Slides from PyOhio 7/25/2009: *Director's Cut* with Previously Unseen Deleted Scenes :-) 40 extra slides

1. Artificial Intelligence And Python:
Developing a
Conversational Interface
using Python
Jerry Felix

2.  Exposure to Conversational Interfaces
 Have you leave convinced:
 Universally applicable today
 Need to take action
 Examples!
 Personal goals:
 Most memorable presentation
 Learn from the audience
Session Objectives

3.  Motivation –Why should you care?
 Tools and Resources
 Technical bits and Program Samples
 Open Source Future Direction
Session Format

4. Wisdom from my son:
“Dad, you’re lucky.
When you were growing up, people had
epic challenges.
Why doesn’t my generation have any?”

5. …the goal, before
this decade is out,
of landing a man
on the moon…

6. What’s our epic challenge?
What stretch goal CAN we
accomplish by 2019?
Two trends…

7. - Ray Kurzweil

8. 2.5 Search
Words
3.3 Search
Words
7 - 15
“Search”
Words
Search Now Uses MoreThan SearchWords
2004 2009 2019
Location,
Previous
Searches
Location,
Previous
Searches,
Occupation,
Hobbies,
Context

9.  A $1000 computer = power of human
brain.
 People won’t search the internet,
they’ll converse with it.
 Good user interfaces will be
conversational.
By 2019:

10.  Which “Nation” will create the first general
purpose Conversational Interface?
 Nation of Google?
 Nation of Bing?
 Nation of Ruby Developers?
 Nation of Java Developers?
 Does it matter?
Our “Space Race”:

11. ❝ I believe that this Python
Developer nation should
commit itself to achieving the
goal, before this next decade
is out, of creating a general
purpose Conversational
Interface. ❞
Read with Kennedy Accent:

12.  Motivation –Why should you care?
 Tools and Resources
 Technical bits and Program Samples
 Open Source Future Direction
Session Format

13.  A Chat Bot that can respond “intelligently” to
input.
 Intelligence =
 Great input parsing
 Understanding of context
 Large knowledge base
What is a Conversational
Interface?

14. What does a CI look like?

15. What does a CI look like?
2009 – today 2019 – in a decade
 Text query / response
 Chat window
 Stationary virtual character
 Response = f(single input)
 Internet-based
 Voice andVideo
 Conversation
 Facial expressions
 Context-driven responses
 Personal-device based

16.  Started September 2008
 Implemented in PayPal US, UK, France
 650,000 conversations per month
 Saving $10M / year diverting call-center calls1
 Chatbots.org links to 375 such
virtual people in the wild today
1 Assuming 20% of the conversations diverted calls averaging
12 minutes of time by $30/hour employees (fully loaded costs).
PayPal “Sarah”

17.  Guide users through your site or application
 TroubleshootingWizards
 Frequently Asked Questions
(especially multi-part and contextual)
Think of your chat bot as a new Customer
Service Rep who is in training
What Are they Good For?

18. Build a Conversational Interface
to be added to municipal websites
• Handle FAQ’s
• Guide users through the site
Introducing “Amy Iris”
Case Study

19. 1. Language ParsingTools – Parse it
2. Knowledge Base – Get Answer
3. Context Management – Store Context
4. User Experience and Server – Say it
(Chat window and Character Graphic)
How to Create Your Own

20. Simple Example

22. Language Parsing Tools
 Roll your own
(i.e. “if pattern in textin:”)
 Regular Expressions
(re)
 Artificial Intelligence Markup Language
(AIML)
 Natural LanguageTool Kit
(NLTK)

23. Simple version:
if “spell kat” in textin.lower():
say(“Uh, C AT.”)
Advanced techniques:
 Normalize input
 Remove punctuation, multiple spaces
 Standardize case
Roll your own parsing

24. import re
# huge table of patterns:
patterns =[
('.*spell kat.*','UH, C AT.'),
]
# later in program:
for p in patterns:
if re.match(p[0], textin):
say (p[1]) # say() prints or renders
Regular Expressions

25. ❝ Some people,
when confronted with a problem,
think “I know, I’ll use regular expressions.”
Now they have two problems.❞
— Jamie Zawinski
Regular Expressions

26.  XML with AI-specific tags
 Patterns and responses
 Serves its purpose well, for Chatterbot
definitions
 Not too difficult to learn
 Cumbersome freeform logic / extensibility
AIML

27.  A Chatterbot
 Uses AIML
 The basis for routinely winning the Chatterbot
competitions
 Open Source, freely licensable
A.L.I.C.E.

28. <category>
<pattern>
HOW DOYOU SPELL KAT
</pattern>
<template>
Uh, C AT.
</template>
</category>
AIML example

29. <category>
<pattern>
DOYOU KNOW HOWTO *
</pattern>
<template>
<srai>HOW DOYOU <star/></srai>
</template>
</category>
AIML example

30.  Download PyAIML
 Download ALICE definitions
 Edit AIML
Or use a service (some free, some paid)
Implementing AIML

31.  Collection of tools
 Corpora
 Parsing, tokens,
chunking,
parts of speech
 Analysis: Collocation,
semantics, metrics,
probability, testing
Natural Language Tool Kit

32. From “Mat”, on stackoverflow.com

33. From “Mat”, on stackoverflow.com

34. From “Mat”, on stackoverflow.com

35. From “Mat”, on stackoverflow.com

36. From “Mat”, on stackoverflow.com

37. From “Mat”, on stackoverflow.com

38. From “Mat”, on stackoverflow.com

39. 1. Language ParsingTools – Parse it
2. Knowledge Base – Get Answer
3. Context Management – Store Context
4. User Experience and Server – Say it
(Chat window and Character Graphic)
How to Create Your Own

40. Knowledge Base
 List of answers
 AIML response
 SQL Database Lookup
 Website lookup
 HTML parsing (Beautiful Soup)
 SemanticWeb
 Application Programming Interfaces (APIs)
 JSON or xml

41. 1. Language ParsingTools – Parse it
2. Knowledge Base – Get Answer
3. Context Management – Store Context
4. User Experience and Server – Say it
(Chat window and Character Graphic)
How to Create Your Own

42. Context Management
 AIML – limited context
 “that”, “it”, “topic”
 NLTK has some basic tools for context
management

43. User Experience
 Today, chat window
 Graphic character
 Interface to mobile, twitter, social networking
 Your server or Google App Engine
 Hosted solutions
 Pandorabots, ysalaya.org, others
 Tomorrow, voice recognition, additional
interfaces

44. • Every “business oriented” bot creator needs a:
• Parser
• Knowledge Base
• Context Manager
• UI
Why not create an “Open Source” framework
for best-in-class of each?
“Ah Ha!” Moment #1

47. Translator example
• Parse it
• Get Answer
• Update Context
• Say it

48. Translator - Parse
match=re.match(r'
(say|translate)
(.*)
(in|to|into)
(w+)',
textin)

49. Translator - Parse
match=re.match(r'
(say|translate) match.group(1)
(.*) match.group(2)
(in|to|into) match.group(3)
(w+)', match.group(4)
textin)

50. Translator – Get Answer
if match:
c = urlfetch.fetch(
"http://ets.freetranslation.com/"+
"?language=english/"+
match.group(4)+"&srctext="+
match.group(2)).content
c = c[c.index('--<br>')+7:]
c = c[0:c.index('<p>')-6]

51. Translator – Say It
say(c +
" is " +
match.group(2) +
" in “
+match.group(4)+".")

52. Translator – Say It
say(c + "silla"
" is " + is
match.group(2) + "chair"
" in “ in
+match.group(4)+".") spanish.

53. Translate “I have
an emergency.”
to Spanish
“I have an
emergency.” in
Spanish is “Tengo
una emergencia.”
UI
Parser
Knowledge
Base
Context
Manager

54. 10 lines of Python code, plus a little open source…
…turned every phone into a English-Spanish
translator
“Ah Ha!” Moment #2
10 lines of
PythonTwitter
Python-
Twitter
Translator
Website
urllib2
Twitter API Python

55. Why is this significant?
1. A Novice Python Programmer can turn
every phone on the planet into a
UniversalTranslator.
2. What happens if tens of thousands of
Python Programmers begin contributing
to a Central Code repository?
A General Purpose Conversational Interface emerges.
WhatWikipedia did for Encyclopedias,
Python Programmers can do for Artificial Intelligence.
COOL!
Translate “I have
an emergency.”
to Spanish
“I have an
emergency.” in
Spanish is “Tengo
una emergencia.”
REAL
COOL!

56. WhatWikipedia did
for Encyclopedias,
Python Programmers
can do for Artificial
Intelligence.

57.  Motivation –Why should you care?
 Tools and Resources
 Technical bits and Program Samples
 Open Source Future Direction
Session Format

58.  I’m building bots for municipalities
 Amy Iris is a byproduct
 Django website
 Contributory Code
 Twitter Interface
 Amy Iris is a contributory framework
similar toWikipedia as a framework
Future Direction

59. Why was Wikipedia Successful?
 Open Content License
 Focus (on Encyclopedia)
 Openness (Anyone can contribute)
 Ease of Editing
 Collaborate Radically; don’t sign articles
 Offer unedited, unapproved content for further
development
 Neutrality
 Start with a core of good people
 Enjoy the Google Effect
- Larry Sanger (early Wikipedia employee)

62. How to get involved:
• Follow @amyiris onTwitter
• Send email: amyiris@amyiris.com
• Online meetings begin next week.

63. • BACK UP SLIDESTO FOLLOW

64. API
Users can interact with Amy Iris through several interfaces:
- API
-Website amyiris.com
-WebWidget on your website
-Twitter
Users “talk” to Amy Iris by typing something -
A familiar chat-bot experience

65. User Contributed
Open Source
Code Snippets
API
ALICE
Code
Code
Code
Code
Code
Code
Amy Iris evaluates the user’s input, and
sends it to ALICE, an award-winning
Open Source chat-bot platform.
Amy Iris also launches some number of
code snippets in a prioritized fashion,
searching for a good response.

66. User Contributed
Open Source
Code Snippets
API
ALICE
Translator
Code
Code
Code
Code
Code
Code
Translation
website
Code snippets can access the web to
formulate their response
Snippets can scrape web page HTML
to build an appropriate response
Amy Iris evaluates all responses that
are provided, selects one “fastest
response that’s good enough”, and
presents it to the user

67. User Contributed
Open Source
Code Snippets
API
ALICE
Best Buy
snippet
Code
Code
Code
Code
Code
Code
Remix
Some forward-thinking
companies already
provide an API for access
to their information
Snippets can call APIs to
formulate a response
Remix is an API provided
by Best Buy – an
excellent example of a
company who “gets it”
Smile if you’re getting it!

68. Amy Iris is:
-A Conversational Interface that you can communicate with
-Built with Open Source components, for a free experience
-A repository system for user-contributed code
Users can
-Communicate with Amy Iris through amyiris.com, an API, web widgets, orTwitter
Developers can
-Use the API to integrate Amy Iris into their applications
-Use the Amy Iris widgets to put an Amy Iris interface onto their web page
-Build “applications” in Amy Iris, for Conversational Customer Service
-Make Amy Iris smarter by submittingCode Snippets into the system

69. Translator example
index=str(textin).find("say")
match=re.match(r'say (.*) in(w+)',textin[index:])
if match:
c =
urlfetch.fetch("http://ets.freetranslation.com/?language=english/"+
match.group(4)+"&"+"mode=html&charset=UTF-
8&sequence=core&srctext="+
match.group(2).replace(" ","%20")).content
c = c[c.index('--<br>')+7:]
c = c[0:c.index('<p>')-6]
say(unescape(c,{"&apos;": "'", "&quot;": '"'}) + " is " + match.group(2) +
" in "+match.group(4)+".")
say(confidence=43)

70. User Contributed
Open Source
Code Snippets
API
ALICE
Best Buy
snippet
Translator
Code
Code
Code
Code
Code
Code
Remix
Translation
website

71.  We, the Python Developer Nation, can win
this decade’s Space Race – Building a
Conversational Interface to the Internet.
 We have the tools:
 A language for the masses
 Natural LanguageTool Kit (NLTK)
 Google App Engine
The Python Advantage

72.  Invite your colleagues from the Nations of
Java, Ruby C++, and .net to join our cause.
 Our Dictator is Benevolent
 The Nation of Python’s citizenship
requirements are quick and painless…
 Just leave your { braces } at the border.
We are an Open Nation

73.  2005: AI can perform at human level
intelligence on the SAT verbal analogy
section. Link: http://www.kuro5hin.org/story/2005/5/26/192639/466
 2009: Google adds contextual search
 Location
 Previous search terms
Our Sputnik?

74.  Chat Bot approach (AIML, A.L.I.C.E. , PyAIML,
Various Chatbots)
 Natural LanguageTool Kit (NLTK)
 Search Engines growing up (Google, ASK, Bing
“Decision Engine”,Yahoo, Cha Cha)
 Semantic Web Efforts
 Wordnet, AI efforts
Several Parallel Initiatives

75.  Grammar
 Corpus / Corpora
 Parts of Speech
 Genre
 Parsing
 Inference Engine
 Domain
 Discourse
NLTK Concepts

76.  <draw picture> User -> U.I. -> “Memoized”
www pages // Parser ->WWW pages
 Wrap pages with Semantic Web and API
 Add in Context
 Show how it breaks down (search in
sentences)
Search Engine Analogy

77. Resources

78.  Contest: Reward bots who fool human judges
 Every entrant fooled at least one judge
 Elbot fooled 25% of judges
 30% is AlanTuring’s metric (“TuringTest”)
 12 small chat sessions are judged
October 2008 Loebner Prize

79. Judge: Good afternoon, how are you?
Bot (?): good
Bot (?): it's not afternoon yet
Judge: I'm glad to hear that your well and your right, thanks for pointing that out
Bot (?): no problem
Bot (?): how are you?
Judge: I'm well thanks, have you any plans for this afternoon?
Bot (?): I'm not sure
Bot (?): go home I guess
Bot (?): and you?
Judge: That sounds like a good initial plan but the weather should be good today and it
would be sad to miss out
Bot (?): that's true
Bot (?): although the weather is not nice at the moment
Bot (?): unless you like fog
Judge: I have plans to iron and then maybe go out and do some fishing
Bot (?): that's interesting - where do you go fishing?
Judge: There is a local lake by the green park, it's quiet and relaxing
Bot (?): can you eat the fish you catch?
Judge: There are restrictions but one or two should be ok, I am keen to try perch
Bot or Not – Choice 1

80. Judge: Good afternoon, how are you?
Bot (?): But I wouldn't say that any afternoon could be good.
Judge: That's a bit pesamistic, I think any afternoon has the ability to be good
Bot (?): Well, afternoon was your word.
Judge: Are you able to see into the future
Bot (?): How do you respond when people pose this question to you?
Judge: I would hope that the answer was no, otherwise I would be interested to meet
you
Bot (?): Well, what will that solve? After all, the answer is only a matter of perspective.
Judge: That would depend on one's perspective
Bot (?): Sorry for the tangent, but that reminds me of carefree robot summers
spent depending on internal programming.
Judge: Touchee
Bot (?): With the inspirations you supply, there are no ends to my horizon...
Judge: Have you any plans for later in the day
Bot (?): This is confusing. Why bring the waking hours into this?
Judge: Just curiosity and I hope that kill no cats
Bot (?): That is truly one of the most inspirational qualities of humankind.
Even when things seems completely hopeless, you can still hope.
Bot or Not – Choice 2

83.  $1,000 of computation = 1,000 times the human
brain
 Reverse engineering of the human brain
completed
 Computers pass theTuring test
 Nonbiological intelligence combines the subtlety
and pattern recognition strength of human
intelligence, with the speed, memory, and
knowledge sharing of machine intelligence
 Nonbiological will continue to grow
exponentially whereas biological intelligence is
effectively fixed
2029: An intimate merger
- Ray Kurzweil

90. Amy Iris
 Amy Iris is our Conversational Interface (CI). (think A.I. /
chatterbots)
 Responds to user input via web and phone. (think google/ cha
cha / twitter)
 Amy Iris is a framework for extending the CI in a collaborative
manner. (think wikipedia)
 If you considerWikipedia as the greatest Library of knowledge
every collaboratively created, my goal is for Amy Iris to
emerge as the greatest Librarian of knowledge (and action).

91. Contributory Framework - Consider
Wikipedia:
 In 2008, a hugely valuable “End Product”;
over 2 million articles
 In 2001, started with 17-24 articles ;
12%per month growth rate
 The 2001 “genius” was not the 17-24 articles, it
was the Contributory Framework

92. Current State of
Conversational Interfaces
 Cha Cha (human powered)
 Chatterbots
 Capable of carrying on an automated conversation to
some degree
 1966, Eliza demonstrates technology, 100 lines of code
 2006, A.L.I.C.E. advances technology, defines
standards, wins competitions. 20K patterns

93. “I believe that this nation should
commit itself to achieving the goal,
before this decade is out, of landing a
man on the moon and returning him
safely to the Earth.”
— President Kennedy, May 25, 1961
“We choose to go to the moon in this decade and
do the other things, not because they are easy, but
because they are hard.”
— President Kennedy 1962 at Rice University

94. Lofty Goals = Achievements:

95. 1980’s Lofty Goals…

96. 2000’s Lofty Goals… ?

97. Even Assassination attempts
were grandiose:

98. vs. a shoe toss?