IoT, Automation and AI to enrich Human Experience
Hassan SawafDirector of Applied Science & Artificial Intelligence
Amazon Web Services
Agenda
• My Motivation
Personal Background:
• Serial Entrepreneur since mid-90s
• Speech Recognition, Machine Translation
and Computer Vision since 1996
• Daimler Benz, AIXPLAIN AG, AppTek Inc., SAIC, eBay
• now Amazon (AWS AI)
Agenda
• My Motivation
Personal Experiences:
• Business ideas often require complex AI services
• E.g. “real-time speech translation”
• E.g. “personal voice assistant”
• Expensive R&D necessary to establish robust AI services
• Challenges can me prohibitive
for small and large enterprises
Alexa
• Goal:
Ubiquitous
Computing
• Alexa Skills Kit
• Alexa Voice Services
• Alexa Fund
• Smart Home
AWS Internet of Things
• AWS IoT service since October 2015
• Check it out on:
https://aws.amazon.com/iot-platform
AWS Internet of Things
• AWS IoT service since November 2016
• Check it out on:
https://aws.amazon.com/greengrass
AWS Machine Learning
• AWS ML service since November 2016
• Check it out on:
https://aws.amazon.com/machine-learning
AWS Lex
• AWS Lex service since November 2016
• Check it out on:
https://aws.amazon.com/iot-platform
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
AI ServicesAmazon
Rekognition
Amazon
Polly
Amazon
Lex
Amazon AI: Democratized Artificial Intelligence
AI ServicesAmazon
Rekognition
Amazon
Polly
Amazon
Lex
More to come
in 2017
Amazon AI: Democratized Artificial Intelligence
AI Services
AI Platform
Amazon
Rekognition
Amazon
Polly
Amazon
Lex
More to come
in 2017
Amazon
Machine Learning
Amazon Elastic
MapReduce
Spark &
SparkML
More to come
in 2017
Amazon AI: Democratized Artificial Intelligence
AI Services
AI Platform
AI Engines
Amazon
Rekognition
Amazon
Polly
Amazon
Lex
More to come
in 2017
Amazon
Machine Learning
Amazon Elastic
MapReduce
Spark &
SparkML
More to come
in 2017
Apache
MXNetTensorFlow Caffe Theano KerasTorch CNTK
Amazon AI: Democratized Artificial Intelligence
AI Services
AI Platform
AI Engines
Amazon
Rekognition
Amazon
Polly
Amazon
Lex
More to come
in 2017
Amazon
Machine Learning
Amazon Elastic
MapReduce
Spark &
SparkML
More to come
in 2017
Apache
MXNetCaffe Theano KerasTorch CNTK
Amazon AI: Democratized Artificial Intelligence
TensorFlow
P2 ECS Lambda GreenGrass FPGAEMR/Spark
More to
come
in 2017
Hardware
Recommendations & Ranking At Netflix
Personalized ranking,
page generation,
search, similarity, ratings
In 140 new countries,
simultaneously
Apache MXNet
Programmable Portable High Performance
Near linear scaling
across hundreds of GPUs
Highly efficient
models for mobile
and IoT
Simple syntax,
multiple languages
Why Apache MXNet?
Most Open Best On AWS
Optimized for
deep learning on AWS
Accepted into the
Apache Incubator
(Integration with AWS)
Ideal
Inception v3Resnet
Alexnet
88%Efficiency
0
64
128
192
256
1 2 4 8 16 32 64 128 256
Amazon AI: Scaling With MXNet
MXNet Overview
• Founded by: U.Washington, Carnegie Mellon U. (~1.5yrs old)
• Recently Accepted to the Apache Incubator
• State of the Art Model Support: Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM)
• Ultra-scalable: Near-linear scaling equals fastest time to model
• Multi-language: Support for Scala, Python, R, etc.. for legacy code leverage and easy integration with Spark
• Ecosystem: Vibrant community from Academia and Industry
Open Source Project on Github | Apache-2 Licensed
Collaborations and Community
4th DL Framework in Popularity
(Outpacing Torch, CNTK and Theano)
0 27.5 55 82.5 110 137.5
TensorFlow
Caffe
Keras
MXNet
Theano
Deeplearning4j
CNTK
Torch7
Popularity
Diverse Community(Spans Industry and Academia)
0 15000 30000 45000 60000
Bing Xu (Apple)
Tianqi Chen (UW)
Mu Li (CMU/AWS)
Eric Xie (UW/AWS)
Yizhi Liu (Mediav)
Chiyuan Zhang (MIT)
Tianjun Xiao (Micrsoft)
Yutian Li (Face++)
Guo Jian (Tusimple)
Guosheng Dong (sogou)
Yu Zhang (MIT)
Depeng Liang (?)
Qiang Kou (Indiana U)
Xingjian Shi (HKUST)
Naiyan Wang (Tusimple)
Top Contributors
Roadmap / Areas of Investment
• NNVM Migration (complete)
• Apache project (Accepted and transitioning to Apache)
• Usability
• Keras Integration WIP (Expected by Q2)
• MinPy being merged (Dynamic Computation graphs, Std Numpyinterface)
• Documentation (installation, native documents, etc.)
• Tutorials, examples
• Platform support(Linux, Windows, OS X, mobile …)
• Language bindings(Python, C++, R, Scala, Julia, JavaScript …)
• Sparse datatypes and LSTM performance improvements
• Deploy your model your way: Lambda, EC2/Docker, Raspberry Pi
Application Examples | Python notebooks
• https://github.com/dmlc/mxnet-notebooks
• Basic concepts
• NDArray - multi-dimensional array computation
• Symbol - symbolic expression for neural networks
• Module - neural network training and inference
• Applications
• MNIST: recognize handwritten digits
• Check out the distributed training results
• Predict with pre-trained models
• LSTMs for sequence learning
• Recommender systems
• Train a state of the art Computer Vision model (CNN)
• Lots more..
Call to Action
MXNet Resources:
• MXNet Blog Post | AWS Endorsement
• Read up on MXNet and Learn More: mxnet.io
• MXNet Github Repo
• MXNet Recommender Systems Talk | Leo Dirac
Developer Resources:
• Deep Learning AMI |Amazon Linux
• Deep Learning AMI | Ubuntu – NEW!!!
• P2 Instance Information
• CloudFormation Template Instructions
• Deep Learning Benchmark
• MXNet on Lambda
• MXNet on ECS/Docker
• MXNet on Raspberry Pi | Wine Detector
Thank you!
Joseph Spisak
Manager | Product Mgmt
AI & Deep Learning
Hassan Sawaf
Director
AI & Applied Sciences