sas and artificial intelligence · pattern recognition prediction classification image ......

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SAS and Artificial Intelligence


OBJECTIVE

Tactics StrategyFuture Vision Current Reality

Leading with Intelligence


Artificial Intelligence (AI) is the science of training computers to perform tasks that typically require human intelligence to complete.


Performance Assessment

Extract real-time insights during games

Object Detection


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


A Tactical Approach to AI

Machine Learning Natural Language Processing

Deep Learning Natural Language Understanding


EnergyForecasting

Use short and long-term variability to improve accuracy

Deep Learning


Supply Chain Stability

Improve response to changes in the market

Natural Language Understanding





Pattern Recognition ● Prediction ● Classification ● Image Recognition ● Speech to Text ●Cognitive Search ● Natural Language Interaction ● Natural Language Generation


Manufacturing Optimization

Identify defects during production

Pattern Recognition





Pattern Recognition ● Prediction ● Classification ● Image Recognition ● Speech to Text ●Cognitive Search ● Natural Language Interaction ● Natural Language Generation


Wildlife Conservation

Provide non-invasive monitoring of

endangered species

Image Recognition


We provide elements and capabilities

for people that aspire to build an AI system.


These AI elements are being embedded in the SAS platform.


Our approach to AI is

augmenting human efforts


Change the World

Advancing analytics by embedding AI in the

SAS Platform

Apply analytics to data

The SAS Platform


Input

Deep learning

Learning

MilitarySurveillance

Crop Yields

Fraud MedicalImages

Autonomous Vehicles

CustomerTargeting


Introduction to deep Learning

Agenda

4

1Deep Learning Defined

2Sample Business Applications

3Deep Learning Architectures

Comparison with other Algorithms

5Competitive Landscape

High Level Planning6


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.


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;

C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

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


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.


Deep Learning ARCHITECTURES

Deep Forward Nets

Convolutional Networks

Recurrent Networks

Auto-encoders4


What’s your

vision?

sas and artificial intelligence · pattern recognition prediction classification image ......

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