Post on 12-Apr-2017
WHAT ARE CHATBOTS?
“A chatbot is a service, powered by rules and sometimes artificial intelligence, that you interact with
via a chat interface”Matt Schlicht - Founder of Chatbot magazine
Poncho
Lybrate
Madison Reed
HOW DOES IT WORK?
TWO TYPES OF CHATBOTS
1. Based on rules
2. Based on Artificial
intelligence
Artificial Intelligence (AI)
“An ideal intelligent machine is a flexible rational agent that perceives its environment and takes
actions that maximize its chance of success at some goal”
Russell & Norvig, 2003
• Concept very old: Greek myths about automatons
• Beginnings of modern AI: Greek philosophers describe human thinking as a symbolic system
• Field of AI formally founded in 1956
BRIEF HISTORY OF AI
• 1997: IBM’s Deep Blue beats chess champion Garry Kasparov
BRIEF HISTORY OF AI
2011: IBM’s Watson won the quiz show Jeopardy
https://www.youtube.com/watch?v=Sp4q60BsHoY
• IBM’s Watson • Understands written and
spoken language + visuals
• Constantly learning
BRIEF HISTORY OF AI
Fields in AI
NATURAL LANGUAGE PROCESSING (NLP)
Turing Test
• Natural language understanding
• Natural language generation • Text planning • Sentence planning • Text realisation
COMPONENTS OF NLP
Steps in NLP
Lexical analysis
Syntactic analysis
Semantic analysis
Discourse integration
Pragmatic analysis
Lexical analysis
The quick brown fox jumps over the lazy dog .
article adj. adj. subst. verb prep. article adj. subst.
sentence
punct.
Syntactic analysis
The quick brown fox jumps over the lazy dog.
subjectverb adjunct
predicate
Semantic analysis
The quick brown fox jumps over the lazy dog.
Discourse integration
The quick brown fox jumps over the lazy dog.
He jumps very high.
Pragmatic analysis
The quick brown fox jumps over the lazy dog.
—> A dog is lying down, maybe sleeping (because it’s lazy). A fox takes a leap and jumps over the dog.
• “Intentions” (e.g. there’s beer in the fridge)
• Sarcasm • Irony • Ambiguity • …
—> Paul Grice’s theory of “meaning”
POSSIBLE ISSUES
• Utterer’s Meaning • Timeless Meaning
EXAMPLE
“Flying planes can be dangerous.”
NLP in practice
Example
MACHINE LEARNING (ML)
• Microsoft • Chinese market • Mines Chinese internet
for human conversations
XIAO ICE
This can also backfire!
To summarise
‘I’, ‘need’, ‘a’, ‘bunch’, ‘of’, ‘bananas’, ‘,’, ‘some’, ‘yoghurt’, ‘,’, ‘toilet’, ‘paper’, ‘,’, ‘paper’, ‘towels’, ‘1/2’, ‘lb’, ‘of’, ‘hamburger’, ‘meat’, ‘,’, ‘and’, ‘some’, ‘beer’
NLU
check for appropriate answer in database
‘By’, ‘when’, ‘?’
NLG
BUILDING A BOT
Motion.ai
Wit.ai
Google API
• One topic or several topics?
• How complex are the answers?
• What is the bot’s goal?
WHICH TOOL TO CHOOSE?
• The language is the interface
• Design with language • Cooperate with linguists,
copywriters, novelists and even comedians
TO CONCLUDE: OUR ROLE AS UX DESIGNERS?
THANK YOU!