Date post: | 06-Jan-2017 |
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© 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Swami Sivasubramanian
GM, Amazon AI
November 30, 2016
MAC206
AI and Deep Learning
at Amazon
A Flywheel For Data
Click stream
User activity
Generated content
Purchases
Clicks
Likes
Sensor data
More Users
More Data Better Analytics
Better Products
A Flywheel For Data
Click stream
User activity
Generated content
Purchases
Clicks
Likes
Sensor data
Object Storage
Databases
Data warehouse
Streaming analytics
BI
Hadoop
Spark/Presto
Elasticsearch
More Users
More Data Better Analytics
Better Products
A Flywheel For Data
Click stream
User activity
Generated content
Purchases
Clicks
Likes
Sensor data
Object Storage
Databases
Data warehouse
Streaming analytics
BI
Hadoop
Spark/Presto
Elasticsearch
Artificial
IntelligenceMore Users
More Data Better Analytics
Better Products
A Flywheel For Data
Click stream
User activity
Generated content
Purchases
Clicks
Likes
Sensor data
Object Storage
Databases
Data warehouse
Streaming analytics
BI
Hadoop
Spark/Presto
Elasticsearch
Artificial
IntelligenceMore Users
More Data Better Analytics
Better Products
Artificial Intelligence At AmazonThousands Of Employees Across The Company Focused on AI
Discovery &
Search
Fulfillment &
Logistics
Enhance
Existing Products
Define New
Categories Of
Products
Bring Machine
Learning To All
AI on AWS Today
• Zillow–Zestimate (using Apache Spark)
• Howard Hughes Corp–Lead scoring for luxury real estate purchase predictions
• FINRA–Anomaly detection, sequence matching, regression
analysis, network/tribe analysis
• Netflix–Recommendation engine
• Pinterest –Image recognition search
• Fraud.net–Detect online payment fraud
• DataXu–Leverage automated & unattended ML at
large scale (Amazon EMR + Spark)
• Mapillary–Computer vision for crowd sourced maps
• Hudl–Predictive analytics on sports plays
• Upserve–Restaurant table mgmt & POS for
forecasting customer traffic
• TuSimple–Computer Vision for Autonomous Driving
• Clarifai– Computer Vision APIs
One-Click GPU
Deep Learning
AWS Deep Learning AMI
Up to~40k CUDA cores
MXNet
TensorFlow
Theano
Caffe
Torch
Pre-configured CUDA drivers
Anaconda, Python3
+ CloudFormation template
+ Container Image
Amazon AI: Three New Deep Learning Services
Amazon RekognitionLife-like Speech Image Analysis
Amazon Polly
Amazon AI: Three New Deep Learning Services
Amazon Rekognition Amazon LexLife-like Speech Image Analysis Conversational
Engine
Amazon Polly
Amazon AI: Three New Deep Learning Services
Polly Rekognition LexLife-like Speech Image Analysis Conversational
Engine
The Advent Of Conversational Interactions
1st Gen: Machine-oriented
interactions
2nd Gen: Control-oriented
& translated
The Advent Of Conversational Interactions
1st Gen: Machine-oriented
interactions
2nd Gen: Control-oriented
& translated
3rd Gen:
Intent-oriented
Lex: Build Natural, Conversational Interactions In Voice & Text
Voice & Text
“Chatbots”
Powers
Amazon Alexa
Voice interactions
on mobile, web
& devices
Text interaction
with Facebook Messenger
Enterprise
Connectors
(with more coming) Salesforce
Microsoft Dynamics
Marketo
Zendesk
Quickbooks
Hubspot
Origin
Destination
Departure Date
Flight Booking
“Book a flight to
London”
Automatic
Speech Recognition
Book Flight
London
Origin
Destination
Departure Date
Flight Booking
“Book a flight to
London”
Automatic
Speech Recognition
Natural Language
Understanding
Book Flight
London
Utterances
Flight booking
London Heathrow
Intent /
Slot model
Origin
Destination London Heathrow
Departure Date
Flight Booking
“Book a flight to
London”
Automatic
Speech Recognition
Natural Language
Understanding
Book Flight
London
Utterances
Flight booking
London Heathrow
Intent /
Slot model
Origin Seattle
Destination London Heathrow
Departure Date
Flight Booking
“Book a flight to
London”
Automatic
Speech Recognition
Natural Language
Understanding
Book Flight
London
Utterances
Flight booking
London Heathrow
LocationLocation
Intent /
Slot model
Origin Seattle
Destination London Heathrow
Departure Date
Flight Booking
“Book a flight to
London”
Automatic
Speech Recognition
Natural Language
Understanding
Book Flight
London
Utterances
Flight booking
London Heathrow
Prompt
LocationLocation
“When would you like to fly?”
Intent /
Slot model
Origin Seattle
Destination London Heathrow
Departure Date
Flight Booking
“Book a flight to
London”
Automatic
Speech Recognition
Natural Language
Understanding
Book Flight
London
Utterances
Flight booking
London Heathrow
Prompt
LocationLocation
“When would you like to fly?”
“When would you like to
fly?”
Polly
Intent /
Slot model
Origin Seattle
Destination London Heathrow
Departure Date
Flight Booking
“Next Friday”
“When would you like to
fly?”
Origin Seattle
Destination London Heathrow
Departure Date
Flight Booking
“Next Friday”Automatic
Speech Recognition
Next Friday
Origin Seattle
Destination London Heathrow
Departure Date 11/18/2016
Flight Booking
“Next Friday”Automatic
Speech Recognition
Natural Language
Understanding
Next Friday
Utterances
Flight booking
11/18/2016
Intent /
Slot model
Origin Seattle
Destination London Heathrow
Departure Date 11/18/2016
Flight Booking
“Next Friday”Automatic
Speech Recognition
Natural Language
Understanding
Next Friday
Utterances
Flight booking
11/18/2016
Intent /
Slot model
Origin Seattle
Destination London Heathrow
Departure Date 11/18/2016
Flight Booking
“Next Friday”Automatic
Speech Recognition
Natural Language
Understanding
Next Friday
Utterances
Flight booking
11/18/2016
Confirmation
“Your flight is booked for next Friday”
Intent /
Slot model
Origin Seattle
Destination London Heathrow
Departure Date 11/18/2016
Flight Booking
“Next Friday”Automatic
Speech Recognition
Natural Language
Understanding
Next Friday
Intent /
Slot model
Utterances
Flight booking
11/18/2016
“Your flight is booked for
next Friday”
Confirmation
“Your flight is booked for next Friday”Polly
Origin Seattle
Destination London Heathrow
Departure Date 11/18/2016
Flight Booking
“Next Friday”Automatic
Speech Recognition
Natural Language
Understanding
Next Friday
Grammar
Graph
Utterances
Flight booking
11/18/2016
Hotel Booking
High quality,
through
best-in-class
deep learning
Deep
functionality
Easy to use
& thoughtfully integrated
Built for
production
Low
cost
Lex: Build Natural, Conversational Interfaces In Voice & Text
Amazon AI: Three New Deep Learning Services
Polly Rekognition LexLife-like Speech Image Analysis Conversational
Engine
Polly: Life-like Speech Service
Converts text
to life-like speech
47 voices 24 languages Low latency,
real time
Fully managed
“Today in Seattle, WA, it’s 11°F”
‘"We live for the music" live from the Madison Square Garden.’
1. Automatic, Accurate Text Processing
Polly: A Focus On Voice Quality & Pronunciation
Polly: A Focus On Voice Quality & Pronunciation
2. Intelligible and Easy to Understand
1. Automatic, Accurate Text Processing
2. Intelligible and Easy to Understand
3. Add Semantic Meaning to Text
Richard’s number without semantic meaning
Richard’s number with semantic meaning
Telephone Number
Polly: A Focus On Voice Quality & Pronunciation
1. Automatic, Accurate Text Processing
2. Intelligible and Easy to Understand
3. Add Semantic Meaning to Text
4. Customized Pronunciation
“My last name is Nguyen.”
Polly: A Focus On Voice Quality & Pronunciation
1. Automatic, Accurate Text Processing
“My last name is Nguyen.”
Polly: Life-like Speech Service
High quality,
through
best-in-class
deep learning
Deep
functionality
Easy to use
& thoughtfully integrated
Built for
production
Low
cost
Amazon AI: Three New Deep Learning Services
Polly Rekognition LexLife-like Speech Image Analysis Conversational
Engine
Rekognition: Search & Understand Visual Content
Real-time &
batch image
analysis
Object & Scene
DetectionFacial Detection Face SearchFacial Analysis
Demographic Data
Facial Landmarks
Sentiment Expressed
Image Quality
Facial Analysis
Brightness: 25.84
Sharpness: 160
General Attributes
High quality,
through
best-in-class
deep learning
Deep
functionality
Easy to use
& thoughtfully integrated
Built for
production
Low
cost
Rekognition: Search & Understand Visual Content
Amazon AI: New Deep Learning Services
Polly Rekognition LexLife-like Speech Image Analysis Conversational
Engine
Deep Learning
Frameworks
MxNet, TensorFlow,
Theano, Caffe, Torch
© 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Dan Law, Wei Lin, Steve Varner
November 30, 2016
AI for Public SafetyLeveraging Amazon Rekognition, Amazon Lex,
and Amazon Polly to Find Missing Persons
Learn about:
• A difficult public safety challenge
• How we currently address it
• How Amazon AI can help (with video
and live demo)
• How we apply Amazon Rekognition,
Amazon Lex, and Amazon Polly
What to expect from the session
A public safety challenge: Finding missing persons
~ 100,000 active missing persons cases in U.S. at any given time
~ 60% are adults, ~40% are children
The National Missing and Unidentified Persons System (NamUs) currently has:
~ 13,000 open missing persons cases
~ 11,000 open unidentified remains cases
How can AI apply?
Image analytics and facial recognition can continually monitor
for missing persons
Tools that understand natural language can enable officers to keep eyes up and hands free
Amazon Rekognition, Amazon Lex, and Amazon Polly Can Support This
COMMANDCENTRAL
INGEST, MANAGEMENT, SEARCH
INTELLIGENT MIDDLEWARE
How do we employ Amazon AI tools?
BIO MONITORWEAPONVEHICLE OFFICER
SMART DEVICE & BODYCAM
Amazon Rekognition
Amazon S3 Amazon LexAmazon
Polly
AWS (GovCloud)
EDGE
AWS Lambda
FACE
DETECTOR
COUCH (Amazon EC2)
MICROSERVICES (Amazon EC2)
FACE PATH
VOICE PATH
© 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Salil Verma, Senior Director, IT
OhioHealth