The document provides an overview of artificial intelligence (AI) and machine learning, including key milestones in the development of AI and deep learning techniques. It discusses early pioneers in AI from the 1950s and developments that enabled recent advances, such as increased data, GPUs, cloud computing, and algorithms. Examples are given of applying deep learning techniques to problems like image recognition, natural language processing, and generating images/text. The document also discusses Amazon AI services for vision, speech, text analysis and language translation that make ML accessible.

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Moving Forward with AI
©2018 Amazon Web Services, Inc. or its affiliates, All rights reserved
Adrian Hornsby, Cloud Architecture Evangelist
@adhorn

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Myth: AI is dark magic

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What are we talking about?
AI
Machine
Learning
Deep
Learning

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One of the ”Founding Father" of Artificial Intelligence
John McCarthy
1955

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Photo from the 1956 Dartmouth
Conference with Marvin Minsky,
Ray Solomonoff, Claude Shannon,
John McCarthy, Trenchard More,
Oliver Selfridge and Nathaniel
Rawchester

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Frank Rosenblatt, 1957
Perceptron

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First known deep network
https://devblogs.nvidia.com/deep-learning-nutshell-history-training/
Alexey Grigorevich Ivakhnenko, 1965

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Paul Werbos, 1975
Backpropagation

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LeCun, 1989
First application of
backpropagation
https://www.youtube.com/watch?v=FwFduRA_L6Q

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The curse of dimensionality

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The Advent of AI
Data
GPUs
& Acceleration
Cloud
Computing
Algorithms

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Machine Learning in a nutshell
Binary classification (Logistic
regression)
Multi-category classification
(Multinomial logistic regression)
Regression
(Linear regression)

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What is a Deep Learning?

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Predicting the price of a house with humans
Price
City
ZipCode Life Quality
Parking
Size
# Room
Accessibility
Family Friendly

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Predicting the price of a house with neural network
Price
City
ZipCode Life Quality
Parking
Size
# Room
Accessibility
Family Friendly
Input Output
Discovered by the neural network

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Deep Learning – Neural Networks
Output
Layer
Input
Layer
Hidden
Layers
Many
More…

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https://ml4a.github.io/ml4a/neural_networks/
784 x 10 + 10 x 10 = 7940 weights
Deep Learning Training

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Learning = Minimizing the loss (error) function
Backpropagation

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Deep Learning is a Big Deal
It’s able to do better than other ML and Humans

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https://github.com/precedenceguo/mx-rcnn https://github.com/zhreshold/mxnet-yolo
CNN: Object Detection

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CNN: Object Detection

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FDA-approved
medical imaging
https://www.periscope.tv/AWSstartups/1vAGRgevBXRJl
https://www.youtube.com/watch?v=WE81dncwnIc
CNN: Object Segmentation

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CNN: Text Detection and Recognition
https://github.com/Bartzi/stn-ocr

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https://github.com/tornadomeet/mxnet-face
CNN: Face Detection

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CNN: Real-Time Pose Estimation
https://github.com/dragonfly90/mxnet_Realtime_Multi-Person_Pose_Estimation

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CapsNet: Capsule Networks
Spatial Memory
https://medium.com/ai%C2%B3-theory-practice-business/understanding-hintons-capsule-networks-part-i-intuition-b4b559d1159b

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CapsNet: Capsule Networks
Spatial Memory
https://arxiv.org/pdf/1710.09829v1.pdf

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LSTM: Long Short Term Memory Networks
Oh I remember!
https://github.com/awslabs/sockeye

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PredNet: Prediction Networks
What comes next
https://coxlab.github.io/prednet/

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Generative Adversarial Networks (GAN)
The future at work (already) today
Generating new ”celebrity” faces
https://github.com/tkarras/progressive_growing_of_gans

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Generative adversarial networks (GAN)
The future at work (already) today
Semantic labels → Cityscapes street views
https://tcwang0509.github.io/pix2pixHD/

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Wait! There’s more!
Models can also generate text from text, text from images, text from
video, images from text, sound from video,
3D models from 2D images, etc.

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How do people ”build” Neural Nets?

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Data Visualization &
Analysis
Business Problem –
M L problem fram ing Data Collection
Data Integration
Data Preparation &
Cleaning
Feature Engineering
M odel Training &
Param eter Tuning
M odel Evaluation
Are Business
Goals met?
M odel Deploym ent
M onitoring &
Debugging
YesNo
DataAugmentation
Feature
Augmentation
The AI Process
Re-training
Predictions

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Where to look at in your organisation ?
• Where data is being analysed to help making decisions.
• Sales
• Marketing
• Social media
• Customer supports
• Logs
• Etc.

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Thorn is a global nonprofit organization fighting
child sexual exploitation and child traffickers.
Used by 5,300 officers in
over 18 countries and
identifies an average of 5
kids per day.
https://www.youtube.com/watch?v=lTlv273XUNM

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“Data is gold”

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Did I hear Zoo? Transfer Learning

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https://www.youtube.com/watch?v=qGotULKg8e0
• Over 10 million images from 300,000 hotels
• Fine-tuned a pre-trained Convolutional Neural
Network using 100,000 images
• Hotel descriptions now automatically feature the
best available images
Expedia
Ranking hotel images using deep learning
https://news.developer.nvidia.com/expedia-ranking-hotel-images-with-deep-learning/

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Pro-tip
• Make it ridiculously easy to collect and store any type of data.
• One line of code should be all it takes for anyone in the
company to start collecting and storing new data type.

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Where do you start?
The Low Hanging Fruits

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Put AI in the hands of every developer and
data scientist
Machine Learning at AWS: Our mission

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Amazon ML

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FRAMEWORKS AND INTERFACES
PLATFORMSERVICES
APPLICATIONSERVICES
Amazon
Rekognition
Amazon Polly Amazon Lex
Machine Learning for everyone.
Amazon
Rekognition Video
Amazon Transcribe
Amazon Comprehend
Amazon SageMaker AWS DeepLens Amazon EMR
Deep Learning AMI
Amazon Translate

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FRAMEWORKS AND INTERFACES
PLATFORMSERVICES
APPLICATIONSERVICES
Amazon
Rekognition
Amazon Polly Amazon Lex
Machine Learning for everyone.
Amazon
Rekognition Video
Amazon Transcribe
Amazon Comprehend
Amazon SageMaker AWS DeepLens Amazon EMR
Deep Learning AMI
Amazon Translate

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Amazon Rekognition
Deep learning-based image & video analysis

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Marinus Analytics uses facial recognition to stop
human trafficking
“Now with Traffic Jam’s
FaceSearch, powered by
Amazon Rekognition,
investigators are able to
take effective action by
searching through millions
of records in seconds to
find victims.”
http://www.marinusanalytics.com/articles/2017/10/17/amazon-rekognition-helps-marinus-analytics-fight-human-trafficking

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Amazon Polly
“What's the
difference between a
hippo and a zippo?
One is really heavy,
and the other is a
little lighter.”
Amazon Polly: Text In, Life-like Speech Out
The Text-To-Speech technology behind Amazon Polly takes advantage of
bidirectional long short-term memory (LSTM)*

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“With Amazon Polly our users benefit from
the most lifelike Text-to-Speech voices
available on the market.”
Severin Hacker
CTO, Duolingo

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Amazon Lex
“What’s the weather
forecast?”
“It will be sunny
and 25°C”
Weather
Forecast
Amazon Lex
Build Conversational Chatbots

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59. https://www.capitalone.com/applications/alexa/

60. https://www.youtube.com/watch?v=TeLvFqLW_0A
Speech Recognition and Natural Language
Understanding
« Amazon Lex integrates easily into our existing
applications, as well as our new cloud-native
serverless architectures, enabling us to rapidly
take advantage of these powerful technologies
to improve and extend the capabilities we can
offer our employees and customers.»
Gillian Armstrong, Technologist, Liberty Mutual

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Amazon Comprehend
Discover insights from text
Entities
Key Phrases
Language
Sentiment
Amazon
Comprehend Topic Modeling

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« Amazon Comprehend helps us analyze
the key sentiments, objects, and geos in
our 30 million plus reviews & testimonies
[…] so our customers can make the best
decision possible for their travel.”
Matt Fryer, VP and Chief Data Science Officer
Hotels.com
https://aws.amazon.com/solutions/case-studies/expedia/
https://news.developer.nvidia.com/expedia-ranking-hotel-images-with-deep-learning/

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“Hello, this isAllan
speaking”
Amazon Transcribe
Automatic speech recognition service
Amazon
Transcribe

64. ringDNA
• RingDNA is an end-to-end
communications platformfor sales
teams.
• Hundreds of enterprise organizations
use RingDNA to dramatically increase
productivity, engage in smarter sales
conversations, gain predictive sales
insights and improve their win rate.
Speech to Text
"A critical component of RingDNA’s Conversation AI
requires best of breed speech-to-text to deliver
transcriptions of every phone call. RingDNA is excited
about Amazon Transcribe since it provides high-quality
speech recognition at scale, helping us to better
transcribe every call to text "
Howard Brown, CEO & Founder, RingDNA
https://www.youtube.com/watch?v=1ZJ_f1bDdog

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“Hello, what’s up? Do you
want to go see a movie
tonight?”
Amazon Translate
Natural and fluent language translation
"Bonjour, quoi de neuf ? Tu
veux aller voir un film ce
soir ?"
Amazon
Translate

66. Hotels.com
Natural Language Processing
« Amazon Comprehend helps us
analyze the key sentiments, objects, and
geos in our 30 million plus reviews &
testimonies. Now we are able to discover
new insights into the unique experiences
available at each property, so our
customers can make the best
decision possible for their travel.”
Machine Translation
« We operate 90 localized websites in 41
languages. (…)
Having evaluated Amazon Translate
and several other solutions, we believe
that Amazon Translate presents a quick,
efficient and most importantly, accurate
solution.»
Matt Fryer, VP and Chief Data Science Officer, Hotels.com

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Moving deeper in the AI rabbit-hole

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FRAMEWORKS AND INTERFACES
PLATFORMSERVICES
APPLICATIONSERVICES
Amazon
Rekognition
Amazon Polly Amazon Lex
Machine Learning for everyone.
Amazon
Rekognition Video
Amazon Transcribe
Amazon Comprehend
Amazon SageMaker AWS DeepLens Amazon EMR
Deep Learning AMI
Amazon Translate

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Amazon ML Lab
Lots of companies doing
Machine Learning
Unable to unlock
business potential
Brainstorming Modeling Teaching
Lack ML
expertise
Leverage Amazon experts with decades of ML
experience with technologies like Amazon Echo,
Amazon Alexa, Prime Air and Amazon Go
Amazon ML Lab
provides the missing ML
expertise
https://aws.amazon.com/ml-solutions-lab/

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There’s Never Been A Better Time
To Build New Businesses

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Thank You.
Adrian Hornsby, Cloud Architecture Evangelist
@adhorn