x.ai // data driven nyc // november 2014
TRANSCRIPT
a personal assistant who schedules meetings for you
DATA DRIVEN NYC NOVEMBER 2014 NEW YORK CITY
x.ai
Solution is middlewareinput
Client
EmailCalendar
Server
EmailCalendar
output
ai trainer
Cc : amy @ x.ai
ai
calendar insert
negotiation
ArchitectureIn order to understand automation we need to understand architecture
Natural Language
Processing
Architectural design
Meeting Preference
Analysis
Cerebellum. Conversation ModelArchitectural
Design
User CC Amy
Amy
Users
New Meeting request
Propose time to participant
Request location from host
Reject time / propose time Accept Time Location sent
Intermediate States Meeting Invite
Accept Time
Accept Location
New Meeting
preferences
Initial State
Architectural Design
Changing meeting states trigger actions
Meeting invite
Not really a tree structure but more “guided conversation structure”
Pituitary gland. Conversation ModelArchitectural
Design
- Reduce ambiguity with each step- Clear, geared at facilitating data detection from response- Designed to optimize convergence- Maximize happiness
Amy’s goals:
Architectural Design
Pituitary gland. Conversation Model*actual example
Natural language processing
Temporal Lobe. Natural Language Processing
When Amy receives an email:
Classification problem What is the intent of the email ?
Information extraction problemWhat is the relevant data ?
This email exemplifies the problem perfectly
The categorization problemNatural language
processing
person is accepting a time proposal
Need to detect the intent combination from possible intents- Similar to article topic modeling but with shorter and noisier documents- But stronger features…
Examples of features- Characteristic N-grams- Characteristic named entities- Characteristic POS rules- Correlation to previous emails
The categorization problemNatural language
processing
One-vs-all non-linear svm’s as a first stab:- Optimize kernel / kernel params / features per intent- Optimize for precision- Calculate Platt confidence level as a function of “prediction score”
Categorization automated after confortable confidence → further human verification/training
person is accepting a time proposal
Information extraction modelNatural language
processing
what time is the person accepting
1 Detection (SUTime CRF model – StanfordNLP + x.ai layers / re-training - Temporal expressions → “1-2 works”, “early next week”, “lunch”, ... - Locations → “my office”, “Starbucks”, “Peking”, “Wall Street 48 5th floor” - People → Marcos, Alex, Dennis, Matt
2 Resolution – x.ai logical email context layers - “1-2 works” → 2014-11-13T18:00:00 UTC Duration: 1h - “my office” → Wall Street 48 5th Floor - “Marcos” → [email protected] userId:u23687khje672876
*can be broken down into two problems of varying difficulty
Classifier
New Meeting
Accept Time
Decline Time
Set preferences
Information extraction modelNatural language
processing
Information model based on class
Etc..
Class Information Model
Look for time constraints
Find time in thread
Look for locations / times / preferences
Hippocampus: User PreferencesUser
Preferences
Different ways to obtain user preferences :- User tells Amy his/her preferences- We deduce it from calendar
Figure out “meeting density” - Distribution over the week- Per type of meeting : lunch, “afternoon business”, etc… - Preferred meeting locations
A beautiful probability model emerges when we combine preferences for several users which- Simultaneously makes everyone happy- Does not make anyone sad- Work in progress… :-)
UserPreferences
Hippocampus: User Preferences
More modules are involved Many more modules are involved and under development.
Actively looking for new smart people to join the team at x.ai / jobs :-)
- Marcos Jimenez (PhD, UC Berkeley, CERN)- Diedi Hu (PhD Columbia, CERN)- Nikhil Raju (Columbia)- Sid Chandra (Columbia)- Angela Zhou (Columbia)
marcos @ x.aichief data scientist and co-founder
48 Wall Street, 5th FloorNew York, NY 10005
E: [email protected]: @xdotai