Resurgence of AI and use cases in the enterprise. Think Big, a Teradata company.

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Artificial Intelligence
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Applications and Approaches
© 2017 Teradata
​Enterprise Artificial Intelligence
Laura Frølich
Data Scientist

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Analytics Evolution
Descriptive
Predictive
Prescriptive
What is happening?REPORTING
ANALYZING
PREDICTING
MACHINE LEARNING
ARTIFICIAL INTELLIGENCE
Is it real? Why is it
happening?
What are the hidden
patterns? What will
happen next?
Self-learning systems
with linear regression.
Deep learning.
OPERATIONALIZING What is happening
right now?
ACTIVATING Make it happen with
automation.
2010s
2000s
1990s

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The Resurgence of Artificial Intelligence
• Significant advances in hardware capability
• Rapid progress in research and applications
using neural networks
• Significant technology investments
• Increasing amounts of data
By 2019, deep learning will provide best-
in-class performance for demand, fraud,
and failure prediction. - Gartner

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Companies mentioning “Artificial
Intelligence” Rising Rapidly
The Resurgence of AI

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“
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

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The Resurgence of AI
7 November 2016
12 October 2014

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Deep Learning
How is it different?
• Multiple layers in neural network with intermediate data
representations to facilitate dimensional reduction.
• Interpret non-linear relationships in the data.
• 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.

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Deep Learning Innovation in Computer Vision
Continuous Improvement in
Supervised Learning Methods
2016 Image-Net Results

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• Context
• Applications
• Conclusions
Agenda

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• Good fit for AI
– Massive data amounts
– Complex patterns
• Bad fit for AI
– Small data amount
– Limited time for training
– Interpretability required
• Caveats
– Amplification of existing human
biases
– Blind spots/adversarial challenges
- Not unique to deep learning though
AI in applications
Intriguing properties of neural networks, 2014, Szegedy et al.

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• Many of these use cases already have working
solutions using non-DL Machine Learning Techniques
• Deep Learning is delivering improvement in
performance on complex problems
Source: http://deeplearning4j.org/use_cases
AI Has Many Applications Across Industries

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Mobile Personalization
• Google Play Store production and other leading digital companies
– Generalize rules (e.g., categories of interest)
– Memorize exceptions (e.g., common pairs)
• Projects in banking, telco, retail
Source: Google

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Banking Anti-Fraud: Business Drivers
• Goal: fraud detection across products
• Trends
– Evolution of new payment methods
– Mobile payments exploding
– Fraud evolving rapidly, increased sophistication
• Traditional approach is hand-written rules
• Cost, delay and customer impact of false positives

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• Phased implementation
approach
– Simulated result
– Champion/challenger testing
– Production deployment
• Significant improvements over
traditional rules-based
techniques
• Techniques
– Random Forest
– Recurrent Neural Networks
• Tools: Spark, Hadoop, TensorFlow
Banking Anti-Fraud: Solution Approach

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• Provide smart assistance to drivers
– Navigation and safety
– Realtime Pricing
– Vehicle comfort
– Parking assistance
• Leverage video and other sensors
• Techniques:
– Object Detection, Segmentation,
Motion Detection, etc.
– Scene Labeling: Convolutional Neural
Network, MultiNet
• Tools: TensorFlow, Darknet
Connected Car Assistance
Real-Time
Streaming
Streaming
Results
Traffic Data
Service
Navigation
Update
Object Detection
Object
Segmentation
Motion Detection
GPU Training
TF Serving
Online
Inference
Model
Update
s

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• 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
• Techniques:
– Convolutional Neural Network
– Image Processing
– Natural Language Processing
• Tools: Spark, Hadoop, TensorFlow
Automated Check Processing
Check Images
To Hadoop
ImageMagick
Processing
Handwriting
Recognition

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• Market Context
• Applications
• Conclusions
Agenda

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Challenges
• Technology
– Research-driven, rapid change
– GPU deployment and integration
– Framework immaturity
– Research quality model code
– Complexity
• Point solutions rarely meet bar for enterprise
• Limited access to talent
• Data
– Governance and quality
– Volume, kinds
– Labeling / supervision
• Deployment and integration

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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

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Analytics Ops for Cross-Functional AI Teams
Constant
Monitoring
Test and
Deploy-
ment
A/B Testing
Automated
Training &
Scoring
Application
Integration

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Our Approach
Teradata Deep Learning CommunityTeradata Labs
Dozens of Experts in Deep Learning,
Image/Audio/Video Processing,
Computer Vision, GPU
200+ Practitioners delivering
Artificial Intelligence Business Value
on Customer Projects
500+ Solution Architects, Business
Consultants and Software Engineers with
knowledge of Artificial Intelligence Tools,
Techniques and Technologies. Deep
expertise in retail and across industries.
Experts
Practitioners
Interest
Industry
Collaborations
Academic
Collaborations
Analytics
Ops
Data
Management

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Industry Timeline Projection

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Conclusion
• AI moving beyond labs to production
• A strategy and roadmap is critical
• Pilot now, build capabilities