AI and Python: Developing a Conversational Interface using Python

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

Session Objectives

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 Format

Motivation – Why should you care?
Tools and Resources
Technical bits and Program Samples
Open Source Future Direction

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

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

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

Trend #1
- Ray Kurzweil

Search Now Uses More Than Search Words
2004 2009 2019
Location, Previous Searches, Occupation, Hobbies, Context
Trend #2
7 - 15 “Search” Words
Location, Previous Searches
3.3 Search Words
2.5 Search Words

By 2019:

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

Why you should care!
Every interface. Every application.

Our “Space Race”:

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?

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

Session Format

Tools and Resources

What is a Conversational Interface?

A Chat Bot that can respond “intelligently” to input.
Intelligence =
Great input parsing
Understanding of context
Large knowledge base

What does a CI look like?

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 and Video
Conversation
Facial expressions
Context-driven responses
Personal-device based

PayPal “Sarah”

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).

What Are they Good For?

Guide users through your site or application
Troubleshooting Wizards
Frequently Asked Questions (especially multi-part and contextual)

Think of your chat bot as a new Customer Service Rep who is in training

Case Study
Build a Conversational Interface to be added to municipal websites

Handle FAQ’s
Guide users through the site

Introducing “Amy Iris”

How to Create Your Own
Language Parsing Tools – Parse it
Knowledge Base – Get Answer
Context Management – Store Context
User Experience and Server – Say it(Chat window and Character Graphic)

SimpleExample

Language Parsing Tools

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

Roll your own parsing
Simple version:
if “spell kat” in textin.lower():
say(“Uh, C A T.”)
Advanced techniques:

Normalize input
Remove punctuation, multiple spaces
Standardize case

Regular Expressions
import re
# huge table of patterns:
patterns =[
('.*spell kat.*','UH, C A T.'),
]
# later in program:
for p in patterns:
if re.match(p[0], textin):
say (p[1]) # say() prints or renders

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

AIML

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

A.L.I.C.E.

A Chatterbot
Uses AIML
The basis for routinely winning the Chatterbot competitions
Open Source, freely licensable

AIML example
<category>
<pattern>
HOW DO YOU SPELL KAT
</pattern>
<template>
Uh, C A T.
</template>
</category>

AIML example
<category>
<pattern>
DO YOU KNOW HOW TO *
</pattern>
<template>
<srai>HOW DO YOU <star/></srai>
</template>
</category>

Implementing AIML

Download PyAIML
Download ALICE definitions
Edit AIML

Or use a service (some free, some paid)

Natural Language Tool Kit

Collection of tools
Corpora
Parsing, tokens, chunking, parts of speech
Analysis: Collocation, semantics, metrics, probability, testing

From “Mat”, on stackoverflow.com

Knowledge Base

List of answers
AIML response
SQL Database Lookup
Website lookup
HTML parsing (Beautiful Soup)
Semantic Web
Application Programming Interfaces (APIs)
JSON or xml

Context Management

AIML – limited context
“that”, “it”, “topic”
NLTK has some basic tools for context management

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

“Ah Ha!” Moment #1

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?

Translator example

Parse it
Get Answer
Update Context
Say it

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

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)

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]

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

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

Translate “I have an emergency.” to Spanish
“I have an emergency.” in Spanish is “Tengounaemergencia.”
UI
Context Manager
Parser
Knowledge
Base

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

Translate “I have an emergency.” to Spanish
“I have an emergency.” in Spanish is “Tengounaemergencia.”
Why is this significant?
A Novice Python Programmer can turn every phone on the planet into a Universal Translator.
What happens if tens of thousands ofPython Programmers begin contributingto a Central Code repository? A General Purpose Conversational Interface emerges.
What Wikipedia did for Encyclopedias,
Python Programmers can do for Artificial Intelligence.
COOL!
REAL COOL!

What Wikipedia did for Encyclopedias,
Python Programmers can do for Artificial Intelligence.

Session Format

Tools and Resources

Future Direction

I’m building bots for municipalities
Amy Iris is a byproduct
Django website
Contributory Code
Twitter Interface
Amy Iris is a contributory frameworksimilar to Wikipedia as a framework

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)

How to get involved:

Follow @amyiris on Twitter
Send email: amyiris@amyiris.com
Online meetings begin next week.

BACK UP SLIDES TO FOLLOW

API
Users can interact with Amy Iris through several interfaces:

API
Website amyiris.com
Web Widget on your website
Twitter

Users “talk” to Amy Iris by typing something -
A familiar chat-bot experience

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.
API
ALICE
User Contributed
Open Source
Code Snippets
Code
Code
Code
Code
Code
Code

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
API
ALICE
User Contributed
Open Source
Code Snippets
Translation website
Translator
Code
Code
Code
Code
Code
Code

API
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!
ALICE
User Contributed
Open Source
Code Snippets
Best Buy snippet
Code
Code
Code
Code
Code
Code

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, or Twitter

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 submitting Code Snippets into the system

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,{"'": "'", """: '"'}) + " is " + match.group(2) + " in "+match.group(4)+".")
say(confidence=43)

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

The Python Advantage

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
Google App Engine

We are an Open Nation

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.

Our Sputnik?

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

Several Parallel Initiatives

Chat Bot approach (AIML, A.L.I.C.E. , PyAIML, Various Chatbots)
Search Engines growing up (Google, ASK, Bing “Decision Engine”, Yahoo, Cha Cha)
Semantic Web Efforts
Wordnet, AI efforts

NLTK Concepts

Grammar
Corpus / Corpora
Parts of Speech
Genre
Parsing
Inference Engine
Domain
Discourse

Search Engine Analogy

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)

Resources

October 2008 Loebner Prize

Contest: Reward bots who fool human judges
Every entrant fooled at least one judge
Elbot fooled 25% of judges
30% is Alan Turing’s metric (“Turing Test”)
12 small chat sessions are judged

Bot or Not – Choice 1
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 2
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.

2029: An intimate merger

$1,000 of computation = 1,000 times the human brain
Reverse engineering of the human brain completed
Computers pass the Turing 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

- Ray Kurzweil

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 consider Wikipedia 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).

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

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

“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

Lofty Goals = Achievements:

1980’s Lofty Goals…

2000’s Lofty Goals… ?

Even Assassination attempts were grandiose:

vs. a shoe toss?