Introduction to AI

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Introduction to AI
Boaz Ziniman, Technical Evangelist – Amazon Web Services
boazz@amazon.com
@ziniman

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
If I asked you…
“How important is it for your company to invest in Machine
Learning and AI?”
Most of you would probably answer:
“We’re not a Machine Learning company.”
”They’re not central to our business.”
“We have more important topics to address.”

Now, if I asked you…
“How important is it for your organization to build better
products and deliver a better customer experience?”
Most of you would certainly answer:
“This is our number one priority.”
But isn’t this the same question, really?

Artificial Intelligence: design software applications which exhibit
human-like behavior, e.g. speech, natural language processing,
reasoning or intuition
Machine Learning: teach machines to learn without being
explicitly programmed
Deep Learning: using neural networks, teach machines to learn
from complex data where features cannot be explicitly expressed

More Data Better Analytics
A Flywheel For Data

A Flywheel For Data
Better Products

A Flywheel For Data
Better ProductsMore Users

A Flywheel For Data
Click stream
User activity
Generated content
Purchases
Clicks
Likes
Sensor data

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

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
Intelligence

The Advent of
Deep Learning
Algorithms

The Advent of
Deep Learning
Data
Algorithms

The Advent of
Deep Learning
Data
GPUs
& Acceleration
Algorithms

The Advent of
Deep Learning
Data
GPUs
& Acceleration
Programming
models
Algorithms

Myth #1 - AI is the flavour of the month

John McCarthy (1927-2011)
1956 - Coined the term “Artificial Intelligence”
1958 - Invented LISP
1971 - Received the Turing Award
Marvin Minsky (1927-2016)
1959 - Co-founded the MIT AI Lab
1968 - Advised Kubrick on “2001: A Space Odyssey”
1969 - Received the Turing Award
Fact #1 - AI is 60 years old

Myth #2 - AI is dark magic - aka « You’re not smart enough »

Fact #2 - AI is math, code and chips
A bit of Science, a lot of Engineering

Myth #3 - AI is for complex use cases

Fact #3: AI shines on intuitive problems

Amazon.com,1995

Two Decades of Recommender Systems at Amazon.com (2017)
https://www.computer.org/csdl/mags/ic/2017/03/mic2017030012.html
G.D. Linden, J.A. Jacobi, and E.A. Benson,
Collaborative Recommendations Using Item-to-
Item Similarity Mappings, US Patent 6,266,649, to
Amazon.com, Patent and Trademark Office, 2001
(filed 1998).

https://arxiv.org/abs/1712.05690
Sockeye

Blazing Text
https://dl.acm.org/citation.cfm?id=3146354

AI @ Amazon

30,000 skills

Jeff Bezos’ letter to Amazon shareholders
“We are solving problems with machine learning and
artificial intelligence that were in the realm of science
fiction for the last several decades. Natural language
understanding, machine vision problems, it really is an
amazing renaissance.”
https://www.geekwire.com/2017/jeff-bezos-explains-amazons-artificial-
intelligence-machine-learning-strategy/

A few examples
Fraud detection Detecting fraudulent transactions, filtering spam
emails, flagging suspicious reviews, …
Personalization Recommending content, predictive content loading,
improving user experience, …
Targeted marketing Matching customers and offers, choosing marketing
campaigns, cross-selling and up-selling, …
Content classification Categorizing documents, matching hiring managers
and resumes, understanding visual content, …
Churn prediction Finding customers who are likely to stop using the
service, upgrade targeting, …
Customer support Predictive routing of customer emails, social media
listening, …
Predictive maintenance Detect early signs of equipment failure

More AI/ML is built on AWS than anywhere else

Our mission
Put Machine Learning in the hands of every
developer and data scientist

Application
Services
Platform
Services
Frameworks
&
Infrastructure
API-driven services: Vision & Language Services, Conversational Chatbots
Deploy machine learning models with high-performance machine learning
algorithms, broad framework support, and one-click training, tuning, and
inference.
Develop sophisticated models with any framework, create managed, auto-
scaling clusters of GPUs for large scale training, or run inference on trained
models.
AWS ML Stack

AI Services

ringDNA
RingDNA is an end-to-end communications
platform for sales teams.
Hundreds of enterprise organizations use
RingDNA to 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

The Washington Post
Text to Speech
« This is a new technology that can give users more choice and
better accessibility to our content, so we wanted to create an
experiment to dive deeper into the user experience. After a month,
we’ll take what we’ve learned about how users engage with this
feature to develop our first iteration of a product with Amazon
Polly. »
Joseph Price, Product Manager, The Washington Post
https://www.washingtonpost.com/pr/wp/201
7/06/09/the-washington-post-to-start-
experimenting-with-audio-articles-using-
amazon-polly
Natural Language Processing
« The Post strives to give its nearly 100 million readers the best
experience possible and relevant content recommendations are a
key part of that mission. With Amazon Comprehend, we can
leverage the continuously-trained NLP capabilities like key phrase
and topic APIs to potentially allow us to provide even better
content personalization, SEO, and ad targeting capabilities. »
Dr. Sam Han, Director of Data Science, The Washington
Post

Hotels.com
Matt Fryer, VP and Chief Data Science Officer, Hotels.com
Machine Translation
At Hotels.com, we are committed to offering all of our customers the most
relevant and up to date information about their destination. To achieve
that, we operate 90 localized websites in 41 languages. We have more than
25M customer reviews and more are coming in every day, making a great
candidate for machine translation. Having evaluated Amazon Translate and
several other solutions, we believe that Amazon Translate presents a quick,
efficient and most importantly, accurate solution.

Expedia
• Expedia have over 10 million images
from 300,000 hotels
• Using great images boosts
conversion.
• Using Keras and GPU instances, they
fine-tuned a pre-trained
Convolutional Neural Network using
100,000 images
• Hotel descriptions now
automatically feature the best
available images
https://news.developer.nvidia.com/expedia-
ranking-hotel-images-with-deep-learning/

Marinus Analytics
Marinus Analytics provides law enforcement
with tools founded in artificial intelligence.
Traffic Jam, is a suite of tools for use by law
enforcement agencies on sex trafficking
investigations.
Before using Amazon Rekognition, their only
recourse was manual processing; this was time-
intensive or not possible.
Now, 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

Thank You!
Boaz Ziniman, Technical Evangelist – Amazon Web Services
boazz@amazon.com
@ziniman

Introduction to AI

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