The success of deep learning has again raised the hope for artificial intelligence (AI). As the understanding of the strengths of deep learning deepens, a desire to combine it with other approach emerges. Clojure’s data oriented programming is shown to offer a perfect bridge between symbolic and data driven AI. In this talk, we introduce REP, a domain specific language embedded in Clojure that provides the quickest path to authoring a chat bot. We show how Clojure enables us to combine the strength of symbolic and data driven approach of machine learning to build an AI platform that is easy to use, even for non-programmers.

1. A CLOJURE FUSION OF SYMBOLIC
AND DATA DRIVEN AI
Huahai Yang, Ph.D.
Juji, Inc.
Nov. 29, 2018
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2. Psychologist
Computer scientist
Coding in Clojure since 2012
Huahai Yang
 huahaiy 
Cofounder & CTO
Juji Inc.
© 2018 Juji, Inc.
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3. JUJI BUILDS CHATBOT PLATFORM
A challenging problem
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4. JUJI BUILDS CHATBOT PLATFORM
It is not hard to pass Turing Test, done in 70s'
PARRY: 33 psychiatrists cannot tell it from
paranoid patients
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5. PARRY SAMPLE
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6. JUJI BUILDS CHATBOT PLATFORM
It is harder to build useful
chatbots
interview people to collect
feedback
receive visitors to sites
screen job candidates
check up on trainees
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7. JUJI BUILDS CHATBOT PLATFORM
When used for survey
2X completion rate
76% better quality responses
 the whole time i was doing this survey it felt like i was
talking to a friend and sharing the same common
ground. i loved that i wish it didnt have to end
 very dynamic and very fluid conversation you have
great quality thanks
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8. JUJI APPROACH:
SYMBOLIC + DATA DRIVEN
Symbolic system as the
bones
Data-driven component
as the flesh
Done in a Clojure DSL,
REP
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9. AI SUMMER IS BACK
Watson Jeopardy beats human
AlphaGo beats human
Many AI assistants on phone and in home
Many commercial products in enterprises
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10. RISE OF DEEP LEARNING (DL)
Recently hugely successful
For many: AI = DL
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11. DL SOLVES PERCEPTION PROBLEM
DL maps raw data (pixels, text characters) into:
known labels (classification)
desirable numbers (regression)
fixed length vectors (embedding)
 Perception is the organization, identification, and
interpretation of sensory information in order to
represent and understand the presented information, or
the environment.
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12. PERCEPTION FEELS LIKE INTELLIGENCE
Reporter: "How many moves do you see ahead
while playing chess?"
Capablanca: "Only one, but it's always the right
one."
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13. PERCEPTION IS NOT YET INTELLIGENCE
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14. INTELLIGENCE CANNOT BE SOLVED WITH
DATA ALONE
Bottom-up
data driven
sub-symbolic
Top-down
goal/hypothesis driven
symbolic (human-readable)
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15. TIME TO BRING BACK SYMBOLIC AI
(Semi-)solving perception lays the foundation for
symbolic AI
The same forces leading to the rise of DL apply to
symbolic AI
More powerful hardware, help graph search
More abundant realistic data, help knowledge
base construction
Better so ware tools and practices
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16. TRADE-OFFS
Data driven
Easy to defeat/abuse by adversaries (e.g. Tay)
Hard to debug and bend it to the creator's will
By design, unlikely to be fixable
Symbolic
Easy to build rigid/brittle systems
Hard to develop, hard for human to think like
machines
In principle fixable, in practice, not so easy
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17. DL IS FOOLED
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18. GIBBON
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19. TWO ROADS TO INTEGRATION
Implement symbolic phenomenon with sub-
symbolic system
Mimic brain
Not yet practical
Symbolic + sub-symbolic
Engineer's method
Practical today
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20. SYMBOLIC + DATA DRIVEN
Symbolic system as the bones
for its potential for growth and adaptability,
despite the rigidity
DL/ML components as the flesh
for its flexibility and ease of development, despite
the obscurity
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21. JUJI ARCHITECTURE
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22. EDN DATA ALL THE WAY
1 User select chat template
2 User configure chat in GUI
3 Generate script from GUI
4 Chat: script compiles and runs
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23. DEFTOPIC: THE BUILDING BLOCK
Topic: a set of rules
01 (deftopic hello-world ; topic name
02 [] ; parameters
03
04 [] ; trigger
05 ["Hello world!"]) ; action
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24. PRODUCTION RULE
Rule: trigger (if), action (then) and associated
followup topics
Followup topics are primed when a rule fired
01 [:1 hello hi hey howdy] ; alternative pattern trigger
02 ["Nice to meet you!"] ; action: a string output
03 (talk-about-wheather) ; followup topic invocation
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25. TOPIC COMPOSITIONS
A topic may include rules of other topics
01 (deftopic greetings
02 []
03 {:include-before [(morning-greetings)
04 (evening-greetings)]}
05
06 [:1 hello hi hey howdy]
07 ["Hello"])
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26. PATTERNS
Token based regular expressions
01 [I love :1-. pizza] ; sequence with a wild car
02 [love ? [:1- pizza bacon]] ; nested, inner alternativ
03 [:0. "I love pizza"] ; start and string pattern
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27. ML BASED TAG AND CLASS PATTERNS
Tags for annotating text
keywords for placeholders of content classes
01 [he #pos/verb dog tree] ; parts of speech tag
02 [she love :phrase/NP] ; noun phrase class
03 [I work at :entity/org] ; organization entity class
04 [did in :entity/duration] ; duration entity class
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28. DL/ML FOR CLASSIFICATION FUNCTIONS
Neural networks are universal function
approximator, should be used as such
Patterns are and together in a rule
Rules are or together, so a topic matches a DNF
01 [programming
02 (input-in-this-category? "self-intro-relevance" 0.7)
03 ["You must be a smart person"]
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29. DL FOR SIMILARITY BASED MATCHES
Calculate similarity using Tensorflow sentence
embedding
01 [(> (max-similarity-score
02 ["What does your product cost?"
03 "How much does your product cost?"
04 "What's the price of your product?"
05 "How expensive is your product?"])
06 0.9)]
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30. ROLES
ML/DL models cover broad cases
Symbolic covers specific cases
misses by DL/ML
detailed refinement
01 [(input-in-this-category? "self-intro-relevance"
02 0.7)]
03 ([programming]
04 "You must be a smart person."
05
06 [art]
07 "I enjoy art too."
08
09 "Thank you for the introduction.")
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31. META-CIRCULARITY
Turn a topic into a function, then use the function in
another topic
01 [(create-topic-func
02 custom/why-u-here :extract-why-u-here)
03 "I see, you are here to "
04 (exec-topic-func :extract-why-u-here)]
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32. AUTOMATIC DIALOG MANAGEMENT
REP is a declarative language
System pushes topics around
Agenda queue
Ad-lib queue
Exception queue
Main stack
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33. CONCLUSION
Symbolic + data driven = practical AI today
Clojure is a great choice for doing so
Lisp was and still is the language of symbolic AI
Data orientation of Clojure makes it easy to
integrate both
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34. Huahai Yang
Juji, Inc.
https://juji.io
© 2018 Juji, Inc.
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