Demystifying Artificial Intelligence delivered by Data Science

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1 Demystifying Artificial Intelligence delivered by Data Science Status Quo of Machine Learning, Cognitive and Advanced Analytics The Three Legged Problem: Assets, People, and Process Brian Ray Cognitive Team Lead Products & Solutions Deloitte Consulting LLP 111 S Wacker Drive Chicago, IL or brray@deloitte.com or linked in

2 Switch and bait! I m really here to promote my B&B OR my video series:

3 Demystify 1. An incredibly brief intro to ML 2. The Status Quo of AI 3. The Key factors in the Three Legged Problem: 1. Process: project management aimed at agile and practical 2. People: Data Scientists, Engineers, and SME 3. Assets: Data, Tools, and Platforms 4. Examples ASSETS PEOPLE PROCESS

4 An incredibly brief intro to ML In two slides

5 Identify existing Mail (unsupervised learning) Explore Cluster Mail features: Stock Size (Width x Height) Finish (matte, gloss) Thickness Says open now Feature modeling Invoices Personal Junk

6 Identify a *new* piece of mail based on previous (supervised learning) HISTORICAL Mail features: Stock: Cover Size (Width x Height): 8 x 9 Finish (matte, gloss): semi-gloss Thickness:.02 Says open now : No Is it Junk? Model YES (.90% confident) Train 825 examples Invoices Personal Junk Is it junk x 165 (20%)? Yes and guessed yes: 100 No and guessed no: 50 No and guessed yes: 10 Yes and guessed no: 5 Precision.91 (100 / 110) Recall.95 (100 / 105) F1 = 2*((.91*.95)/( )) =.929

7 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.

8 The Status Quo of AI Spoiler: It s here! Finally (AI winters: and )

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12 Google Search worldwide from /2/14 11/2/15 11/2/16 11/2/17 machine learning IOT blockchain big data

13 2006 Gartner Emerging Technologies Hype Curve

14 2017 Gartner Emerging Technologies Hype Curve

15 The evolution to AI

16 In practice

17 Cognitive Advantage Capability Spectrum Deloitte is equipped with a wide spectrum of automation and cognitive technologies to deliver value through the Cognitive Advantage framework Process Automation Cognitive Automation Cognitive Insights Cognitive Engagement Mimics Human Actions Rules-based, deterministic processes, such as invoice processing, leave of absence processing, etc. Comprehends Human Intelligence Comprehension of a sentence or multiple sentences in a document, such as or a commercial contracts Augments Human Intelligence Used for predictive decision making to answer probabilistic questions, such as with finance planning and strategy to customer trends and interactions 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 Screen scraping data collection - Deloitte, The Robots are Coming 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 Data input and output in any format Pattern recognition within unstructured data - Deloitte, Automate This 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

18 It s a three legged stool ASSETS PROCESS PEOPLE

19 Business Issues Customer Retention Customer Acquisition Profitability Reliability Risk Fraud Productivity Predict Bank deflection Assess Campaign Success Price is Right? Part Expiration Predict High Risk Insurance Real time Fraud Detection with Shop floor optimizer Understanding Regulation Reform Tool

20 TERMS ASSETS Taxonomies Tensorflow Streaming Data Data Lake Machine Learning Models SaaS Platforms Unlabeled and Unstructured Data Cloud Computing PROCESS Agile IoT EDA Deep Learning Blockchain Automated Machine Learning PEOPLE Data Scientists Engineers Subject Mater Experts Design UI/UX Business Analysts NLP Traditional statistics

21 Assets Data, Tools, Services, and Platforms

22 Assets AI and ML

23 Identify what data is important to your business owners and users Explore which data sets are available and where additional context can be created by new sources Assets 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 Outside data set owned by another business unit Outside (free) data set Outside data set that can be purchased Unlabeled data Poor quality data Initial data set

24 What does a proposed Cognitive Platform look like? INFORMATION AND DATA SOURCES COGNITIVELY AUGMENTED APPLICATIONS / USE CASES Customer Data Sales data, Customer segmentation Input Output Capacity Demand Future demand prediction Data Stores Planning, Procurement, and manufacturing KPIs Social/ Public Data News and Economic Reports, Facebook Text & Images Supplier catalogues, online pricing data Paper / Fax / Prints Legal contracts Information Sensing & Recognition HWR VR IR NLP Knowledge Learning & Representation ML SCE IRVL TAE Reasoning & Decision Making Natural & Visual Interaction PIE DRE NLG VDA COGNITIVE COMPUTING PLATFORM Workflow Integration Web Server App Server Database Big Data Cloud Events HYBRID REFERENCE ARCHITECTURE Legend CI APIs / Services Analytics Graphical UI Reporting Optimization Model Determine build plan best path and optimal service Customer Service Automated customer interface for customer service requests Cognitive Platform AI platform to address all 17 capabilities and more HWR Hand Writing Recognition NLP Natural Language Processing PIE Probabilistic Inference Engine DRE Deterministic Rules Engine SCE Semantic Computing Engine NLG Natural Language Generation ML Machine Learning VR Voice Recognition IR Image Recognition VDA Virtual Decision Assistant TAE Text Analytics Engine IRVL Information Retrieval CI Cognitive Insights

25 Assets

26 Assets

27 Assets Obligatory nascar slide Ecosystem Partners Platform Partners Tools * *** * ** *

28 People

29 Unicorn Hunting People

30 Put together a team that will bring the right skills to each phase Blend teams with business, technology + science talent People

31 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 People 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?..

32 People

33 Process Agile, EDA, Data Science Modeling

34 How different are we? Business Process VS Engineering VS Data Science. PROCESS

35 Business Waterfall Process PROCESS

36 Engineering Iterative Process PROCESS

37 Data Science Recursive Process PROCESS 2 Exploratory Data Analysis Sorting / Aggregation data for discovering meaningful relationships Suggesting and verifying hypothesis Supporting model selection Providing a basis for further data collection Exploratory Data Analysis 3 Feature Engineering Categorical encoding Adding (polynomial) terms Word Embedding TF-IDF Feature Engineering 8 Model Ensembling Model inference, averaging and voting Boosting Bagging Stacking Ensemble pruning Model Ensembling Error Analysis 7 Error Analysis Researching error patterns Fixing high variance problems Fixing high bias problems Comparison with state-ofthe-art models where available Data Processing 1 Data Processing Imputing missing values Document conversion and decomposition Centering and scaling Transformations to resolve skewness Transformations to resolve outliers Dimensionality Reduction Assessing assumptions Feature Selection 4 Predictive Modeling Feature Selection Wrapper methods (AIC, backward / forward / stepwise selection, genetic algorithms ) Filter methods (Chi2, Bonferroni correction) 5 Model Selection & Assessment Predictive modeling Linear models Basis expansions and regularization Additive models and Trees - based models Neural networks 6 Model Selection & Assessment Model selection Model assessment Resampling techniques (k-fold cross validation, bootstrap) Bayesian approach and BIC Gino Tesei

38 Approach 1. Data Scientists interactively build models 2. Wrap Models to be Packaged using Engineering Allow Scientists to use existing tools for developing Predictive models on client data Results Package models into containers to allow deployment 3. Deployment Integration into Production Enable real time prediction and integration with client systems and workflows Data Results Client Systems

39 Implementation Example: Complaint System 1. Text Classification Machine Learning Models 2. Deloitte Open-text Classification Engine (DOTCE) Random Forest NLP Parts of speech Elastic Search PCA Term Frequency Machine Learning Rules Results 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% Complaints Results CRM 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%.)

40 Thank You Q&A Brian Ray Cognitive Team Lead Products & Solutions Deloitte Consulting LLP 111 S Wacker Drive Chicago, IL or brray@deloitte.com or linked in

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