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Demystifying Artificial Intelligence delivered by
Data ScienceStatus Quo of Machine Learning, Cognitive and Advanced Analytics
The Three Legged Problem: Assets, People, and Process
Brian RayCognitive Team Lead | Products & SolutionsDeloitte Consulting LLP111 S Wacker Drive Chicago, IL 60606
@brianray or [email protected] or linked in http://www.linkedin.com/in/brianray
Switch and bait!I’m really here to promote my B&B…
OR my video series:
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Demystify
1. An incredibly brief intro to ML 2. The Status Quo of AI3. The Key factors in the Three Legged Problem:
1. Process: project management aimedat agile and practical
2. People: Data Scientists, Engineers, and SME3. Assets: Data, Tools, and Platforms
4. Examples
ASSETS
PEOPLE
PROCESS
Identify existing Mail (unsupervised learning)
Explore Cluster
Feature modeling
Invoices
Personal
Junk
Mail features:
• Stock• Size (Width x Height)• Finish (matte, gloss)• Thickness• Says “open now”
Identify a *new* piece of mail based on previous (supervised learning)
HISTORICAL
Invoices
Personal
Junk
Mail features:
• Stock: Cover• Size (Width x Height): 8 x 9• Finish (matte, gloss): semi-gloss• Thickness: .02 • Says “open now”: No
Model
Is it junk x 165 (20%)?Yes and guessed yes: 100No and guessed no: 50No and guessed yes: 10Yes and guessed no: 5
Train
825 examples
Precision .91 (100 / 110)Recall .95 (100 / 105)
F1 = 2*((.91*.95)/(.91+.95)) = .929
Is it Junk?YES (.90% confident)
4-tiered definition of "Analytics": ranging from "Traditional" to "Cognitive/AI"
A. Traditional Analytics / Statistical Modeling – datasets with homoscedasticity (variability of a variable is equal across the range of values[2]) where the distribution of variables is known.B. Advanced Analytics – Mostly done by machine learning via supervised and unsupervised learning. Sometimes deterministic vs probabilistic.C. Predictive Analytics – Same as ‘B’; however, the model is *wired* up to do real time prediction. May also include retraining.D. Cognitive Analytics (AI)— New brand of Data Science Analytics in practice that uses 2 or more predictive models (Like that from ‘C’) to mimic human thinking to help add insights and solve problems in business or daily life.
https://www.linkedin.com/pulse/new-4-tiered-definition-analytics-ranging-from-traditional-brian-ray
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machine learning IOT blockchain big data
The evolution to AI
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Cognitive Insights
Used for predictive decision making to answer probabilistic questions, such as with finance planning and strategy to customer trends and interactions
“Augments Human Intelligence”
Process Automation
Rules-based, deterministic processes, such as invoice processing, leave of absence processing, etc.
“Mimics Human Actions”
Cognitive Engagement
“Mimics Human behavior and Intelligence”
Systems that completely replicate human behavior, emotions and interactions
Cognitive Automation
Software used to capture and interpret existing applications for the purpose of automating transaction processing, data manipulation, and communication across multiple IT systems
- Deloitte, “The Robots are Coming”
• Screen scraping data collection
• Rules based business process management
• Tactical toolset to automate repetitive tasks
• Cheaper and faster step towards process efficiency, compliance improvement and error reduction
Cognitive Insights
Automate non routine tasks involving intuition, judgment, creativity, persuasion, or problem solving
- Deloitte, “Automate This”
• Data input and output in any format
• Pattern recognition within unstructured data
• Replication of judgment based tasks through natural language processing
• Basic learning capabilities for continuous improvement to quality and speed applying machine learning algorithm
Cognitive Engagement
“The theory and development of computer systems able to perform tasks that normally require human intelligence.”
- Deloitte, DU Press “Cognitive Technologies”
• Natural language recognition and processing
• Dealing with unstructured super data sets
• Hypothesis based predictive analysis
• Self-learning rules continuously rewritten to improve performance
Cognitive Automation
Comprehension of a sentence or multiple sentences in a document, such as email or a commercial contracts
“Comprehends” Human Intelligence”
Deloitte is equipped with a wide spectrum of automation and cognitive technologies to deliver value through the Cognitive Advantage framework
Cognitive Advantage Capability Spectrum
Business Issues
Fraud
Customer Retention
Profitability
Reliability
Risk
Productivity
Customer Acquisition
Real time Fraud Detection with
Predict Bank deflection
Price is Right?
Part Expiration
Predict High Risk Insurance
Shop floor optimizer
Assess Campaign Success
Understanding Regulation Reform Tool
ASSETS
PEOPLE
PROCESS
Data Scientists
Machine Learning
IoT
Blockchain
Deep Learning
Tensorflow
Traditional statistics
Engineers
Data Lake
SaaS Platforms
Cloud Computing
Automated Machine Learning
Streaming Data
Unlabeled and Unstructured Data
Subject Mater Experts
Business Analysts
Design UI/UX
TERMS
Agile
EDA
Models
Taxonomies
NLP
• Explore which data sets are available and where additional context can be created by new sources
Identify what data is important to your business owners and users
Initial data set
Outside data set owned by another business unit
Outside (free) data set
Outside data set that can be purchased
• Existing easily accessible datasets
• Accessible data sets owned by other business units or ones where there is an appetite to acquire
• Not easily accessible sets:
• Data that is not machine readable
• Unlabeled data
• Poor quality data
Assets
Information Sensing & Recognition
Knowledge Learning & Representation
Reasoning & Decision Making
Natural & Visual Interaction
HWR IR
VR NLP
ML IRVL
SCE TAE
PIE DRE NLG VDA
COGNITIVE COMPUTING PLATFORM
HYBRID REFERENCE ARCHITECTURE
IntegrationWorkflow Web Server App Server
Database Big Data Cloud Events
APIs / Services Graphical UI
Analytics Reporting
CI
Information Retrieval
Hand Writing Recognition Natural Language Processing Probabilistic Inference Engine Deterministic Rules Engine
Semantic Computing Engine Machine Learning Voice Recognition
Image Recognition Virtual Decision Assistant Text Analytics Engine
Natural Language Generation
NLP PIE DRE
SCE ML VR
IR VDA TAE IRVL
HWR
NLG
Legend
CI Cognitive Insights
INFORMATION AND DATA SOURCES
Data StoresPlanning, Procurement, and manufacturing KPIs
Social/ Public DataNews and Economic Reports, Facebook
Text & ImagesSupplier catalogues, online pricing data
Paper / Fax / PrintsLegal contracts
Sales data, Customer segmentation
Customer Data
COGNITIVELY AUGMENTED APPLICATIONS / USE CASES
Input Output
What does a proposed Cognitive Platform look like?
Future demand prediction
Capacity Demand
Determine build plan best path and optimal service
Optimization Model
Automated customer interface for customer service requests
Customer Service
AI platform to address all 17 capabilities and more
Cognitive Platform
Put together a team that will bring the right skills to each phase
Blend teams with business, technology + science talent
People
Why is making Machine Learning real at-scale is still somewhat elusive?• Technical, business, and organizational challenges
• It feels risky and daunting. I don’t know how to begin
• My business stakeholders do not not buy into it
• My Data Scientists are not able to communicate the value
• We don’t have enough test data that can be relied upon
• My techies don’t have the right skills for this
• How do I develop a business case?
• …..
People
Data Science Recursive Process
Predictive Modeling
Feature Selection
Model Selection & Assessment
Model Ensembling
Error Analysis
Feature Engineering
Data Processing
Exploratory Data Analysis
Data Processing • Imputing missing values• Document conversion and decomposition • Centering and scaling • Transformations to resolve skewness • Transformations to resolve outliers• Dimensionality Reduction• Assessing assumptions
Exploratory Data Analysis • Sorting / Aggregation data
for discovering meaningful relationships
• Suggesting and verifying hypothesis
• Supporting model selection • Providing a basis for further
data collection
Feature Engineering • Categorical encoding• Adding (polynomial) terms • Word Embedding • TF-IDF
Feature Selection • Wrapper methods (AIC,
backward / forward / stepwise selection, genetic algorithms )
• Filter methods (Chi2, Bonferroni correction)
Predictive modeling• Linear models • Basis expansions and
regularization• Additive models and Trees -
based models• Neural networks
Model Selection & Assessment• Model selection • Model assessment • Resampling techniques (k-fold
cross validation, bootstrap)• Bayesian approach and BIC
Error Analysis • Researching error patterns• Fixing high variance
problems • Fixing high bias problems • Comparison with state-of-
the-art models where available
Model Ensembling • Model inference, averaging
and voting • Boosting• Bagging • Stacking • Ensemble pruning
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PROCESS
Approach
1. Data Scientists interactively build models
2. Wrap Models to be Packaged using Engineering
3. Deployment Integration into Production
Package models intocontainers to allow deploymentAllow Scientists to use
existing tools for developing Predictive models on client data
ClientSystemsData
Enable real timeprediction and integration with client systems and workflows
Results
Results
Implementation Example: Complaint System
1. Text ClassificationMachine Learning Models
2. Deloitte Open-text Classification Engine (DOTCE)
3. 133 models in a resource limited environment, each over
370,000 narratives, nearly 50,000,000 predictions in less
than 2.5 hrs. With accuracy between 70-90%
CRMComplaints
Results
Results
NLP
Parts of speech
Term Frequency
Machine Learning
Rules
Random Forest
Elastic Search
PCA
Each document is 1,000 words long. Would have taken humans 31,000 hours (Average readers only reach around 200 wpm with a typical comprehension of 60%.)
Thank You Q&A
Brian RayCognitive Team Lead | Products & SolutionsDeloitte Consulting LLP111 S Wacker Drive Chicago, IL 60606
@brianray or [email protected] or linked in http://www.linkedin.com/in/brianray