aws re:invent 2016: machine learning state of the union mini con (mac206)

© 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

More Data Better Analytics

A Flywheel For Data


Better Products

A Flywheel For Data

More Users


Better Products

A Flywheel For Data

Click stream

User activity

Generated content

Purchases

Clicks

Likes

Sensor data

More Users


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


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


Better Products

Artificial Intelligence

At Amazon

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

The Advent Of

Deep Learning

Algorithms

The Advent Of

Deep LearningData

Algorithms

The Advent Of

Deep LearningData

GPUs

& Acceleration

Algorithms

The Advent Of

Deep LearningData

GPUs

& Acceleration

Programming

models

Algorithms

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

MXNet: Scalable Deep Learning Framework

Can We Help Customers

Put Intelligence At The Heart Of

Every Application & Business?

Amazon AIIntelligent Services Powered By Deep Learning

Amazon AI: Three New Deep Learning Services

Amazon PollyLife-like Speech


Amazon RekognitionLife-like Speech Image Analysis

Amazon Polly


Amazon Rekognition Amazon LexLife-like Speech Image Analysis Conversational

Engine

Amazon Polly


Polly Rekognition LexLife-like Speech Image Analysis Conversational

Engine

The Advent Of Conversational Interactions

1st Gen: Machine-oriented

interactions



interactions

2nd Gen: Control-oriented

& translated



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

Origin

Destination

Departure Date

Flight Booking

“Book a flight to

London”

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


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


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


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


Departure Date

Flight Booking

“Next Friday”

“When would you like to

fly?”

Origin Seattle


Departure Date

Flight Booking

“Next Friday”Automatic

Speech Recognition

Next Friday

Origin Seattle


Departure Date 11/18/2016

Flight Booking


Speech Recognition

Natural Language

Understanding

Next Friday

Utterances

Flight booking

11/18/2016

Intent /

Slot model

Origin Seattle



Flight Booking


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



Flight Booking


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



Flight Booking


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



Engine

Polly: Life-like Speech Service

Converts text

to life-like speech

47 voices 24 languages Low latency,

real time

Fully managed

Let’s hear from Polly..

“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


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




3. Add Semantic Meaning to Text

4. Customized Pronunciation

“My last name is Nguyen.”



“My last name is Nguyen.”

Polly: Life-like Speech Service

High quality,

through

best-in-class

deep learning

Deep

functionality

Easy to use


Built for

production

Low

cost



Engine

Rekognition: Search & Understand Visual Content

Real-time &

batch image

analysis

Object & Scene

DetectionFacial Detection Face SearchFacial Analysis

Object and Scene Detection

Maple

Villa

Plant

Garden

Water

Swimming Pool

Tree

Potted Plant

Backyard

Demographic Data

Facial Landmarks

Sentiment Expressed

Image Quality

Facial Analysis

Brightness: 25.84

Sharpness: 160

General Attributes

Face Comparison

Face Recognition

High quality,

through

best-in-class

deep learning

Deep

functionality

Easy to use


Built for

production

Low

cost

Rekognition: Search & Understand Visual Content

Amazon AI: New Deep Learning Services


Engine

Deep Learning

Frameworks

MxNet, TensorFlow,

Theano, Caffe, Torch


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


Salil Verma, Senior Director, IT

OhioHealth

Thank you!

[email protected]

THANK YOU!

aws re:invent 2016: machine learning state of the union mini con (mac206)

Technology