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AI @ AWS: A look into AI Innovations by Amazon and

AWS Customers

Swami Sivasubramanian

VP, Amazon AI

swami@amazon.com

Thousands of Amazon Engineers Focused on AI

Fulfilment & Logistics

Thousands of Amazon Engineers Focused on AI

Fulfilment & Logistics

Search & Discovery

Thousands of Amazon Engineers Focused on AI

Fulfilment & Logistics

Search & Discovery

Existing Products

Fulfilment & Logistics

Search & Discovery

Existing Products

New Products

Thousands of Amazon Engineers Focused on AI

Data Training Prediction

How to build these artificial intelligence systems?

Lots of GPUs and CPUs

Serverless

At the Edge, On IoT Devices

Prediction

The Challenge For Artificial Intelligence: SCALE

Tons of GPUs

Elastic capacity

Training

Pre-built images

Aggressive migration

New data created on AWS

Data

PBs of existing data

“The future is here,

it’s just not evenly distributed yet”

William Gibson

Amazon AIIntelligent Services Powered By Deep Learning!

Amazon AI: Artificial Intelligence In The Hands Of Every Developer

Infrastructure!

Engines!

Services!

Platforms!

Infrastructure! CPU!

Engines! Apache!MXNet! TensorFlow! Caffe2 &!

Caffe! Theano! Keras! CNTK!

Services!Rekognition! Polly!

Chat!

Platforms!

IoT!

Speech!Vision!

Lex!

Mobile!

Amazon AI: Artificial Intelligence In The Hands Of Every Developer

Amazon !ML!

Spark &!EMR! Kinesis! Batch! ECS!

GPU!

Torch!

Rekognition: Object & Scene Detection

Rekognition: Facial Detection

Rekognition: Facial Search

Automating Footage Tagging with Amazon Rekognition

Built in 3 weeks Index against 99,000 people Saving ~9,000 hours a year in labor

Quickly Identifying Persons of Interest with Amazon Rekognition

More than 300,000 photo leads indexed within 1-2 days Identification process went from 2-3 days, to minutes – greatly increasing the ability for law enforcement to act quickly Within 1 week of going live, the solution identified a suspect for a cold case that lead to an arrest through due process

Polly: Life-like Speech Service

Converts text to life-like

speech

48 voices 24 languages Low latency, real time

Fully managed

High quality language education using Amazon Polly Text-to-Speech (TTS)

>170M users: Most popular language-learning platform Each lesson targets different linguistic skills and concepts. Amazon Polly has been the A/B winner against their previous TTS providers in all six voice tests they use to evaluate impact to their business.

•  Media and Entertainment!

•  Education!

•  Accessibility!

•  Content Creation!

•  Telephony/Contact Center!

•  Internet of Things!

•  Gaming!

Amazon Polly: Use Cases & Customers

Amazon Lex: Conversational Chatbots

I’d like to book a hotel.

Sure! What city would you like to book it in?

Amazon Lex: Conversational Chatbots

I’d like to book a hotel.

Sure! What city would you like to book it in?

New York City Destination: New York City

Amazon Lex: Conversational Chatbots

I’d like to book a hotel.

Sure! What city would you like to book it in?

New York City

When would you like to check in?

Next Friday

Destination: New York City

Check In: 5/26/2017

Amazon Lex: Conversational Chatbots

Lex Bots

Salesforce

Microsoft Dynamics

Marketo

Zendesk

Web

Devices

Apps Facebook Messenger,

Slack,

Amazon Connect

Mobile Mobile Hub integration

Quickbooks

Customer support made refreshingly easy

Freshbots combine Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU) capabilities of Amazon Lex with in-house workflows to automate customer service Users transact using natural language leading to significant productivity improvements Customer support agents can make intelligent decisions to enhance customer experience

An Amazon Lex voice chatbot to register Heart Walk participants

Nearly 300 Heart Walk fundraising events annually in the U.S., totaling nearly 1 million participants Amazon Lex enables Heart Walk participants to use natural language to register for the event Voice interaction eliminates the need to read text, and accelerates the registration process

Amazon Machine Learning: Predictive models in minutes

Integrated with S3 and Redshift for data access

Efficient common ML

transformations

Simple regression

model generation

Real-time and batch

predictions at scale

Model evaluation and interpretation

tools

Amazon AI: Democratized Artificial Intelligence

Infrastructure! CPU!

Engines! Apache!MXNet! TensorFlow! Caffe2 &!

Caffe! Theano! Keras! CNTK!

Services!Rekognition! Polly!

Chat!

Platforms!

IoT!

Speech!Vision!

Lex!

Mobile!

Amazon !ML!

Spark &!EMR! Kinesis! Batch! ECS!

GPU!

Torch!

One-Click GPU or CPU Deep Learning

AWS Deep Learning AMI

Up to~40k CUDA cores

Apache MXNet

TensorFlow

Theano

Caffe2 & Caffe

Torch

CNTK

Keras

Pre-configured CUDA drivers,

MKL

Anaconda, Python3

Ubuntu and Amazon Linux

+ CloudFormation template

+ Container Image

Computer vision for crowd-sourced maps using deep learning on Amazon

Gained insight from large volumes of unstructured public data to improve global mobility and transportation Accelerated training and inference of deep neural networks for graphic-intensive workloads using AWS EC2 P2 instances Applied their fine grain computer vision algorithms to 142M images and nearly 2M miles of mapped roads

Deep Learning Model Optimization on AWS

Improved accuracy and lower training costs using MXNet and other frameworks using AWS EC2 P2 and SigOpt HPO Dramatically reduces computation costs by 8X or more compared to traditional optimization methods

0.2-0.1...0.7

Input Output

1 1 11 0 10 0 0

3

mx.sym.Pooling(data, pool_type="max", kernel=(2,2), stride=(2,2)

lstm.lstm_unroll(num_lstm_layer, seq_len, len, num_hidden, num_embed)

4 2 2 0 4=Max

13...4

0.2-0.1...0.7

mx.sym.FullyConnected(data, num_hidden=128)

2

mx.symbol.Embedding(data, input_dim, output_dim = k)

Queen

4 2 2 0 2=Avg

Input Weights

cos(w, queen) = cos(w, king) - cos(w, man) + cos(w, woman)

mx.sym.Activation(data, act_type="xxxx")

"relu"

"tanh"

"sigmoid"

"softrelu"

Neural Art

Face Search

Image Segmentation

Image Caption

“People Riding Bikes”

Bicycle, People, Road, SportImage Labels

Image

Video

Speech

Text

“People Riding Bikes”

Machine Translation

“Οι άνθρωποι ιππασίας ποδήλατα”

Events

mx.model.FeedForward model.fit

mx.sym.SoftmaxOutput

Anatomy of a Deep Learning Model

mx.sym.Convolution(data, kernel=(5,5), num_filter=20)

Deep Learning Models

Ex. MXNet User | TuSimple|Autonomous Driving

Ex. MXNet User |TwoSense| Biometrics for Mobile Devices

Deep Learning @Amazon

•  Applied Research

•  Core Research

•  Alexa

•  Demand Forecasting

•  Risk Analytics

•  Search

•  Recommendations

•  AI Services | Rek, Lex, Polly

•  Q&A Systems

•  Supply Chain Optimization

•  Advertising

•  Machine Translation

•  Video Content Analysis

•  Robotics

•  Lots of Computer Vision..

•  Lots of NLP/U..

*Teams are either actively evaluating, in development, or transitioning to scale production

Running AI In Production on AWS Today

Thank you!!THANK YOU! !

swami@amazon.com!