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SAS and Artificial Intelligence
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OBJECTIVE
Tactics StrategyFuture Vision Current Reality
Leading with Intelligence
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Artificial Intelligence (AI) is the science of training computers to perform tasks that typically require human intelligence to complete.
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Performance Assessment
Extract real-time insights during games
Object Detection
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Strategic AI Applications
Retail
Conversational Chat Bots
Contextual Marketing
Personalized Virtual Shopper
Fraud Detection
Credit Analysis
Automated Financial Advisors
Banking
Smart Cities
Sensor Fusion
Facial Recognition
Government
Predictive Diagnostics
Biomedical Imaging
Health Monitor
Heath and Life Sciences
Supply Chain Optimization
Automated Defect Detection
Energy Forecasting
Manufacturing and Energy
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A Tactical Approach to AI
Machine Learning Natural Language Processing
Deep Learning Natural Language Understanding
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EnergyForecasting
Use short and long-term variability to improve accuracy
Deep Learning
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A Tactical Approach to AI
Machine Learning Natural Language Processing
Deep Learning Natural Language Understanding
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Supply Chain Stability
Improve response to changes in the market
Natural Language Understanding
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A Tactical Approach to AI
Machine Learning Natural Language Processing
Deep Learning Natural Language Understanding
Pattern Recognition ● Prediction ● Classification ● Image Recognition ● Speech to Text ●Cognitive Search ● Natural Language Interaction ● Natural Language Generation
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Manufacturing Optimization
Identify defects during production
Pattern Recognition
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A Tactical Approach to AI
Machine Learning Natural Language Processing
Deep Learning Natural Language Understanding
Pattern Recognition ● Prediction ● Classification ● Image Recognition ● Speech to Text ●Cognitive Search ● Natural Language Interaction ● Natural Language Generation
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Wildlife Conservation
Provide non-invasive monitoring of
endangered species
Image Recognition
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We provide elements and capabilities
for people that aspire to build an AI system.
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These AI elements are being embedded in the SAS platform.
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Our approach to AI is
augmenting human efforts
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Change the World
Advancing analytics by embedding AI in the
SAS Platform
Apply analytics to data
The SAS Platform
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Input
Deep learning
Learning
MilitarySurveillance
Crop Yields
Fraud MedicalImages
Autonomous Vehicles
CustomerTargeting
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Introduction to deep Learning
Agenda
4
1Deep Learning Defined
2Sample Business Applications
3Deep Learning Architectures
Comparison with other Algorithms
5Competitive Landscape
High Level Planning6
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The Basics Deep Learning Defined
Deep learning is a sub-class of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers with complex structures.
Deep learning platform is a platform which help users to build different deep learning architectures or facilitate users to apply deep learning to a wide range of business applications with apps and services.
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Deep learning Why Deep learning?
• Very flexible structures of networks;
• Learning often improves with more data;
• Requires minimal feature engineering;
• Perform best in speech, text, image and video recognition;
Problems in Deep learning?
• Long training time;
• Hard to train due to the unstable gradient, numerous hype-
parameters and flexible architectures;
• Overfitting;
• The extracted features may be non-interpretable;
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deep Learning WHY IS DEEP LEARNING SO HOT?
• Drastically increased chip processing abilities such as general purpose graphical processing units (GPGPUs) and MPP platforms;
• Significantly increased size of data for training, making the overfitting problem much
less severe;
• Great advances of machine learning algorithms for deep learning such as min-batch
random gradient descent;
Google Trends over time – Deep Learning
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Business applications
UNLIMITED BOUNDS
Finance: Time series prediction and forecasting; fraud detection
Cybersecurity: Protection of information systemsMedical applications: Diagnostics, image recognitionAgriculture: Image recognition and feature codingMilitary Surveillance: Video recognition, planning and
optimizationComputer recognition: Image tagging and speech, text
recognitionAutonomous vehicles – cars, drones, robots, etc.
And much, much more.
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Deep Learning ARCHITECTURES
Deep Forward Nets
Convolutional Networks
Recurrent Networks
Auto-encoders4
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What’s your
vision?
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