Distinguishing the hype from reality can be a bit confusing, especially when you consider the attention that AI gets from the media and commentators. So, how can your organisation get started and put AI to work for you? That is the question I will answer in this talk. From greater customer intimacy, increasing competitive advantage and improving efficiency, I will discuss and show how AI can be used today and help the organisation in more impactful ways.

1. Adrian Hornsby, Cloud Architecture Evangelist @ AWS
@adhorn
Moving forward with AI

2. If I asked you…
“How important is it for your company to invest in
Machine Learning and AI?”
What we often hear:
“We’re not a Machine Learning company.”
”They’re not central to our business.”
“We have more important topics to address.”

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

4. A Flywheel For Data
More Data Better Analytics

5. A Flywheel For Data
More Data Better Analytics
Better Products

6. A Flywheel For Data
More Data Better Analytics
Better ProductsMore Users

7. A Flywheel For Data
Click stream
User activity
Generated content
Purchases
Clicks
Likes
Sensor data
More Data Better Analytics
Better ProductsMore Users

8. 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
More Data Better Analytics
Better ProductsMore Users

9. Artificial
Intelligence
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
More Data Better Analytics
Better ProductsMore Users

10. What is Artificial Intelligence?

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

12. 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

13. Artificial Neural Network

14. The (60+ years) rise of Artificial Intelligence

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

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

17. Frank Rosenblatt, 1957
Perceptron

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

19. Paul Werbos, 1975
Backpropagation

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

21. The curse of dimensionality

22. The Advent of AI
Algorithms

23. The Advent of AI
Data
Algorithms

24. The Advent of AI
Data
GPUs
& Acceleration
Algorithms

25. The Advent of AI
Data
GPUs
& Acceleration
Cloud
Computing
Algorithms
AWS

26. More AI/ML is built on AWS than anywhere else

27. More AI/ML is built on AWS than anywhere else

28. Common neural networks
& use cases

29. Convolutional Neural Networks (CNN)
Conv 1 Conv 2 Conv n
…
…
Feature Maps
Labrador
Dog
Beach
Outdoors
Softmax
Probability
Fully
Connected
Layer

30. https://www.youtube.com/watch?v=qGotULKg8e0
• Over 10 million images from 300,000 hotels
• Using Keras and EC2 GPU instances
• Fine-tuned a pre-trained Convolutional Neural
Network using 100,000 images
• Hotel descriptions now automatically feature the
best available images
CNN: Object Classification
Nuno Castro - Ranking hotel images using deep learning

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

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

33. Autonomous Driving Systems
CNN: Object Segmentation

34. CNN: Object Segmentation

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

36. Long Short Term Memory Networks (LSTM)
• LSTM are capable of learning long-term
dependencies
• Designed to recognize patterns in sequences
of data such as:
• text
• genomes
• handwriting
• spoken words
• numerical times series data coming from
sensors, stock markets, etc.

37. LSTM: Machine Translation
https://github.com/awslabs/sockeye

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

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

40. Data, Algorithms, Humans and
Artificial Intelligence

41. Data Visualization &
Analysis
Business Problem –
ML problem framing Data Collection
Data Integration
Data Preparation &
Cleaning
Feature Engineering
Model Training &
Parameter Tuning
Model Evaluation
Are Business
Goals met?
Model Deployment
Monitoring &
Debugging
YesNo
DataAugmentation
Feature
Augmentation
The AI Process
Re-training
Predictions

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

43. Most important for you to do today?
“Data is gold”

44. 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.

45. How do you start?
The Low Hanging Fruits

46. Put AI in the hands of every developer and data scientist
AI @ AWS: Our mission

47. FRAMEWORKS AND INTERFACES
PLATFORM SERVICES
APPLICATION SERVICES
Amazon
Rekognition
Amazon
Polly
Amazon
Lex
AI platform on AWS
Amazon
Rekognition
Video
Amazon
Transcribe
Amazon
Comprehend
Amazon
SageMaker
AWS DeepLens Amazon EMR
Deep Learning
AMI
Amazon
Translate

48. Amazon
Rekognition
Object and scene detection
Facial analysis
Face comparison
Person Tracking
Celebrity recognition
Image moderation
Text-in-Image
Amazon Rekognition (Image & Video)
Deep learning-based visual analysis service

50. 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

51. City of Orlando
Real-time video analysis
”The City of Orlando is excited to work with Amazon to
pilot the latest in public safety software through a
unique, first-of-it's-kind public-private partnership.
Through the pilot, Orlando will utilize Amazon
Rekognition Video and Amazon Kinesis Video
Streams technology in a way that will use existing City
resources to provide real-time detection and
notification of persons-of-interests, further increasing
public safety, and operational efficiency opportunities
for the City of Orlando and other cities across the
nation”.
John Mina, Police Chief, City of Orlando

52. Amazon Polly
Hei! Jeg heter Liv.
Skriv inn noe her,
så leser jeg det
opp.
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)*
* https://www.allthingsdistributed.com/2016/11/amazon-ai-and-alexa-for-all-aws-apps.html

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

54. ”
“ Amazon Polly delivers
incredibly lifelike voices
which captivate and engage
our readers.
John Worsfold
Solutions Implementation Manager, RNIB
• RNIB delivers largest library of
audiobooks in the UK for nearly 2
million people with sight loss
• Naturalness of generated speech is
critical to captivate and engage readers
• No restrictions on speech
redistributions enables RNIB to create
and distribute accessible information in
a form of synthesized content
RNIB provides the largest library in the UK for people with sight loss

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

57. Capital One
https://www.capitalone.com/applications/alexa/

58. Liberty Mutual
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

59. “Hello, this is Allan
speaking”
Amazon Transcribe
Automatic speech recognition service
Amazon
Transcribe

60. ringDNA
• RingDNA is an end-to-end
communications platform for 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

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

62. 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 Keyphrase
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

63. “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

64. 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

65. Twitter Stream
API
Kinesis
Lambda
S3 Athena
Translate Comprehend
Transcribe
1 day to build, $17/day to run
(to analyze tweets for
AWS-size customers)
Multilingual Social Analytics

66. Moving deeper in the AI rabbit-hole

67. 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/

69. Capital One
• The arrival of chatbots and
robo-advisors is the tip of the
disruptive iceberg in the
industry.
• Capital One is applying AI to
the customer experience with
nuanced fraud and lending
decisions, in addition to
chatbots.
Deep Learning
« We’re focused on building capabilities around what we call
explainable AI. We think it’s important to have models that
aren’t just black-box models but ones that enable us to
understand why deep learning and neural net models are
making the decisions they’re making. Financial services can
help people achieve their dreams, but when it’s done poorly,
an institution can get in the way of someone’s dreams. »
Rob Alexander
Chief Information Officer, Capital One
http://www.zdnet.com/video/how-capital-one-
builds-its-ai-and-machine-learning-efforts-on-aws/

73. Thorn
Thorn is a global nonprofit organization fighting
child sexual exploitation and child traffickers.
Thorn and AWS-partner, MemSQL, built an age
progressed facial recognition service using data
analytics and deep learning on AWS compute-
optimized C5 to identify missing children by
matching images against child abuse material.
Thorn can apply 5,000 data points to a single face
and classify, correlate, and match the image to an
image in a database. As a result, the organization’s
solution can make a positive image match in 200
milliseconds, compared to 20 minutes previously.
Spotlight is 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

74. 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/

75. Digital Globe
http://blog.digitalglobe.com/industry/using-machine-learning-
to-save-money-on-cloud-data-storage/
• In the last 18 years DigitalGlobe has
been operating Earth imaging
satellites, they have collected over
100 PB of imagery.
• There is a trade-off between how
quickly data can be accessed and
how much it will cost to store.
• Working with the ML Lab, Digital
Globe built a predictive model that
will reduce cloud storage costs for
their imagery archive by 50%.

76. Wrapping up

77. 1. Understand what AI is.
2. Take great care of your data.
3. Find the processes that need improvements.
4. Start with the low hanging fruits.
5. Slowly develop yourself into an AI-powered organisation.

78. There’s Never Been A
Better Time
To Build New Businesses
@adhorn