Reading

43. PRODUCT
Gather Results

44. PRODUCT
Summarize Results

45. PRODUCTTransparently …

46. PRODUCT
Machine Learning All Around

47. Evid Science
• https://evidscience.com/product/

48. ActiveSearch for Medical Entities

49. AI for the Community

50. AI Community
• Journal of Artificial Intelligence Research
• https://jair.org
• AI Access
• AI Resources

51. We did not cover :)
R&D
Tools
Solutions
Active Search
Complex Information Linkage
RSX
Spin-offs
MachineReading
AutoScience
Evid Science
OpenAI
AI Resources
EntityBase
ConnectD
OpenWatch
CodeFault
TBN

52. Active Interests in AI & Data Science
• Health informatics and Biomedical research
• Cybersecurity
• Homeland security, emergency and disaster response
• Data driven engineering design
• Precision agriculture, E-Governance
• Transportation safety
• ….

53. Acknowledgements
• Dr. Steven Minton, President and CEO InferLink
• Dr. Greg Barish, CTO InferLink & CEO Cytenna
• Dr. Matt Michelson, CEO Evid Science
• Dr. Pedro Szekely, Research Associate Professor USC/ISI

54. www.inferlink.com
www.cytenna.com
www.evidscience.com
nashish@inferlink.com
thank you !