artificial intelligence -...
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Artificial IntelligenceSteve Holder
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Artificial Intelligence
AlgorithmsMachine Learning
Deep Learning
Automation
Natural Language Processing
Computer Vision
Cognitive Computing
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Artificial Intelligenceis the science of training systems to emulate human tasks
through learning and automation.
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AI and Machine LearningThere is a little Hype
Machine Learning is at the “Peak of Inflated
Expectations”
Right next to:
• Blockchain
• Cognitive Experts
• Software Defined Security
• Autonomous vehicles
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1950s 1980s 2010’s Present
Artificial Intelligence
Neural NetworkStatistics
Machine Learning
Machine Learning
Text, Image, Speech
Cognitive Computing
Deep Networks
Deep Learning
MACHINE LEARNING AND AI
Natural Language Processing
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Databases
Statistics
KDD
AI
Computational Neuroscience
Data Mining
Data Science
MachineLearning
PatternRecognition
Related Disciplines
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How Machines Learn
▪ Machines use algorithms to:
- Study data to detect patterns.
- Apply defined rules to achieve a specified outcome.
▪ As new data is provided, the machine’s performance improves.
▪ Leading to increased or improved “intelligence.”
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Machine Learning and AI
▪ Categorize or catalog like people or things.
▪ Predict likely outcomes or actions based on identified patterns.
▪ Identify hitherto unknown patterns and relationships.
▪ Detect anomalous or unexpected behaviors.
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Why Now?
Data Computing Power Algorithms
We have data to find new signals
• Lower cost data. • Organizations can store
it all.• Analyst can leverage
image and text data.
We can actually process the math now
• Advanced computing technology is commoditized.
• The algorithms love it.
Refined, powerful algorithms
• Machine Learning algorithms are understood.
• Better able to automate.
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So what’s the difference?It’s about the process
Traditional AnalyticsHumans know best
• Assumption based• Easy to interpret• Outcome is known• Fewer variables, less data• Humans do the work:
• Select the inputs• Craft the outputs
Machine Learning Machines are smarter
• Fewer assumptions• Harder to interpret• Outcome can be unknown• Larger problems, variables and data• Machines do the work:
• Create the data• Determine the algorithms
• T-tests, ANOVA, GLMs
• MIXED models • No Variable
Selection
• Stepwise methods
• Data partitioning • Tune models on
validation data• LARS, LASSO
• Decision Trees• Bagging &
Boosting• Ensemble
Models
• Random Forests• Gradient Boosting • Neural Networks• Support Vector
Machines
• Deep Learning Neural Networks
• Hyper-Parameter Auto-Tuning
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Types of Machine Learning Methods
SUPERVISED LEARNING
• Labeled data • Classification, Prediction• Algorithms: Logistic
Regression, Gradient Boosting etc.
UNSUPERVISED LEARNING
• Unlabeled data• Clustering, Feature
Extraction• Algorithms: K-means
clustering, PCA, etc.
SEMI-SUPERVISED LEARNING
• Labeled and unlabeled data
• Classification, Prediction• Algorithms:
Autoencoders, TSVM etc.
REINFORCEMENT LEARNING
• Agent, environment and actions
• Robotics, Gaming and Navigation
• Monte-Carlo methods etc.
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SAS PlatformMark Miskiman
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The Analytic Process
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The Analytic Process
Foundation for Analytics• Access - All sources• Build – Merge and transform• Profile – Explore your data• Fix Quality - Standardize• Governance and Lineage
Creation of Analytic Assets• Centrally Manage • Deploy in database• Create Efficiencies• Control & Adjust
Broad Range of Capabilities• Report – BI• Discover – Data Visualization• Forecast – Time Series• Optimize – Operations Research • Explain – Statistics • Predict – Data Mining, Text
Analytics, Machine Learning & AI
ModelAccess Explore AnalyzeCleanse MonitorIntegrate Govern Embed
DISCOVERYDATA DEPLOYMENT
AUTOMATE & ORCHESTRATE
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The Analytic Process
Broad Range of Capabilities• Report – BI• Discover – Data Visualization• Forecast – Time Series• Optimize – Operations Research • Explain – Statistics • Predict – Data Mining, Text
Analytics, Machine Learning & AI
Foundation for Analytics• Access - All sources• Build – Merge and transform• Profile – Explore your data• Fix Quality - Standardize• Governance and Lineage
Creation of Analytic Assets• Centrally Manage • Deploy in database• Create Efficiencies• Control & Adjust
Access Cleanse MonitorIntegrate Govern Embed
DISCOVERYDATA DEPLOYMENT
Analyze
Optimize
Forecast
Explore
Discover
Report
Model
Predict
Explain
Build Views
Connect
Transform
Join
Standardize
ProfileCentrally Manage
Create Efficiencies
Control & Adjust
AUTOMATE & ORCHESTRATE
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SAS for DataProducing data that cultivates analytics assets
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ACCESSAccess data & assess the data structure and content.
• Browse and search available
data & metadata.
• Filter, copy & profile data.
• Lift data into memory.
• Access new data sources (files,
social media, data connections).
INTEGRATESelect data of interest, manipulate & structure it for analysis.
GOVERNAutomate data preparation tasks, monitor jobs & share plans across users.
CLEANSEPut data into a consistent, trusted format.
• Select columns & apply filters.
• Calculate Columns.
• Convert data types.
• Append, join & transpose data.
• Insert code (DATASTEP, CASL).
• Create jobs.
• Parse
• Field Extraction
• Standardize
• Match
• Identification Analysis
• Change Case
• Gender Analysis
• Manager user permissions.
• Automate jobs, view code and
logs.
• Share tables and data preparation
plans across users.
• Open APIs.
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SAS for DiscoveryBuilding analytics that answer any question
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DATA DISCOVERYExplore, report, collaborate and share.
FORECASTINGLeverage time series data to forecast future results
DATA MININGExplore, find and predict patterns in data.
STATISTICSUnderstand and explain patterns in data.
• Time series forecasting.
• Simultaneous scenario
comparison.
• AI enabled forecasting
tournaments.
• Hierarchical forecasting with
reconciliation.
• Human guided algorithm assisted
discovery & modeling
• Algorithm-driven variable selection
• Deeper statistical methods for
bigger, more complex data
• Model assessments, comparisons
& tournaments on hold-out data
• Interactive Ad hoc exploration
of data.
• Discover relationships, trends
and outliers.
• Create reports and
dashboards.
• Distribute and share content
with others.
• Linear & logistic regression
• Survival analysis
• Generalized linear model, Mixed
Models
• Classification trees
• Clustering
• Fit Distributions
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• Unsupervised- and supervised-
learning algorithms
• Build machine learning
pipelines
• Automated model tuning
• Model assessment
comparisons & tournaments,
and scoring
MACHINE LEARNINGAutomate discovery and modeling, prediction & deployment.
SAS for DiscoveryBuilding analytics that answer any question
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• Optimize outcome based on
constraints.
• Use cases include:
• Optimize scheduling & routing
• Site selection
• Revenue management
• Product stocking, bundling
TEXT MININGExplain & Predict patterns in unstructured text data.
• Text Analytics pipeline for
modeling
• Content Parsing, Categorization
and Analysis.
• Information retrieval, word
searches
• Word associations and linkages
• Stylometry & Interpret natural
language
OPTIMIZATIONFind optimal solutions given constraints.
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SAS for DeploymentRealize the value from analytics assets in action
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GOVERNProvide oversight and governed analytics.
EMBEDEmbed and execute models, leverage compute platforms.
AUTOMATEMove from insight to action.
MONITORGain visibility into analytic outcomes and approach
• Execute models in database &
Hadoop.
• No Language Conversion.
• Generate score code for
complex models.
• Automate Model execution.
• Automate Model retraining.
• Create AI enabled model
tournaments.
• Central model management.
• Repository for SAS Models and
Open Source.
• Model and data History,
version control.
• Model and data lineage with
governance.
• Model performance reporting.
• Compare multiple models.
• Assess model accuracy (Lift, ROC,
K-S)
• Champion/Challenger modeling
• Model retraining including open
source
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Pragmatic AI Steve Holder
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What people expect from AI?
Better Results Auto Magic Lower Cost Human Interfaces
• Less assumptions• Better math• More data
• Throw data at it• Remove Bias• Automate the
process
• Machines do the work
• Model tournament
• Better UI• Better interaction• Add speech, text
and images
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We provide elements and capabilities
for people that aspire to build an AI system.
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What falls under AI?
Chatbots
Smart Q&A and Search System
Deep Learning AlgorithmsRoboadvisor
Natural Language Processing
Self Learning Models
Reinforcement Algorithms
Machine LearningFacial recognition
Conversational system
CAPABILITIESAPPLICATIONS
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MACHINE LEARNING AND AI
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ALGORITHMS
• Deliver robust analytic algorithms.
• Classic Statistics, Machine Learning and Deep Learning.
• More accurate analytics.
AUTOMATION
• Embed AI in SAS
• Automate the analytics lifecycle.
• Generate model tournaments for best results.
• Retrain models when they decay.
TEXT AND IMAGE PROCESSING
• Image processing, classification and recognition.
• Text processing, Analytics, & Sentiment Analysis.
• Speech to Text for SAS
• Natural language generation.
AI APPLICATIONS
• Create AI applications
• Natural language interfaces to SAS.
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Data Manipulation
Data Manipulation
• In-Memory Data Manipulation• Frequency / Crosstab• Data Transpose• Variable Binning • Variable Cardinality Analysis• Variable Summary• Sampling and Partitioning• Missing Value Imputation• Variable Selection • Model Assessment• DS2• FedSQL• Image Processing
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Analytics
Statistics
• Cox Proportional Hazards• Decision Trees• Design Matrix• General Additive Models• Generalized Linear Models • K-means and K-modes Clustering• Linear Regression• Logistic Regression• Nonlinear Regression • Ordinary Least Squares Regression • Partial Least Squares Regression• Pearson Correlation• Quantile Regression• Shewhart Control Chart Analysis
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Machine Learning
Machine Learning
• Bayesian Networks• Boolean Rules• Factorization Machines• Frequent Item Set Mining• Gradient Boosting• K Nearest Neighbor• Market Basket Analysis• Moving Windows PCA• Network Analytics/Community
Detection• Random Forest• Principal Component Analysis • Support Vector Data Description• Support Vector Machines• Text Mining• Variable Clustering
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Deep Learning
Machine Learning
Deep Forward Neural Networks (DNNs)Convolutional Neural Networks (CNNs)Support VGG-like modelsSupport ResNet modelsRecurrent Neural Networks(RNNs)Support LSTM modelSupport GRU modelAutoencoders for neural networks
Image processing extensionsAugment image actionConvert image table action Match image action2D/3D medical image visualization
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ModelAccess Explore AnalyzeCleanse MonitorIntegrate Govern Embed
DISCOVERYDATA DEPLOYMENT
AUTOMATE & ORCHESTRATE
Automate the Analytic Process
Data AnalysisFeature
EngineeringModel Training Deploy
More Interations
Better Analytics
IncreasedProductivity
Better Insights
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AI Applications
Personalized Recommendation
IoT Analytics
Person/Object Identification Three-Dimensional Scans
Personalized Offers
1 2
3 4
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Over 3000 IMAGES analyzed in < 10 minutesModel Accuracy 78%
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ScaleHyper-parameter Auto-Tuning
SAS Auto Tuning provides:
• Automated hyper-parameters search and selection
• Guided parameter selection and defaults.
• Avoids over-fitting
• More accurate models faster vs. hand-tuning
• Patented optimization algorithm to select right combination of hyperparameters
What are hyper parameter?• Machine learning models have parameters that tune the
model for results and performance.
• Data scientist try combinations of parameters to maximize results.
• This is a manual and time consuming process.
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x1
x2
y = f(x1) + g(x2)
Standard Grid Search
ScalePatented Optimization
x1
x2
Random Searchx1
x2
Latin Hypercube
= individual model train and assessment
• Better hyper-parameters coverage• Optimal computation time
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TrustAnalytic Accuracy
• Provide new machine learning algorithms in increase accuracy
- Gradient Boosting, Factorization Machines, Support Vector Machines, Principal
Component Analysis, Deep Neural Networks
• Automated Hyperparameter auto tuning
Model accuracy using
Cut off at 80% probability Cut off at 60% probability
Number of false positives prevented
False positivesNumber of false
positives preventedFalse
positives
SAS VDMML 96 out of 98 (98%) 2 114 out of 121 (94%) 7
Other tools at Media Company
168 out of 192 (88%)
24 184 out of 247 (74%) 63
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Diversity
Visual Interfaces
Programming Interfaces
API Interfaces
Support all users
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DiversitySupport for all languages
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Workers
Controller
proc print data = hmeq (obs = 10);
run;
df = s.CASTable(‘hmeq’)
df.head(10)
df <- defCasTable(s, ‘hmeq’)
head(df, 10)
[table.fetch]
table.name = “hmeq”
from = 1 to = 10
Translated Command
APIs
TrustConsistent Execution
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Mastering CAS ActionsHow you will become great at SAS Viya Programming
Action Sets
Actions
Parameters
Options
PROC
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SAS for DeploymentRealize the value from analytics assets in action
41
GOVERNProvide oversight and governed analytics.
EMBEDEmbed and execute models, leverage compute platforms.
AUTOMATEMove from insight to action.
MONITORGain visibility into analytic outcomes and approach
• Execute models in database &
Hadoop.
• No Language Conversion.
• Generate score code for
complex models.
• Automate Model execution.
• Automate Model retraining.
• Create AI enabled model
tournaments.
• Central model management.
• Repository for SAS Models and
Open Source.
• Model and data History,
version control.
• Model and data lineage with
governance.
• Model performance reporting.
• Compare multiple models.
• Assess model accuracy (Lift, ROC,
K-S)
• Champion/Challenger modeling
• Model retraining including open
source
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ScaleLess Technical Integration
Decentralized Centralized
…
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Data Ingestion
Data Storage Data Preparation Analytics & Modelling
Inventory & Document
Deployment & Publish
Monitoring & Retraining
Open Source
Skills Required
• Source Data structures
• Sqoop
• Hadoop & HDFS• Query in SQL, HQL, Pig• Writing customized UDF in Java• Data wrangling in R or Python• Knowledge about the models being built
• Python, PySpark, Scala• Machine Learning
Algorithms• Modelling• Spark - Parallelized
Computing
• Git, • Best Practices for
Version Control
• Unix - Bash Scripting
• Docker Containers
• Dev Ops skills
How SAS can help
• Enable the business users with access to Hadoop
• SAS can execute queries in Hadoop environment and minimize data duplication
• Provide a variety of user experiences
• Leverage open programming skills
• Consistent execution• Embedded In memory• Ability to deploy
models
• Central Model Management Repository
• Inventory all models• Model history and
lineage• Version control
• Execution in Hadoop
• No recording required
• Accessible via Restful API
• Monitor model performance
• Automatic model retraining
• Out of the box Modelperformance statistics
Open Source Analytic Lifecycle
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ScaleDeploy models with an Analytics Store
Why do we need an analytics store?• Machine Learning models generate huge amounts of code (up to >50M)
• Gradient Boosting, Random Forests, factorization machines etc.
• Generation and execution of this code in production can be difficult to manage.
What does the SAS Astore provider?
• A SAS Astore is a binary file that captures the state of a predictive model
• It is transportable
• Can be moved between databases and Hadoop
• No SAS import/export needed
• Allows for model deployment and execution
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Delivers capabilities to build applications
Not limited to the cloud or just one cloud
Governed processes
SAS AI Approach
Simplified end-to-end integration
Integrated analytical streaming engine
Supported globally (tech, language...)
SAS analytic standard in the enterprise
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• Leader in Advanced Analytics, AI, and Data Management
• Over 35% Market share for Advanced analytics according to IDC
• More than 83,000 sites, across 149 countries
About SAS
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