This document discusses AI in the enterprise from past, present, and future perspectives. It provides an overview of the history and recent developments in AI and deep learning, including improved performance on tasks like image recognition. Case studies are presented showing how various large companies have successfully applied deep learning techniques like convolutional neural networks to problems in different industries involving computer vision, predictive maintenance, fraud detection, and more. The importance of data quantity for deep learning performance is highlighted. The final sections discuss challenges in AI adoption and the importance of piloting models before full production deployment.
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AI in the Enterprise: Past, Present & Future - StampedeCon AI Summit 2017
1. AI in the Enterprise: Past, Present & Future
Paul Huibers, Think Big Analytics
2. 2
“
By 2020 AI will be a top five
investment priority for more
than 30% of CIOs.
—Gartner BI Summit,
February, 2017
“The Resurgence of AI
By 2019, deep learning will provide best-
in-class performance for demand, fraud,
and failure prediction. - Gartner
4. 4
• Introduction to AI/DL
• AI in Industry
• Case Study: Financial Fraud
• Pilot to Production
Agenda
5. 5
What is AI?
Artificial Intelligence is usually defined as the
science of making computers do things that
require intelligence when done by humans.
6. 6
AI: A Brief History… …and now, Deep Learning!
• 1940’s – early concepts developed
• 1980’s – more concepts
– copy the brain, neurons, perceptron
– backpropagation for training
• 1990’s – LeCun handwriting reader
• AI winter
• 2009 – Netflix Prize $1M
• 2010 – first ImageNet competition
• 2012 - AI/deep learning comes of age
• ImageNet classification error:
– 2011: 25% using traditional methods
– 2012: 16% achieved by a ConvNet
– 2013: 11%
– 2014: 6.7%
– 2015: 3.6%
– 2016: < 3%
• 3% ~ human error rate (expert group)
• 0.3% mislabeling
• (1000 categories of images)
• What changed since the 1990s?
– 10,000X computing power, GPUs
– massive labeled datasets
8. 8
ImageNet
1.2 million images 1000 categories lots of animals…
a jungle of viewpoints,
lighting conditions, and variations of all
imaginable types.
…a jungle of viewpoints, lighting conditions, and variations of all imaginable types. – Karpathy
9. 9
What is Deep Learning?
• A machine learning method that involves learning data representations
rather than task-specific algorithms
• Deep Neural Networks – an artificial neural network with multiple hidden
layers of “neurons” between the input and the output
• Artificial Neural Networks – computing systems inspired by biological
neural networks, involving a collection of connected units, with learned
weights and activation functions between the units
How is it achieved?
10. 10
Deep Neural Networks
How are they different?
• Multiple hidden layers in neural network with intermediate data representations
to facilitate dimensional reduction
• Interpret non-linear relationships in the data through activation functions
• Derive patterns from data with very high dimensionality
Why do we care?
• Ability to create value with little
or no domain knowledge
required
• Ability to incorporate data from
across multiple, seemingly
unrelated sources
• Ability to tolerate very noisy data
11. 11
Data Quantity Drives Deep Learning Performance
Andrew Ng
Amount of Labeled Data
ModelPerformance
1990’s
Small Training Sets
Traditional ML
Small NN
Medium NN
Large NN
12. 12
Deep Learning Architectures
Convolutional Neural Network (ConvNet or CNN)
• CNN = Convolution + Pooling + ReLu + Fully Connected
• Convolution Layers are composable so can be chained
• Primary use: any problem that has a high
dimensional input (ex.: Image Labeling)
13. 13
Specialized APIs General Purpose Frameworks
AI Framework Landscape
Vision
Language
Speech
Keras
• Pretrained (fast)
• Public
• Google/Microsoft/Amazon
• Need to be trained (expensive)
• Private
• Fully customizable
14. 14
Touched by AI…
• Cognitive successes
• Siri, Alexa, OK Google!
– Understanding words
– Understanding context
– Language translation
• Face detection in images
• Recommender systems
• How about some practical
examples from industry?
15. 15
• Introduction to AI/DL
• AI in Industry
• Case Study: Financial Fraud
• Pilot to Production
Agenda
16. 16
Proven applications of Deep Learning
ANOMALY
DETECTION
Enables real-time
detection of
abnormal patterns of
data, usually time-
series events.
PREDICTIVE
MAINTENANCE
Improves preventative
measures &
performance with
greater accuracy at
the asset &
component level
RECOMMENDER
SYSTEMS
Enable more effective
search rankings based
on context, in
accordance with a
particular objective
such as purchase or
click-through
SPEECH
RECOGNITION
Enable capture of
voice to text with
higher fidelity of
speech transcription
and improved
precision of speaker
identification
COMPUTER
VISION
Enables dramatically
more accurate visual
recognition tasks
that include image
classification,
detection and
localization
DOCUMENT
AUTOMATION
Enables automation
of manual, paper-
based processes
that are human-
intensive with higher
speed, accuracy
and fidelity
17. 17
Industry Specific Use Cases
High-Dimensional Data
Image
Video
Audio
Time Series
Text
• Many already have working solutions using non-DL Machine Learning Techniques
• Deep Learning is delivering improvement in performance on complex problems
Automotive Retail
• Navigation, Guidance, Assistance
• Predictive Maintenance
• Visual Search
• Recommendation
• Text Analytics
• Assistants
• Brand Analytics
Manufacturing & High-Tech Health Care
• Image/Audio/Video
• Reinforcement Learning – Systems
Optimization
• Plant Operations Optimization
• Image-based Analysis
• Drug Discovery
Financial Services & Insurance Cross-Industry
• Anti-Fraud
• Portfolio Optimization
• Damage Assessment
• Cyber Security
• Call Center Audio
18. 18
Large European Logistics Provider
• Increasing use of plastic bags in
shipping
• Challenges with existing package
sorting and identification system
• Use Deep Learning Image
Analytics to improve identification
and sorting
• Tools: TensorFlow, Hadoop
• Techniques:
– Deep Learning: Convolutional
Neural Network
19. 19
• Road objects, traffic and accident
events are manually reported or not
at all
• Automated object detection and
scene labeling system from car
camera feed to improve navigation
and traffic
• Tools: Darknet, Caffe, TensorFlow
• Techniques:
– Object Detection: Single Shot MultiBox
Detector (SSD), You Only Look Once
(YOLO)
– Scene Labeling: Convolutional Neural
Network
Large Auto Parts Manufacturer Use Case
Real-Time
Streaming
Streaming
Results
Traffic Data Service
Navigation Update
Darknet/Darkflow –
Object Detection
TensorFlow – Scene
Labeling
Cloud GPU
Based Training
TF Serving
Cloud GPU
Based Inference
Model
Updates
20. 20
• Handwritten check volume is
decreasing however processing
checks has many fixed costs
• Handwriting recognition to reduce
manual processing and fraud
examination resulting in cost savings
• Tools: Spark, Hadoop, TensorFlow
• Techniques:
– Convolutional Neural Network
– Image Processing
Large US Multinational Bank
Check Images
To Hadoop
ImageMagick
Processing
Handwriting
Recognition
Fraud
Detection
21. 21
• Predict failure of pistons on large
container ships to reduce unplanned
and costly maintenance
• Utilized sensor data to predict piston
wear between 70-80%
• Failures are extremely infrequent so there
is a risk of overfitting
• Tools: R, Hadoop, Spark, AWS
• Techniques:
– ROC curve
– Internet of Things data
– Methods to prevent overfitting
Large Container Shipping Company Container Ship Sensors
Predict
Failures
1 month (December)
High
Low
Abnormalcylinderbehaviour*
Lead time
Port stays
PROB1
0.0
0.2
0.4
0.6
0.8
2015-12-06 2015-12-13 2015-12-20 2015-12-27
Piston ring change
Cylinders
Piston ring(s) changed Threshold
Abnormal behavior:
Everything above
the threshold
triggers an alarm
Other cylinders
below threshold
Worn piston ring was
changed
Each point is a
combination of
selected sensor data
for a specific cylinder
22. 22
Large European Railway
• Detecting rail switch failures
• Allows for switches to be fixed
ahead of time thus not delaying
trains
• Tools: R (Shiny and Studio),
Hadoop, Spark
• Techniques:
– Survival Analysis
– Machine Learning
– Internet of Things data
Railway Switch Sensor Data
Visualize
Failures
and Act
23. 23
• Fraud detection across products
• Trends
– Mobile payments exploding
– Fraud evolving rapidly, increased
sophistication
• Significant improvements over
traditional rules-based techniques
• Tools: Spark, Hadoop, TensorFlow
• Techniques:
– Boosted Decision Trees
– Convolutional Neural Network
Large European Bank
24. 24
State of AI in Industry
Successes
• Computer vision (e.g., ImageNet)
• Speech & NLP
• Simplification of general-purpose
ML (e.g., recommendation)
• Rapid advance of state of art,
growth of expertise & applications
• Major investment programs in
industry
Challenges
• Research-driven, fundamentals
change
• Mostly empirical, little theory
• Complexity in solution design
• Limited access to talent
• AI/DL still requires governed data,
and Analytics Ops integration
• Gaps in enterprise deployment
beyond lock-in clouds
25. 25
• Introduction to AI/DL
• AI in Industry
• Case Study: Financial Fraud
• Pilot to Production
Agenda
38. 38
• Introduction to AI/DL
• AI in Industry
• Case Study: Financial Fraud
• Pilot to Production
Agenda
39. 39
Operationalization is Hard
“We evaluated some of the new methods offline
but the additional accuracy gains that we
measured did not seem to justify the engineering
effort needed to bring them into a production
environment.”
- Netflix, 2012
40. 40
Focus First on Pilot into Production
Sets up Phase Two: Scale COE, Standardize Capabilities
Investigate
Test
Engineer
SimulateIntegration
Analyze
Data
Go Live
Handover
Validate
Activities: Define business
opportunity, understand data
available, test model
approaches, potentially
generate data
Outcome: Proposed solution
approach
Discovery/Insights
Activities: Architecture
selection, software engineering
of model and simulation
Outcome: Predicted impact of
model
Live Test
Activities: Integration into
live business process
(Champion/Challenger),
analysis, iteration
Outcome: Benefit
measurement, live learnings,
improvement
Production
Activities: Go Live, Analytics
Ops integration, Hand Over
Outcome: System scaled,
application teams and ops
trained and operating
Assessment
Insights
Production
Live Test
Cross-Functional
Teams
Cross-Functional Teams
41. 4141
For more information, please contact:
Paul.Huibers@ThinkBigAnalytics.com
603-395-6567
Thank You StampedeCon!
stampedecon.com/ai-summit-2017-st-louis/
42. 42
The Future… and more…
Architectural innovations: RNN, LSTM, GAN and more
Better training through new optimization, new activation functions and more
Transfer learning, pre-training and more
Theory catching up with practice (Tishby) – relevant information, bottlenecks
10,000X speed improvement would make many things possible – Moore’s Law
Unsupervised learning
Learning with few samples
AGI – artificial general intelligence
Singularity