Chatbots paris meetup may 24 2016

• Panorama des chatbots Fréderic Cavazza, Marketing
technologist
• Anatomie d'un chatbot Yan Georget, de Botfuel.io
• Chatops Victor Demonchy/Charles Judith de Criteo
• Qu'est-ce que le traitement du langage et
comment est-il utilisé par les bots? Paul Renvoisé de
Recast.ai
• Comment fonctionne Jam? Marjolaine Grondin

FredCavazza.net@fredcavazza #ChatbotsParis
Des agents au
commerce
conversationnel
#ChatbotsParis, 24 mai 2016

Frédéric Cavazza ?
• Consultant et conférencier
• 20 ans d’expérience dans le web
• FredCavazza.net
• 2 livres :

Genèse des agents
conversationnels

Les chatbots il y a 10 ans
La première expérience ouverte à grande échelle

Les premiers agents conversationnels
Un agent conversationnel est un logiciel qui dialogue
avec un utilisateur à travers une interface textuelle

Et il n’y a pas que des femmes !

Qu’est-ce qui a changé depuis ?
• Des ressources informatiques beaucoup
moins chères
• D’énorme progrès en intelligence artiﬁcielle
• Des services beaucoup plus facilement
connectables entre-eux
• 2 milliards d’utilisateurs de smartphones
dont les usages sont concentrés sur les
applications de messagerie

L’avènement 
des chatbots

Un écosystème qui se structure

Des chatbots par milliers
• Diffusion d’informations
• Suggestion de produits
• Achats automatisés
• Commande de services
• Opérations courantes
• …

Des app stores aux bot stores
L’optimisation du référencement sera une étape obligatoire

L’année 0 du commerce
conversationnel

Les géants s’affrontent
AppleFacebook Google
Microsoft
Amazon

Le commerce conversationnel est-il viable ?
Le web et le SMS restent des canaux bien plus pragmatiques

Un gros potentiel pour le CRM
20% des questions pourraient être immédiatement traitées

Merci de votre attention

GatewaysMultiple messaging
platforms
- audiences
- formats
- interactions
Scalability
Not only messaging
platforms but
- webchat
- SMS

Engine
Know the user
- context
Understand the user
- intent
recognition
Bot testing and AB
testing
Bot modelization:
- black box
- (lack of) sdks &
languages

NaturalLanguageProcessing
Entities extraction
- regular
expressions
- probabilistic
grammars
Intent recognition
- regular
expressions
- logistic
regression
- bayesian
classification

Analytics
Rich interactions
- conversations
- sentiment
analysis
Optimize conversions
Optimize UX

Services
APIs
- storage
- payment
- catalogs
Non-blocking IO
Humans

Fuelingthenextgenerationofbots
hi@botfuel.io

Copyright © 2016 Criteo
#ChatOps @Criteo
Charles JUDITH
Victor DEMONCHY

About us
• DevOps Engineer
• FOSS Monitoring Addict
• Blogger
• DevOps Engineer
• Monitoring newbie
• Criteo internal traveler
Charles JUDITH Victor DEMONCHY

Why #ChatOps ?
• Automate repetitive/tedious tasks
• Reduce context switching
• Gather information
• Execute actions
• Share knowledge

How we started
• Define the needs
• Study the existing bots
• Start small and simple
• Make it collaborative (share !)
• Provide dev environment
https://victorops.com/chatops-for-dummies/

It’s a trap !
• Reinvent the wheel
• Replicate CLI actions without adding value
• Restrict access to a few people
• Think only about personal/team usages

Benefits
• IM is a central communication point
• Democratize access to information
• Give visibility to teams
• Reduce newcomers bootstrap time
• Save time for everyone !

The chatbots competition
• Three chatbots in the competition
• Errbot (Python)
• Lita (Ruby)
• Hubot (NodeJS / Coffeescript)

The chosen one
• We picked Errbot
• Python was more popular
• Good documentation
• Active development
• Interesting features

Meet Bob (the Builder) !
• Started as a 10% project
• Two months old
• Hackathon last month
• Basic features
• Technical usages for now

The future
• Have a proper hosting
• Provide documentation
• Promote usage
• Gather feedback
• Develop new features
• Open source (if possible !)

DEFAULT
REASONING
ARTIFICIAL
INTELLIGENCE
DIALOG
MODELING
BIG
DATA
SPEECH
RECOGNITION
IMAGE
RECOGNITION
MACHINE
LEARNING
NATURAL
LANGUAGE
PROCESSING
ROBOTICS
NATURAL
LANGUAGE
PROCESSING

NATURAL
LANGUAGE
GENERATION
NATURAL
LANGUAGE
PROCESSING
TEXT
CLASSIFICATION
TOKENIZATION
LEMMATISATION
LANGUAGE
DETECTION
PART OF SPEECH
TAGGING
DEPENDENCY
PARSING
NAMED ENTITY
RECOGNITION

Gur jbexref jrer birejbexrq The workers were overworked
The workers were overworked Les travailleurs étaient surchargés
The workers at the plant were overworked ???plant

RULE-BASED STATISTICAL
p(Ck|Fx) =
p(Ck)p(Fx|Ck)
p(Fx)
if contains(x) & has(y) then C
STATISTICAL

He‘s the king of Scotland
LOCATIONACTION JOB
PRONOUN VERB DETERMINER NOUN PREPOSITION PROPER NOUN
He ‘s the king of Scotland
subject
nominal modiﬁer
clausal complement
determiner prepositional modiﬁer
He be the king of Scotland

DATA ANONYMIZATION
In November 1991, the husband of Ms Marina Babic, of Serb ethnicity, was
abducted from his home. He was later found dead. In September 1999, Sisak
police began conducting interviews about Serb killings in this city from 1991 to
1995, including Mr Aleksandar Babic, the husband of Ms Marina Babic.
In DATE, the husband of Ms PERSON, of Serb ethnicity, was abducted from his
home. He was later found dead. In DATE, LOCATION police began conducting
interviews about Serb killings in this city from 1991 to 1995, including Mr
PERSON, the husband of Ms PERSON.

TEXT CLASSIFICATION
A B C
SENTIMENT ANALYSIS
LANGUAGE DETECTION
INTENT DETECTION
READABILITY ASSESSMENT

Chatbots paris meetup may 24 2016

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