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Adrian Hornsby, Cloud Architecture Evangelist @ AWS
@adhorn
AI in Finance: Moving forward!
AI in Finance: Moving forward!
What is Artificial Intelligence?
“A system or service which can perform tasks
that usually require human intelligence”
One of the ”Founding Father" of Artificial Intelligence
John McCarthy
1955
Photo from the 1956 Dartmouth
Conference with Marvin Minsky,
Ray Solomonoff, Claude Shannon,
John McCarthy, Trenchard More,
Oliver Selfridge and Nathaniel
Rawchester
Frank Rosenblatt, 1957
Perceptron
Robert Schlaifer, 1959
Bayesian Decision Theory
First known deep network
https://devblogs.nvidia.com/deep-learning-nutshell-history-training/
Alexey Grigorevich Ivakhnenko, 1965
1969 First financial application
Paul Werbos, 1975
Backpropagation
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
AI winter 1969 – 1990
LeCun, 1989
First application of
backpropagation
https://www.youtube.com/watch?v=FwFduRA_L6Q
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.
The curse of dimensionality
The Advent of AI
Algorithms
The Advent of AI
Data
Algorithms
The Advent of AI
Data
GPUs
& Acceleration
Algorithms
The Advent of AI
Data
GPUs
& Acceleration
Cloud
Computing
Algorithms
AWS
Common AI Algorithms
& use cases
AI in Finance: Moving forward!
ML Supervised Learning Algorithms
Binary classification (Logistic
regression)
Multi-category classification
(Multinomial logistic regression)
Regression
(Linear regression)
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, …
Predicting the price of a house with humans
Price
City
ZipCode Life Quality
Parking
Size
# Room
Accessibility
Family Friendly
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
Artificial Neural Network
Convolutional Neural Networks (CNN)
Conv 1 Conv 2 Conv n
…
…
Feature Maps
Labrador
Dog
Beach
Outdoors
Softmax
Probability
Fully
Connected
Layer
CNN: Object Detection and Classification
https://github.com/precedenceguo/mx-rcnn https://github.com/zhreshold/mxnet-yolo
CNN: Face Detection
https://github.com/tornadomeet/mxnet-face
Autonomous Driving Systems
CNN: Object Segmentation
FDA-approved
medical imaging
https://www.periscope.tv/AWSstartups/1vAGRgevBXRJl
https://www.youtube.com/watch?v=WE81dncwnIc
CNN: Object Segmentation
CNN: Neural Style Transfer
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
Generative Adversarial Networks (GAN)
The future at work (already) today
Generating new ”celebrity” faces
https://github.com/tkarras/progressive_growing_of_gans
Generative adversarial networks (GAN)
The future at work (already) today
Semantic labels → Cityscapes street views
https://tcwang0509.github.io/pix2pixHD/
CapsNet: Capsule Networks
Spatial Memory
https://medium.com/ai%C2%B3-theory-practice-business/understanding-hintons-capsule-networks-part-i-intuition-b4b559d1159b
CapsNet: Capsule Networks
Spatial Memory
https://arxiv.org/pdf/1710.09829v1.pdf
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
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.
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.
https://mxnet.apache.org/model_zoo/index.html
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/
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Amazon AI
AI in Finance: Moving forward!
AI in Finance: Moving forward!
AI in Finance: Moving forward!
AI in Finance: Moving forward!
AI in Finance: Moving forward!
AI in Finance: Moving forward!
Put AI in the hands of every developer and data scientist
AI @ AWS: Our mission
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.
Application
Services
API-driven services: Vision & Language Services, Conversational Chatbots
AWS ML Stack
Amazon Rekognition
Deep learning-based image & video analysis
AI in Finance: Moving forward!
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
http://timescapes.org/trailers/
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
http://timescapes.org/trailers/
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
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
“With Amazon Polly our users benefit from
the most lifelike Text-to-Speech voices
available on the market.”
Severin Hacker
CTO, Duolingo
AI in Finance: Moving forward!
Amazon Lex
“What’s the weather
forecast?”
“It will be sunny
and 25°C”
Weather
Forecast
Amazon Lex
Build Conversational Chatbots
https://www.capitalone.com/applications/alexa/
https://www.libertymutual.com/liberty-mutual-mobile/amazon-alexa
“Hello, this is Allan
speaking”
Amazon Transcribe
Automatic speech recognition service
Amazon
Transcribe
“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
Amazon Comprehend
Discover insights from text
Entities
Key Phrases
Language
Sentiment
Amazon
Comprehend
Topic Modeling
AI in Finance: Moving forward!
« 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/
Moving deeper into the AI rabbit-hole
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.
ML Applications on Amazon EMR
Amazon EMR
(Elastic MapReduce)
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
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
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/
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/
« 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/
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/
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/
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
FinTech startups have found a home on AWS
Percentage of the 2016 Forbes
FinTech 50 that use AWS.
96%
Financial institutions across market segments are transforming on
AWS
Banking & Payments InsuranceCapital Markets
Capital One
Go build!
@adhorn
Demo!
https://github.com/awslabs/lambda-refarch-imagerecognition
http://bit.ly/adhornlightbulb
Interactive Serverless applications
AWS IoT IoT
shadow
Amazon
Cognito
MQTT over WebSockets
AWS
LambdaAlexa
Amazon
S3
Interactive Serverless applications
Go build!
@adhorn

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AI in Finance: Moving forward!

  • 1. Adrian Hornsby, Cloud Architecture Evangelist @ AWS @adhorn AI in Finance: Moving forward!
  • 3. What is Artificial Intelligence?
  • 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
  • 9. First known deep network https://devblogs.nvidia.com/deep-learning-nutshell-history-training/ Alexey Grigorevich Ivakhnenko, 1965
  • 10. 1969 First financial application
  • 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
  • 13. AI winter 1969 – 1990
  • 14. LeCun, 1989 First application of backpropagation https://www.youtube.com/watch?v=FwFduRA_L6Q
  • 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.
  • 16. The curse of dimensionality
  • 17. The Advent of AI Algorithms
  • 18. The Advent of AI Data Algorithms
  • 19. The Advent of AI Data GPUs & Acceleration Algorithms
  • 20. The Advent of AI Data GPUs & Acceleration Cloud Computing Algorithms AWS
  • 23. ML Supervised Learning Algorithms Binary classification (Logistic regression) Multi-category classification (Multinomial logistic regression) Regression (Linear regression)
  • 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
  • 28. Convolutional Neural Networks (CNN) Conv 1 Conv 2 Conv n … … Feature Maps Labrador Dog Beach Outdoors Softmax Probability Fully Connected Layer
  • 29. CNN: Object Detection and Classification https://github.com/precedenceguo/mx-rcnn https://github.com/zhreshold/mxnet-yolo
  • 31. Autonomous Driving Systems CNN: Object Segmentation
  • 33. CNN: Neural Style Transfer
  • 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/
  • 37. CapsNet: Capsule Networks Spatial Memory https://medium.com/ai%C2%B3-theory-practice-business/understanding-hintons-capsule-networks-part-i-intuition-b4b559d1159b
  • 38. CapsNet: Capsule Networks Spatial Memory https://arxiv.org/pdf/1710.09829v1.pdf
  • 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/
  • 44. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon AI
  • 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.
  • 53. Application Services API-driven services: Vision & Language Services, Conversational Chatbots AWS ML Stack
  • 54. Amazon Rekognition Deep learning-based image & video analysis
  • 56. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. http://timescapes.org/trailers/
  • 57. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. http://timescapes.org/trailers/
  • 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
  • 67. Amazon Comprehend Discover insights from text Entities Key Phrases Language Sentiment Amazon Comprehend Topic Modeling
  • 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/
  • 70. Moving deeper into the AI rabbit-hole
  • 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.
  • 72. ML Applications on Amazon EMR Amazon EMR (Elastic MapReduce)
  • 73. 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
  • 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
  • 84. Demo!
  • 87. AWS IoT IoT shadow Amazon Cognito MQTT over WebSockets AWS LambdaAlexa Amazon S3 Interactive Serverless applications