Self-Driving cars. Commercial drones. Smart cameras. Movie and music creation. Powerful & intelligent robots. Over the past few years, a new revolution has brought AI almost to the level of science-fiction. However, most companies are not worried about far-off futuristic applications of AI, they want to know what AI can do - today - for their organisations. 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. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Adrian Hornsby, Cloud Architecture Evangelist @ AWS
@adhorn
Moving forward with AI

3. What is Artificial Intelligence?

4. “A system or service which can perform tasks
that usually require human intelligence”

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

6. 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

7. Artificial Neural Network

8. The (60 years) rise of Artificial Intelligence

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

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

11. Frank Rosenblatt, 1957
Perceptron

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

13. Paul Werbos, 1975
Backpropagation

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

15. The curse of dimensionality

16. The Advent of AI
Algorithms

17. The Advent of AI
Data
Algorithms

18. The Advent of AI
Data
GPUs
& Acceleration
Algorithms

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

24. Common neural networks
& use cases

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

27. 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

28. ImageNet Classification Error Over Time

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

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

31. Autonomous Driving Systems
CNN: Object Segmentation

32. CNN: Object Segmentation

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

34. CNN: Neural Style Transfer

35. 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.

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

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

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

39. Data, Algorithms, Humans and
Artificial Intelligence

40. Ground Truth Generation
Training

41. How much data do you need?

42. Predicting the price of a house castle
150+ rooms

43. Rule of thumbs
• Data should cover as many combinations of features as
possible
• More data is almost always better
• Approx. 10x more than the number of features

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

45. 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.

46. What processes should you boost
with AI?

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

48. How do you start?
The Low Hanging Fruits

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

52. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

53. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

54. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

55. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

56. 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.
AI @ AWS – Stack
AI in the hands of every developer and data scientist

57. Application Services
The low hanging fruits
• API-driven
• Not training required
• Pre-trained on general datasets
• No infrastructure to manage
• Use it now with one line of code
Application
Services
API-driven services: Vision & Language Services, Conversational Chatbots

58. 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

60. 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

61. 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

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

63. ”
“ 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

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

66. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

67. “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

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

69. Amazon Comprehend
Discover insights from text
Entities
Key Phrases
Language
Sentiment
Amazon
Comprehend

70. STORM
WORLD SERIES
STOCK MARKET
WASHINGTON
LIBRARY OF
NEWS ARTICLES *
Amazon
Comprehend
* Integrated with Amazon S3 and AWS Glue
Amazon Comprehend
Support for large data sets and topic modeling

71. 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

72. Moving deeper in the rabbit-hole

73. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
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

74. Say hello to Transfer Learning (hidden gem 1)
• Initialise parameter with pre-trained model
• Use pre-trained model as fixed feature extractor and
build model based on feature
• Why?
• It takes a long time and a lot of resources to train a neural
network from scratch.

75. Model Zoos (hidden gem 2)
• Full implementations of many state-of-the-art models
reported in the academic literature.
• Complete models, with scripts, pre-trained weights and
instructions on how to build and fine tune these
models.

76. https://mxnet.apache.org/model_zoo/index.html

77. End-to-End
Machine Learning
Platform
Zero setup Flexible Model
Training
Pay by the second
$
Amazon SageMaker
Build, train, and deploy machine learning models at scale

78. Wrapping up

79. 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.

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