4. “A system or service which can perform tasks
that usually require human intelligence”
5. One of the ”Founding Father" of Artificial Intelligence
John McCarthy
1955
6. Photo from the 1956 Dartmouth
Conference with Marvin Minsky,
Ray Solomonoff, Claude Shannon,
John McCarthy, Trenchard More,
Oliver Selfridge and Nathaniel
Rawchester
12. Protrader expert system by K.C Chen and Ting-peng Lian
Chen and Lian were able to
predict the 87 point drop in
Dow Jones Industrial Average
in 1986
15. Expert System Development – 90’s
• PlanPower & Client Profiling System created by Applied Expert
Systems
• providing tailored financial plans
• Chase Lincoln First Bank and Arthur D. Little Inc.
• investment & debt planning
• retirement & life-insurance planning
• budget recommendations,
• income tax planning and savings achievement
• U.S Department of Treasury created FinCEN Artificial Intelligence
system.
• money laundering.
24. ML use-cases
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,
…
Churn prediction Finding customers who are likely to stop using the service, free-
tier upgrade targeting, …
Customer support Predictive routing of customer emails, social media listening, …
25. Predicting the price of a house with humans
Price
City
ZipCode Life Quality
Parking
Size
# Room
Accessibility
Family Friendly
26. 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
34. 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
• Machine Translation
• Genomes
• Handwriting
• Spoken words
• Numerical times series data coming from
sensors, stock markets, etc.
https://github.com/awslabs/sockeye
35. Generative Adversarial Networks (GAN)
The future at work (already) today
Generating new ”celebrity” faces
https://github.com/tkarras/progressive_growing_of_gans
36. Generative adversarial networks (GAN)
The future at work (already) today
Semantic labels → Cityscapes street views
https://tcwang0509.github.io/pix2pixHD/
39. 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
40. 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.
41. 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.
43. 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/
51. Put AI in the hands of every developer and data scientist
AI @ AWS: Our mission
52. Application
Services
Platform
Services
Frameworks
&
Infrastructure
API-driven services: Vision & Language Services, Conversational Chatbots
AWS ML Stack
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.
58. 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
59. 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
60. “With Amazon Polly our users benefit from
the most lifelike Text-to-Speech voices
available on the market.”
Severin Hacker
CTO, Duolingo
62. Amazon Lex
“What’s the weather
forecast?”
“It will be sunny
and 25°C”
Weather
Forecast
Amazon Lex
Build Conversational Chatbots
65. “Hello, this is Allan
speaking”
Amazon Transcribe
Automatic speech recognition service
Amazon
Transcribe
66. “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
69. « 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/
71. Platform
Services
Frameworks
&
Infrastructure
AWS ML Stack
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.
74. Amazon EC2 P3 Instances (October 2017)
• Up to eight NVIDIA Tesla V100 GPUs
• 1 PetaFLOPs of computational
performance – 14x better than P2
• 300 GB/s GPU-to-GPU communication
(NVLink) – 9X better than P2
• 16GB GPU memory with 900 GB/sec peak
GPU memory bandwidth
T h e f a s t e s t , m o s t p o w e r f u l G P U i n s t a n c e s i n t h e c l o u d
75. For market surveillance, each night FINRA
loads approximately 35 billion rows of
data into Amazon S3 and Amazon EMR
to monitor trading activity on exchanges
and market centers in the US.
https://aws.amazon.com/solutions/case-studies/finra/
76. Data analytics applications:
On a nightly basis, Nasdaq
loads approximately 5 billion
rows of data into Redshift within
a 4-6 hour window.
https://aws.amazon.com/solutions/case-studies/nasdaq-omx/
77. « Amazon Machine Learning helps
us reduce complexity and make
sense of emerging fraud patterns.
We can see correlations we
wouldn’t have been able to see
otherwise and answer questions it
would have taken us way too long
to answer ourselves »
Oliver Clark, CTO
https://aws.amazon.com/solutions/case-studies/fraud-dot-net/
78. Credit-risk simulation application:
«This requires high computational
power. We need to perform at least
5,000,000 simulations to get
realistic results.»
Javier Roldán
Bankinter Director of Technological Innovation
https://aws.amazon.com/solutions/case-studies/bankinter/
79. Capital One is applying AI to the
customer experience with nuanced
fraud and lending decisions, in
addition to chatbots.
http://www.zdnet.com/video/how-capital-one-builds-its-ai-and-machine-learning-efforts-on-aws/
80. Global Systemically Important
Banks (G-SIBs) are AWS customers.
80%
A top 10 bank in the US is
migrating core systems to AWS
and planning to own or lease no
data centers by 2018
A US-based G-SIB is running
11 mission critical grid/HPC
workloads on AWS
A European G-SIB is moving its
cash management platform
and a mission critical pricing
engine to AWS
81. FinTech startups have found a home on AWS
Percentage of the 2016 Forbes
FinTech 50 that use AWS.
96%
82. Financial institutions across market segments are transforming on
AWS
Banking & Payments InsuranceCapital Markets
Capital One